Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model

Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a trade-off between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource, is considered in the proposed model. Additionally, a bounded rational behavior model (Quantal Response, QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference

Soluble interleukin-2 receptor combined with interleukin-8 is a powerful predictor of future adverse cardiovascular events in patients with acute myocardial infarction

BackgroundLittle is known about the role of interleukin (IL) in patients with acute myocardial infarction (MI), especially soluble IL-2 receptor (sIL-2R) and IL-8. We aim to evaluate, in MI patients, the predictive value of serum sIL-2R and IL-8 for future major adverse cardiovascular events (MACEs), and compare them with current biomarkers reflecting myocardial inflammation and injury.MethodsThis was a prospective, single-center cohort study. We measured serum concentrations of IL-1β, sIL-2R, IL-6, IL-8 and IL-10. Levels of current biomarkers for predicting MACEs were measured, including high-sensitivity C reactive protein, cardiac troponin T and N-terminal pro-brain natriuretic peptide. Clinical events were collected during 1-year and a median of 2.2 years (long-term) follow-up.ResultsTwenty-four patients (13.8%, 24/173) experienced MACEs during 1-year follow-up and 40 patients (23.1%, 40/173) during long-term follow-up. Of the five interleukins studied, only sIL-2R and IL-8 were independently associated with endpoints during 1-year or long-term follow-up. Patients with high sIL-2R or IL-8 levels (higher than the cutoff value) had a significantly higher risk of MACEs during 1-year (sIL-2R: HR 7.7, 3.3–18.0, p < 0.001; IL-8: HR 4.8, 2.1–10.7, p < 0.001) and long-term (sIL-2R: HR 7.7, 3.3–18.0, p < 0.001; IL-8: HR 4.8, 2.1–10.7, p < 0.001) follow-up. Receiver operator characteristic curve analysis regarding predictive accuracy for MACEs during 1-year follow-up showed that the area under the curve for sIL-2R, IL-8, sIL-2R combined with IL-8 was 0.66 (0.54–0.79, p = 0.011), 0.69 (0.56–0.82, p < 0.001) and 0.720 (0.59–0.85, p < 0.001), whose predictive value were superior to that of current biomarkers. The addition of sIL-2R combined with IL-8 to the existing prediction model resulted in a significant improvement in predictive power (p = 0.029), prompting a 20.8% increase in the proportion of correct classifications.ConclusionsHigh serum sIL-2R combined with IL-8 levels was significantly associated with MACEs during follow-up in patients with MI, suggesting that sIL-2R combined with IL-8 may be a helpful biomarker for identifying the increased risk of new cardiovascular events. IL-2 and IL-8 would be promising therapeutic targets for anti-inflammatory therapy

Energy Efficiency Evaluation of Power Equipment Based on DEA

As the current situation of imperfect model algorithm of electrical equipment for energy efficiency evaluation, we set up energy efficiency DEA model. In the model, we got average load rate, average power factor and tri-phase unbalance factor as inputs indexes. And we got economic output of per unit of power consumption and energy pollution as outputs indexes. Then we transformed multiple pollutants into a pollutant index through principal component analysis. And we got it as the desired output. The result of 12 industrial enterprises in the energy efficiency of electrical equipment shows that the evaluation model is suitable for energy efficiency evaluation and system analysis. The DEA model is useful to further improvement in the energy efficiency of equipment