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Sexual harassment are situations where the unwelcome sexual conduct of co‐workers or supervisors interferes with an individual’s ability to work or creates an intimidating or offensive atmosphere. It involves situations where a workplace superior or co‐worker demands some degree of sexual favour and threatens to or actually does retaliate in a way that has a tangible effect on the working conditions of the harassment victim if he or she refuses to acquiesce.

Three-axis accelerometers play a vital role in monitoring the vibrations in aircraft machinery, especially in variable flight temperature environments. The sensitivity of a three-axis accelerometer under different temperature conditions needs to be calibrated before the flight test. Hence, the authors investigated the efficiency and sensitivity calibration of three-axis accelerometers under different conditions. This paper aims to propose the novel calibration algorithm for the three-axis accelerometers or the similar accelerometers.

The authors propose a hybrid genetic algorithm–particle swarm optimisation–back-propagation neural network (GA–PSO–BPNN) algorithm. This method has high global search ability, fast convergence speed and strong non-linear fitting capability; it follows the rules of natural selection and survival of the fittest. The authors describe the experimental setup for the calibration of the three-axis accelerometer using a three-comprehensive electrodynamic vibration test box, which provides different temperatures. Furthermore, to evaluate the performance of the hybrid GA–PSO–BPNN algorithm for sensitivity calibration, the authors performed a detailed comparative experimental analysis of the BPNN, GA–BPNN, PSO–BPNN and GA–PSO–BPNN algorithms under different temperatures (−55, 0 , 25 and 70 °C).

It has been showed that the prediction error of three-axis accelerometer under the hybrid GA–PSO–BPNN algorithm is the least (approximately ±0.1), which proved that the proposed GA–PSO–BPNN algorithm performed well on the sensitivity calibration of the three-axis accelerometer under different temperatures conditions.

The designed GA–PSO–BPNN algorithm with high global search ability, fast convergence speed and strong non-linear fitting capability has been proposed to decrease the sensitivity calibration error of three-axis accelerometer, and the hybrid algorithm could reach the global optimal solution rapidly and accurately.

The purpose of this paper is to provide energy-efficient task scheduling and resource allocation (RA) in cloud data centers (CDC).

Task scheduling and RA is proposed in this paper for cloud environment, which schedules the user’s seasonal requests and allocates resources in an optimized manner. The proposed study does the following operations: data collection, feature extraction, feature reduction and RA. Initially, the online streaming data of seasonal requests of multiple users were gathered. After that, the features are extracted based on user requests along with the cloud server, and the extracted features are lessened using modified principal component analysis. For RA, the split data of the user request is identified and that data is pre-processed by computing closed frequent itemset along with entropy values. After that, the user requests are scheduled using the normalized K-means algorithm (NKMA) centered on the entropy values. Finally, the apt resources are allotted to that scheduled task using the Cauchy mutation-genetic algorithm (CM-GA). The investigational outcomes exhibit that the proposed study outruns other existing algorithms in respect to response time, execution time, clustering accuracy, precision and recall.

The proposed NKMA and CM-GA technique’s performance is analyzed by comparing them with the existing techniques. The NKMA performance is analyzed with KMA and Fuzzy C-means regarding P_rc (Precision), R_ca (Recall), F ms (f measure), Acr (Accuracy)and C_t (Clustering Time). The performance is compared to about 500 numbers of tasks. For all tasks, the NKMA provides the highest values for P_rc, R_ca, F_ms and A_cr, takes the lowest time (C_t) for clustering the data. Then, the CM-GA optimization for optimally allocating the resource in the cloud is contrasted with the GA and particle swarm optimization with respect to R_t (Response Time), P_t (Process Time), A_wt (Average Waiting Time), A_tat (Average Turnaround Time), L_cy (Latency) and T_p (Throughput). For all number of tasks, the proposed CM-GA gives the lowest values for R_t, P_t, A_wt, A_tat and L_cy and also provides the highest values for T_p. So, from the results, it is known that the proposed technique for seasonal requests RA works well and the method optimally allocates the resources in the cloud.

The proposed approach provides energy-efficient task scheduling and RA and it paves the way for the development of effective CDC.

This paper aims to assess the fiscal sustainability in Egypt during the period 1990–2018 using deficit accounts (DA) approach. It also tries to investigate the possibility of applying generational accounts (GA) in Egypt as a new approach to assess fiscal sustainability.

This paper tries to assess fiscal sustainability in Egypt during 1990–2018 using DA and GA approaches. DA approach includes primary deficit indicator, tax gap indicator, augmented Dickey-Fuller stationarity test for debt/GDP ratio and Johansen co-integration test between government revenues and expenditures. However, concerning the possibility of applying GA in Egypt, field study form was designed including specific questions to academic and executive economic experts to investigate if it is possible to apply GA in Egypt.

The empirical findings of the field study indicate that Egypt witnessed fiscal sustainability during the period 1990–2018 using DA. On the other hand, there are various obstacles, including administrative, technical, legal and political obstacles which hinder Egypt from applying GA to assess fiscal sustainability.

To the best of the authors' knowledge, this paper assesses fiscal sustainability in Egypt using DA for a longer and updated time series within 1990–2018. In addition, it is the first paper to examine the possibility of assessing fiscal sustainability using GA approach in Egypt.

In this study, the performance of ordinal GA-based trading strategies is evaluated under six classes of time series model, namely, the linear ARMA model, the bilinear model, the ARCH model, the GARCH model, the threshold model and the chaotic model. The performance criteria employed are the winning probability, accumulated returns, Sharpe ratio and luck coefficient. Asymptotic test statistics for these criteria are derived. The hypothesis as to the superiority of GA over a benchmark, say, buy-and-hold, can then be tested using Monte Carlo simulation. From this rigorously-established evaluation process, we find that simple genetic algorithms can work very well in linear stochastic environments, and that they also work very well in nonlinear deterministic (chaotic) environments. However, they may perform much worse in pure nonlinear stochastic cases. These results shed light on the superior performance of GA when it is applied to the two tick-by-tick time series of foreign exchange rates: EUR/USD and USD/JPY.

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper presents a hybrid approach of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) specifically for the Integrated Process Planning and Scheduling (IPPS) problems. GA and ACO have given different performances in different cases of IPPS problems. In some cases, GA has outperformed, and so do ACO in other cases. This hybrid method can be constructed as (I) GA to improve ACO results or (II) ACO to improve GA results. Based on the performances of the algorithm pairs on the given problem scale. This proposed hybrid GA-ACO approach (hAG) runs both GA and ACO simultaneously, and the better performing one is selected as the primary algorithm in the hybrid approach. hAG also avoids convergence by resetting parameters which cause algorithms to converge local optimum points. Moreover, the algorithm can obtain more accurate solutions with avoidance strategy. The new hybrid optimization technique (hAG) merges a GA with a local search strategy based on the interior point method. The efficiency of hAG is demonstrated by solving a constrained multi-objective mathematical test-case. The benchmarking results of the experimental studies with AIS (Artificial Immune System), GA, and ACO indicate that the proposed model has outperformed other non-hybrid algorithms in different scenarios.

Acknowledging fiscal crises as critical junctures for policy makers, this paper investigates how the recent fiscal crisis has affected the paradigmatic approach to the design of an ongoing governmental accounting (GA) reform.

This paper analyses the Italian GA harmonization as a peculiar instance of an ongoing GA reform at the crisis outbreak. A longitudinal narrative analysis of official documents is complemented with semi-structured interviews with key policy makers and participant observations.

The fiscal crisis is found to play an indirect role in the Italian GA reform, which, promoting centralization of competencies in the fields of GA, determines the intensification of the approach adopted before the crisis outbreak.

This paper extends the knowledge on the nature of post-crisis reforms by highlighting how fiscal crises can work as catalysts for paradigmatic approaches to ongoing GA reforms. This paper analyses the designing of a GA reform, whereas the long-term adaptations and outcomes of the reform are not taken into consideration.

The tight link between GA and financial management issues featuring the current paradigmatic approaches to reforms suggests the need to design GA reforms consistently with fiscal and financial management policies.

Whereas the extant literature on the nature of post-crisis reforms analyses the latter as responses to the former, this paper enlarges the knowledge on the topic by focusing on a peculiar instance of a GA reform that was ongoing at the crisis outbreak.

The purpose of this study is to reduce the number of mutations and, consequently, reduce the cost of mutation test. The results of related studies indicate that about 40% of injected faults (mutants) in the source code are effect-less (equivalent). Equivalent mutants are one of the major costs of mutation testing and the identification of equivalent and effect-less mutants has been known as an undecidable problem.

In a program with n branch instructions (if instruction) there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Given the role and impact of data in a program, some of data and codes propagates the injected mutants more likely to the output of the program. In this study, firstly the error-propagation rate of the program data is quantified using static analysis of the program control-flow graph. Then, the most error-propagating test paths are identified by the proposed heuristic algorithm (Genetic Algorithm [GA]). Data and codes with higher error-propagation rate are only considered as the strategic locations for the mutation testing.

In order to evaluate the proposed method, an extensive series of mutation testing experiments have been conducted on a set of traditional benchmark programs using MuJava tool set. The results depict that the proposed method reduces the number of mutants about 24%. Also, in the corresponding experiments, the mutation score is increased about 5.6%. The success rate of the GA in finding the most error-propagating paths of the input programs is 99%. On average, only 7.46% of generated mutants by the proposed method are equivalent. Indeed, 92.54% of generated mutants are non-equivalent.

The main contribution of this study is as follows: Proposing a set of equations to measure the error-propagation rate of each data, basic-block and execution path of a program. Proposing a genetic algorithm to identify a most error-propagating path of program as locations of mutations. Developing an efficient mutation-testing framework that mutates only the strategic locations of a program identified by the proposed genetic algorithms. Reducing the time and cost of mutation testing by reducing the equivalent mutants.

This paper aims to investigate the preventive effects of a concomitant supplementation of a lyophilized aqueous extract of Globularia alypum (Ga) leaves in a high cholesterol-diet (HC-D) on lipid profile and lecithin cholesterol acyltransferase (LCAT) activity in hypercholesterolemic rats.

Twenty-four male Wistar rats weighing 232 ± 10 g were divided into four groups (n = 6). Two control groups were fed a standard-diet (St-D) supplemented (C-Ga) or not (C) with 1.66% Ga leaf extract. The two others experimental groups were fed HC-D, which contains the St-D plus 1% of cholesterol and 0.5% of cholic acid supplemented (HC-Ga) or not (HC) with the same amount of Ga. At d28, feces were collected and fasting rats were anesthetized; bloods and livers were removed to measure biochemical parameters.

In hypercholesterolemic (HC) rats, Ga supplementation in HC-D induced a significant reduction in ALT (−64%, p = 0.002) and AST (−71%; p = 0.005) activities, in plasma TC (−55%; p = 0.03) and TG (−54%; p = 0.01) concentrations, in cholesterol contents of atherogenic lipoproteins VLDL (−78%; p = 0.004) and LDL-HDL1 (−64%; p = 0.003) and inversely, an increase in those of anti-atherogenic HDL2 (+14%; p = 0.002). Feeding the HC-D-Ga exhibited a reduction in atherogenic index Apo B/Apo A-I (−72%; p = 0.002), an increase in faecal lipids, cholesterol excretion and in plasma apo A-I (+60%; p = 0.002) and HDL2-cholesteryl esters (+32%, p = 0.04) and then improved LCAT activity (+31%; p = 0.03).

In hypercholesterolemic rats, Globularia alypum extract was effective in preventing lipid disorders by its hypolipidemic action, had an anti-atherogenic potential and a protective effect against cardiovascular risk by enhancing LCAT activity.

This study aims to examine whether Globularia alypum (Ga) lyophilized aqueous leaves extract treatment improves cardiometabolic syndromes such as hyperglycemia, lipid profiles and oxidative damage resulting from a high-fructose diet induced in hypertriglyceridemic rats.

A total of 24 male Wistar rats weighing 80 ± 5 g were first randomly divided into 2 groups. A total of 12 control rats (C) were fed a standard-diet (St-D) and 12 high fructose (HF) rats were fed a high-fructose diet (HF-D) containing St-D in which cornstarch was substituted by fructose (61.4%). After 15 weeks of feeding, body weight (BW) was about 320 ± 20 g and hypertriglyceridemia was noted in HF vs C group (2.69 ± 0.49 mmol/L) vs (1.25 ± 0.33 mmol/L). Each group of rats was then divided into two equal groups (n = 6) and fed during four weeks either a St-D or HF-D, treated or not with 1% of Ga extract (C-Ga) and (HF-Ga). After 28 days, fasting rats were anesthetized and blood and tissues were removed to measure biochemical parameters.

The results showed no significant differences in BW and insulinemia between all groups. Ga extract supplementation reduced glycemia (−36%), glycosylated hemoglobin (−37%), Homeostasis Model of Assessment-Insulin Resistance index (−34%) and triacylglycerol’s contents in plasma (−33%), very low density lipoproteins–low density lipoproteins (VLDL-LDL) (−48%), liver (−52%) and aorta (−39%); total cholesterol concentrations in aorta was 3.7-fold lower in HF-Ga vs HF group. Ga treatment reduced lipid peroxidation in plasma, VLDL-LDL, red blood cells (RBC), liver, muscle and kidney by improving superoxide dismutase (SOD) activity and glutathione peroxidase (GPx) in RBC and catalase (CAT) activity in kidney (p < 0.05). Moreover, Ga ameliorates glutathione (GSH) production in RBC (+41%) and kidney tissues (+35%).

Ga extract ameliorated cardiometabolic syndrome by its hypotriglyceridemic effect and prevented development of insulin resistance. It reduces lipid peroxidation by enhancing non-enzymatic (GSH) and enzymatic (SOD, GPx and CAT) antioxidant defense systems in high-fructose hypertriglyceridemic rats. Therefore, supplementation of Ga leaves extract as an adjuvant could be used for the treatment of hypertriglyceridemia and the prevention and/or the management of cardio-metabolic adverse effects.