Multiple Performance Factors Research Articles

Five Influential Factors for Clinical Team Performance in Urgent, Emergency Care Contexts.

In deployed contexts, military medical care is provided through the coordinated efforts of multiple interdisciplinary teams that work across and between a continuum of widely distributed role theaters. The forms these teams take, and functional demands, vary by roles of care, location, and mission requirements. Understanding the requirements for optimal performance of these teams to provide emergency, urgent, and trauma care for multiple patients simultaneously is critical. A team's collective ability to function is dependent on the clinical expertise (knowledge and skills), authority, experience, and affective management capabilities of the team members. Identifying the relative impacts of multiple performance factors on the accuracy of care provided by interdisciplinary clinical teams will inform targeted development requirements. A regression study design determined the extent to which factors known to influence team performance impacted the effectiveness of small, six to eight people, interdisciplinary teams tasked with concurrently caring for multiple patients with urgent, emergency care needs. Linear regression analysis was used to distinguish which of the 11 identified predictors individually and collectively contributed to the clinical accuracy of team performance in simulated emergency care contexts. All data met the assumptions for regression analyses. Stepwise linear regression analysis of the 11 predictors on team performance yielded a model of five predictors accounting for 82.30% of the variance. The five predictors of team performance include (1) clinical skills, (2) team size, (3) authority profile, (4) clinical knowledge, and (5) familiarity with team members. The analysis of variance confirmed a significant linear relationship between team performance and the five predictors, F(5, 240) = 218.34, P < .001. The outcomes of this study demonstrate that the collective knowledge, skills, and abilities within an urgent, emergency care team must be developed to the extent that each team member is able to competently perform their role functions and that smaller teams benefit by being composed of clinical authorities who are familiar with each other. Ideally, smaller, forward-deployed military teams will be an expert team of individual experts, with the collective expertise and abilities required for their patients. This expertise and familiarity are advantageous for collective consideration of significant clinical details, potential alternatives for treatment, decision-making, and effective implementation of clinical skills during patient care. Identifying the most influential team performance factors narrows the focus of team development strategies to precisely what is needed for a team to optimally perform.

Energy and Path-Aware-Reliable Routing in Underwater Acoustic Wireless Sensor Networks

In underwater acoustic sensor networks (UASNs), energy awareness, best path selection, reliability, and scalability are among the key factors that decide information delivery to the sea surface. Existing protocols usually do not combine such performance-affecting factors in information routing. As a result, the performance of such protocols usually deteriorates if multiple performance factors are taken into account. To cope with such performance deterioration, this article proposes two routing protocols for UASNs: energy and path-aware reliable routing (EPRR) and cooperative EPRR (Co-EPRR). Compared with the counterpart systems, the proposed protocols have been designed to deal with the problem of long propagation delays and achieve network reliability. The EPRR scheme uses nodes’ physical distance from the surface with its depth, which minimized the delay of packet transmission. The channel interaction time has been reduced, therefore, reducing unwanted channel effects on the data. Furthermore, the density of the nodes in the upper part of the network prevents data loss and limits the rapid death of the nodes. The second proposed scheme, Co-EPRR, uses the concept of routing information from the source to the destination on multiple paths. In Co-EPRR routing, the destination node can receive more than one copy of the data packet. This reduces unfavorable channel effects during data delivery. Both the schemes show good performance in terms of packet delivery ratio, received packet analysis, and end-to-end delay.

Evaluating and monitoring distribution network efficiency with multivariate process control methods

In this paper, we present a methodology, by utilising a nonparametric multivariate cumulative sum control chart and data envelopment analysis in tandem, to evaluate and monitor distribution network performance by simultaneously considering multiple performance factors and their interrelationships. While these methods were previously applied individually, we demonstrate that their combined use can provide more powerful results in performance assessment, monitoring and improvement. Our approach contributes to the domain of supply chain analytics and risk management as it can provide new insights into network performance based on real time and past data. We present the application of our approach on data obtained from The Dow Chemical Company.

MPF-MLBS: A Multi-path Load Balancing Strategy for SDN Networks Based on Multiple Performance Factors

Aiming at the common load balancing problems in the network, a multi-path load balancing strategy (MPF-MLBS) based on multi-performance factor (MPF) for Software Defined Network (SDN) networks is proposed, which can be divided into two stages: algorithm design and strategy implementation. In the algorithm design stage, the advantages and disadvantages of the existing load balancing algorithms are analyzed, combined with the characteristics of the SDN network architecture, and the bandwidth, delay and link rate of the network link are comprehensively considered. Based on this, multiple performance factors are defined, and a load balancing algorithm based on multiple performance factors (MPF-CMP) is designed and implemented. In the strategy implementation stage, build a multi-path network topology based on the SDN architecture, and use the depth-first traversal algorithm to traverse the global network to obtain the required link information; Subsequently, the MPF-CMP algorithm and OpenFlow group table technology are combined to complete the proportional distribution of network traffic to each available path, thereby achieving multi-path load balancing of the SDN network. The simulation experiment results show that the strategy effectively exerts the SDN controller's overall network monitoring and scheduling functions, can obtain link information in real time and distribute and transmit network traffic according to the situation. The overload of a single path is avoided, the data packet transmission volume of all available paths is effectively increased, the flow transmission efficiency of the entire network is improved, and the multi-path load balancing of the SDN network is realized.

Solar assisted heat engine systems: multi-objective optimisation and decision making

Identifying, analysing and optimising the effect of various parameters of a solar assisted heat engine system contribute significantly in assessing the system's performance. Optimum parameters can be identified by performing multi-objective optimisation (MOO) considering multiple performance factors. Usually, non-dominated solutions set can be found for these MOO problems and selecting a most suitable solution from non-dominated solutions set is difficult. Hence, in this study, the applicability of multi attribute decision-making methods in MOO problems has been discussed and a decision making procedure is suggested on the basis of average rank to identify the best solution in a Pareto front. Two MOO case-studies of solar-assisted engine systems are optimised by using the multi-objective-Jaya-algorithm and its variants known as multi-team perturbation guiding Jaya algorithm and adaptive multi-team prturbation guiding Jaya algorithm. In both the case studies, the proposed algorithms have improved the performance of the considered systems. Furthermore, the performances of the proposed algorithms are compared using the coverage, spacing, and hypervolume indicators.

New Miller Codes for Run-Length Control in Visible Light Communications

Designing run-length limited codes for visible light communication systems must account for multiple performance factors, including spectral efficiency, power efficiency, dc balance, and flicker avoidance. This paper reports a new class of enhanced Miller codes, termed eMiller codes, which are capable of achieving highly desirable performances in all of these accounts. An improved Viterbi algorithm (VA), termed $mn$ VA, is developed to help further enhance the performance of eMiller codes by preserving multiple candidate sequences at each decoding stage. This performance-enhancing algorithm introduces little complexity increase compared with the original VA. Analysis on flicker control, power spectral density, and minimum Hamming distance demonstrates the all-around wellness of these new codes. Extensive simulations are carried out to evaluate eMiller codes by themselves and in practical visible light communication (VLC) systems. It is shown that the original VA already allows eMiller codes to deliver a performance noticeably better than conventional Miller and FM0/FM1 codes (and on par with Manchester codes). This result is particularly exciting, as eMiller codes are also more spectrally efficient than Manchester codes. The $mn$ VA further allows eMiller codes to surpass Manchester codes and 4B6B codes in practical RS-coded VLC systems. Simulation results confirm the superb performance of the RS-eMiller schemes.

비모수 분석방법에 의한 국내 건설조직 성과 측정 방향에 관한 연구 - DEA를 이용한 국내 연구 문헌 고찰을 기반으로 -

Abstract : Data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) with multiple performance factors that are grouped into outputs and inputs. DEA has proven to be superior to simple aggregation of performance measures, and is also useful for evaluating the performance of construction companies for comparison with competitor performance. The purpose of this study was to survey literatures on the application of DEA methodology and to propose a methodological scheme to measure the performance of construction organizations. Articles on previous studies were surveyed and examined as part of a comprehensive review. The survey revealed that the application of DEA in the construction industry was limited. Further, the survey indicated that there is a need for the development of a methodological framework on the special goals and subjects of performance measurement, methods of data structure and collection, selection of appropriate DEA models, analysis of results, and post-test. Based on the survey, this study identified and discussed the types of major issues and topics for future studies from a methodological perspective, which could be helpful to researchers interested in using DEA to study performance issues in construction organizations.

Modeling undesirable factors in efficiency evaluation

Data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) with multiple performance factors which are grouped into outputs and inputs. Once the efficient frontier is determined, inefficient DMUs can improve their performance to reach the efficient frontier by either increasing their current output levels or decreasing their current input levels. However, both desirable (good) and undesirable (bad) factors may be present. For example, if inefficiency exists in production processes where final products are manufactured with a production of wastes and pollutants, the outputs of wastes and pollutants are undesirable and should be reduced to improve the performance. Using the classification invariance property, we show that the standard DEA model can be used to improve the performance via increasing the desirable outputs and decreasing the undesirable outputs. The method can also be applied to situations when some inputs need to be increased to improve the performance. The linearity and convexity of DEA are preserved through our proposal.

Avoidance learning in autoimmune mice

Previous studies have shown that autoimmune mice perform very poorly on active avoidance learning tasks. In the current studies, mice with lupus-like systemic autoimmunity were able to learn active, as well as passive, avoidance protocols with shock as reinforcement. Therefore, the behavioral deficits seen in active avoidance tasks are not a consequence of the use of electric shock. Rather, the current findings suggest that the inability of autoimmune mice to learn shock motivated responding is due to multiple performance factors, including shock level and properties of the testing apparatus.