A Digital Twin for Decision Making on Livestock Feeding

Author(s):  
David Raba ◽  
Rafael D. Tordecilla ◽  
Pedro Copado ◽  
Angel A. Juan ◽  
Daniel Mount

Looking for an accurate and cost-effective solution to measure feed inventories, forecast the feed demand and allow feed suppliers to optimize inventories, production batches, and delivery routes.

2020 ◽  
pp. medethics-2020-106177
Author(s):  
Harleen Kaur Johal ◽  
Christopher Danbury

Conflict is an important consideration in the intensive care unit (ICU). In this setting, conflict most commonly occurs over the ‘best interests’ of the incapacitated adult patient; for instance, when families seek aggressive life-sustaining treatments, which are thought by the medical team to be potentially inappropriate. Indeed, indecision on futility of treatment and the initiation of end-of-life discussions are recognised to be among the greatest challenges of working in the ICU, leading to emotional and psychological ‘burnout’ in ICU teams. When these disagreements occur, they may be within the clinical team or among those close to the patient, or between the clinical team and those close to the patient. It is, therefore, crucial to have a theoretical understanding of decision-making itself, as unpicking misalignments in the family’s and clinical team’s decision-making processes may offer strategies to resolve conflict. Here, we relate Kahneman and Tversky’s work on cognitive biases and behavioural economics to the ICU environment, arguing that these biases could partly explain disparities in the decision-making processes for the two conflicting parties. We suggest that through the establishment of common ground, challenging of cognitive biases and formulation of mutually agreeable solutions, mediation may offer a pragmatic and cost-effective solution to conflict resolution. The litigation process is intrinsically adversarial and strains the doctor–patient–relative relationship. Thus an alternative external party should be considered, however mediation is not frequently used and more research is needed into its effectiveness in resolving conflicts in the ICU.


2019 ◽  
Vol 2 (4) ◽  
Author(s):  
Ralitsa Akins

There is a paucity of publications about new regional medical campuses. The authors, members of the Association of American Medical Colleges (AAMC) and its Group on Regional Medical Campuses (GRMC), offer a historical perspective about the role of Regional Medical Campuses (RMCs), and provide a roadmap to establishing a new RMC, including logistics, resources, curriculum, student services, faculty, affiliations and networking within the community. A checklist designed to support leadership decision-making is also included. The RMC is an efficient model for increasing opportunities for clinical training, accommodating expansion of graduate medical education, and offering a cost-effective solution to train future physicians. Conflicts of Interest: None


Author(s):  
Guang Zou ◽  
Kian Banisoleiman ◽  
Arturo González

A challenge in marine and offshore engineering is structural integrity management (SIM) of assets such as ships, offshore structures, mooring systems, etc. Due to harsh marine environments, fatigue cracking and corrosion present persistent threats to structural integrity. SIM for such assets is complicated because of a very large number of rewelded plates and joints, for which condition inspections and maintenance are difficult and expensive tasks. Marine SIM needs to take into account uncertainty in material properties, loading characteristics, fatigue models, detection capacities of inspection methods, etc. Optimising inspection and maintenance strategies under uncertainty is therefore vital for effective SIM and cost reductions. This paper proposes a value of information (VoI) computation and Bayesian decision optimisation (BDO) approach to optimal maintenance planning of typical fatigue-prone structural systems under uncertainty. It is shown that the approach can yield optimal maintenance strategies reliably in various maintenance decision making problems or contexts, which are characterized by different cost ratios. It is also shown that there are decision making contexts where inspection information doesn’t add value, and condition based maintenance (CBM) is not cost-effective. The CBM strategy is optimal only in the decision making contexts where VoI > 0. The proposed approach overcomes the limitation of CBM strategy and highlights the importance of VoI computation (to confirm VoI > 0) before adopting inspections and CBM.


2021 ◽  
Vol 13 (5) ◽  
pp. 2703
Author(s):  
Rodrigo A. Estévez ◽  
Stefan Gelcich

The United Nations calls on the international community to implement an ecosystem approach to fisheries (EAF) that considers the complex interrelationships between fisheries and marine and coastal ecosystems, including social and economic dimensions. However, countries experience significant national challenges for the application of the EAF. In this article, we used public officials’ knowledge to understand advances, gaps, and priorities for the implementation of the EAF in Chile. For this, we relied on the valuable information held by fisheries managers and government officials to support decision-making. In Chile, the EAF was established as a mandatory requirement for fisheries management in 2013. Key positive aspects include the promotion of fishers’ participation in inter-sectorial Management Committees to administrate fisheries and the regulation of bycatch and trawling on seamounts. Likewise, Scientific Committees formal roles in management allow the participation of scientists by setting catch limits for each fishery. However, important gaps were also identified. Officials highlighted serious difficulties to integrate social dimensions in fisheries management, and low effective coordination among the institutions to implement the EAF. We concluded that establishing clear protocols to systematize and generate formal instances to build upon government officials’ knowledge seems a clear and cost effective way to advance in the effective implementation of the EAF.


Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2338
Author(s):  
Sofia Agostinelli ◽  
Fabrizio Cumo ◽  
Giambattista Guidi ◽  
Claudio Tomazzoli

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.


Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2963
Author(s):  
Melinda Timea Fülöp ◽  
Miklós Gubán ◽  
György Kovács ◽  
Mihály Avornicului

Due to globalization and increased market competition, forwarding companies must focus on the optimization of their international transport activities and on cost reduction. The minimization of the amount and cost of fuel results in increased competition and profitability of the companies as well as the reduction of environmental damage. Nowadays, these aspects are particularly important. This research aims to develop a new optimization method for road freight transport costs in order to reduce the fuel costs and determine optimal fueling stations and to calculate the optimal quantity of fuel to refill. The mathematical method developed in this research has two phases. In the first phase the optimal, most cost-effective fuel station is determined based on the potential fuel stations. The specific fuel prices differ per fuel station, and the stations are located at different distances from the main transport way. The method developed in this study supports drivers’ decision-making regarding whether to refuel at a farther but cheaper fuel station or at a nearer but more expensive fuel station based on the more economical choice. Thereafter, it is necessary to determine the optimal fuel volume, i.e., the exact volume required including a safe amount to cover stochastic incidents (e.g., road closures). This aspect of the optimization method supports drivers’ optimal decision-making regarding optimal fuel stations and how much fuel to obtain in order to reduce the fuel cost. Therefore, the application of this new method instead of the recently applied ad-hoc individual decision-making of the drivers results in significant fuel cost savings. A case study confirmed the efficiency of the proposed method.


2021 ◽  
Vol 9 (6) ◽  
pp. 596
Author(s):  
Murugan Ramasamy ◽  
Mohammed Abdul Hannan ◽  
Yaseen Adnan Ahmed ◽  
Arun Kr Dev

Offshore vessels (OVs) often require precise station-keeping and some vessels, for example, vessels involved in geotechnical drilling, generally use Spread Mooring (SM) or Dynamic Positioning (DP) systems. Most of these vessels are equipped with both systems to cover all ranges of water depths. However, determining which system to use for a particular operational scenario depends on many factors and requires significant balancing in terms of cost-benefit. Therefore, this research aims to develop a platform that will determine the cost factors for both the SM and DP station-keeping systems. Operational information and cost data are collected for several field operations, and Artificial Neural Networks (ANN) are trained using those data samples. After that, the trained ANN is used to predict the components of cost for any given environmental situation, fieldwork duration and water depth. Later, the total cost is investigated against water depth for both DP and SM systems to determine the most cost-effective option. The results are validated using two operational scenarios for a specific geotechnical vessel. This decision-making algorithm can be further developed by adding up more operational data for various vessels and can be applied in the development of sustainable decision-making business models for OVs operators.


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