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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.

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.

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.

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.

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.

This study aims to explore how firms can design their government affairs (GAs) units in ways that improve their ability to monitor and influence legislative developments in their firms’ corporate political environments.

This conceptual work is informed by existing research into organizational design, brought to life with illustrative examples of firms’ political actions derived from interviews conducted with practitioners in the field.

In line with organizational design thinking, the authors find that high-performing GA units need to be designed and built using a blend of mutually reinforcing organizational mechanisms. GA units should be staffed by autonomous managers with mixed skills-sets. Moreover, they should not be constrained by formal rules, but instead given autonomy and support to create lateral relations with other business units.

This study provides a “recipe” that managers can follow to create opportunities for the exchange of political information within their firms and enable and motivate GAs practitioners to monitor and influence political developments more effectively.

This research exposes important, organizational antecedents of firms’ political strategies, which have not been systematically explored in the existing literature.

Flexible job-shop scheduling is significant for different manufacturing industries nowadays. Moreover, consideration of transportation time during scheduling makes it more practical and useful. The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem (MOFJSP) considering transportation time.

A hybrid genetic algorithm (GA) approach is integrated with simulated annealing to solve the MOFJSP considering transportation time, and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.

The performance of the proposed algorithm is tested on different MOFJSP taken from literature. Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution, especially when the number of jobs and the flexibility of the machine increase.

Most of existing studies have not considered the transportation time during scheduling of jobs. The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs. Meanwhile, GA is one of primary algorithms extensively used to address MOFJSP in literature. However, to solve the MOFJSP, the original GA has a possibility to get a premature convergence and it has a slow convergence speed. To overcome these problems, a new hybrid GA is developed in this paper.

Resource selection/optimization problems are often characterized by two related problems: numerical function and combinatorial optimization. Although techniques ranging from classical mathematical programming to knowledge‐based expert systems (KBESs) have been applied to solve the function optimization problem, there still exists the need for improved solution techniques in solving the combinatorial optimization. This paper reports an exploratory work that investigates the integration of genetic algorithms (GAs) with organizational databases to solve the combinatorial problem in resource optimization and management. The solution strategy involved using two levels of knowledge (declarative and procedural) to address the problems of numerical function, and combinatorial optimization of resources. The research shows that GAs can be effectively integrated into the evolving decision support systems (DSSs) for resource optimization and management, and that integrating a hybrid GA that incorporates resource economic and productivity factors, would facilitate the development of a more robust DSS. This helps to overcome the major limitations of current optimization techniques such as linear programming and monolithic techniques such as the KBES. The results also highlighted that GA exhibits the chaotic characteristics that are often observed in other complex non‐linear dynamic systems. The empirical results are discussed, and some recommendations given on how to achieve improved results in adapting GAs for decision support in the architecture, engineering and construction (AEC) sector.