1,690 research outputs found
Climate change impact and adaptation: Lagoonal fishing communities in west Africa
Lagoons are a common feature of the low-lying West African coastline. These lagoons are resource-rich and biodiverse. The small-scale fishing communities, which border them, are dependent on the resources and ecosystem services for their livelihoods and well-being. Climate change has had significant and diverse effects on both the lagoons and their surrounding communities. Sea level rise has caused erosion of the coast and increased the risk of floods. Changes to rainfall patterns have caused shifts in lagoon ecosystems and physical cycles. Of particular relevance to lagoon fishing communities is the fluctuation in quantity and distribution of fish catch that they rely upon for economic livelihood. Understanding the vulnerability of these communities to the effects of climate change is critical to supporting and developing successful adaptations. Using a case study from Ghana, sustainable livelihoods approach (SLA) and vulnerability framework are used to characterize the community vulnerability, giving insight into the temporal and spatial dynamics of vulnerability and how subsections of the community may be identified and prioritized for adaptation interventions. A scalar analysis of the relevant coastal and environmental frameworks and policy to support climate change adaptation in coastal communities reveals the common challenges in implementing adaptation interventions and strategies in the region. A policy gap exists between high level, institutional coastal, and climate directives and implementation of climate adaptations at the local level. That gap might be bridged by a participatory approach that places coastal communities at the center of creating and enacting climate change adaptations
Characterising the vulnerability of fishing households to climate and environmental change: Insights from Ghana
Rural coastal communities in the global south are mostly natural resource-dependent and their livelihoods are therefore vulnerable to the impacts of climate and environmental changes. Efforts to improve their adaptive capacity often prove mal-adaptive due to misunderstanding the dynamics of the unique socioeconomic factors that shape their vulnerability. By integrating theories from climate change vulnerability and the Sustainable Livelihoods Approach, this study draws upon household survey data from a fishing community in Ghana to assess the vulnerability of fishing households to climate change and explore how their vulnerability is differentiated within the community. The findings suggest that household incomes in the last decade have reduced significantly, attributable to an interaction of both climatic and non-climatic factors. Analysis of the characteristics of three vulnerability groups derived by quantile clustering showed that the most vulnerable household group is not necessarily women or poorer households as expected. Rather, it is dynamic and includes all gender and economic class categories in varying proportions depending on the success or failure of the fishing season. The findings suggest furthermore that the factors that significantly differentiates vulnerability between households differ, depending on whether households are categorised by economic class, gender of household-head or vulnerability group. Consequently, the study highlights the importance of looking beyond existing social categorizations like gender and economic classes when identifying and prioritizing households for climate change adaptive capacity building
Solving relativistic hydrodynamic equation in presence of magnetic field for phase transition in a neutron star
Hadronic to quark matter phase transition may occur inside neutron stars (NS)
having central densities of the order of 3-10 times normal nuclear matter
saturation density (). The transition is expected to be a two-step
process; transition from hadronic to 2-flavour matter and two-flavour to
equilibrated charge neutral three-flavour matter. In this paper we
concentrate on the first step process and solve the relativistic hydrodynamic
equations for the conversion front in presence of high magnetic field. Lorentz
force due to magnetic field is included in the energy momentum tensor by
averaging over the polar angles. We find that for an initial dipole
configuration of the magnetic field with a sufficiently high value at the
surface, velocity of the front increases considerably.Comment: 16 pages, 4 figures, same as published version of JPG, J. Phys. G:
Nucl. Part. Phys. 39 (2012) 09520
Scalable Anytime Algorithms for Learning Fragments
International audienceAbstract Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas. We consider the problem of learning formulas in fragments of LTL without the U -operator for classifying traces; despite a growing interest of the research community, existing solutions suffer from two limitations: they do not scale beyond small formulas, and they may exhaust computational resources without returning any result. We introduce a new algorithm addressing both issues: our algorithm is able to construct formulas an order of magnitude larger than previous methods, and it is anytime, meaning that it in most cases successfully outputs a formula, albeit possibly not of minimal size. We evaluate the performances of our algorithm using an open source implementation against publicly available benchmarks
The conversion of Neutron star to Strange star : A two step process
The conversion of neutron matter to strange matter in a neutron star have
been studied as a two step process. In the first step, the nuclear matter gets
converted to two flavour quark matter. The conversion of two flavour to three
flavour strange matter takes place in the second step. The first process is
analysed with the help of equations of state and hydrodynamical equations,
whereas, in the second process, non-leptonic weak interaction plays the main
role. Velocities and the time of travel through the star of these two
conversion fronts have been analysed and compared.Comment: 18 pages including 9 figure
Calibration of a solid state nuclear track detector (SSNTD) with high detection threshold to search for rare events in cosmic rays
We have investigated a commercially available polymer for its suitability as
a solid state nuclear track detector (SSNTD). We identified that polymer to be
polyethylene terephthalate (PET) and found that it has a higher detection
threshold compared to many other widely used SSNTDs which makes this detector
particularly suitable for rare event search in cosmic rays as it eliminates the
dominant low Z background. Systematic studies were carried out to determine its
charge response which is essential before any new material can be used as an
SSNTD. In this paper we describe the charge response of PET to 129Xe, 78Kr and
49Ti ions from the REX-ISOLDE facility at CERN, present the calibration curve
for PET and characterize it as a nuclear track detector
Ensemble Machine Learning Model for Phishing Intrusion Detection and Classification from URLs
Phishing sounds like fishing (which means to cash fish) is a term used for an attempt to commit financial fraud on the internet. An e-mail scam is carried out on individuals or corporate organizations in an attempt to defraud them by falsely obtaining their sensitive details such as usernames, passwords, credit card information, and account numbers. For example, an email may be sent to an individual and appears with a link to click, such as “click me” showing that the recipient has won a certain amount of money, and thereafter requesting him to provide account information for verification. Unfortunately, the credentials are actually transmitted to a phisher who may exploit the person's account when the receiver sends the account details for validation. This research’s focus is to utilize different machine learning classification models to predict whether a given URL is legitimate or a phishing URL. A legitimate URL directs users to a benign authentic webpage and typically serves the user’s request. In contrast, a phishing URL directs users to a fraudulent website, usually impersonating another entity, luring visitors to believe otherwise, and eventually allowing the attacker to perform limitless post-exploitation attacks. Given the little-to-no internet safety awareness of average individuals, this paper aims to take an adaptive approach to detect phishing URLs on the client-side, which can significantly protect users from falling victims to cyber-attacks such as stealing important personal credentials. The proposed approach is to build a machine-learning powered tool that can help individuals stay safe and assist security researchers in identifying patterns and relations that correlate to these attacks, which will help maintain high-security standards for everyday internet users
Ensemble Machine Learning Model for Phishing Intrusion Detection and Classification from URLs
Phishing sounds like fishing (which means to cash fish) is a term used for an attempt to commit financial fraud on the internet. An e-mail scam is carried out on individuals or corporate organizations in an attempt to defraud them by falsely obtaining their sensitive details such as usernames, passwords, credit card information, and account numbers. For example, an email may be sent to an individual and appears with a link to click, such as “click me” showing that the recipient has won a certain amount of money, and thereafter requesting him to provide account information for verification. Unfortunately, the credentials are actually transmitted to a phisher who may exploit the person's account when the receiver sends the account details for validation. This research’s focus is to utilize different machine learning classification models to predict whether a given URL is legitimate or a phishing URL. A legitimate URL directs users to a benign authentic webpage and typically serves the user’s request. In contrast, a phishing URL directs users to a fraudulent website, usually impersonating another entity, luring visitors to believe otherwise, and eventually allowing the attacker to perform limitless post-exploitation attacks. Given the little-to-no internet safety awareness of average individuals, this paper aims to take an adaptive approach to detect phishing URLs on the client-side, which can significantly protect users from falling victims to cyber-attacks such as stealing important personal credentials. The proposed approach is to build a machine-learning powered tool that can help individuals stay safe and assist security researchers in identifying patterns and relations that correlate to these attacks, which will help maintain high-security standards for everyday internet users
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