数值地形图的生成及其水文地貌特征评价

数值地形图(DEM)是利用地理信息系统(GIS)进行流域水文模拟的基础, 其精度直接影响模拟结果在没有现成的数值地形图时, 应用人员往往利用传统的地形图来生成它. 本文分别采用Arc/Info地理信息系统软件提供的哈奇逊法和不规则三角网五次插值法, 对一个实验流域的高程取样点进行插值, 生成了4种不同的数值地形图, 从凹洼分布、流域界定、河网提取、地面坡度和地形指数、以及地面径流汇成等方面, 对它们的水文地貌精度进行了比较. 结果表明, 两种方法都能生成由高程点群所反映的地形, 然而由于哈奇逊法能够结合流域的河网结构及其具有物理基础的水文地貌加强法来处理凹洼问题, 因此大大地提高了其所生成的流域数值地形图水文地貌特征精度.     

Emotional Loneliness and Coping Strategies: A Reference to Older Malaysians at Nursing Homes

This article presents an in-depth qualitative study using a phenomenological approach to understand loneliness among elderly individuals in Malaysia. The objective of the study was to understand how the Malaysian elderly perceive and understand social isolation as well as loneliness, with the aim of identifying the factors that cause emotional loneliness among the elderly in nursing homes. In addition, this study also explored their coping strategies when dealing with loneliness. Semi-structured interviews were conducted with ten elderly participants from two different nursing homes in Kuala Lumpur and Selangor with representatives from the three major ethnic groups of Malaysia. Based on the results, there are several factors that cause the elderly to feel lonely – health factors, lack of family ties, and the lack of communication and cognitive factors, such as memory and perception. It was also found that internal (expectations and optimism) and external (work and activities) coping strategies play major roles in overcoming loneliness. In conclusion, some recommendations are made to respective party families and the government to consider when developing plans to help the elderly overcome loneliness, which could strengthen the family and social support system in Malaysia.     

Development of a food frequency questionnaire for Sri Lankan adults

ABSTRACT: BACKGROUND: Food Frequency Questionnaires (FFQs) are commonly used in epidemiologic studies to assess long-term nutritional exposure. Because of wide variations in dietary habits in different countries, a FFQ must be developed to suit the specific population. Sri Lanka is undergoing nutritional transition and diet-related chronic diseases are emerging as an important health problem. Currently, no FFQ has been developed for Sri Lankan adults. In this study, we developed a FFQ to assess the regular dietary intake of Sri Lankan adults. METHODS: A nationally representative sample of 600 adults was selected by a multi-stage random cluster sampling technique and dietary intake was assessed by random 24-h dietary recall. Nutrient analysis of the FFQ required the selection of foods, development of recipes and application of these to cooked foods to develop a nutrient database. We constructed a comprehensive food list with the units of measurement. A stepwise regression method was used to identify foods contributing to a cumulative 90% of variance to total energy and macronutrients. In addition, a series of photographs were included. RESULTS: We obtained dietary data from 482 participants and 312 different food items were recorded. Nutritionists grouped similar food items which resulted in a total of 178 items. After performing step-wise multiple regression, 93 foods explained 90% of the variance for total energy intake, carbohydrates, protein, total fat and dietary fibre. Finally, 90 food items and 12 photographs were selected. CONCLUSION: We developed a FFQ and the related nutrient composition database for Sri Lankan adults. Culturally specific dietary tools are central to capturing the role of diet in risk for chronic disease in Sri Lanka. The next step will involve the verification of FFQ reproducibility and validity.     

利用双抛物线型土壤蓄水容量曲线对新安江产流模型的改进

提出了双抛物线型土壤蓄水容量关系曲线，对新安江模型产流计算公式进行了改进。利用珠江三角洲不同土壤水分状态的多年水文资料，对改进前和改进后新安江模型产流计算公式进行了比较。结果表明，双抛物线型关系曲线能够代表流域中多种土壤水分状态并存的情况，包括湿润、干燥以及从湿润到干燥过渡阶段，而原单抛线型的模型则是双抛物线的一个特例，只能反映单一土壤水分状态的分布。对于那些发生在湿润季节的暴雨，两种曲线的模拟精度相当，但对于发生在旱季及旱湿过渡时期的暴雨，双抛物线明显优于单抛物线。     

Use of Radial Basis Function Type Artificial Neural Networks for Runoff Simulation

In this paper, the applicability of the radial basis function (RBF) type artificial neural networks (ANNs) approach for modeling a hydrologic system is investigated. The method differs from the more widely used multilayer perceptron (MLP) approach in that the nonlinearity of the model is embedded only in the hidden layer of the network. Search for optimal model parameters is carried out in two steps, each of which can be made to be more efficient and much faster than in MLP. This approach is applied to simulate runoff discharges in a small catchment. The results show that the models based on RBF networks can predict runoff with accuracy comparable with that with the MLP approach. An added advantage of RBF network-based models is that they can be developed with relative ease and with much less time compared with their MLP counterparts.     

An integrated strategy to control post-harvest decay of Embul banana by combining essential oils with modified atmosphere packaging

Mature Embul (Musa, AAB) bananas were treated with emulsions of either cinnamon bark or leaf (Cinnamomum zeylanicum) or clove (Syzygium aromaticum) oils to control post‐harvest diseases, packed under modified atmosphere (MA) using low‐density polyethylene (0.075 mm, LDPE) bags, and stored in a cold room (14 ± 1 °C, 90% RH) or at ambient temperature (28 ± 2 °C). The effects of the essential oils on post‐harvest diseases; physico‐chemical properties and organoleptic properties were determined and compared with controls and bananas treated with benomyl. Treatments with cinnamon bark and leaf oils controlled crown rot, whereas clove oil treatment did not affect crown rot development. Treatment with emulsions of cinnamon oils combined with MA packaging can be recommended as a safe, cost‐effective method for extending the storage life of Embul bananas up to 21 days in a cold room and 14 days at 28 ± 2 °C without affecting the organoleptic and physico‐chemical properties.     

Utilisation of beef lung protein powder as a functional ingredient to enhance protein and iron content of fresh pasta

Beef lungs are an underutilised co-product of the meat industry that could be used as an ingredient to supplement the protein content of cereal foods. Beef lung powder (BLP) had a protein content of 87% (dry weight basis), an amino acid score of 1, and contained 1 mg g⁻¹ iron. Fresh semolina pasta was used as a model food, and BLP was incorporated up to 20%. Incorporation of 10% BLP improved the indispensable amino acid score of the pasta from 0.48 to 0.91. At that level of incorporation, higher cooking loss and redder (increased ‘a’ value) and darker (decreased ‘L’ value) colour resulted. The fresh pasta with BLP had a lower glycaemic response compared to control samples. BLP improved the functional value of the fresh pasta and is a potential ingredient in the development of new food products.     

Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

Deep learning techniques, particularly convolutional neural networks (CNNs), have exhibited remarkable performance in solving vision-related problems, especially in unpredictable, dynamic, and challenging environments. In autonomous vehicles, imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs. In this regard, globally, researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results. Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs. However, to the best of our knowledge, these techniques are yet to be applied to address the problem of imitation-learning-based steering angle prediction. Thus, in this study, we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters, which are employed to solve the steering angle prediction problem. To validate the performance of each hyperparameters’ set and architectural parameters’ set, we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set: optimizer, Adagrad; learning rate, 0.0052; and nonlinear activation function, exponential linear unit. As per our findings, we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones. Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach. Infield testing was also performed using the model trained with the optimal architecture, which we developed using our approach.     

Controlling a robot manipulator with fuzzy voice commands using a probabilistic neural network

Natural language commands are generated by intelligent human beings. As a result, they contain a lot of information. Therefore, if it is possible to learn from such commands and reuse that knowledge, it will be a very efficient process. In this paper, learning from such information rich voice commands for controlling a robot is studied. First, new concepts of fuzzy coach-player system and sub-coach are proposed for controlling robots with natural language commands. Then, the characteristics of the subjective human decision making process are discussed and a Probabilistic Neural Network (PNN) based learning method is proposed to learn from such commands and to reuse the acquired knowledge. Finally, the proposed concept is demonstrated and confirmed with experiments conducted using a PA-10 redundant manipulator.