Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment Size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flood Water Volume. A cross-validation R² value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.     

Use of distributed data sources in facility location

Facility location decisions are usually determined by cost and coverage related factors although empirical studies show that such factors as infrastructure, labor conditions and competition also play an important role in practice. The objective of this paper is to develop a multi-objective facility location model accounting for a wide range of factors affecting decision-making. The proposed model selects potential facilities from a set of pre-defined alternative locations according to the number of customers, the number of competitors and real-estate cost criteria. However, that requires large amount of both spatial and non-spatial input data, which could be acquired from distributed data sources over the Internet. Therefore, a computational approach for processing input data and representation of modeling results is elaborated. It is capable of accessing and processing data from heterogeneous spatial and non-spatial data sources. Application of the elaborated data gathering approach and facility location model is demonstrated using an example of fast food restaurants location problem.     

An empirical evaluation of multi-media based learning of a procedural task

The present study investigated the effects of multi-media modules and their combinations on the learning of procedural tasks. In the experiment, 72 participants were classified as having either low- or high spatial ability based on their spatial ability test. They were randomly assigned to one of the six experimental conditions in a 2 × 3 factorial design with verbal modality (on-screen text procedure vs. auditory procedure) and the format of visual representation (static visual representation vs. static visual representation with motion cues vs. animated visual representation). After they completed their learning session, the ability to perform the procedural task was directly measured in a realistic setting. The results revealed that: (1) in the condition of static visual representation, the high spatial ability group outperformed the low spatial ability group, (2) for the low spatial ability participants, the animated visual representation group outperformed the static visual representation group, however, the static visual representation with motion cues group did not outperform the static visual representation group, (3) the use of animated visual representation helped participants with low spatial ability more than those with high spatial ability, and (4) a modality effect was found for the measure of satisfaction when viewing the animated visual representation. Since the participants with low spatial ability benefited from the use of animation, the results might support an idea that people are better able to retrieve the procedural information by viewing animated representation. The findings also might reflect a preference for the auditory mode of presentation with greater familiarity with the type of visual representation.     

Multiplicative noise removal combining a total variation regularizer and a nonconvex regularizer

A novel variational model for removing multiplicative noise is proposed in this paper. In the model, a novel regularization term is elaborately designed which is inherently equivalent to a combination of the classical total variation regularizer and a nonconvex regularizer. The proposed regularization term, on the one hand, can better remove the noise in homogeneous regions of a noisy image and, on the other hand, can preserve edge details of the image during the denoising process. In order to solve the model efficiently, we design an alternating iteration process in which two coupling minimization problems are solved. For each of the two minimization problems, the existence and uniqueness of their solutions are proved under some necessary assumptions. Numerical results are reported to demonstrate the effectiveness of the proposed regularization term for multiplicative noise removal.     

A composite likelihood approach for spatially correlated survival data

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.     

A mobility constraint model to infer sensor behaviour in forest fire risk monitoring

Wireless sensor networks (WSNs) play an important role in forest fire risk monitoring. Various applications are in operation. However, the use of mobile sensors in forest risk monitoring remains largely unexplored. Our research contributes to fill this gap by designing a model which abstracts mobility constraints within different types of contexts for the inference of mobile sensor behaviour. This behaviour is focused on achieving a suitable spatial coverage of the WSN when monitoring forest fire risk. The proposed mobility constraint model makes use of a Bayesian network approach and consists of three components: (1) a context typology describing different contexts in which a WSN monitors a dynamic phenomenon; (2) a context graph encoding probabilistic dependencies among variables of interest; and (3) contextual rules encoding expert knowledge and application requirements needed for the inference of sensor behaviour. As an illustration, the model is used to simulate the behaviour of a mobile WSN to obtain a suitable spatial coverage in low and high fire risk scenarios. It is shown that the implemented Bayesian network within the mobility constraint model can successfully infer behaviour such as sleeping sensors, moving sensors, or deploying more sensors to enhance spatial coverage. Furthermore, the mobility constraint model contributes towards mobile sensing in which the mobile sensor behaviour is driven by constraints on the state of the phenomenon and the sensing system.     

A national cross-sectional study in the Danish wood and furniture industry on working postures and manual materials handling

Musculoskeletal disorders constitute a major problem in the wood and furniture industry and identification of risk factors is needed urgently. Therefore, exposures to different work tasks and variation in the job were recorded based on an observation survey in combination with an interview among 281 employees working in wood working and painting departments. A questionnaire survey confirmed high frequencies of symptoms from the musculoskeletal system: The one-year prevalence of symptoms from the low back was 42% and symptoms from the neck/shoulder was 40%. The exposure was evaluated based on: (1) classification of work tasks, (2) work cycle time, (3) manual materials handling, (4) working postures, and (5) variation in die job. Among the employees 47% performed feeding or clearing of machines, 35% performed wood working or painting materials, and 18% performed various other operations. Among the employees 20% had no variation in their job while 44% had little variation. Manual materials handling of 375 different burdens was observed, which most often occurred during feeding or clearing of machines. The weight of burdens lifted was 0·5-87·0 kg, where 2% had a weight of more than 50 kg. Among the lifting conditions 30% were evaluated as implying a risk of injury. An additional risk factor was the high total tonnage lifted per day, which was estimated to range from 132 kg to 58 800 kg. Working postures implied a risk of injury due to prolonged forward and lateral flexions of the neck, which was seen most frequently during wood working or painting materials. These data substantiate the finding that work tasks mainly during feeding or clearing of machines imply a risk of injury to the low back and a risk of injury to the neck and shoulder area mainly during wood working or painting materials. Optimal strategies for job redesign may be worked out by using these data in order to prevent occupational musculoskeletal disorders.     

Psychological impact of air velocity variations in a ventilated room

Two experiments investigated the psychological impact of two velocity conditions (constant low velocity (V1) and variations of low and high velocity (V2)) in two temperature conditions (Experiment 1: an air temperature increase from 21°C to 24°C; Experiment 2: an air temperature increase from 25°C to 27°C) in females and males, aged 16 to 18 years, under realistic classroom conditions during an exposure period of 80 min. It was predicted that the V2 room condition compared to the V1 room condition would be more beneficial for subjects' perceived room temperature and air quality, self-reported affect and cognitive performance. The results obtained showed no significant effects on cognitive performance. However and as predicted, in Experiment 1, the subjects in the V2 compared to those in the V1 room condition felt that the air temperature decreased (while it de facto increased) and reported a constant level of high activation. In Experiment 2, the subjects in the V2 room condition felt that the air temperature increased less and reported that their unactivated unpleasantness increased less and activated pleasantness decreased less than it did for subjects in the V1 room condition. All this indicates, as was suggested by Wigö et al. (2002 Wigö, H., Knez, I. and Sandberg, M. 2002. Effects of velocity variations in ventilated room on comfort, affect and cognitive performance. Proceedings of 9th International Conference on Indoor Air Quality and Climate, 4: 635–640.  [Google Scholar]), that a cooling effect, induced by air velocity variations, might be beneficial for subjects in a ventilated room and that their perceived pleasantness of the indoor climate could be met at a higher room temperature than otherwise.     

DEM与空间叠加分析在土地定量评价中的研究

以湖北省建始县为例，研究了海拔、坡度和坡向与该区农业生产条件的关系，建立了土地适宜性评价模型。在MapGIS支持下，该文综合应用数字高程模型（DEM）及空间叠加分析技术实现了该模型在土地适宜性评价中的应用。实践表明，该数字化定量评价方法所得的结果与实际情况相符，具有高效、准确和可视化的特点，可为政府决策提供支持。     

基于面向对象空间数据库的空间对象模型设计及其存储策略的探讨

分析空间信息的结构和面向对象数据库技术的基础上，着重讨论了如何利用面向对象数据库的技术来对空间信息进行存储，展现了利用面向对象设计建立空间信息结构模型的先进性，并针对空间信息的结构提出了在面向对象空间数据库的存储策略。一方面为面向对象空间数据库的设计与建立提供参考，另一方面，通过借鉴相对成熟的面向对象技术，为空间信息的存储管理提供了思路和策略。