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The purpose of this paper is to develop an active larval indices surveillance system and compare the outcomes of the implementation in primary care units (PCUs) at low and high risk of dengue.

The study design was conducted by implementing a community participation action research system in low and high dengue risk PCUs in Lansaka district, Nakhon Si Thammarat province, in the Southern Region of Thailand. There were five phases to the process including preparation of all stakeholders, situation assessment, development of the surveillance system, program implementation and evaluation. The system was developed in ten villages that were categorized as either low dengue risk PCUs (comprising six villages) or high dengue risk PCUs (four villages). A village was assigned as being at high or low dengue risk according to pre-determined criteria. The low dengue risk PCU assessments were conducted on a seven-step active larval indices surveillance system where PCU officials were additionally involved in coordinating, teaching, coaching and supporting the village health volunteers (VHVs) for dengue prevention activities. The high dengue risk PCUs, on the other hand, only followed a basic larval indices surveillance system with no follow-up support.

The outcomes of using intervention systems showed that the VHVs’ dengue knowledge and larval indices understanding in both PCUs increased significantly (p<0.01). Furthermore, the low dengue risk PCUs had a higher larval indices level than the high dengue risk PCU (p<0.01).

This study showed that the low dengue risk PCU followed an active larval indices surveillance system at the sub-district level which is appropriate for villages. This study also revealed that VHVs are needed to strengthen the capacity in terms of knowledge and skills of developing such a system to ensure reduced levels of dengue in the community.

Estuaries and shallow coastal areas are among the most productive ecosystems in the world, being recognized as important nursery areas for marine fish. The aim of this paper is to provide a comprehensive overview of the causes and consequences of recruitment variability in marine fish, contributing to ecosystem‐based management strategies of estuarine and coastal areas.

The authors conducted a literature review, exploring the role of estuaries as nursery areas for marine fish, and analyzed the connectivity between estuaries and coastal areas, the main processes driving recruitment variability in marine fish during their pelagic (larval) and estuarine residency (juveniles) phases, and how it can be translated into variable coastal stocks.

Recruitment variability in marine fish is still one of the most important issues in marine fisheries ecology. In this paper, the authors demonstrate the potential of several processes for inducing variability in recruitment, including density‐independent mechanisms such as temperature, hydrodynamics, river flow and large‐scale factors, as well as density‐dependent processes, related with food abundance, competition and predation. Lastly, the authors evaluated the connectivity between estuaries and the ocean, and how this relationship can influence coastal stocks in the future. The main findings were analyzed in the context of climate change, which has been demonstrated to influence marine life at the individual, population and ecosystem levels.

This paper is a valuable tool for marine researchers and stakeholders, since it summarizes some of the most important processes that drive recruitment variability in marine fish, and how this information can be used for establishing sustainable ecosystem management programmes.

This column inaugurates a new facet of the coverage of reference materials contained in RSR: the inclusion of sections devoted to reviews of useful reference books published around the world. Inasmuch as RSR's emphasis is on American reference sources and American libraries, the titles selected for evaluation were chosen on the basis of their appeal and relevance to libraries both in the U.S. and in other countries.

This study proposes a mobile learning model that employs digital libraries to support investigative learning activities. A student‐centered mobile learning activity with self‐guided exploration for physical ecology observation has been conducted to demonstrate the benefits of using digital libraries to support investigation‐based ecology learning activities.

An investigation‐based mobile learning model is proposed and an experiment is designed to show the effectiveness of the learning model, in which the students are asked to answer a series of questions by observing the real‐world learning objects and searching for supplemental materials from a digital library.

The instructional experiment conducted in an elementary school with 64 sixth grade students shows that the innovative approach is able to improve the learning achievement, learning effectiveness, as well as the learning attitudes of the students.

The findings of this paper imply that the use of the investigative learning model will significantly promote the utilization rate of digital libraries.

An investigative model for using digital libraries to support mobile learning is proposed in this paper. It provides good guidance to teachers for designing learning activities with digital libraries, and a good way for students to learn, utilizing the materials in digital libraries.

In order to mitigate the effects of climate change, the UK government has set a target of achieving net zero greenhouse gas (GHG) emissions by 2050. Agricultural GHG emissions in 2017 were 45.6 million tonnes of carbon dioxide equivalent (CO₂e; 10% of UK total GHG emissions). Farmland hedgerows are a carbon sink, storing carbon in the vegetation and soils beneath them, and thus increasing hedgerow length by 40% has been proposed in the UK to help meet net zero targets. However, the full impact of this expansion on farm biodiversity is yet to be evaluated in a net zero context. This paper critically synthesises the literature on the biodiversity implications of hedgerow planting and management on arable farms in the UK as a rapid review with policy recommendations. Eight peer-reviewed articles were reviewed, with the overall scientific evidence suggesting a positive influence of hedgerow management on farmland biodiversity, particularly coppicing and hedgelaying, although other boundary features, e.g. field margins and green lanes, may be additive to net zero hedgerow policy as they often supported higher abundances and richness of species. Only one paper found hedgerow age effects on biodiversity, with no significant effects found. Key policy implications are that further research is required, particularly on the effect of hedgerow age on biodiversity, as well as mammalian and avian responses to hedgerow planting and management, in order to fully evaluate hedgerow expansion impacts on biodiversity.

Communications regarding this column should be addressed to Mrs. Cheney, Peabody Library School, Nashville, Tenn. 37203. Mrs. Cheney does not sell the books listed here. They are available through normal trade sources. Mrs. Cheney, being a member of the editorial board of Pierian Press, will not review Pierian Press reference books in this column. Descriptions of Pierian Press reference books will be included elsewhere in this publication.

The purpose of this paper is to propose parallelization of a successful sequential ant‐based clustering algorithm (SABCA) to increase time performance.

A SABCA is parallelized through the chosen parallelization library MPI. Parallelization is performed in two stages. In the first stage, data to be clustered are divided among processors. After the sequential ant‐based approach running on each processor clusters the data assigned to it, the resulting clusters are merged in the second stage. The merging is also performed through the same ant‐based technique. The experimental analysis focuses on whether the implemented parallel ant‐based clustering method leads to a better time performance than its fully sequential version or not. Since the aim of this paper is to speedup the time consuming, but otherwise successful, ant‐based clustering method, no extra steps are taken to improve the clustering solution. Tests are executed using 2 and 4 processors on selected sample datasets. Results are analyzed through commonly used cluster validity indices and parallelization performance metrices.

As a result of the experiments, it is seen that the proposed algorithm performs better based on time measurements and parallelization performance metrices; as expected, it does not improve the clustering quality based on the cluster validity indices. Furthermore, the communication cost is very small compared to other ant‐based clustering parallelization techniques proposed so far.

The use of MPI for the parallelization step has been very effective. Also, the proposed parallelization technique is quite successful in increasing time performance; however, as a future study, improvements to clustering quality can be made in the final step where the partially clustered data are merged.

The results in literature show that ant‐based clustering techniques are successful; however, their high‐time complexity prohibit their effective use in practical applications. Through this low‐communication‐cost parallelization technique, this limitation may be overcome.

A new parallelization approach to ant‐based clustering is proposed. The proposed approach does not decrease clustering performance while it increases time performance. Also, another major contribution of this paper is the fact that the communication costs required for parallelization is lower than the previously proposed parallel ant‐based techniques.

The purpose of this paper is to provide a review of the issues related to cluster analysis, one of the most important and primitive activities of human beings, and of the advances made in recent years.

The paper investigates the clustering algorithms rooted in machine learning, computer science, statistics, and computational intelligence.

The paper reviews the basic issues of cluster analysis and discusses the recent advances of clustering algorithms in scalability, robustness, visualization, irregular cluster shape detection, and so on.

The paper presents a comprehensive and systematic survey of cluster analysis and emphasizes its recent efforts in order to meet the challenges caused by the glut of complicated data from a wide variety of communities.