1,721,179 research outputs found
Seismic assessment of a RC case study building using the simple lateral mechanism analysis, SLaMA, method
No full textThe seismic assessment of an existing structure is a complex procedure. In the evaluation of the structural capacity, difficulties arise in the definition of the lateral resisting members, in the estimation of the lateral capacity from component to system level. The identification of the local and global mechanisms, depending on the hierarchy of strength at subassembly level, is a challenging task. Nowadays, advanced numerical analysis procedures, implemented in user-friendly commercial software, are commonly adopted in the current practice to overcome the difficulties in the use of non-linear analyses. Nevertheless, their accuracy strongly depends on the ability of the numerical model to capture all the probable failure mechanisms. To help the user in the assessment of the probable failure mechanism and to have a first estimation of the building lateral capacity, the Simple Lateral Mechanism Analysis (SLaMA) method has been recently developed and included in the NZSEE 2016 guidelines. SLaMA is a simple and reliable "by-hand" tool to derive the local and global mechanism of a structural system and the corresponding capacity curve. This paper deals with the application of the SLaMA to an existing RC building severely damaged during the Christchurch earthquake (2011, New Zealand). The lateral capacity of four 2D resisting systems was assessed following the SLaMA method. The results of a refined nonlinear numerical model were used to assess the accuracy and reliability of SLaMA. The good match confirmed that SLaMA can be a useful and simple tool to have a first estimation of the building lateral response. It allows to identify the main structural weaknesses driving the user to the development of more refined nonlinear models
Resilience in healthcare after Covid-19: Rethinking technologies, operations, supply chain and network
Full text linkThe concept of resilience, defined as the ability of organizations to adapt their operations before, during, and after disruptions while ensuring the delivery of essential services, has garnered increasing attention in healthcare research. Despite its theoretical significance, hospital resilience remains underexplored in practice, with limited consensus on its contextual applications, contributing factors, and empirical validation. This thesis addresses these gaps by examining hospital resilience through the combined lenses of the Resource-Based View (RBV) and Contingent RBV (CRBV), focusing on the organizational characteristics, resources, and capabilities that underpin resilience across different crisis phases. Adopting a resource-based perspective enables a deeper understanding of hospital dynamics during crises by identifying the resources and capabilities critical for maintaining operations, adapting to challenges, and achieving resilience outcomes. Moreover, this approach emphasizes the importance of context, highlighting how internal and external contingencies shape the effectiveness of resilience strategies.
First, a systematic literature review is conducted to systematize knowledge on resources and capabilities that strengthen hospital resilience, distinguishing their roles across the five resilience dimensions: anticipation, adaptation, response, recovery, and learning. This review integrates fragmented findings, highlights synergies among organizational elements, and proposes an integrated framework to guide future research.
Building on these insights, the thesis empirically investigates the role of digital technologies, staff skills, and information integration capabilities in enhancing hospital resilience during crises. Through structural equation modeling (SEM) on data from 130 Italian hospitals, findings reveal that digital technologies and staff skills significantly contribute to resilience outcomes, with external information integration acting as a critical mediator.
The final phase of the thesis examines the moderating role of contingencies, including service complexity and operational efficiency, on the relationships between resources, capabilities, and resilience. In this phase, data were collected through an online survey of Italian hospitals and supplemented by secondary data from databases provided by the Italian Minestry of Health to measure the moderating factors. The analysis confirms the direct effects of ICTs, digital skills, and information integration on hospital resilience, while demonstrating how these effects are influenced by contextual factors. Results show that both service complexity and operational efficiency condition the effectiveness of resources, underscoring the nuanced dynamics of hospital resilience in varying contexts.
This research contributes to the theoretical and practical advancement of hospital resilience by providing a structured framework and empirical evidence to inform resource allocation, emergency planning, and strategic decision-making. It highlights the necessity of integrating diverse organizational elements and considering contextual factors to enhance resilience and ensure high-quality, safe patient care in increasingly volatile healthcare environments
Sampled-data set stabilization of switched boolean control networks
No full textIn this paper, the set stabilization of switched Boolean control networks (SBCNs) under sampled-data feedback control is addressed. Here, the control input is switching signal-dependent, and SBCNs can switch only at the sampling instants. First, the sampled point control invariant subset (SPCIS) of SBCNs is defined, and an algorithm is provided to obtain the largest SPCIS under arbitrary switching signal. Based on the largest SPCIS, some necessary and sufficient conditions are presented for the set stabilization of SBCNs by switching signal-dependent sampled-data (SSDSD) state feedback control. Furthermore, a constructive procedure is given to design all possible SSDSD state feedback controllers. Finally, some examples are presented to illustrate the effectiveness of the obtained results
Non Linear Discrete Time Epidemiological Model for X-Linked Recessive Diseases
No full textWe developed a discrete time, structured, mathe- matical model describing the epidemiology of X-linked recessive diseases, a class of genetic disorders.The model accounts for both de novo mutations and distinct reproduction rates of pro- creating couples depending on their health conditions. We found the exact solution to the model when de novo mutations are not significant and negligible reproduction rates are assigned to affected males. Our results have relevance for both system modeling and genetic epidemiology
Control of switched boolean control networks by state feedback
No full textControl of infectious diseases using bacteriophage therapy is regaining interests in modern medicine and systems biology as an alternative treatment for antibiotic-resistant bacteria. A key issue is to control the phage's replication process: indeed, phage may switch either towards lytic state or lysogeny state during its reproduction due to some conditions. However, only lytic state is relevant for bacteria elimination. In this paper, we model switching replication dynamics of the lambda phage, i.e., a class of bacteriophage, as a switched Boolean control network (SBCN) and we address the problem of designing all possible switching signal-dependent state feedback controllers to stabilize the phage system at lytic developmental pathway. An algebraic state-space representation method is adopted to determine all possible families of reachable sets. Necessary and sufficient conditions for the stabilization of SBCN to a given equilibrium point under arbitrary switching signals are presented, where the control input is switching signal-dependent. Moreover, all possible switching signal-dependent state feedback controllers are obtained based on all complete families of reachable sets. Results are relevant for both the development of SBCN theory and for practical applications. At last, the example of bacteriophage lambda is analyzed to show the effectiveness of the main results
A Production and Inventory Control Model for Make-to-stock Manufacturing Systems
No full textWe present a production and inventory control model for multi stage manufacturing systems. We first describe some inefficiencies of the most common production control systems based on the WIP feedback signal. We then address the showed inefficiencies in an novel production control scheme where the WIP signal is replaced with a signal proportional to the pipeline output. We also briefly discuss some implementation concerns
Model of Eukaryotic Cell Protein Control Schemes via Manufacturing System Simulator
No full textThe folding and transport of proteins in the Endoplasmic Reticulum (ER) of mammalian cells exhibit similarities to industrial manufacturing processes, in that they are complex systems regulated by control mechanisms. Recently, two such control systems have been identified: the Unfolded Protein Response (UPR) and AutoRegulation of ER eXport (AREX), which allow the ER to adapt to fluctuations and stress. However, the challenges of modeling their activities arise from the lack of data and the complexity of the signaling pathways that activate them. In this study, we utilize a simulation tool commonly employed in manufacturing plants to develop a model that replicates the protein production process in the ER and the actions of the UPR and AREX in mitigating stress conditions. Our simulations provide insights into the behavior of the cell and represent the first attempt to integrate the entire protein production process and the control activity in the ER. The simulation results demonstrate the potential of regarding the ER as a manufacturing process and provide a novel approach to understanding the complex regulation of the ER
Feedback stabilization control design for switched Boolean control networks
No full textIn this paper, the design of all possible switching signal-dependent state feedback and output feedback stabilizers for switched Boolean control networks (SBCNs) under arbitrary switching signal is investigated. By making use of the algebraic state-space approach to SBCNs, necessary and sufficient conditions for the stabilization at a given equilibrium point are presented. A new algorithm is proposed to determine all possible complete families of controllable sets for SBCNs, thereby all possible switching signal-dependent state feedback and output feedback controllers are obtained. Furthermore, we derive necessary and sufficient conditions for the solvability of the output regulation problem, and we show that, upon satisfying such conditions, also this problem can be solved by means of switching signal-dependent state feedback and output feedback controllers. At last, the proposed results are supported by examples
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