The effects of explicit pronunciation instruction on the degree of perceived foreign accent in the speech of EFL learners

This paper reports on a study that attempted to examine the effect of explicit pronunciation instruction of some English segments (individual sounds) on the degree of perceived foreign accent in EFL Arab learners’ speech. Nine Arab learners of English in an EFL (English as a foreign language) setting were assigned to two groups, experimental and control. Five utterances loaded with the taught segments were collected from both groups before and after instruction. While the experimental group received instruction on these segments, the control group did not. 13 native English listeners were recruited to rate all the elicited sentences for the degree of perceived foreign accent. The results did not show any effect of explicit pronunciation instruction on the degree of perceived foreign accent, as there were no differences between the ratings before and after the instruction

Development of a microfluidic approach to the analysis of carbamate pesticides in drinking water

During the last decade, public concern over carbamate pesticide residues has increased remarkably and their accurate determination in environmental samples is gaining great importance. These compounds are present in environmental samples at low concentration levels; one or several pre-concentration steps are therefore required to isolate the target analytes, bring them to an appropriate concentration level, and remove matrix interference components. This work describes the development of an analytical approach to ultimately allow the combined extraction and detection of eserine, as an example of a carbamate pesticide, within a single microfluidic device.The current study has been based on the use of the well-known approach using solid-phase extraction (SPE) for eserine sample preparation, with a silica-based monolith used as an SPE sorbent. A silica-based monolith rod was fabricated by the sol-gel process and modified with octadecyl groups for eserine extraction. This sorbent material was found to have a good surface area of approximately 312 m2 g·1 after the modification step. A high extraction efficiency of 96.58% recovery was demonstrated for eserine using the octadecylated silica monolith. The SPE approach was rapid, taking less than I0 min, and used a low volume of sample of 300 μL.In this study, a very sensitive and rapid microfluidic-chemiluminescence method was developed for the determination of eserine. To the researcher's knowledge, there has been no published data to date in the literature for the determination of eserine by a chemiluminescence method

Regenerative Suspension System Modeling and Control

Many energy indicators show an increase in the world’s energy deficit. Demand for portable energy sources is growing and has increased the market for energy harvesters and regenerative systems. This work investigated the implementation of a regenerative suspension in a two-degree-of freedom (2-DOF) quarter-car suspension system. First, an active controller was designed and implemented. It showed 69% improvement in rider comfort and consumed 8 – 9 W of power to run the linear motor used in the experiment. A regenerative suspension system was then designed to save the energy normally spent in active suspensions, approximately several kilowatts in an actual car. Regenerative suspension is preferable because it can regenerate energy. Experimental investigations were then conducted to find generator constants and damping coefficients. Additionally, generator damping effects and power regeneration in the quarter-car test bed were also investigated. The experiments showed that a linear regenerative damper can suppress up to 22% of vibrations and harvest 2% of the disturbance power. Since both harvesting and damping capabilities were noticeable in this test bed, it was used to implement regenerative suspension, and a regenerative controller was developed to provide riders with additional comfort. To implement this regenerative controller, an electronic interface was designed to facilitate controlling the regenerative force and storing energy after the rectification process. The electronic interface used was a symmetrical-bridgeless boost converter (SBBC) due to its few components and even fewer control efforts. The converter was then modeled in a manner that made the current and voltage in phase for the maximum power factor. The converter control allowed the motor’s external load to be presented as of variable resistance with the unity power factor. The generator was then considered a voltage source for energy regeneration purposes. The controller was designed to control regenerative force at a frequency of 20 kHz. This frequency was sufficient to enable another controller to manipulate the desired regenerative damping force, which was chosen to be 1 kHz. The input to this controller was the generator voltage used to determine the polarity of pulse-width modulation (PWM). Therefore, a combination of converter and controller was able to take the place of an active controller. A different controller was then designed to manipulate the desired damping force. This regenerative controller was designed in a manner similar to that of a semi-active controller. It improved vibration suppression and enhanced harvesting capabilities. The regenerative suspension showed better results than a passive suspension. The improvements are minimal at this time, but there is the potential for greater improvement with a more efficient controller. The harvested energy was so small in this experiment because the damper was inefficient. In practice, the damper’s efficiency should be improved. A regenerative damper will be more economical than a passive damper, and suppress more vibration at the same time. The active suspension system showed superior performance. Conversely, the regenerative system showed only modest performance but also regenerated energy. However, a regenerative suspension can be combined with an active suspension to enhance the rider’s comfort and provide energy regeneration

Diversity-based adaptive genetic algorithm for a workforce scheduling and routing problem

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise total operational cost. One of the main obstacles in designing a genetic algorithm for this highly-constrained combinatorial optimisation problem is the amount of empirical tests required for parameter tuning. This paper presents a genetic algorithm that uses a diversity-based adaptive parameter control method. Experimental results show the effectiveness of this parameter control method to enhance the performance of the genetic algorithm. This study makes a contribution to research on adaptive evolutionary algorithms applied to real-world problems

A study of genetic operators for the Workforce Scheduling and Routing Problem

The Workforce Scheduling and Routing Problem (WSRP) is concerned with planning visits of qualified workers to different locations to perform a set of tasks, while satisfying each task time-window plus additional requirements such as customer/workers preferences. This type of mobile workforce scheduling problem arises in many real-world operational scenarios. We investigate a set of genetic operators including problem-specific and well-known generic operators used in related problems. The aim is to conduct an in-depth analysis on their performance on this very constrained scheduling problem. In particular, we want to identify genetic operators that could help to minimise the violation of customer/workers preferences. We also develop two cost-based genetic operators tailored to the WSRP. A Steady State Genetic Algorithm (SSGA) is used in the study and experiments are conducted on a set of problem instances from a real-world Home Health Care scenario (HHC). The experimental analysis allows us to better understand how we can more effectively employ genetic operators to tackle WSRPs

A study of genetic operators for the Workforce Scheduling and Routing Problem

The Workforce Scheduling and Routing Problem (WSRP) is concerned with planning visits of qualified workers to different locations to perform a set of tasks, while satisfying each task time-window plus additional requirements such as customer/workers preferences. This type of mobile workforce scheduling problem arises in many real-world operational scenarios. We investigate a set of genetic operators including problem-specific and well-known generic operators used in related problems. The aim is to conduct an in-depth analysis on their performance on this very constrained scheduling problem. In particular, we want to identify genetic operators that could help to minimise the violation of customer/workers preferences. We also develop two cost-based genetic operators tailored to the WSRP. A Steady State Genetic Algorithm (SSGA) is used in the study and experiments are conducted on a set of problem instances from a real-world Home Health Care scenario (HHC). The experimental analysis allows us to better understand how we can more effectively employ genetic operators to tackle WSRPs

DiVE-Arabic: Gulf Arabic Dialogue in a Virtual Environment

Documentation of communicative behaviour across languages seems at a crossroads. While methods for collecting data on spoken or written communication, backed up by computational techniques, are evolving, the actual data being collected remain largely the same. Inspired by the efforts of some innovative researchers who are directly tackling the various obstacles to investigating language in the field (e.g. see various papers collected in Enfield & Stivers 2007), we report here about ongoing work to solve the general problem of collecting in situ data for situated linguistic interaction. The initial stages of this project have involved employing a portable format designed to increase range and flexibility of doing such collections in the field. Our motivation is to combine this with a parallel data set for a typologically distinct language, in order to contribute a parallel corpus of situated language use

Solutions to graded reflection equation of GL-type

We list solutions of the graded reflection equation associated with the fundamental vector representation of the quantum supergroup of GL-type.Comment: arXiv admin note: text overlap with arXiv:math/020429

Genetic algorithms for workforce scheduling and routing problem

The Workforce Scheduling and Routing Problem (WSRP) is described as the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling scheduling and routing constraints while aiming to minimise the total operational cost. With current computational capabilities, small WSRPs are solvable using exact methods. However, it is difficult to solve when they are larger. The difficulty of WSRP is further increased when processing conflicting assignments or dealing with workers unavailability at customer's areas. Genetic Algorithms (GAs) have proved their effectiveness in these regards, because of their search capability to acquire good solutions in a reasonable computational time. A GA consists of many components, which can be chosen and combined in numerous procedures. In the case of solving scheduling and routing problems separately, different GAs have been proposed. When solving WSRP problem instances, it has been quite common to use the design components, intended for scheduling or routing problems. In this thesis, 42 real-world Home Health Care (HHC) planning problem datasets are used as instances of the WSRP. Different GA components are presented in this study, tailored for the combined settings. This has made major contributions to understanding how GAs works in a challenging real-world problem. Research interests in this work are categorised into two parts. The first part aims to understand how to employ different genetic operators effectively when solving WSRPs. The work intends to design and select the best combination of components that provide good solutions. Accordingly, seven well-known crossovers, three mutation operators and eight cost-based operators are implemented. In addition, two repair heuristics to tackle infeasibility. Nevertheless, a direct chromosome representation has resulted in poor solutions. Thus, there is a need for more tailored components for this problem. Therefore, an indirect chromosome representation, designed specifically to tackle WSRPs, is presented. The aim is to ensure initial solutions feasibility. Due to the quality of solutions, the GA introduced is considered an effective baseline evolutionary algorithm for WSRP. This work also suggested that each problem set requires different parameter settings. The second research interest intends to increase the GA efficiency. One approach is to investigate the effect of using adaptive components on the quality of WSRPs solutions. The aim is to adaptively alter parameter values instead of tuning an algorithm to a specific instance. Three aspects are adjusted during the run according to different rules: operator rates, population size, and crossover operator function. Thus, six variations of a diversity-based adaptive GA is presented. Not only the adaptive GA has improved the results, especially for large WSRP scenarios, but also it reduces the computational time. Another aspect investigated is the effect of using a group of crossover operators rather than using one operator throughout the search. Six crossover operators, well known and problem-specific are used as part of a multiple crossover GA framework. To evaluate an operator effectiveness, a reinforcement-learning model is developed with three performance measurements. The most successful variant of this algorithm finds the best-known results for the larger problem instances and matching the best-known results for some of the smaller ones. When combining this method with the adaptive GA, it provided some of the best results, as compared to established algorithms. The presented methods have contributed in reducing the operational costs for this constrained combinatorial optimisation problem

Selecting genetic operators to maximise preference satisfaction in a workforce scheduling and routing problem

The Workforce Scheduling and Routing Problem (WSRP) is a combinatorial optimisation problem that involves scheduling and routing of workforce. Tackling this type of problem often requires handling a considerable number of requirements, including customers and workers preferences while minimising both operational costs and travelling distance. This study seeks to determine effective combinations of genetic operators combined with heuristics that help to find good solutions for this constrained combinatorial optimisation problem. In particular, it aims to identify the best set of operators that help to maximise customers and workers preferences satisfaction. This paper advances the understanding of how to effectively employ different operators within two variants of genetic algorithms to tackle WSRPs. To tackle infeasibility, an initialisation heuristic is used to generate a conflict-free initial plan and a repair heuristic is used to ensure the satisfaction of constraints. Experiments are conducted using three sets of real-world Home Health Care (HHC) planning problem instances