Time-of-day effects of alcohol intake on simulated driving performance in women.

There is a circadian propensity for sleepiness in the early afternoon (contrasting with an alertness peak early evening) which potentiates the sedating effects of alcohol. However, little research has been done to examine these effects on driving performance. Four units of alcohol (95 ml of 40% spirit) or placebo were given double blind, with a snack to two groups ('early afternoon' and 'early evening') of 12 young women, at either 1310 h or 1810 h. Blood alcohol levels (BACs) were estimated by breathalyser. BACs were within the UK legal driving maximum. Subjects underwent 40 min of monotonous motorway driving in a car simulator. Self-ratings on sleepiness and alcohol effects showed a significantly greater impact of alcohol in the early afternoon relative to the early evening. Whilst neither time of day nor alcohol affected lateral corrective steering movements, alcohol significantly increased the average following-distance and the variability in this distance, especially during the early afternoon. In some subjects from the early afternoon group, this impairment seemed to be to dangerous levels following alcohol.     

In-car usage-based insurance feedback strategies. A comparative driving simulator study

Usage-Based Insurances (UBI) enable policyholders to actively reduce the impact of vehicle insurance costs by adopting a safer and more eco-friendly driving style. UBI is especially relevant for younger drivers, who are a high-risk population. The effectiveness of UBI should be enhanced by providing in-car feedback optimised for individual drivers. Thirty young novice drivers were therefore invited to complete six experimental drives with an in-car interface that provided real-time information on rewards gained, their driving behaviour and the speed limit. Reward size was either displayed directly in euro, indirectly as a relatively large amount of credits, or as a percentage of the maximum available bonus. Also, interfaces were investigated that provided partial information to reduce the potential for driver distraction. Compared to a control no-UBI condition, behaviour improved similarly across interfaces, suggesting that interface personalisation after an initial familiarisation period could be feasible without compromising feedback effectiveness.

Practitioner Summary: User experiences and effects on driving behaviour of six in-car interfaces were compared. The interface provided information on driving behaviour and rewards in a UBI setting. Results suggest that some personalisation of interfaces may be an option after an initial familiarisation period as driving behaviour improved similarly across interfaces.     

The effects of extended work under sleep deprivation conditions on team-based performance

Teamwork is becoming increasingly common in today's workplaces; however, little research has examined how well teams perform under sleep deprivation conditions. The purpose of the current study was to examine the effect of extended work under sleep deprivation conditions on team performance. A total of 24 participants were sleep deprived for 30 h and completed 16 h of sustained operations during the last portion of the sleep deprivation period. The participants completed the Wombat, a complex task including vigilance and cognitive components, with a partner in four 24-min testing sessions during the sustained operations period. The results indicated that team performance increased during the work period while, within each testing session, team performance on vigilance tasks remained stable and overall performance decreased. The current results suggest that performance on two-person teams results in improved performance but does not fully counteract the decreases in performance within each work period. Performance in two-person teams increased across an extended work shift under sleep deprivation conditions. However, vigilance performance remained stable while overall performance decreased when examining performance in 8-min segments. These results suggest that averaging team-based performance over a longer testing period may mask the negative effects of sleep deprivation.

Statement of Relevance: Performance in two-person teams increased across an extended work shift under sleep deprivation conditions. However, vigilance performance remained stable while overall performance decreased when examining performance in 8-min segments. These results suggest that averaging team-based performance over a longer testing period may mask the negative effects of sleep deprivation.     

How landscape ruggedness influences the performance of real-coded algorithms: a comparative study

Ruggedness has a strong influence on the performance of algorithms, but it has been barely studied in real-coded optimization, mainly because of the difficulty of isolating it from a number of involved topological properties. In this paper, we propose a framework consisting of increasing ruggedness function sets built by a mechanism which generates multiple funnels. This mechanism introduces different levels of sinusoidal distortion which can be controlled to isolate the singular influence of some related features. Some commonly used measures of ruggedness have been applied to analyze these sets of functions, and a numerical study to compare the performance of some representative algorithms has been carried out. The results confirm that ruggedness has an influence on the performance of the algorithm, proving that it depends on the multi-funnel structure and peak features, such as height and relative size of the global peak, and not on the number of peaks.     

DRIVABILITY: a new concept for modelling driving performance

Various types of driver models have been proposed in the literature, such as taxonomic, functional, and motivational. Recently, the promising Michon model was extended, leading to the widely used GADGET matrix. Nevertheless, the correlation of such models to actual road accidents and their causes has so far been weak. In addition, the use of those models for predicting driver behavioural adaptation has not proven feasible. This paper introduces a new concept for modelling driver's performance, that of DRIVABILITY. DRIVABILITY is defined as a combination of permanent and temporary factors that affect a driver's performance. Furthermore, this paper proposes a DRIVABILITY index and a methodology to measure it, in order to move from over-simplistic, hierarchical modelling to a multi-dimensional sphere. The usability of the newly proposed concept is shown through its application in three different example cases, including a system monitoring driver hypovigilance, a system for driver basic training, and an elderly driver's assessment scheme.     

Night-time driving: the use of seat-belts and alcohol

Abstract

The objective risks of accident involvement associated with night driving, seat-belt use and alcohol intoxication have been reported from many countries. However, much less is known about the risks associated with combinations of these activities, even though these are common. A brief review of the relevant literature suggests that young drivers and drinking drivers are less likely to wear seat-belts at night. The results of a night-time survey in the Netherlands support this finding, with the lowest rate of belt use, 21%, being for young drivers with blood alcohol levels in excess of the legal limit (0·50%). Thus those drivers who are most likely to become involved in a crash are least likely to protect themselves from injury. The implications of these findings for accident countermeasures are briefly outlined.     

Classification of adaptive memetic algorithms: a comparative study.

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.     

Portable devices for measuring performance in the field: The effects of sleep deprivation and night shift on the performance of computer operators

The performance of twelve computer operators was measured in the field, on two new portable tests of reaction time, during the first night of the night shift at 0400 h and at the beginning day shift (0800 h), over three successive three-week comparisons. The results were: (i) mean reaction time increased significantly on the night shift as compared with the day: (ii) the adverse effect of the night shift was absent during the first comparison and became increasingly apparent on the second and third replications. These results: (i) provide evidence of inferior performance on the night as compared with the day shift in the field : (ii) show that data from previous studies of sleep deprivation in the laboratory are able to provide a good indication of what was to be expected in the field: (iii) emphasize the need for longitudinal studies in order to assess the true effects of stressful influences.     

Novel methodologies and a comparative study for manufacturing systems performance evaluations

The purpose of this work is to establish complex fuzzy methodologies in the evaluation of a manufacturing system’s performance. Many empirical studies have been presented about the evaluation of manufacturing system’s performance. However, the performance evaluation is quite subjective, since it relies on the individual judgment of the managers who have different, various and multi-factor assessment methods of a system’s performance. In this study, two fuzzy modeling designs were developed and in the construction of the models, a hierarchy process was used. In the first method, the performance factors and the Analytic Hierarchy Process (AHP) were fuzzified and the use of fuzzy numbers and a fuzzy AHP for this problem was recommended. Also, the relative importance of these factors with respect to each other and their contribution to the overall performance was quantified with fuzzy linguistic terms. In the other method, we proposed Approximate Reasoning (AR) based on experts’ knowledge which is represented with the collection of the rules. These fuzzy rule bases are “if-then” linguistic rules that are formed with linguistic variables such as poor, below average, average, above average and superior. Additionally, the problem was structured with the normal AHP and System-With-Feedback (SWF), Finally, these methods were compared. The results showed that fuzzy AHP leads to the best result. It is expected that the recommended models would have an advantage in the competitive manufacturing including cost, flexibility, quality, speed and dependability.     

面向多肽的分子对接算法性能对比研究

蛋白质-多肽间相互作用是计算生物学中的热门方向,对其结合的精确预测对于了解体内复杂的信号通路以及蛋白质功能,及进一步进行药物设计有着至关重要的作用。蛋白质-多肽对接方法能够有效地模拟蛋白质-多肽间相互作用,其对接性能直接决定模拟实验的准确性和成功与否。针对当前主要的蛋白质-多肽对接方法,综合测试了其算法性能;同时将本实验室开发的基于全信息粒子群算法的分子对接工具FIPSDock（Fully-Informed-Particle-Swarm-Dock）延伸到蛋白质-多肽对接领域,并评估其性能。利用标准蛋白质-多肽对接数据集（peptiDB）的21个测试例及13个定制测试例进行性能测试,主要对比分析了各算法的成功率和对接精度。结果表明FIPSDock可用于蛋白质-多肽对接,并相对于其他对接算法展示出优异的性能。     

A comparative study of the improvement of performance using a PSO modified by ACO applied to TSP

Swarm-inspired optimization has become very popular in recent years. Particle swarm optimization (PSO) and Ant colony optimization (ACO) algorithms have attracted the interest of researchers due to their simplicity, effectiveness and efficiency in solving complex optimization problems. Both ACO and PSO were successfully applied for solving the traveling salesman problem (TSP). Performance of the conventional PSO algorithm for small problems with moderate dimensions and search space is very satisfactory. As the search, space gets more complex, conventional approaches tend to offer poor solutions. This paper presents a novel approach by introducing a PSO, which is modified by the ACO algorithm to improve the performance. The new hybrid method (PSO–ACO) is validated using the TSP benchmarks and the empirical results considering the completion time and the best length, illustrate that the proposed method is efficient.     

Mobile learning vs. traditional classroom lessons: a comparative study

Different methods can be used for learning, and they can be compared in several aspects, especially those related to learning outcomes. In this paper, we present a study in order to compare the learning effectiveness and satisfaction of children using an iPhone game for learning the water cycle vs. the traditional classroom lesson. The iPhone game includes multiple interaction forms and combined augmented reality (AR) mini‐games with non‐AR mini‐games. The traditional classroom lesson had the same learning content as the iPhone game. Thirty‐eight children participated in the study. The analyses showed that the children made significant learning gains about the water cycle, regardless of the method used. Even though the results showed that the iPhone method achieved higher knowledge results than the traditional classroom lesson, no statistically significant differences were found between the iPhone and the classroom lesson. When analysing the motivational outcomes, the results showed that the children found the iPhone game to be more satisfying than the classroom lessons. Since the iPhone game achieved similar learning results and a higher motivational effect than the classroom lesson, this suggests that games of this kind could be used as a tool in primary schools to reinforce students' lessons.     

Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design

Computational Intelligence methods, which include Evolutionary Computation and Swarm Intelligence, can efficiently and effectively identify optimal solutions to complex optimization problems by exploiting the cooperative and competitive interplay among their individuals. The exploration and exploitation capabilities of these meta-heuristics are typically assessed by considering well-known suites of benchmark functions, specifically designed for numerical global optimization purposes. However, their performances could drastically change in the case of real-world optimization problems. In this paper, we investigate this issue by considering the Parameter Estimation (PE) of biochemical systems, a common computational problem in the field of Systems Biology. In order to evaluate the effectiveness of various meta-heuristics in solving the PE problem, we compare their performance by considering a set of benchmark functions and a set of synthetic biochemical models characterized by a search space with an increasing number of dimensions. Our results show that some state-of-the-art optimization methods – able to largely outperform the other meta-heuristics on benchmark functions – are characterized by considerably poor performances when applied to the PE problem. We also show that a limiting factor of these optimization methods concerns the representation of the solutions: indeed, by means of a simple semantic transformation, it is possible to turn these algorithms into competitive alternatives. We corroborate this finding by performing the PE of a model of metabolic pathways in red blood cells. Overall, in this work we state that classic benchmark functions cannot be fully representative of all the features that make real-world optimization problems hard to solve. This is the case, in particular, of the PE of biochemical systems. We also show that optimization problems must be carefully analyzed to select an appropriate representation, in order to actually obtain the performance promised by benchmark results.     

Randomized constraint solvers: a comparative study

The complexity of constraints is a major obstacle for constraint-based software verification. Automatic constraint solvers are fundamentally incomplete: input constraints often build on some undecidable theory or some theory the solver does not support. This paper proposes and evaluates several randomized solvers to address this issue. We compared the effectiveness of a symbolic solver (CVC3), a random solver, two heuristic search solvers, and seven hybrid solvers (i.e. mix of random, symbolic, and heuristic solvers). We evaluated the solvers on a benchmark generated with a concolic execution of 9 subjects. The performance of each solver was measured by its precision, which is the fraction of constraints that the solver can find solution out of the total number of constraints that some solver can find solution. As expected, symbolic solving subsumes the other approaches for the 4 subjects that only generate decidable constraints. For the remaining 5 subjects, which contain undecidable constraints, the hybrid solvers achieved the highest precision (fraction of constraints that a solver can find a solution out of the total number of satisfiable constraints). We also observed that the solvers were complementary, which suggests that one should alternate their use in iterations of a concolic execution driver.