Search results

This paper aims to present a case study-based approach to identify resource-poor communities with limited abilities to cope with the adverse effects of climate change. The study area is the Nkonkobe Local Municipality, in the Eastern Cape which is one of South Africa’s provinces ranked as being extremely vulnerable to the adverse effects of climate change because of high incidences of poverty and limited access to public services such as water and education. Although adaptive capacity and vulnerability assessments help to guide policy formulation and implementation by identifying communities with low coping capacities, policy implementers often find it difficult to fully exploit the utility of these assessments because of difficulties in identifying vulnerable communities. The paper attempts to bridge this gap by providing a user-friendly, replicable, practically implementable and adaptable methodology that can be used to cost-effectively and timeously identify vulnerable communities with low coping capacities.

A geostatistical approach was used to assess and evaluate adaptive capacities of resource-poor communities in the Nkonkobe Local Municipality. The geospatial component of this approach consisted of a multi-step Geographical Information Systems (GIS) based technique that was improvised to map adaptive capacities of different communities. The statistical component used demographic indicators comprising literacy levels, income levels, population age profiles and access to water to run automated summation and ranking of indicator scores in ArcGIS 10.2 to produce maps that show spatial locations of communities with varying levels of adaptive capacities on a scale ranging from low, medium to high.

The analysis identified 14 villages with low adaptive capacities from a total of 180 villages in the Nkonkobe Local Municipality. This finding is important because it suggests that our methodology can be effectively used to objectively identify communities that are vulnerable to climate change.

The paper presents a tool that could be used for targeting assistance to climate change vulnerable communities. The methodology proposed is of general applicability in guiding public policy interventions aimed at reaching, protecting and uplifting socio-economically disadvantaged populations in both rural and urban settings.

The approach’s ability to identify vulnerable communities is useful because it aids the identification of resource-poor communities that deserve priority consideration when planning adaptation action plans to deliver support and assistance to those least capable of effectively coping with the adverse effects of climate change induced vulnerabilities.

The study investigated teaching research data management (RDM) courses in higher learning institutions (HLIs) in Tanzania to enable postgraduate students to work with their research data.

The study triangulated research methods. Postgraduate students were investigated using survey questionnaires to learn about their needs and perceptions of the teaching RDM courses in HLIs. Key informants (academicians, information and communication technologists and library staff) were also investigated using in-depth interviews to explore their experiences and knowledge of teaching RDM courses. SPSS statistical software was used for analysing quantitative data; qualitative data were analysed thematically.

A total of 70 questionnaires were distributed to postgraduate students with a returning rate of 44 (69%). On the other hand, 12 key informants were interviewed. A low level of RDM literacy was revealed among 38 (86%) respondents. Most respondents 40 (91%) reported the need for HLIs to start teaching RDM courses. A lack of skills and knowledge in teaching RDM courses was revealed among key informants. The competency-based, adaptive and constructive teaching techniques were selected for teaching RDM courses, whereas intensive training and online tutorials were revealed as teaching formats.

This study focused on teaching RDM courses in HLIs. The survey questionnaires were distributed to all 2nd year postgraduate students, however, the findings cannot be generalised to all postgraduate students due to the response rate obtained. The findings obtained from key informants can also not be used as a basis for generalization across HLIs.

This study concluded that postgraduate students need to be well equipped with skills and knowledge on RDM and its related concepts; teaching RDM courses should be regarded as a continuous programme for benefit of students, researchers and the community at large.

Appropriate teaching of RDM courses among students not only ensures that students meet the funders’ and publishers’ requirements, but also encourages students to store and share their research among researchers worldwide; thus increasing collaboration and visibility of the datasets and data owners through data citations and acknowledgements.

This is a comprehensive study that provides findings for HLIs to teach RDM courses in HLIs, especially for postgraduate students. The findings revealed the need for teaching RDM courses in HLIs. The study provides the basis for further RDM research in HLIs and research institutions.

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

This study documented adaptation strategies employed by rural women in Vhembe district as they reel with climate change impacts. Women are heavily plagued by climate change impacts than any other genders worldwide. This is attributed to their high dependence on the natural resources for survival, low adaptive capacity, illiteracy, social ascribed roles that limit their participation in climate change initiatives like men and high poverty levels. Despite the daunting fact of women's vulnerability to climate change and its vagary impacts, women are agents of social change who have not remained passive victims to climate change and its impacts.

This study adopted a qualitative methodology guided by multi-case study design. A sample of 25 participants was selected through simple random and purposive sampling techniques. Data were collected using Focus Group Discussions and individual interviews and analysed thematically. Rural women and traditional leaders served as key informants and participated in the study.

The study established that the effects of climate change on rural women are real; however, there is a cocktail of strategies employed by women in Vhembe district in response to these effects. The strategies include livelihood and crop diversification, use of indigenous knowledge systems and harnessing of social capital among other household-based adaptation strategies.

The study recommends that the best way of assisting rural women in adapting to climate change is through the amelioration of poverty, enhancing capacity building for women and elimination of all systems that serve as barriers to effective adaptation.

The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.