Search results for: vocational capabilities

Authors: Loretta Ichim, Dan Popescu, Grigore Stamatescu

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The paper reports the collaboration between a top university from Romania and three companies for the implementation of a research project in a multidisciplinary domain, focusing on the impact and benefits both for the education and industry. The joint activities were developed under the Space Technology and Advanced Research Program (STAR), funded by the Romanian Space Agency (ROSA) for a university-industry partnership. The context was defined by linking the European Space Agency optional programs, with the development and promotion national research, with the educational and industrial capabilities in the aeronautics, security and related areas by increasing the collaboration between academic and industrial entities as well as by realizing high-level scientific production. The project name is Multisensory Robotic System for Aerial Monitoring of Critical Infrastructure Systems (MUROS), which was carried 2013-2016. The project included the University POLITEHNICA of Bucharest (coordinator) and three companies, which manufacture and market unmanned aerial systems. The project had as main objective the development of an integrated system for combined ground wireless sensor networks and UAV monitoring in various application scenarios for critical infrastructure surveillance. This included specific activities related to fundamental and applied research, technology transfer, prototype implementation and result dissemination. The core area of the contributions laid in distributed data processing and communication mechanisms, advanced image processing and embedded system development. Special focus is given by the paper to analyzing the impact the project implementation in the educational process, directly or indirectly, through the faculty members (professors and students) involved in the research team. Three main directions are discussed: a) enabling students to carry out internships at the partner companies, b) handling advanced topics and industry requirements at the master's level, c) experiments and concept validation for doctoral thesis. The impact of the research work (as the educational component) developed by the faculty members on the increasing performances of the companies’ products is highlighted. The collaboration between university and companies was well balanced both for contributions and results. The paper also presents the outcomes of the project which reveals the efficient collaboration between high education and industry: master thesis, doctoral thesis, conference papers, journal papers, technical documentation for technology transfer, prototype, and patent. The experience can provide useful practices of blending research and education within an academia-industry cooperation framework while the lessons learned represent a starting point in debating the new role of advanced research and development performing companies in association with higher education. This partnership, promoted at UE level, has a broad impact beyond the constrained scope of a single project and can develop into long-lasting collaboration while benefiting all stakeholders: students, universities and the surrounding knowledge-based economic and industrial ecosystem. Due to the exchange of experiences between the university (UPB) and the manufacturing company (AFT Design), a new project, SIMUL, under the Bridge Grant Program (Romanian executive agency UEFISCDI) was started (2016 – 2017). This project will continue the educational research for innovation on master and doctoral studies in MUROS thematic (collaborative multi-UAV application for flood detection).

Keywords: education process, multisensory robotic system, research and innovation project, technology transfer, university-industry partnership

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Authors: Priyanka Botny Srinath, Alessandro Suglia, Mel McKendrick

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Background: Organizational cultural studies integrate sociology and anthropology, portraying man as a creator of symbols, languages, beliefs, and ideologies -essentially, a creator and manager of meaning. In our research, we leverage analytical measures to discern whether an organization embodies a singular culture or a myriad of subcultures. Fast-forward to 2023, our research thesis focuses on digitally measuring culture, coining it as the "Work Culture Quotient." This entails conceptually mapping common experiential patterns to provide executives insights into the digital organization journey, aiding in understanding their current position and identifying future steps. Objectives: Finding the new age skills that help in defining the culture; understand the implications of post-COVID effects; derive a digital framework for measuring skillsets. Method: We conducted two comprehensive Delphi studies to distill essential insights. Delphi 1: Through a thematic analysis of interviews with 20 high-level leaders representing companies across diverse regions -India, Japan, the US, Canada, Morocco, and Uganda- we identified 20 key skills critical for cultivating a robust organizational culture. The skills are -influence, self-confidence, optimism, empathy, leadership, collaboration and cooperation, developing others, commitment, innovativeness, leveraging diversity, change management, team capabilities, self-control, digital communication, emotional awareness, team bonding, communication, problem solving, adaptability, and trustworthiness. Delphi 2: Subject matter experts were asked to complete a questionnaire derived from the thematic analysis in stage 1 to formalise themes and draw consensus amongst experts on the most important workplace skills. Results: The thematic analysis resulted in 20 workplace employee skills being identified. These skills were all included in the Delphi round 2 questionnaire. From the outputs, we analysed the data using R Studio for arriving at agreement and consensus, we also used sum of squares method to compare various agreements to extract various themes with a threshold of 80% agreements. This yielded three themes at over 80% agreement (leadership, collaboration and cooperation, communication) and three further themes at over 60% agreement (commitment, empathy, trustworthiness). From this, we selected five questionnaires to be included in the primary data collection phase, and these will be paired with the digital footprints to provide a workplace culture quotient. Implications: The findings from these studies bear profound implications for decision-makers, revolutionizing their comprehension of organizational culture. Tackling the challenge of mapping the digital organization journey involves innovative methodologies that probe not only external landscapes but also internal cultural dynamics. This holistic approach furnishes decision-makers with a nuanced understanding of their organizational culture and visualizes pivotal skills for employee growth. This clarity enables informed choices resonating with the organization's unique cultural fabric. Anticipated outcomes transcend mere individual cultural measurements, aligning with organizational goals to unveil a comprehensive view of culture, exposing artifacts and depth. Armed with this profound understanding, decision-makers gain tangible evidence for informed decision-making, strategically leveraging cultural strengths to cultivate an environment conducive to growth, innovation, and enduring success, ultimately leading to measurable outcomes.

Keywords: leadership, cooperation, collaboration, teamwork, work culture

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Authors: Riccardo Fratini, Riccardo Santini, Jacopo Serafini, Massimo Gennaretti, Stefano Panzieri

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This paper presents a nonlinear differential model, for a three-bladed horizontal axis wind turbine (HAWT) suited for control applications. It is based on a 8-dofs, lumped parameters structural dynamics coupled with a quasi-steady sectional aerodynamics. In particular, using the Euler-Lagrange Equation (Energetic Variation approach), the authors derive, and successively validate, such model. For the derivation of the aerodynamic model, the Greenbergs theory, an extension of the theory proposed by Theodorsen to the case of thin airfoils undergoing pulsating flows, is used. Specifically, in this work, the authors restricted that theory under the hypothesis of low perturbation reduced frequency k, which causes the lift deficiency function C(k) to be real and equal to 1. Furthermore, the expressions of the aerodynamic loads are obtained using the quasi-steady strip theory (Hodges and Ormiston), as a function of the chordwise and normal components of relative velocity between flow and airfoil Ut, Up, their derivatives, and section angular velocity ε˙. For the validation of the proposed model, the authors carried out open and closed-loop simulations of a 5 MW HAWT, characterized by radius R =61.5 m and by mean chord c = 3 m, with a nominal angular velocity Ωn = 1.266rad/sec. The first analysis performed is the steady state solution, where a uniform wind Vw = 11.4 m/s is considered and a collective pitch angle θ = 0.88◦ is imposed. During this step, the authors noticed that the proposed model is intrinsically periodic due to the effect of the wind and of the gravitational force. In order to reject this periodic trend in the model dynamics, the authors propose a collective repetitive control algorithm coupled with a PD controller. In particular, when the reference command to be tracked and/or the disturbance to be rejected are periodic signals with a fixed period, the repetitive control strategies can be applied due to their high precision, simple implementation and little performance dependency on system parameters. The functional scheme of a repetitive controller is quite simple and, given a periodic reference command, is composed of a control block Crc(s) usually added to an existing feedback control system. The control block contains and a free time-delay system eτs in a positive feedback loop, and a low-pass filter q(s). It should be noticed that, while the time delay term reduces the stability margin, on the other hand the low pass filter is added to ensure stability. It is worth noting that, in this work, the authors propose a phase shifting for the controller and the delay system has been modified as e^(−(T−γk)), where T is the period of the signal and γk is a phase shifting of k samples of the same periodic signal. It should be noticed that, the phase shifting technique is particularly useful in non-minimum phase systems, such as flexible structures. In fact, using the phase shifting, the iterative algorithm could reach the convergence also at high frequencies. Notice that, in our case study, the shifting of k samples depends both on the rotor angular velocity Ω and on the rotor azimuth angle Ψ: we refer to this controller as a spatial repetitive controller. The collective repetitive controller has also been coupled with a C(s) = PD(s), in order to dampen oscillations of the blades. The performance of the spatial repetitive controller is compared with an industrial PI controller. In particular, starting from wind speed velocity Vw = 11.4 m/s the controller is asked to maintain the nominal angular velocity Ωn = 1.266rad/s after an instantaneous increase of wind speed (Vw = 15 m/s). Then, a purely periodic external disturbance is introduced in order to stress the capabilities of the repetitive controller. The results of the simulations show that, contrary to a simple PI controller, the spatial repetitive-PD controller has the capability to reject both external disturbances and periodic trend in the model dynamics. Finally, the nominal value of the angular velocity is reached, in accordance with results obtained with commercial software for a turbine of the same type.

Keywords: wind turbines, aeroelasticity, repetitive control, periodic systems

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Authors: M. Agostini, A. Sonato, G. Greco, M. Travagliati, G. Ruffato, E. Gazzola, D. Liuni, F. Romanato, M. Cecchini

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Since their first demonstration in the early 1980s, surface plasmon resonance (SPR) sensors have been widely recognized as useful tools for detecting chemical and biological species, and the interest of the scientific community toward this technology has known a rapid growth in the past two decades owing to their high sensitivity, label-free operation and possibility of real-time detection. Recent works have suggested that a turning point in SPR sensor research would be the combination of SPR strategies with other technologies in order to reduce human handling of samples, improve integration and plasmonic sensitivity. In this light, microfluidics has been attracting growing interest. By properly designing microfluidic biochips it is possible to miniaturize the analyte-sensitive areas with an overall reduction of the chip dimension, reduce the liquid reagents and sample volume, improve automation, and increase the number of experiments in a single biochip by multiplexing approaches. However, as the fluidic channel dimensions approach the micron scale, laminar flows become dominant owing to the low Reynolds numbers that typically characterize microfluidics. In these environments mixing times are usually dominated by diffusion, which can be prohibitively long and lead to long-lasting biochemistry experiments. An elegant method to overcome these issues is to actively perturb the liquid laminar flow by exploiting surface acoustic waves (SAWs). With this work, we demonstrate a new approach for SPR biosensing based on the combination of microfluidics, SAW-induced mixing and the real-time phase-interrogation grating-coupling SPR technology. On a single lithium niobate (LN) substrate the nanostructured SPR sensing areas, interdigital transducer (IDT) for SAW generation and polydimethylsiloxane (PDMS) microfluidic chambers were fabricated. SAWs, impinging on the microfluidic chamber, generate acoustic streaming inside the fluid, leading to chaotic advection and thus improved fluid mixing, whilst analytes binding detection is made via SPR method based on SPP excitation via gold metallic grating upon azimuthal orientation and phase interrogation. Our device has been fully characterized in order to separate for the very first time the unwanted SAW heating effect with respect to the fluid stirring inside the microchamber that affect the molecules binding dynamics. Avidin/biotin assay and thiol-polyethylene glycol (bPEG-SH) were exploited as model biological interaction and non-fouling layer respectively. Biosensing kinetics time reduction with SAW-enhanced mixing resulted in a ≈ 82% improvement for bPEG-SH adsorption onto gold and ≈ 24% for avidin/biotin binding—≈ 50% and 18% respectively compared to the heating only condition. These results demonstrate that our biochip can significantly reduce the duration of bioreactions that usually require long times (e.g., PEG-based sensing layer, low concentration analyte detection). The sensing architecture here proposed represents a new promising technology satisfying the major biosensing requirements: scalability and high throughput capabilities. The detection system size and biochip dimension could be further reduced and integrated; in addition, the possibility of reducing biological experiment duration via SAW-driven active mixing and developing multiplexing platforms for parallel real-time sensing could be easily combined. In general, the technology reported in this study can be straightforwardly adapted to a great number of biological system and sensing geometry.

Keywords: biosensor, microfluidics, surface acoustic wave, surface plasmon resonance

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Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

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The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

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