Patterns of species composition of beach seine fisheries off North-Western coast of Sri Lanka,fishers’ perceptions and implications for co-management

As in many developing countries, small-scale fisheries including beach seining contribute significantly livelihoods and food security of coastal communities. Beach seining in Sri Lanka is seasonal mainly during calm season deprived of strong monsoonal winds, and essentially a multi-species fishery. Knowledge about the seasonal occurrence of pelagic species is important to be known for proper planning of the fishing activity, especially due to the reason that beach seine fishers in many parts of Sri Lanka make decisions to attach the cod-end of correct type depending on the target species. The possibility of identifying pattern of seasonal occurrence of target fish species in beach seine fishing sites off the southern region of north-western coast of Sri Lanka was therefore investigated using Self Organizing Maps (SOM). The analysis indicated that beach seine fishers’ local knowledge to predict the occurrence of certain species in the fishing sites to adjust their fishing strategies to target desirable species was consistent with the findings of SOM approach. Consequently, it was concluded that as beach seine fishers use indirect indicators such as colour of sea water and behaviour sea birds predict the species occurrence fairly accurately, their local knowledge can be incorporated in the management planning of beach seine fisheries in the North Western coastal area of Sri Lanka.     

Modeling heat and mass transfer during ground freezing subjected to high seepage velocities

Natural or induced groundwater flow may negatively influence the performance of artificial ground freezing: high water flow velocities can prevent frozen conditions from developing. Reliable models that take into consideration hydraulic mechanisms are then needed to predict the ground freezing development. For forty years, numerous thermo-hydraulic coupled numerical models have been developed. Some of these models have been validated against experimental data but only one has been tested under high water flow velocity conditions. This paper describes a coupled thermo-hydraulic numerical model completely thermodynamically consistent and designed to simulate artificial ground freezing of a saturated and non-deformable porous medium under seepage flow conditions. On some points, less restrictive assumptions than the ones usually used in the literature are considered. As for the constant-porosity assumption, its validity is verified. The model appears to be well validated against analytical solutions and a three-dimensional ground freezing experiment under high seepage flow velocity conditions. It is used to highlight key thermo-hydraulic mechanisms associated with phase change in a porous medium.     

Failure Process after Peak Strength of Artificial Joints by Fractal Dimension

Because lack of information when the joints exhibit strain softening behavior, the transition from peak to residual values is assumed to decrease either linearly or exponentially. Also, displacement of slide side after peak is much larger than the peak displacement and the stress approaches the residual state, thus studying failure process after peak strength is very important. In this research, three types of artificial joints with tooth-shaped asperity under repeated direct-shear were tested. Continuously movement of slide side after peak strength was monitored during shearing test. Reduction of shear parameters was examined according to two failure criteria (Barton and Patton). JRC value (Joint Roughness Coefficient) for a given profile was estimated by fractal dimension. One of the results of this study is that Barton’s criterion predicts a good estimation of residual strength and the second result is when the amount of fractal dimension of a joint surface increases, the JRC amount also increases, and with having value of h (height average) and l (base average) of a tooth-shaped asperity can determine the JRC of joint surface. M. Askari is a M.Sc Student of Engineering Faculty, Mining Engineering Department, Tarbiat Modares University, Tehran.     

Development and application of reservoir models and artificial neural networks for optimizing ventilation air requirements in development mining of coal seams

In longwall development mining of coal seams, planning, optimizing and providing adequate ventilation are very important steps to eliminate the accumulation of explosive methane–air mixtures in the working environment. Mine operators usually try to supply maximum ventilation air based on the capacity of the system and the predicted need underground. This approach is neither economical nor safer as ventilation capacity may decrease in time depending on various mining and coalbed parameters. Thus, it is important to develop better engineered approaches to optimize mine ventilation effectiveness and, therefore, to ensure a safer work environment.This study presents an approach using coalbed methane reservoir modeling and an artificial neural network (ANN) design for prediction and optimization of methane inflows and ventilation air requirements to maintain methane concentrations below statutory limits. A coalbed reservoir model of a three-entry development section, which is typical of Pittsburgh Coalbed mines in the Southwestern Pennsylvania section of Northern Appalachian Basin, was developed taking into account the presence and absence of shielding boreholes around the entries against methane inflow. In the model, grids were dynamically controlled to simulate the advance of mining for parametric simulations.Development and application of artificial neural networks as an optimization tool for ventilation requirements are introduced. Model predictions are used to develop, train, and test artificial neural networks to optimize ventilation requirements. The sensitivity and applications of proposed networks for predicting simulator data are presented and discussed. Results show that reservoir simulations and integrated ANN models can be practical and powerful tools for predicting methane emissions and optimization of ventilation air requirements.     

Graphic Biostratigraphic Correlation Using Genetic Algorithms

The most generally used method for estimating the basin-wide sequence and scaling of first and last occurrences, based on their occurrence in local sections, is Shaw’s graphic correlation method. The key step in this method is the determination of the line of correlation (LOC), which represents the best estimate of the correlation between two local sections, or between a local section and a composite standard. In general, available techniques for fitting the LOC for multiple sections are tedious, subjective, or computationally expensive. A new method employing genetic algorithms can dramatically reduce the effort involved in determining the LOC and produces stable biostratigraphic correlations and composite range charts objectively and efficiently. Genetic algorithms are an artificial intelligence technique that excels in locating the optimum solution from a large number of alternative choices. In the case of the LOC, the alternative choices are the number of line segments comprising the complete line and the positions of each segment’s beginning and end points. For a given number of segments, a wide range of alternative LOCs can be rapidly evaluated and a potential optimum fit determined. It is also possible to estimate the point when no further refinement of the fit by adding line segments is necessary. Genetic algorithms can also be applied to other methods for quantitative biostratigraphy.     

一种园林火灾智能检测与定位系统的基本方案研究

介绍了红外检测传感器的检测原理；基于模糊理论，研究了传感器检测信息的处理方法；提出了用系流气球平台代替园林火灾观测嘹望台，应用GPS与红外检测器结合来实现园林火灾的自动监测和定位；建立了园林火灾智能监测系统设计框架。     

Experimental and theoretical investigations of drainage in horizontal rough-walled fractures with different correlation structures

Experiments on the immiscible displacement of water by air were performed in two horizontal artificial open rough-walled fractures with different correlation structures. The aperture field of the first fracture has a Gaussian variogram with a finite correlation length, while that of the second one has a power-law variogram, for which no finite correlation length can be assigned. We simulated the displacement process using an invasion percolation model, which takes the in-plane curvature of the interface between water and air into account. The correlation structure of the aperture field has no effect on the irregularity of the shape of the air clusters. However, the correlation structure in combination with the in-plane curvature influences the density of the air clusters and thus has an impact on characteristic properties of the flow. We analyze the model parameters and properties with respect to the fracture properties. The stochastic average of the air clusters for models with different curvature numbers and fields with different correlation lengths is then calculated in order to analyze their influence on continuum approach models.     

Emotionally intelligent research

Key to this paper is not so much the emotions of the researcher that emanate from fieldwork, but those of the researched (and their emotional intelligence). Using a vignette of research on the impact of the 2001 foot and mouth disease outbreak on rural economies in the north of England, the paper explores emotions as clues and ways of seeing. It is broadly influenced by a social constructionist approach that views emotion as created and understood in the context of relationships with others and made meaningful through discourses, language and signifiers. It does not, though, deny the physical sensations that accompany emotion and the ways in which emotions are embodied as individuals interact with others (human and non-human).     

遗传神经网络测井解释技术在宝北区块的应用初探

将遗传算法与人工神经网络(BP网络)的原理结合,对BP网络进行了改进,提高了收敛速度,并防止陷入局部极小。应用改进后的BP算法,建立了针对测井解释的神经网络模型,并应用此模型定量计算了宝北区块的多口井的渗透率值,其解释结果及精度均令人满意。     

我国煤矿坑道钻探装备技术进展与展望

煤矿坑道钻探装备技术在发展过程中受到诸多不利条件的限制,导致新技术发展滞后,随着我国煤矿大力推进智能化建设,给煤矿坑道钻探装备技术带来了革命性的发展机遇。通过分析了国外坑道钻探装备的技术特点和发展状况,以及国内坑道钻探装备技术的三个阶段发展,包括初级阶段的分体式钻机与稳定组合钻具轨迹控制方法,蓬勃发展阶段的履带式钻机、定向钻进装备技术及松软煤层钻进技术,新发展阶段的自动化和智能化装备技术,提出了今后我国煤矿智能化发展阶段钻探装备技术需要解决的关键问题,为推进钻探装备技术的智能化水平提高提供支撑。