Two Dimensional Nano-Structures

The properties of a step-like defect on the surface of ultrathin topological insulator nanofilm have been studied. The reflectance of an electron from such a defect for different parameters of the nanofilm and the different parameters of the defect has been calculated. An electron incident on a steplike defect not only produces reflected and transmitted waves but also generates the modes, which are localized at the steplike defect. Such modes result in an enhancement of electron density at the defect by ≈ 60%. The magnitude of the enhancement depends on the parameters of the nanofilm and the height of the step and is the largest in the case of total electron reflection. Next, the quantum dots in 2D materials such as topological insulator nanofilm, germanene, and phosphorene were introduced. Intopologicalinsulator, We introduce a quantum dot as a bump at a surface of nanofilm. Such quantum dot can localize an electron if the size of the dot is large enough, ∼ 5 nm. The other type of quantum dot is created in germanene. The band gap of buckled graphene-like materials such as germanene, depends on the external electric field. Then a specially design profile of the electric field can produce trapping potential for electrons. Another type of quantum dot can be designed using phosphorene. Phosphorene itself has a band gap. By considering the piece of cylindrical phosphorene layer, an electron can be confined. We study the energy spectra of such defined quantum dots. The intraband and interband optical transitions within the dots have also been studied. The effects of the temperature and the substrate modify the model parameters and should not change the results considerably

姿勢－呼吸フィードバック制御機構の実装と評価

国立大学法人長岡技術科学大

The strategic importance of supply chain management in small and medium sized enterprises :a case study of the garment industry in Sri Lanka

PhD ThesisThis research concerns SMEs in the Sri Lankan garment manufacturing and export industry and explores success factors in managing their supply chains. The overall aim of the study is to better understand successful supply chain management (SCM) practices which have been implemented by SMEs in the Sri Lankan garment exporting industry and the obstacles faced in their implementation. Eight telephone interviews were followed by 20 in-depth, face-to-face interviews with senior managers in Sri Lanka. Documentary evidence was also collected and analysed. Combined with a literature review on manufacturing operations, the data collection led to the development of three criteria for the selection of cases for the research: 1) maintaining direct contacts with foreign buyers, 2) shorter lead times and 3) high value added of products. Based on these three criteria, a sample of six cases: three firms that exercised 'more successful‘ SCM strategies and three characterised by 'less successful‘ SCM strategies were selected. Data were analysed using NVivo10 software with a combination of theoretically derived codes and indigenous codes as the coding strategy. Successful SCM strategies and constraints on improving SCM performance were identified based on each factor considered: lead time, value added and direct contacts related. While both macro and micro-environmental factors influence SME performance, the micro-environmental ones (in particular the lack of strategic business thinking, a weak resource base, resistance to business risk and low profit marginal niches) were far more salient. Further to this, the lack of a fabric manufacturing base within Sri Lanka is a common barrier for both 'more‘ and 'less successful‘ companies while company-specific successful strategies and constrains also were evident. The absence of direct contact with foreign buyers is critical for 'less successful‘ companies as it has led these companies to work with intermediaries. Critical supply chain decisions have to be channelled via buying offices, which leave these companies at risk. The findings add to a growing body of literature on the role of international buying offices and their impact on the implementation of SCM strategies‘ by exporting SMEs

Multiscale modelling of woven and knitted fabric membranes

Light-weight fabric membranes have gained increasing popularity over the past years due to their tailorable structural and material performances. These tailorable properties include stretch forming and deep drawing formability that exhibits excellent stretchability and drapeability properties of textiles and textile composites. Since the inception of computerised numerical control for three-dimensional textile-manufacturing machines, technical textiles paved their way to numerous applications, certainly not limited to; aerospace, biomedical, civil engineering, defence, marine and medical industries. Digital interlooping and digital interlacing technology in additive manufacturing greatly advanced the manufacturing processes of textiles. In this work, we consider two branches of technical fabrics, namely plain-woven and weft-knitted. Multiscale modelling is the tool of choice for homogenising periodic structures and has been used extensively to model and analyse the mechanical behaviour of woven and knitted fabrics. But there is a plethora of literature discussing the demerits of such conventional multiscale modelling. These demerits include higher computational costs, rigid numerical models, ineffcient algorithmic computations and inability to incorporate geometric nonlinearities. We propose a data-driven nonlinear multiscale modelling technique to analyse the complex mechanical behaviour of plain-woven and weft-knitted fabrics with a neat extension to fabric material designing. We show how the integration of statistical learning techniques mitigates the weaknesses of conventional multiscale modelling. Moreover, we discuss the avenues that will open in many potential fields with regard to material modelling, structural engineering and textile industries. In the proposed data-driven nonlinear computational homogenisation technique, we effi ciently integrate the microscale and macroscale using Gaussian Process Regression (GPR) statistical learning technique. In the microscale, representative volume elements (RVEs) are modelled using nite deformable isogeometric spatial rods and deformation is homogenised using periodic boundary conditions. This nite deformable rod is profi cient in handling large deformations, rod-to-rod contacts, arbitrary cross-section de finitions and follower loads. Respecting the principle of separation of scales, we construct response databases by applying different homogenised strain states to the RVEs and recording the respective incremental volume-averaged energy values. We use GPR to learn a model using a 5-fold cross-validation technique by optimising the log marginal likelihood. In the macroscale, textiles are modelled as nonlinear orthotropic membranes for which the stresses and material constitutive relations are predicted by the trained GPR model. This coupling between GPR and membrane models is achieved through a systematic and seamless nite element integration using C++ and Python environments. A neat extension to material designing is also discussed with potentials to extend the work into other related fi elds.Cambridge trust and Trinity Hall scholarshi

Hierarchical and Distributed Architecture for Large-Scale Residential Demand Response Management

The implementation of smart grid brings several challenges to the power system. The ‘prosumer’ concept, proposed by the smart grid, allows small-scale ‘nano-grids’ to buy or sell electric power at their own discretion. One major problem in integrating prosumers is that they tend to follow the same pattern of generation and consumption, which is un-optimal for grid operations. One tool to optimize grid operations is demand response (DR). DR attempts to optimize by altering the power consumption patterns. DR is an integrated tool of the smart grid. FERC Order No. 2222 caters for distributed energy resources, including demand response resources, in participating in energy markets. However, DR contribution of an average residential energy consumer is insignificant. Most residential energy consumers pay a flat price for their energy usage and the established market for residential DR is quite small. In this dissertation, a survey is carried out on the current state-of-the-art in DR research and generalizations of the mathematical models are made. Additionally, a service provider model is developed along with an incentive program and user interfaces (UI). These UIs and incentive program are designed to be attractive and easily comprehended by a large customer base. Furthermore, customer behavior models are developed that characterize the potential customer base, allowing a demand response aggregator to understand and quantify the quality of the customer. Optimization methods for DR management with various characteristics are also explored in this dissertation. Moreover, A scalable demand response management framework that can incorporate millions of participants in the program is introduced. The framework is based on a hierarchical architecture. To improve DR management, hierarchical load forecasting method is studied. Specifically, optimal combination method for hierarchical forecast reconciliation is applied to the DR program. It is shown that the optimal combination for reconciliation of hierarchical predictions could reduce the stress levels of the consumer close to the ideal values for all scenarios

Mediating and moderating effects of entrepreneurial self-efficacy and absorptive capacity on the relationship among cognitive factors, strategic orientation and firm performance of small and medium scale hotel and restaurant industry in Sri Lanka

The low performance of small and medium scale enterprises deprives their multiple contributions to the economies in their mobility towards the development. Though the issue has attracted the attention of many researchers, even today it is compelling due to the emerging global competition in the context of developing countries. The previous findings on the understanding of the complex relationships among factors influencing firm performance remain fragmented and unexplained. Therefore, the purpose of this study was to test a research model for investigating the effect of cognitive factors and strategic orientation on firm performance. The research model incorporated achievement motivation, personal goal setting, and mastery experience as cognitive variables while entrepreneurial orientation, market orientation, and learning orientation were configured as strategic orientation. The mediating role of self-efficacy and the moderating effect of absorptive capacity were also examined. The survey questionnaire translated into the native language was administered to a sample of 800 owner managers in the small and medium scale hotel and restaurant industry in Sri Lanka resulting in 350 usable responses. The stratified random sampling was the method used for the selection of the respondents to the sample. Data analysis was carried out by applying the structural equation modeling method. The findings widened the knowledge of the complex relationships among variables concerned indicating that the synergetic effect of cognitive variables and strategic orientation on firm performance is indispensable. Self-efficacy was found to be a significant mediating mechanism in the relationship between cognitive dispositions and firm performance. Strategic orientation of the organizations with higher level of absorptive capacity was found to be strongly related to firm performance. The results also extended the contextual validation of the research model in the developing countries

Pathogenicity and Reproduction of Isolates of Reniform Nematode, Rotylenchulus Reniformis, From Louisiana on Soybean and Utility of Single Nucleotide Polymorphisms to Evaluate Genetic Variability

Experiments were conducted to evaluate soybean, Glycine max (L.) Merr., responses to indigenous isolates of the reniform nematode (Rotylenchulus reniformis) in Louisiana and to understand the genetic variability of these native isolates. Microplot and greenhouse experiments were conducted to evaluate the comparative reproduction and pathogenicity of single egg-mass populations of R. reniformis isolated from West Carroll (WC), Rapides, Tensas and Morehouse (MOR) parishes of Louisiana. Data from full-season microplot trials, displayed significant differences in reproduction and pathogenicity of the nematode with the commercial soybean cultivars REV 56R63, Pioneer P54T94R, and Dyna-Gro 39RY57. Significantly low population density was observed in the isolate from the MOR parish compared to that of the least reproducing WC isolate. The MOR isolate was also the most pathogenic and resulted in significantly less soybean plant and pod weights compared to the control. In 60 day greenhouse trials, susceptible cultivar Progeny P4930LL and the resistant germplasm lines PI 90763 and PI 548316 were added together with the same cultivars used in the microplot trials. Similar to the microplot trials, the MOR isolate had the least level of reproduction compared to that of WC and presented the greatest level of pathogenicity. In both microplot and greenhouse trials, the soybean cultivar REV 56R63 had a significant reduction in reniform numbers compared to cultivars Pioneer P54T94R and Dyna-Gro 39RY57. The second set of experiments were conducted to understand the amount of genetic variability present in the 13 reniform nematode isolates from Louisiana, Mississippi, Arkansas, South Carolina and Georgia with the use of Single Nucleotide Polymorphism (SNP) analysis. Thirty one chosen SNPs were tested against the reniform nematode isolates using kompetitive allele-specific PCR genotyping assay. Out of the 31 SNPs tested, 26 SNPs amplified genomic DNA of the reniform nematode isolates. Four SNPs out of all tested were able to distinguish genetic differences between and among tested geographic isolates of reniform nematode from Louisiana, Mississippi, and Arkansas. Even with limited numbers of samples, a genetic variability was observed with 3 SNPs between South Carolina, and Georgia isolates. The results obtained in this study might be extremely useful in resistance breeding programs as well as providing soybean cultivar recommendations for growers in different geographical locations

Study of artificially disordered optical fibers

Light transmission through photo-sensitive multi-mode fibers (MMF) with controlled volume disorder is investigated. Experiment shows that a segment of disordered MMF as short as 10 cm is sufficient to distribute power uniformly over all co-propagating modes and the intensity at the output surface of the fiber follows the Rayleigh negative exponential function. To explain the experimental findings, a comprehensive theoretical model is developed with three main results. First, statistical properties of all components of the dielectric tensor are obtained and analyzed in the framework a microscopical model of photo-sensitivity in a germano-silicate glasses. Secondly, it is shown that induced birefringence is insufficient to explain mode mixing, and that cross-polarization mode coupling is essential. Such a coupling is shown to originate from the spatial correlation in the off-diagonal elements of the dielectric tensor. Third, a hybrid theory to describe propagation in a fiber with a spatially correlated disorder is developed. The proposed theory treats the deterministic part of the light via coupled-amplitude equations, and the randomly-phased component with coupled-power equations. The complete theory developed in this work has a predictive power -- it can guide the design of an artificial disorder based on the desired transmission properties of the fiber. Experiment shows that mixing all co-propagating modes can, indeed, be attained in a short segment of a suitably designed disordered MMF without a prohibitive loss. Such fibers can be useful for e.g. maximizing the information capacity multi-mode fiber links --Abstract, page v

Modified biochar adsorbents for aqueous contaminant remediation

Continuous population growth and rapid industrial advancement and development have paved the way for ever increasing environmental pollution. At present, water pollution is a serious global issue that threatens environmental sustainability. The contamination of aquatic bodies with potentially toxic organic and inorganic substances are the result of world-wide anthropogenic activities. These pollutants can have detrimental health consequences on humans and ecosystems. Over the past decades, techniques such as chemical precipitation, ion-exchange, adsorption, membrane filtration, and electrocoagulation-flocculation have been developed and employed for the treatment of drinking and wastewater. Among the currently available techniques, pollutant removal by adsorption is most promising due to its cost-effectiveness, simplicity in operation, environmental friendliness, and abundance of adsorbents. This study emphasized the utilization of biochar (BC), after appropriate surface modification, for the removal of potentially toxic contaminants. In the first study, a base activated biochar was synthesized by treating the biochar with potassium hydroxide (KOH) at 700 ℃ in a muffle furnace for 1 h. The resulting high surface area biochar (KOHBC) was used for the removal of Cr(VI), Pb(II) and Cd(II). In the second study, a biochar-supported polyaniline hybrid was synthesized for aqueous chromium and nitrate adsorption. Introduction of amine and imine groups to the biochar facilitated the removal of these contaminants. In the final study, a composite containing Fe-Ti oxide/biochar (Fe2TiO5/BC) was synthesized for sorptive removal of metal cations, oxy anions, inorganics, and organic contaminants from aqueous solutions. Additionally, this composite was used as a photocatalyst towards aqueous methylene blue (MB) degradation. The surface chemistry and composition of these adsorbents were examined by PZC SEM, TEM, XPS, FTIR, TGA, elemental analysis, and surface area measurements