Anti-glomerular basement membrane (GBM) disease is a rare, aggressive vasculitis with no validated prediction tools to assist its management. We investigated a retrospective multicenter international cohort with the aim to transfer the Renal Risk Score (RRS) and to identify patients that benefit from rescue immunosuppressive therapy.Of a total 191 patients, 174 patients were included in the final analysis (57% female, median age 59 years). Using Cox and Kaplan-Meier methods, the RRS was found to be a strong and effective predictor for end stage kidney disease (ESKD) with a model concordance of C=0.760. The 36-month renal survival was 100%, 62.4%, and 20.7% in the low-, moderate-, and high-risk groups, respectively (P<0.001). The need for renal replacement therapy (RRT) at diagnosis and the percentage of normal glomeruli in the biopsy were independent predictors of ESKD (P<0.001, P<0.001).Considering the 129 patients initially requiring RRT, the best predictor for renal recovery was the percentage of normal glomeruli (C=0.622; P<0.001), a split either side of 10% providing good stratification. A model with the predictors RRT and normal glomeruli (N) achieved superior discrimination (C=0.840, P<0.001). Dividing patients into four risk groups led to a 36-month renal survival of 96.4% (no RRT, N≥10%), 74.0% (no RRT, N<10%), 42.3% (RRT, N≥10%) and 14.1% (RRT, N<10%), respectively.In summary, we demonstrate that the RRS concept is transferrable to anti-GBM disease. Stratifying patients according to the need for RRT at diagnosis and renal histology improves prediction, highlighting the importance of normal glomeruli. Here, we propose a stratification to assist in the management of anti-GBM disease.

Flow Shop Scheduling Problem (FSSP) has significant application in the industry, and therefore it has been extensively addressed in the literature using different optimization techniques. Current research investigates Permutation Flow Shop Scheduling Problem (PFSSP) to minimize makespan using Hybrid Evolution Strategy (HESSA). Initially, a global search of the solution space is performed using an Improved Evolution Strategy (IES), then the solution is improved by utilizing local search abilities of Simulated Annealing (SA). IES thoroughly exploits the solution space using the reproduction operator, in which four offsprings are generated from one parent. A double swap mutation is used to guide the search to more promising areas in less computational time. The mutation rate is also varied for the fine-tuning of results. The best solution of the IES acts as a seed for SA, which further improved the results by exploring better neighborhood solutions. In SA, insertion mutation is used, and the cooling parameter and acceptancerejection criteria induce randomness in the algorithm. The proposed HESSA algorithm is tested on wellknown NP-hard benchmark problems of Taillard (120 instances), and the performance of the proposed algorithm is compared with the famous techniques available in the literature. Experimental results indicate that the proposed HESSA algorithm finds 54 Upper bounds for Taillard instances, while 38 results are further improved for the Taillard instances.

In this paper, a robust schedule has been proposed to deal with uncertainities for m-machines permutation flow shop problems. A robust schedule ensures that the expected finish time is always less than the makespan. To use the global search ability of the evolution strategy (ES) and local search ability of Tabu Search (TS), a hybrid evolution strategy (HES) is proposed by combining Improved ES with TS to generate the robust schedules. The robust schedule is first generated using ES and then the solution is optimized using TS for maximum exploitation and exploration of the solution space. For maximum exploitation in ES, (1+9) reproduction operator and double swap mutation is used. Also variable mutation rate is used for fine tuning of the results. In TS, the length of Tabu list is fixed, also lower bound is used to save computational time. The hybrid algorithm is tested on Carlier and Reeves benchmark problems taken from the OR-library. Achieved results are compared with other famous techniques available in the literature, and the results show that HES performs better than other techniques and provides an affirmative percentage increase in the probability that the expected finish time is less than the makespan.

Carbonates are characterized by low oil recovery due to their positive surface charge and consequent high affinity to negatively charged crude oil, rendering them to a state of mixed-to-oil wettability. In order to understand the rock/brine/oil interactions and their effect on potential-determining-ions (PDIs) adsorption/desorption during engineered water injection is needed for realistic and representative estimations of oil recovery. Therefore, this study reveals a novel approach to capture various interactions and better predict the effect of PDIs adsorption/desorption as well as concentrations of various ionic species in the effluent using Phreeqc. In this work, we determined adsorption/desorption of PDIs for the first time using surface complexation reactions and then we validated our results with experimental data from the literature. Our results showed that the presence of PDIs and their respective adsorption/desorption results in surface charge decrease and increase in pH. Also, this study found that ionic adsorption depends on ionic strength and species activity where calcium adsorption remained constant while magnesium and sulfate adsorptions varied with ionic strength. Moreover, magnesium ion was found to be the most sensitive ionic species to temperature as opposed to calcium and sulfate ions. In addition, sulfate spiking and dilution might decrease sulfate adsorption since the sulfate starts reacting with magnesium and forming complexes. Additionally, deionized water resulted in the highest charge decrease and pH increase with related incremental oil recovery. The adsorption/desorption of ions is case-dependent and thus, the findings cannot be generalized.

Being complex and combinatorial optimization problems, Permutation Flow Shop Scheduling Problems (PFSSP) are difficult to be solved optimally. PFSSP occurs in many manufacturing systems i.e. automobile industry, glass industry, paper industry, appliances industry, and pharmaceutical industry, and the generation of the best schedule is very important for these manufacturing systems. Evolution Strategy (ES) is a subclass of Evolutionary algorithms and in this paper, we propose an Improved Evolution Strategy to reduce the makespan of PFSSP. Two variants of the Improved Evolution Strategy are proposed namely ES5 and ES10. The initial solution is generated using the shortest processing time rule. In ES5, four offsprings are generated from one parent while in ES10, nine offsprings are generated from one parent. The selection pool consists of both the parents and offsprings. Quad swap mutation operator has been proposed to minimize computational time and for the maximum search of solution space. Also, a variable mutation rate is used for the fine-tuning of results, with the increasing number of iterations the mutation rate is reduced. The performances of both ES variants were tested on two test domains. First, it is applied to benchmark the PFSSP of Carlier and Reeves. Computational results are matched with other well-known techniques available in the literature, and the results show the effectiveness and robustness of the proposed techniques. Secondly, ES is applied to the real-life problem for the manufacturing of batteries to demonstrate its effectiveness. Data was taken from Pakistan Accumulator for NS30-40 Plates battery, the company is daily producing 1400 units of NS30-40 Plates battery. ES is applied to different batch sizes i.e. 35, 140, 1120 & 1400. Our results show that a Min %GAP of 1.25 is found using ES10. Hence the company can increase monthly 450 units of NS30 batteries using the ES10 algorithm.