Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank. An evaluation using two bio-inspired algorithms (bat algorithm (BA) and krill herd (KH)) and 12 optimization problems (cross-in-tray, rotated hyper-ellipsoid (RHE), sphere, sum of squares, sum of different powers, McCormick, Zakharov, Rosenbrock, De Jong No. 5, Easom, Branin, and Styblinski–Tang) is presented. Furthermore, an experimental evaluation of the proposed scheme using the industrial three-bar truss design problem is presented. The experimental results demonstrate that the hybrid scheme outperformed the baseline algorithms (mean rank for the hybrid BA-KH algorithm is 1.279 vs. 1.958 for KH and 2.763 for BA).

Modeling of hospital admissions for respiratory diseases as a function of probability distribution functions

Objective: To analyze the adjustments of the weibull, gamma, normal and logistic probability density distributions of the historical series of hospitalizations for respiratory diseases (childhood and adult pneumonia) from 2011 to 2015, in Campo Grande, MS. Methods: The shape and scale parameters of the distributions were determined to verify the quality of the data fit. Results: Four probability density functions (Table 2) were fitted and the R2, MAE, RSME, MAPE tests were used to verify the best density function for hospitalization data. Conclusion: The best fit was the Gamma distribution; the distribution can be used as an alternative distribution that adequately describes the data on hospital admissions for respiratory diseases in Campo Grande.

To procrastinate or not to procrastinate: the optimal timing of containing the global COVID-19 pandemic spread

As global public health is under threat by the 2019-nCoV, an urgent question to ask is what the optimal strategy of epidemic prevention and control (P&C) measures would be, especially in terms of the timing of enforcing aggressive policy response so as to maximise health efficacy and to contain pandemic spread. Here, we developed a logistic probability function configured SEIR model to analyse the COVID-19 outbreak and estimate its transmission pattern under different “anticipate- or delay-to-activate” policy response scenarios in containing the pandemic. We wound that the potential positive effects of stringent P&C measures would be cancelled out in case of significantly delayed action, whereas a partially procastinatory wait-and-see control policy may still be able to contribute to containing the degree of epidemic spread although its effectiveness may be significantly compromised compared to a scenario of early intervention coupled with stringent P&C measures. A laissez faire policy adopted by the government and health authority to tackling the uncertainly of COVID19-type pandemic development during the early stage of the outbreak turns out to be a very bad strategy from optimal control perspective, as significant damages would be produced in that case. Authors Jun Li and Yimin Zhou contributed equally to this work.

An improved logistic probability prediction model for water shortage risk in situations with insufficient data

In drought years, it is important to have an estimate or prediction of the probability that a water shortage risk will occur to enable risk mitigation. This study developed an improved logistic probability prediction model for water shortage risk in situations when there is insufficient data. First, information flow was applied to select water shortage risk factors. Then, the logistic regression model was used to describe the relation between water shortage risk and its factors, and an alternative method of parameter estimation (maximum entropy estimation) was proposed in situations where insufficient data was available. Water shortage risk probabilities in Beijing were predicted under different inflow scenarios by using the model. There were two main findings of the study. (1) The water shortage risk probability was predicted to be very high in 2020, although this was not the case in some high inflow conditions. (2) After using the transferred and reclaimed water, the water shortage risk probability declined under all inflow conditions (59.1% on average), but the water shortage risk probability was still high in some low inflow conditions.

Validation of the Six Sigma Z-score for the quality assessment of clinical laboratory timeliness

Background: The International Federation of Clinical Chemistry and Laboratory Medicine has introduced in recent times the turnaround time (TAT) as mandatory quality indicator for the postanalytical phase. Classic TAT indicators, namely, average, median, 90th percentile and proportion of acceptable test (PAT), are in use since almost 40 years and to date represent the mainstay for gauging the laboratory timeliness. In this study, we investigated the performance of the Six Sigma Z-score, which was previously introduced as a device for the quantitative assessment of timeliness. Methods: A numerical simulation was obtained modeling the actual TAT data set using the log-logistic probability density function. Five thousand replicates for each size of the artificial TAT random sample (n=20, 50, 250 and 1000) were generated, and different laboratory conditions were simulated manipulating the PDF in order to generate more or less variable data. The Z-score and the classic TAT indicators were assessed for precision (%CV), robustness toward right-tailing (precision at different sample variability), sensitivity and specificity. Results: Z-score showed sensitivity and specificity comparable to PAT (≈80% with n≥250), but superior precision that ranged within 20% by moderately small sized samples (n≥50); furthermore, Z-score was less affected by the value of the cutoff used for setting the acceptable TAT, as well as by the sample variability that reflected into the magnitude of right-tailing. Conclusions: The Z-score was a valid indicator of laboratory timeliness and a suitable device to improve as well as to maintain the achieved quality level.

Impact of Basel III framework on financial distress: A case study of Pakistan

Purpose: New liquidity rules phased under the Basel lll define the new stable funding ratios (NSFR) increase the stability of the funding structure of the financial institution. Using a Pakistani banking data, we tested the relevance of both Structural liquidity and Capital ratios as defined in the Basel lll. We used the broad definition of the failure and distress to check the status of the banking sector. If the banks fail, then it denoted by 1 otherwise 0. We use the logistic regression in our study. Estimate from several versions of the logistic probability model indicate that the likelihood of failure and distress decrease with increase liquidity holding while capital ratios are not significant. Our result provides support for the Basel lll that the NSFR has the inverse relation with the bank failure and distress. This study also compared the two versions of the NSFR. NSFR-10 and NSFR-14 are the two versions. Our analysis tells that the NSFR-14 is more reliable as compare to the NSFR-10. We also check the bank situations whether it lies in the failure and distress condition or in active banks. In this study we also check the other variables that have an important impact on the stability and failure and distress of the banks.

Using Skew-Logistic Probability Density Function as a Model for Age-Specific Fertility Rate Pattern

Fertility rate is one of the most important global indexes. Past researchers found models which fit to age-specific fertility rates. For example, mixture probability density functions have been proposed for situations with bi-modal fertility patterns. This model is less useful for unimodal age-specific fertility rate patterns, so a model based on skew-symmetric (skew-normal) pdf was proposed by Mazzuco and Scarpa (2011) which was flexible for unimodal and bimodal fertility patterns. In this paper, we introduce skew-logistic probability density function as a better model: its residuals are less than those of the skew-normal model and it can more precisely estimate the parameters of the model.