Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana'a metropolitan city, Yemen.

An effective and efficient planning of an urban growth and land use changes and its impact on the environment requires information about growth trends and patterns amongst other important information. Over the years, many urban growth models have been developed and used in the developed countries for forecasting growth patterns. In the developing countries however, there exist a very few studies showing the application of these models and their performances. In this study two models such as cellular automata (CA) and the SLEUTH models are applied in a geographical information system (GIS) to simulate and predict the urban growth and land use change for the City of Sana’a (Yemen) for the period 2004–2020. GIS based maps were generated for the urban growth pattern of the city which was further analyzed using geo-statistical techniques. During the models calibration process, a total of 35 years of time series dataset such as historical topographical maps, aerial photographs and satellite imageries was used to identify the parameters that influenced the urban growth. The validation result showed an overall accuracy of 99.6 %; with the producer’s accuracy of 83.3 % and the user’s accuracy 83.6 %. The SLEUTH model used the best fit growth rule parameters during the calibration to forecasting future urban growth pattern and generated various probability maps in which the individual grid cells are urbanized assuming unique “urban growth signatures”. The models generated future urban growth pattern and land use changes from the period 2004–2020. Both models proved effective in forecasting growth pattern that will be useful in planning and decision making. In comparison, the CA model growth pattern showed high density development, in which growth edges were filled and clusters were merged together to form a compact built-up area wherein less agricultural lands were included. On the contrary, the SLEUTH model growth pattern showed more urban sprawl and low-density development that included substantial areas of agricultural lands

Comparing the hierarchy of keywords in on-line news portals

The tagging of on-line content with informative keywords is a widespread phenomenon from scientific article repositories through blogs to on-line news portals. In most of the cases, the tags on a given item are free words chosen by the authors independently. Therefore, relations among keywords in a collection of news items is unknown. However, in most cases the topics and concepts described by these keywords are forming a latent hierarchy, with the more general topics and categories at the top, and more specialised ones at the bottom. Here we apply a recent, cooccurrence-based tag hierarchy extraction method to sets of keywords obtained from four different on-line news portals. The resulting hierarchies show substantial differences not just in the topics rendered as important (being at the top of the hierarchy) or of less interest (categorised low in the hierarchy), but also in the underlying network structure. This reveals discrepancies between the plausible keyword association frameworks in the studied news portals

Languages cool as they expand: Allometric scaling and the decreasing need for new words

We analyze the occurrence frequencies of over 15 million words recorded in millions of books published during the past two centuries in seven different languages. For all languages and chronological subsets of the data we confirm that two scaling regimes characterize the word frequency distributions, with only the more common words obeying the classic Zipf law. Using corpora of unprecedented size, we test the allometric scaling relation between the corpus size and the vocabulary size of growing languages to demonstrate a decreasing marginal need for new words, a feature that is likely related to the underlying correlations between words. We calculate the annual growth fluctuations of word use which has a decreasing trend as the corpus size increases, indicating a slowdown in linguistic evolution following language expansion. This ‘‘cooling pattern’’ forms the basis of a third statistical regularity, which unlike the Zipf and the Heaps law, is dynamical in nature

Individualization as driving force of clustering phenomena in humans

One of the most intriguing dynamics in biological systems is the emergence of clustering, the self-organization into separated agglomerations of individuals. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is clustering of opinions in human populations. The puzzle is particularly pressing if opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing opinion formation models suggest that "monoculture" is unavoidable in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness did not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution of the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct simulation experiments to demonstrate that with this kind of noise, a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting.Comment: 12 pages, 4 figure

Improvement of direct methanol fuel cell performance using a novel mordenite barrier layer

The selective incorporation of a functionalised inorganic component at the interface between the Nafion membrane and the catalyst is demonstrated to increase the power density of a direct methanol fuel cell by 57% with no other change in operating conditions. The simple addition of 0.5 wt% zeolite (mordenite) in the Nafion ‘ink,’ which is used as a glue to fix the precast Nafion membrane onto the catalyst/gas diffusion layer, provides an organophobic quality to the MEA which enhances performance and durability. The targeted addition of such small amounts of the ‘organophobe’ at the interface where the chemical effect is required is a novel approach to improving DMFC MEA's and means that the usual trade-off between methanol permeability and proton conductivity is not observed as proton conductivity is maintained while methanol crossover is reduced

Sustainable environment through using porous materials:a review on wastewater treatment

Porous materials play an important role in creating a sustainable environment by improving wastewater treatment's efficacy. Porous materials, including adsorbents or ion exchangers, catalysts, metal–organic frameworks, composites, carbon materials, and membranes, have widespread applications in treating wastewater and air pollution. This review examines recent developments in porous materials, focusing on their effectiveness for different wastewater pollutants. Specifically, they can treat a wide range of water contaminants, and many remove over 95% of targeted contaminants. Recent advancements include a wider range of adsorption options, heterogeneous catalysis, a new UV/H2O2 procedure, ion exchange, Fenton oxidation, membrane activities, ozonation, membrane bioreactor, electrochemical treatment, wet air oxidation, and a carbon capture methodology utilizing various porous materials. A particular focus for innovative research is on developing technologies to synthesize porous materials and assess their performance in removing various pollutants from wastewater at varying experimental conditions. Porous materials can be essential in designing wastewater treatment systems to address the critical environmental issues of water stress and safe drinking water worldwide.</p

Sustainable environment through using porous materials:a review on wastewater treatment

Porous materials play an important role in creating a sustainable environment by improving wastewater treatment's efficacy. Porous materials, including adsorbents or ion exchangers, catalysts, metal–organic frameworks, composites, carbon materials, and membranes, have widespread applications in treating wastewater and air pollution. This review examines recent developments in porous materials, focusing on their effectiveness for different wastewater pollutants. Specifically, they can treat a wide range of water contaminants, and many remove over 95% of targeted contaminants. Recent advancements include a wider range of adsorption options, heterogeneous catalysis, a new UV/H2O2 procedure, ion exchange, Fenton oxidation, membrane activities, ozonation, membrane bioreactor, electrochemical treatment, wet air oxidation, and a carbon capture methodology utilizing various porous materials. A particular focus for innovative research is on developing technologies to synthesize porous materials and assess their performance in removing various pollutants from wastewater at varying experimental conditions. Porous materials can be essential in designing wastewater treatment systems to address the critical environmental issues of water stress and safe drinking water worldwide.</p

Demand-side management in office buildings in Kuwait through an ice-storage assisted HVAC system with model predictive control

Examining methods for controlling the electricity demand in Kuwait was the main objective and motivation of this researchp roject. The extensiveu se of air-conditioning for indoor cooling in office and large commercial buildings in Kuwait and the Gulf States represents a major part of the power and electricity consumption in such countries. The rising electricity generation cost and growing rates of consumption continuously demand the construction new power plants. Devising and enforcing Demand-SideM anagemen(t DSM) in the form of energye fficient operations trategies was the response of this research project to provide a means to rectify this situation using the demand-side management technique known as demand levelling or load shifting. State of the art demand-sidem anagementte chniquesh ave been examined through the developmenot f a model basedp redictive control optimisations trategyf or an integrateda ndm odulara pproachto the provisiono f ice thermals torage. To evaluate the potential of ice-storage assisted air-conditioning systems in flattening the demand curve at peak times during the summer months in Kuwait, a model of a Heating, Ventilation, and Air-conditioning (HVAC) plant was developed in Matlab. The model engaged the use of model based predictive control (MPQ) as an optimisation tool for the plant as a whole. The model with MPC was developed to chose and decide on which control strategy to operate the integrated ice-storage HVAC plant. The model succeeded in optimising the operation of the plant and introduced encouraging improvement of the performance of the system as a whole. The concept of the modular ice-storage system was introduced through a control zoning strategy based on zonal orientation. It is believed that such strategy could lead to the modularisation of ice-storage systems. Additionally, the model was examined and tested in relation to load flattening and demonstrated promising enhancement in the shape of the load curve and demonstrated flattened demand curves through the employed strategy. When compared with measured data from existing buildings, the model showed potential for the techniques utilised to improve the load factor for office buildings.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Adolescent Self-Organization and Adult Smoking and Drinking over Fifty Years of Follow-Up:The British 1946 Birth Cohort

Variations in markers of adolescent self-organization predict a range of economic and health-related outcomes in general population studies. Using a population-based birth cohort study we investigated associations between adolescent self-organization and two common factors over adulthood influencing health, smoking and alcohol consumption. The MRC National Survey of Health and Development (the British 1946 birth cohort) was used to test associations between a dimensional measure of adolescent self-organization derived from teacher ratings, and summary longitudinal measures of smoking and alcohol consumption over the ensuing five decades. Multinomial regression models were adjusted for sex, adolescent emotional and conduct problems, occupational social class of origin, childhood cognition, educational attainment and adult occupational social class. With all covariates adjusted, higher adolescent self-organization was associated with fewer smoking pack years, although not with quitting; there was no association with alcohol consumption across adulthood (none or heavy compared with light to moderate). Adolescent self-organization appears to be protective against smoking, but not against heavy alcohol consumption. Interpretation of this differential effect should be embedded in an understanding of the social and sociodemographic context in which these health behaviours occur over time