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Article
Publication date: 1 February 1999

Maggie Tsai and Brian H. Kleiner

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…

1912

Abstract

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.

Details

Equal Opportunities International, vol. 18 no. 1
Type: Research Article
ISSN: 0261-0159

Keywords

Book part
Publication date: 1 January 2004

Chueh-Yung Tsao and Shu-Heng Chen

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…

Abstract

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.

Details

Applications of Artificial Intelligence in Finance and Economics
Type: Book
ISBN: 978-1-84950-303-7

Article
Publication date: 14 February 2022

Nur Zulaikha Mohamed Sadom, Farzana Quoquab and Jihad Mohammad

This study aims to shed light on the factors that affect frugality (FR) in the hotel industry. Specifically, it aims to test the role of environmental advertisement (EA…

Abstract

Purpose

This study aims to shed light on the factors that affect frugality (FR) in the hotel industry. Specifically, it aims to test the role of environmental advertisement (EA) and eco-labelling (EL) on FR through green attitude (GA) in the Malaysian hotel industry. It also tested the role of government initiatives (GIS) as the moderator.

Design/methodology/approach

Using the judgemental sampling technique, a total of 259 usable responses were gathered from hotel guests. Partial least squares structural equation modelling was used to test the study hypotheses.

Findings

This study found that EA and EL affect hotel guests’ GA positively. Additionally, the finding revealed that GA exerts a positive influence on FR. Furthermore, this study disclosed that GA mediates the relationship between green marketing strategies (EA and EL) and FR. Contrary to expectation, the moderating role of GIs was not supported in this study.

Originality/value

This is a pioneering study that investigates FR in the hotel industry. Further, this study developed new relationships such as the mediating role of GA between marketing strategies in terms of EA and EL and FR. In addition, the moderating effect of GIs on the relationship between GA and FR, which is comparatively new in the literature was developed. The findings from this study are expected to benefit the hoteliers, governments and the researchers that specialized in consumer behaviour study.

Details

Consumer Behavior in Tourism and Hospitality, vol. 17 no. 3
Type: Research Article
ISSN: 2752-6666

Keywords

Open Access
Article
Publication date: 3 August 2021

Ola Al Sayed, Ashraf Samir and Heba Hesham Anwar

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Review of Economics and Political Science, vol. 6 no. 4
Type: Research Article
ISSN: 2356-9980

Keywords

Article
Publication date: 28 October 2021

Cuicui Du and Deren Kong

Three-axis accelerometers play a vital role in monitoring the vibrations in aircraft machinery, especially in variable flight temperature environments. The sensitivity of…

Abstract

Purpose

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.

Design/methodology/approach

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).

Findings

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.

Originality/value

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.

Details

Sensor Review, vol. 42 no. 1
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 27 September 2021

S. Prathiba and Sharmila Sankar

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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 Prc (Precision), Rca (Recall), F ms (f measure), Acr (Accuracy)and Ct (Clustering Time). The performance is compared to about 500 numbers of tasks. For all tasks, the NKMA provides the highest values for Prc, Rca, Fms and Acr, takes the lowest time (Ct) 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 Rt (Response Time), Pt (Process Time), Awt (Average Waiting Time), Atat (Average Turnaround Time), Lcy (Latency) and Tp (Throughput). For all number of tasks, the proposed CM-GA gives the lowest values for Rt, Pt, Awt, Atat and Lcy and also provides the highest values for Tp. 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.

Originality/value

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

Article
Publication date: 1 April 2002

O.O. UGWU and J.H.M. TAH

Resource selection/optimization problems are often characterized by two related problems: numerical function and combinatorial optimization. Although techniques ranging…

175

Abstract

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.

Details

Engineering, Construction and Architectural Management, vol. 9 no. 4
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 12 December 2016

Simone Bruschetta and Raffaele Barone

The purpose of this paper is to present a model of democratic therapeutic community (DTC) for people with a diagnosis of schizophrenia and psychotic disorder, namely the…

Abstract

Purpose

The purpose of this paper is to present a model of democratic therapeutic community (DTC) for people with a diagnosis of schizophrenia and psychotic disorder, namely the Group-Apartment (GA). The authors will describe it in more detail, discussing the ideas which lie behind it, considering the relative cost of treating people in larger residential DTCs and in GAs, outlining findings from the first data gathered on a GA and looking at the usefulness of this model in post-modern societies, with particular reference to Sicily.

Design/methodology/approach

In brief a GA is a flat, located in an urban apartment building, inhabited by a small group of people. In this paper the authors consider an apartment inhabited by a group of three or four patients with the presence of clinical social workers who work in shifts for several hours a day on all or most days of the week (Barone et al., 2009, 2010). GA is also inspired by the pioneering work of Pullen (1999, 2003), in the UK tradition of the apartment post TC for psychosis.

Findings

GAs in Italy have become one of the main methods of support housing in recovery-oriented treatment, because it allows the empowerment of the users and fights against the stigma of mental illness (Barone et al., 2014; Bruschetta et al., 2014). The main therapeutic activities provided in the GA depend on the type of recovery route being supported, on the level of autonomy being developed and on the level of participation in the democratic life of the local community.

Originality/value

GAs appear better, cheaper and a more appropriate treatment for mental problems in the current financial and social climate than larger institutions. Where they have been tried out, they have been found to be effective, by users and by stakeholders. They exemplify the advantages of the DTC for encouraging recovery, but cost less to run. In accordance with DTC principles, the social democratic process is used not only to evaluate the clinical effectiveness of GAs, but also to build a network to support the development of innovative mental health services and new enabling environments (Haigh et al., 2012).

Details

Therapeutic Communities: The International Journal of Therapeutic Communities, vol. 37 no. 4
Type: Research Article
ISSN: 0964-1866

Keywords

Article
Publication date: 1 April 1996

ALEXANDER M. ROBERTSON and PETER WILLETT

This paper describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked‐output document retrieval system. The GA involves…

Abstract

This paper describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked‐output document retrieval system. The GA involves a fitness function that is based on full relevance information, and the rankings resulting from the use of these weights are compared with the Robertson‐Sparck Jones F4 retrospective relevance weight. Extended experiments with seven document test collections show that the ga can often find weights that are slightly superior to those produced by the deterministic weighting scheme. That said, there are many cases where the two approaches give the same results, and a few cases where the F4 weights are superior to the ga weights. Since the ga has been designed to identify weights yielding the best possible level of retrospective performance, these results indicate that the F4 weights provide an excellent and practicable alternative. Evidence is presented to suggest that negative weights may play an important role in retrospective relevance weighting.

Details

Journal of Documentation, vol. 52 no. 4
Type: Research Article
ISSN: 0022-0418

Open Access
Article
Publication date: 20 July 2020

Mehmet Fatih Uslu, Süleyman Uslu and Faruk Bulut

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This…

Abstract

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.

Details

Applied Computing and Informatics, vol. 18 no. 1/2
Type: Research Article
ISSN: 2210-8327

Keywords

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