Fisheries rehabilitation in post-tsunami Aceh: Status and needs from participatory appraisals

The widespread and long-term nature of the tsunami damage in Aceh province, Indonesia has threatened the continued use of coastal and fisheries resources. This article describes the application of the Rapid Appraisal of Fisheries Management System (RAFMS) methodology and presents key findings from the participatory appraisals in 15 study sites. The focus is on changes in the number and types of fishing boats and fishing effort, consumption and marketing flow patterns and community perspectives on livelihood options. The level of aid (for new boats), mainly from international organizations, has been unevenly distributed with the number of boats in 13 of 15 villages still being well below the pre-tsunami levels. A focus on supplying small vessels may put increased fishing pressure on the near-shore zone. Consumption data and marketing flows suggest that most fishing villages are supplying outside markets and adding considerably to the wider food security of the province. Despite the tsunami, marine fisheries-related livelihoods are still preferred, although there are indications for the potential expansion of livelihoods into the culture of new species. Alternative resource-based livelihoods need to be tested and refined to fit the needs of the current conditions in Aceh to provide viable options for eliminating hunger and reducing poverty

Fisheries rehabilitation in post-tsunami Aceh: Status and needs from participatory appraisals

The widespread and long-term nature of the tsunami damage in Aceh province, Indonesia has threatened the continued use of coastal and fisheries resources. This article describes the application of the Rapid Appraisal of Fisheries Management System (RAFMS) methodology and presents key findings from the participatory appraisals in 15 study sites. The focus is on changes in the number and types of fishing boats and fishing effort, consumption and marketing flow patterns and community perspectives on livelihood options. The level of aid (for new boats), mainly from international organizations, has been unevenly distributed with the number of boats in 13 of 15 villages still being well below the pre-tsunami levels. A focus on supplying small vessels may put increased fishing pressure on the near-shore zone. Consumption data and marketing flows suggest that most fishing villages are supplying outside markets and adding considerably to the wider food security of the province. Despite the tsunami, marine fisheries-related livelihoods are still preferred, although there are indications for the potential expansion of livelihoods into the culture of new species. Alternative resource-based livelihoods need to be tested and refined to fit the needs of the current conditions in Aceh to provide viable options for eliminating hunger and reducing poverty.Disaster, Tsunami

Behavioural Digital Forensics Model: Embedding Behavioural Evidence Analysis into the Investigation of Digital Crimes

© 2018 Elsevier Ltd The state-of-the-art and practice show an increased recognition, but limited adoption, of Behavioural Evidence Analysis (BEA) within the Digital Forensics (DF) investigation process. Yet, there is currently no BEA-driven process model and guidelines for DF investigators to follow in order to take advantage of such an approach. This paper proposes the Behavioural Digital Forensics Model to fill this gap. It takes a multidisciplinary approach which incorporates BEA into in-lab investigation of seized devices related to interpersonal cases (i.e., digital crimes involving human interactions between offender(s) and victim(s)). The model was designed based on the application of traditional BEA phases to 35 real cases, and evaluated using 5 real digital crime cases - all from Dubai Police archive. This paper, however, provides details of only one case from this evaluation pool. Compared to the outcome of these cases using a traditional DF investigation process, the new model showed a number of benefits. It allowed a more effective focusing of the investigation, and provided logical directions for identifying the location of further relevant evidence. It also enabled a better understanding and interpretation of victim/offender behaviours (e.g., probable offenders’ motivations and modus operandi), which facilitated a more in depth understanding of the dynamics of the specific crime. Finally, in some cases, it enabled the identification of suspect\u27s collaborators, something which was not identified via the traditional investigative process

Forensic Investigation of Cyberstalking Cases using Behavioural Evidence Analysis

Behavioural Evidence Analysis (BEA) is, in theory, useful in developing an understanding of the offender, the victim, the crime scene, and the dynamics of the crime. It can add meaning to the evidence obtained through digital forensic techniques and assist investigators with reconstruction of a crime. There is, however, little empirical research examining the application of BEA to actual criminal cases, particularly cyberstalking cases. This study addresses this gap by examining the utility of BEA for such cases in terms of understanding the behavioural and motivational dimensions of offending, and the way in which digital evidence can be interpreted. It reports on the forensic analysis of 20 cyberstalking cases investigated by Dubai Police in the last five years. Results showed that BEA helps to focus an investigation, enables better understanding and interpretation of victim and offender behaviour, and assists in inferring traits of the offender from available digital evidence. These benefits can help investigators to build a stronger case, reduce time wasted to mistakes, and to exclude suspects wrongly accused in cyberstalking cases

An extreme value theory approach to calculating minimum capital risk requirements

This paper investigates the frequency of extreme events for three LIFFE futures contracts for the calculation of minimum capital risk requirements (MCRRs). We propose a semiparametric approach where the tails are modelled by the Generalized Pareto Distribution and smaller risks are captured by the empirical distribution function. We compare the capital requirements form this approach with those calculated from the unconditional density and from a conditional density - a GARCH(1,1) model. Our primary finding is that both in-sample and for a hold-out sample, our extreme value approach yields superior results than either of the other two models which do not explicitly model the tails of the return distribution. Since the use of these internal models will be permitted under the EC-CAD II, they could be widely adopted in the near future for determining capital adequacies. Hence, close scrutiny of competing models is required to avoid a potentially costly misallocation capital resources while at the same time ensuring the safety of the financial system

Reflecting back and forwards: The ebb and flow of peer-reviewed reflective practice research in sport

Researchers in sport have claimed that reflective practice is important for competent practice. Evidence supporting this claim is sparse, highly theoretical and located within a variety of domains. The aim of this study was to assimilate and analyse the last 12 years of reflective practice literature within the sport domain in order to identify new areas of inquiry, emerging trends with regard to findings or methodology, and to identify implications for future research and practice. A sample of 68 papers published between 2001 and 2012 was examined, and investigated for the research locations, data collection methods utilised, and the professions and communities involved. The paper concludes with some suggestions for future research

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.</p