134 research outputs found
2006 Annual Research Symposium Abstract Book
2006 annual volume of abstracts for science research projects conducted by students at Trinity College
Innovative Technologies and Services for Smart Cities
A smart city is a modern technology-driven urban area which uses sensing devices, information, and communication technology connected to the internet of things (IoTs) for the optimum and efficient utilization of infrastructures and services with the goal of improving the living conditions of citizens. Increasing populations, lower budgets, limited resources, and compatibility of the upgraded technologies are some of the few problems affecting the implementation of smart cities. Hence, there is continuous advancement regarding technologies for the implementation of smart cities. The aim of this Special Issue is to report on the design and development of integrated/smart sensors, a universal interfacing platform, along with the IoT framework, extending it to next-generation communication networks for monitoring parameters of interest with the goal of achieving smart cities. The proposed universal interfacing platform with the IoT framework will solve many challenging issues and significantly boost the growth of IoT-related applications, not just in the environmental monitoring domain but in the other key areas, such as smart home, assistive technology for the elderly care, smart city with smart waste management, smart E-metering, smart water supply, intelligent traffic control, smart grid, remote healthcare applications, etc., signifying benefits for all countries
Network resilience
Many systems on our planet are known to shift abruptly and irreversibly from
one state to another when they are forced across a "tipping point," such as
mass extinctions in ecological networks, cascading failures in infrastructure
systems, and social convention changes in human and animal networks. Such a
regime shift demonstrates a system's resilience that characterizes the ability
of a system to adjust its activity to retain its basic functionality in the
face of internal disturbances or external environmental changes. In the past 50
years, attention was almost exclusively given to low dimensional systems and
calibration of their resilience functions and indicators of early warning
signals without considerations for the interactions between the components.
Only in recent years, taking advantages of the network theory and lavish real
data sets, network scientists have directed their interest to the real-world
complex networked multidimensional systems and their resilience function and
early warning indicators. This report is devoted to a comprehensive review of
resilience function and regime shift of complex systems in different domains,
such as ecology, biology, social systems and infrastructure. We cover the
related research about empirical observations, experimental studies,
mathematical modeling, and theoretical analysis. We also discuss some ambiguous
definitions, such as robustness, resilience, and stability.Comment: Review chapter
Multi-Objective Optimization in Metabolomics/Computational Intelligence
The development of reliable computational models for detecting non-linear patterns
encased in throughput datasets and characterizing them into phenotypic classes
has been of particular interest and comprises dynamic studies in metabolomics
and other disciplines that are encompassed within the omics science. Some of the
clinical conditions that have been associated with these studies include metabotypes
in cancer, in
ammatory bowel disease (IBD), asthma, diabetes, traumatic brain
injury (TBI), metabolic syndrome, and Parkinson's disease, just to mention a few.
The traction in this domain is attributable to the advancements in the procedures
involved in 1H NMR-linked datasets acquisition, which have fuelled the generation of
a wide abundance of datasets. Throughput datasets generated by modern 1H NMR
spectrometers are often characterized with features that are uninformative, redundant
and inherently correlated. This renders it di cult for conventional multivariate
analysis techniques to e ciently capture important signals and patterns. Therefore,
the work covered in this research thesis provides novel alternative techniques to
address the limitations of current analytical pipelines. This work delineates 13 variants
of population-based nature inspired metaheuristic optimization algorithms which
were further developed in this thesis as wrapper-based feature selection optimizers.
The optimizers were then evaluated and benchmarked against each other through
numerical experiments. Large-scale 1H NMR-linked datasets emerging from three
disease studies were employed for the evaluations. The rst is a study in patients
diagnosed with Malan syndrome; an autosomal dominant inherited disorder marked
by a distinctive facial appearance, learning disabilities, and gigantism culminating
in tall stature and macrocephaly, also referred to as cerebral gigantism. Another
study involved Niemann-Pick Type C1 (NP-C1), a rare progressive neurodegenerative
condition marked by intracellular accrual of cholesterol and complex lipids including
sphingolipids and phospholipids in the endosomal/lysosomal system. The third
study involved sore throat investigation in human (also known as `pharyngitis'); an
acute infection of the upper respiratory tract that a ects the respiratory mucosa
of the throat. In all three cases, samples from pathologically-con rmed cohorts
with corresponding controls were acquired, and metabolomics investigations were
performed using 1H NMR technique. Thereafter, computational optimizations were
conducted on all three high-dimensional datasets that were generated from the disease
studies outlined, so that key biomarkers and most e cient optimizers were identi ed
in each study. The clinical and biochemical signi cance of the results arising from
this work were discussed and highlighted
Bibliometric Studies and Worldwide Research Trends on Global Health
Global health, conceived as a discipline, aims to train, research and respond to problems of a transboundary nature, in order to improve health and health equity at the global level. The current worldwide situation is ruled by globalization, and therefore the concept of global health involves not only health-related issues, but also those related to the environment and climate change. Therefore, in this Special Issue, the problems related to global health have been addressed from a bibliometric approach in four main areas: environmental issues, diseases, health, education and society
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