8,168 research outputs found

    Development of Flame Retardant and Antibacterial Dual Functionalised Flexible Polyurethane Foam

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    Flexible Polyurethane foam (PUF), with its unique properties, such as lightweight and softness, has been utilised extensively. Nevertheless, owing to the intrinsic high flammability and low ignition temperature, PUF-associated fire risks are always a concern. During PUF’s combustion, excessive heat and toxic gases can be generated, threatening the health and life of human beings and causing huge property loss. Consequently, improving the flame retardancy of the PUF is of importance. Later, the global COVID-19 pandemic broke out in 2019, leading to the public’s increased awareness of maintaining good hygiene conditions. Since PUF products are frequently in contact with humans daily, rendering the PUF with bacterial-killing properties should also be addressed. This dissertation delivers studies on introducing flame retardancy to the PUF via a surface engineering method named the layer-by-layer (LbL) assembly. Due to the consequent COVID-19 situation, this thesis expands the investigations to endow the PUF with antibacterial performances. Preliminary research on fabricating a newly emerged two-dimensional material called MXene (Ti3C2) and chitosan (CH) as flame retardants (FRs) to impart fire safety performances to the PUF was conducted. With only 6.9 wt.% mass added to the PUF, unprecedented fire resistance and smoke suppression properties were received. It was revealed that the FR mechanism was ascribed to the hybrid coating’s excellent barrier and carbonisation effects. Further investigations on improving the PUFs’ biodegradability identified synergistic effects between the MXene with the CH and phytic acid, demonstrating the great potential for reducing the toxicity and improving the eco-friendliness of the PUFs. Additionally, this thesis analysed the FR and antibacterial dual-functionalised PUFs. The synthesised MXene, CH, and silver ion hybridised coating endows the foam with exceptional bactericidal properties with decreases of 99.7 % in gram-negative bacteria and 88.9 % in gram-positive bacteria compared with the unmodified counterpart. Excellent flame retardancy possessed by the dual-functionalised PUFs was discovered. The compatibility of the two functional coatings was evaluated and confirmed. The results manifest the great potential for eradicating the fire risks of PUFs and providing traditional PUF products with antibacterial properties, further expanding PUF’s applications

    Knowledge Distillation and Continual Learning for Optimized Deep Neural Networks

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    Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations. Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally learning new tasks, the model performance on old tasks significantly drops. The ability to accommodate new knowledge while retaining previously learned knowledge is called continual learning. Since the realworld environments in which the model operates are always evolving, a robust neural network needs to have this continual learning ability for adapting to new changes

    Reinforcement Learning Empowered Unmanned Aerial Vehicle Assisted Internet of Things Networks

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    This thesis aims towards performance enhancement for unmanned aerial vehicles (UAVs) assisted internet of things network (IoT). In this realm, novel reinforcement learning (RL) frameworks have been proposed for solving intricate joint optimisation scenarios. These scenarios include, uplink, downlink and combined. The multi-access technique utilised is non-orthogonal multiple access (NOMA), as key enabler in this regime. The outcomes of this research entail, enhancement in key performance metrics, such as sum-rate, energy efficiency and consequent reduction in outage. For the scenarios involving uplink transmissions by IoT devices, adaptive and tandem rein forcement learning frameworks have been developed. The aim is to maximise capacity over fixed UAV trajectory. The adaptive framework is utilised in a scenario wherein channel suitability is ascertained for uplink transmissions utilising a fixed clustering regime in NOMA. Tandem framework is utilised in a scenario wherein multiple-channel resource suitability is ascertained along with, power allocation, dynamic clustering and IoT node associations to NOMA clusters and channels. In scenarios involving downlink transmission to IoT devices, an ensemble RL (ERL) frame work is proposed for sum-rate enhancement over fixed UAV trajectory. For dynamic UAV trajec tory, hybrid decision framework (HDF) is proposed for energy efficiency optimisation. Downlink transmission power and bandwidth is managed for NOMA transmissions over fixed and dynamic UAV trajectories, facilitating IoT networks. In UAV enabled relaying scenario, for control system plants and their respective remotely deployed sensors, a head start reinforcement learning framework based on deep learning is de veloped and implemented. NOMA is invoked, in both uplink and downlink transmissions for IoT network. Dynamic NOMA clustering, power management and nodes association along with UAV height control is jointly managed. The primary aim is the, enhancement of net sum-rate and its subsequent manifestation in facilitating the IoT assisted use case. The simulation results relating to aforesaid scenarios indicate, enhanced sum-rate, energy efficiency and reduced outage for UAV-assisted IoT networks. The proposed RL frameworks surpass in performance in comparison to existing frameworks as benchmarks for the same sce narios. The simulation platforms utilised are MATLAB and Python, for network modeling, RL framework design and validation

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    The combination of solar energy and buildings can greatly save energy, and a great deal of practical and theoretical research has been conducted on solar buildings around the world. Rural areas in southern Shaanxi, China, have wet and cold winters. The average room temperature is 4°C and below 2°C at night, which greatly exceeds the range of thermal comfort that the human body can tolerate. In response to a series of problems such as backward heating methods and low heating efficiency in southern Shaanxi, two fully passive heating methods are proposed for traditional houses in the region. They are rooftop solar heating storage systems and thermal storage wall heating systems (TSWHS), respectively. These two systems have been compared with the status quo heating system to confirm the practicality of the new system and to provide an idea for heating and energy saving in traditional houses in rural areas.挗äčć·žćž‚立性

    Sensitivity analysis for multi-objective optimization weights in energy systems

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    Abstract. This master’s thesis dealt with the production of district heating, the popularity of which is growing due to its low-cost production and environmental friendliness. In the experimental part, multi-objective optimization of district heating production and its consumption was considered. The aim was to maximize profits and minimize emissions on the production side by identifying the optimal weights for the presented objective function. In addition to this, a study was made of how the integration of heat pumps into the district heating network affected the emissions and profits of the production plant. The multi-objective optimization of the experimental part was simulated using MATLABÂź software. The prediction horizon was two days (48 hours). The study focused on tuning parameters in the determined objective function, namely weights for profits and emissions. Simulation scenarios included high and low electricity prices and different numbers of heat pumps. The theory part of the master’s thesis introduced the energy systems of the future and how they can be turned into more sustainable solutions. Based on the results of multi-objective optimization, it can be concluded that there is no single optimal solution that would suit every situation, regardless of the electricity price and the number of heat pumps. However, when comparing all the results, it can be noted that when more heat pumps are integrated into the district heating network, the profits tend to increase and emissions decrease during periods of low and high electricity prices.Herkkyysanalyysi energiajĂ€rjestelmien monitavoiteoptimoinnin painokertoimille. TiivistelmĂ€. TĂ€mĂ€ diplomityö kĂ€sitteli kaukolĂ€mmön tuotantoa, jonka suosio on kasvamassa sen edullisen tuotannon ja ympĂ€ristöystĂ€vĂ€llisyyden vuoksi. Kokeellisessa osassa simuloitiin monitavoiteoptimointia Oulun kaupungin lĂ€mmöntuotantolaitokselle sekĂ€ kaupungin rakennuksille. Tavoitteena oli maksimoida tuotantolaitoksen tulos sekĂ€ minimoida pÀÀstöt mÀÀrittĂ€mĂ€llĂ€ esitetylle kustannusfunktiolle optimaaliset parametrit. TĂ€mĂ€n lisĂ€ksi simuloinneilla tutkittiin, miten lĂ€mpöpumppujen integrointi osaksi kaukolĂ€mpöverkkoa vaikuttaa tuotantolaitoksen pÀÀstöihin sekĂ€ tulokseen. Kokeellisen osan monitavoiteoptimointi simuloitiin MATLABÂź ohjelmistotyökalun avulla. Ennustehorisonttina oli kaksi vuorokautta eli 48 tuntia. TyössĂ€ keskityttiin mÀÀrittĂ€mÀÀn optimaaliset painokertoimet taloudellinen tulos- ja tuotannon pÀÀstöt -muuttujille kustannusfunktiossa. Simulointiskenaarioissa muuttuvina tekijöinĂ€ olivat sĂ€hkön hinta sekĂ€ lĂ€mpöpumppujen mÀÀrĂ€. TĂ€mĂ€n lisĂ€ksi verrattiin kalliin ja edullisen sĂ€hkönhinnan vaikutusta tuotannon tulokseen sekĂ€ pÀÀstöihin. Diplomityön teoriaosuudessa tutustuttiin tulevaisuuden energiajĂ€rjestelmiin, ja siihen miten niistĂ€ voidaan tehdĂ€ kestĂ€vĂ€mpiĂ€. Monitavoiteoptimoinnista saatujen tulosten perusteella voidaan todeta, ettĂ€ yhtĂ€ optimaalista ratkaisua ei saada, joka sopisi jokaiseen tilanteeseen sĂ€hkönhinnasta ja lĂ€mpöpumppujen mÀÀrĂ€stĂ€ huolimatta. Kuitenkin kaikkia tuloksia verrattaessa voidaan todeta, ettĂ€ mitĂ€ enemmĂ€n lĂ€mpöpumppuja on integroituna kaukolĂ€mpöverkkoon, sitĂ€ suurempi on tulos. TĂ€mĂ€n lisĂ€ksi myös pÀÀstöjen kokonaismÀÀrĂ€ nĂ€yttÀÀ laskevan kaupunkitasolla

    On Centrality and Population Size Effects in Urban Pollution: A Meta-Analysis of NO2 and Heat Islands and Spatial Analysis of NO2

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    Load Restoration in Islanded Microgrids: Formulation and Solution Strategies

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    Extreme weather events induced by climate change can cause significant disruptions to the normal operation of electric distribution systems (DS), including isolation of parts of the DS due to damaged transmission equipment. In this paper, we consider the problem of load restoration in a microgrid (MG) that is islanded from the upstream DS because of an extreme weather event. The MG contains sources of distributed generation such as microturbines and renewable energy sources, in addition to energy storage systems. We formulate the load restoration task as a non-convex optimization problem with complementarity constraints. We propose a convex relaxation of the problem that can be solved via model predictive control. In addition, we propose a data-driven policy-learning method called constrained policy optimization. The solutions from both methods are compared by evaluating their performance in load restoration, which is tested on a 12-bus MG

    The nexus between e-marketing, e-service quality, e-satisfaction and e-loyalty: a cross-sectional study within the context of online SMEs in Ghana

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    The spread of the Internet, the proliferation of mobile devices, and the onset of the COVID-19 pandemic have given impetus to online shopping in Ghana and the subregion. This situation has also created opportunities for SMEs to take advantage of online marketing technologies. However, there is a dearth of studies on the link between e-marketing and e-loyalty in terms of online shopping, thereby creating a policy gap on the prospects for business success for online SMEs in Ghana. Therefore, the purpose of the study was to examine the relationship between the main independent variable, e-marketing and the main dependent variable, e-loyalty, as well as the mediating roles of e-service quality and e-satisfaction in the link between e-marketing and e-loyalty. The study adopted a positivist stance with a quantitative method. The study was cross-sectional in nature with the adoption of a descriptive correlational design. A Structural Equation Modelling approach was employed to examine the nature of the associations between the independent, mediating and dependent variables. A sensitivity analysis was also conducted to control for the potential confounding effects of the demographic factors. A sample size of 1,293 residents in Accra, Ghana, who had previously shopped online, responded to structured questionnaire in an online survey via Google Docs. The IBM SPSS Amos 24 software was used to analyse the data collected. Positive associations were found between the key constructs in the study: e-marketing, e-service quality, e-satisfaction and e-Loyalty. The findings from the study gave further backing to the diffusion innovation theory, resource-based view theory, and technology acceptance model. In addition, e-service quality and e-satisfaction individually and jointly mediated the relationship between e-marketing and e-loyalty. However, these mediations were partial, instead of an originally anticipated full mediation. In terms of value and contribution, this is the first study in a developing economy context to undertake a holistic examination of the key marketing performance variables within an online shopping context. The study uniquely tested the mediation roles of both e-service quality and e-satisfaction in the link between e-marketing and e-loyalty. The findings of the study are novel in the e-marketing literature as they unearthed the key antecedents of e-loyalty for online SMEs in a developing economy context. The study suggested areas for further related studies and also highlighted the limitations

    Land Use and Land Cover Mapping in a Changing World

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    It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classiïŹcation systems

    ATR-FTIR Spectroscopy-Linked Chemometrics:A Novel Approach to the Analysis and Control of the Invasive Species Japanese Knotweed

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    Japanese knotweed (Reynoutria japonica), an invasive plant species, causes negative environmental and socio-economic impacts. A female clone in the United Kingdom, its extensive rhizome system enables rapid vegetative spread. Plasticity permits this species to occupy a broad geographic range and survive harsh abiotic conditions. It is notoriously difficult to control with traditional management strategies, which include repetitive herbicide application and costly carbon-intensive rhizome excavation. This problem is complicated by crossbreeding with the closely related species, Giant knotweed (Reynoutria sachalinensis), to give the more vigorous hybrid, Bohemian knotweed (Fallopia x Bohemica) which produces viable seed. These species, hybrids, and backcrosses form a morphologically similar complex known as Japanese knotweed ‘sensu lato’ and are often misidentified. The research herein explores the opportunities offered by advances in the application of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy-linked chemometrics within plant sciences, for the identification and control of knotweed, to enhance our understanding of knotweed biology, and the potential of this technique. ATR-FTIR spectral profiles of Japanese knotweed leaf material and xylem sap samples, which include important biological absorptions due to lipids, proteins, carbohydrates, and nucleic acids, were used to: identify plants from different growing regions highlighting the plasticity of this clonal species; differentiate between related species and hybrids; and predict key physiological characteristics such as hormone concentrations and root water potential. Technical advances were made for the application of ATR-FTIR spectroscopy to plant science, including definition of the environmental factors that exert the most significant influence on spectral profiles, evaluation of sample preparation techniques, and identification of key wavenumbers for prediction of hormone concentrations and abiotic stress. The presented results cement the position of concatenated mid-infrared spectroscopy and machine learning as a powerful approach for the study of plant biology, extending its reach beyond the field of crop science to demonstrate a potential for the discrimination between and control of invasive plant species
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