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Aim: Agar-based phantoms are popular in high intensity focused ultrasound (HIFU) studies, with magnetic resonance imaging (MRI) preferred for guidance since it provides temperature monitoring by proton resonance frequency (PRF) shift magnetic resonance (MR) thermometry. MR thermometry monitoring depends on several factors, thus, herein, the PRF coefficient of agar phantoms was estimated. Materials and Methods: Seven phantoms were developed with varied agar (2, 4, or 6% w/v) or constant agar (6% w/v) and varied silica concentrations (2, 4, 6, or 8% w/v) to assess the effect of the concentration on the PRF coefficient. Each phantom was sonicated using varied acoustical power for a 30 s duration in both a laboratory setting and inside a 3T MRI scanner. PRF coefficients were estimated through linear trends between phase shift acquired using gradient sequences and thermocouple-based temperatures changes. Results: Linear regression (R2 = 0.9707-0.9991) demonstrated a proportional dependency of phase shift with temperature change, resulting in PRF coefficients between -0.00336 ± 0.00029 and -0.00934 ± 0.00050 ppm/°C for the various phantom recipes. Weak negative linear correlations of the PRF coefficient were observed with increased agar. With silica concentrations, the negative linear correlation was strong. For all phantoms, calibrated PRF coefficients resulted in 1.01-3.01-fold higher temperature changes compared to the values calculated using a literature PRF coefficient. Conclusions: Phantoms developed with a 6% w/v agar concentration and doped with 0%-8% w/v silica best resemble tissue PRF coefficients and should be preferred in HIFU studies. The estimated PRF coefficients can result in enhanced MR thermometry monitoring and evaluation of HIFU protocols. [ABSTRACT FROM AUTHOR]

The effective allocation of limited resources and the establishment of targeted goals play a pivotal role in enhancing the overall efficiency of large enterprises and organizations. To achieve optimal organizational efficiency, managers seek dynamic strategies that adapt to the constraints of limited and uncertain historical data. This paper introduces an evaluation of organizational efficiency through a stochastic framework, employing a bilevel data envelopment analysis (DEA) approach. This decision-making process is centralized within a decision-making unit (DMU) overseeing subordinate decision-making units (subDMUs). Discrete scenarios, each associated with a realization probability, define the uncertain parameters in the bilevel DEA-based model. This stochastic approach allows for recourse actions upon scenario realization leading to an enhanced overall organizational strategy. Decision-makers acting within uncertain and dynamic environments can benefit from this research since it allows the investigation of efficiency assessment under alternative scenarios in the presence of volatility and risk. The potential impact of applying this methodology varies depending on the specific domain. Although, the context of this paper focuses on banking, in general, enhancing resource allocation and target setting under stochasticity, contributes to advancing sustainability across all its three dimensions (economic, environmental, social). As mentioned earlier, the practical application of our approach is demonstrated via a case study in the banking sector. [ABSTRACT FROM AUTHOR]

The optimal allocation of limited resources along with output target setting are critical in pursuing the sustainability and competitiveness of organizations. The process of resource distribution is usually implemented through a central unit that routes resources to the subordinate decision-making units (DMUs) along with DMUs lower bounds of desired efficiency. Moreover, the central unit has the authority to set the overall expected output targets so as to maximize organizational effectiveness. In this paper, we tackle evaluation efficiency questions using a type of bilevel network data envelopment analysis (DEA) approach within a stochastic framework. The proposed bilevel DEA model takes into account stochastic conditions and optimizes centralized resource allocation and target setting, imposing lower bounds on the efficiencies of all DMUs affiliated to the organization. Consequently, the total input consumption is minimized, and the total output production is maximized while considering additional bounds and availability constraints for inputs. In the proposed bilevel model, uncertainty is introduced through the upper level (leader) problem that attempts to maximize organizational effectiveness, while in the lower level (follower) problem, the efficiency of the subordinate DMUs is evaluated. A solution methodology for the bilevel network DEA-based model is presented and numerical results are obtained using data from the literature. The obtained results are compared with those published in other case studies for centralized resource allocation DEA models. [ABSTRACT FROM AUTHOR]