Assessing the Risks of Dampness and Mould Growth in Renovated Properties

A large portion of the UK housing stock was built before the introduction of the 1989’s building regulations in which insulated cavity walls became mandatory. It is estimated that 65% of the UK housing stock have uninsulated walls and 49% have single glazed leaky windows making them inefficient in terms of energy performance. There have been great efforts during the recent years to improve the quality and energy performance of such buildings through retrofitting/refurbishment not only to improve the living standards of their occupants but also to achieve UK’s carbon emission targets for 2050. Refurbishing such buildings to improve their quality/energy performance may, at the same time, increase the risk of poor indoor air quality (IAQ), condensation, dampness, and mould growth in these buildings. Many refurbished housing stock in the UK are facing similar problems. Damp and mould issues affect between 30-50% of new or refurbished buildings. There is therefore a need for appropriate design strategies not only to improve the quality and thermal performances of such buildings but also to reduce the aforementioned risks through better design, construction detailing, methods, and management processes. This paper reports on the first phase of a joint university/industry Knowledge Transfer project to address the above issues in renovated student accommodations in North West England. Temperature, relative humidity, CO2, and meter readings are measured and recorded in three case study buildings. Results revealed a direct relationship between energy consumption, IAQ, and occupants’ behaviours in the buildings. CO2, Temperature, and RH levels were more acceptable in one of the case study buildings; however, its energy consumption was 7 times higher when compared with a similar building

Quantum Criticality and Incipient Phase Separation in the Thermodynamic Properties of the Hubbard Model

Transport measurements on the cuprates suggest the presence of a quantum critical point hiding underneath the superconducting dome near optimal hole doping. We provide numerical evidence in support of this scenario via a dynamical cluster quantum Monte Carlo study of the extended two-dimensional Hubbard model. Single particle quantities, such as the spectral function, the quasiparticle weight and the entropy, display a crossover between two distinct ground states: a Fermi liquid at low filling and a non-Fermi liquid with a pseudogap at high filling. Both states are found to cross over to a marginal Fermi-liquid state at higher temperatures. For finite next-nearest-neighbor hopping t' we find a classical critical point at temperature T_c. This classical critical point is found to be associated with a phase separation transition between a compressible Mott gas and an incompressible Mott liquid corresponding to the Fermi liquid and the pseudogap state, respectively. Since the critical temperature T_c extrapolates to zero as t' vanishes, we conclude that a quantum critical point connects the Fermi-liquid to the pseudogap region, and that the marginal-Fermi-liquid behavior in its vicinity is the analogous of the supercritical region in the liquid-gas transition.Comment: 18 pages, 9 figure

Thermodynamics of the Quantum Critical Point at Finite Doping in the 2D Hubbard Model: A Dynamical Cluster Approximation Study

We study the thermodynamics of the two-dimensional Hubbard model within the dynamical cluster approximation. We use continuous time quantum Monte Carlo as a cluster solver to avoid the systematic error which complicates the calculation of the entropy and potential energy (double occupancy). We find that at a critical filling, there is a pronounced peak in the entropy divided by temperature, S/T, and in the normalized double occupancy as a function of doping. At this filling, we find that specific heat divided by temperature, C/T, increases strongly with decreasing temperature and kinetic and potential energies vary like T^2 ln(T). These are all characteristics of quantum critical behavior.Comment: 4 pages, 4 figures. Submitted to Phys. Rev. B Rapid Communications on June 27, 200

Machine Learning in Electronic Quantum Matter Imaging Experiments

Essentials of the scientific discovery process have remained largely unchanged for centuries: systematic human observation of natural phenomena is used to form hypotheses that, when validated through experimentation, are generalized into established scientific theory. Today, however, we face major challenges because automated instrumentation and large-scale data acquisition are generating data sets of such volume and complexity as to defy human analysis. Radically different scientific approaches are needed, with machine learning (ML) showing great promise, not least for materials science research. Hence, given recent advances in ML analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is for ML to engage equivalently with experimental data. For example, atomic-scale visualization of EQM yields arrays of complex electronic structure images, that frequently elude effective analyses. Here we report development and training of an array of artificial neural networks (ANN) designed to recognize different types of hypothesized order hidden in EQM image-arrays. These ANNs are used to analyze an experimentally-derived EQM image archive from carrier-doped cuprate Mott insulators. Throughout these noisy and complex data, the ANNs discover the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state. Further, the ANNs find these phenomena to be unidirectional, revealing a coincident nematic EQM state. Strong-coupling theories of electronic liquid crystals are congruent with all these observations.Comment: 44 pages, 15 figure

Bioconversion of food waste to volatile fatty acids: impact of microbial community, pH and retention time

Bio-based production of materials from waste streams is a pivotal aspect in a circular economy. This study aimed to investigate the influence of inoculum (three different sludge taken from anaerobic digestors), pH (5 & 10) and retention time on production of total volatile fatty acids (VFAs), VFA composition as well as the microbial community during anaerobic digestion of food waste. The highest VFA production was ∼22000 ± 1036 mg COD/L and 12927 ± 1029 mg COD/L on day 15 using the inoculum acclimated to food waste at pH 10 and pH 5, respectively. Acetic acid was the dominant VFA in the batch reactors with initial alkaline conditions, whereas both propionic and acetic acids were the dominant products in the acidic condition. Firmicutes, Chloroflexi and Bacteroidetes had the highest relative abundance in the reactors. VFA generation was positively correlated to the relative abundance of Firmicutes

The modalities of Iranian soft power: from cultural diplomacy to soft war

Through exploring Iran's public diplomacy at the international level, this article demonstrates how the Islamic Republic's motives should not only be contextualised within the oft-sensationalised, material or ‘hard’ aspects of its foreign policy, but also within the desire to project its cultural reach through ‘softer’ means. Iran's utilisation of culturally defined foreign policy objectives and actions demonstrates its understanding of soft power's potentialities. This article explores the ways in which Iran's public diplomacy is used to promote its soft power and craft its, at times, shifting image on the world stage

Control of Carbon Dioxide Concentration in Educational Spaces Using Natural Ventilation

This article was accepted for publication in the International Journal of Ventilation [© VEETECH]. The definitive version is available from: http://www.ijovent.org.uk/This paper reports on research carried out to develop natural ventilation control strategies for densely occupied learning spaces with the intention of improving indoor air quality and heating energy consumption. Investigations were carried out for two test cases according to the characteristics given in CIBSE Guide A (2006) and Building Bulletin (BB) 101 (Department for Education, 2006). The performance of these test cases were assessed using dynamic thermal simulation with fixed CO2 set-points, based on which opening dampers are controlled. Improvements to the control strategy are then proposed. The results show that acceptable indoor air quality can be achieved in almost all cases by adopting typical, traditional control strategies. However, energy consumption can be reduced further by applying more advanced control strategies which use two CO2 set-points to regulate the opening sizes in a non-linear, but stepwise manner. Simulation results predict savings in heating energy consumption of at least 30%

Bosonic t-J Model in a stacked triangular lattice and its phase diagram

In this paper, we study phase diagram of a system of two-component hard-core bosons with nearest-neighbor (NN) pseudo-spin antiferromagnetic (AF) interactions in a stacked triangular lattice. Hamiltonian of the system contains three parameters one of which is the hopping amplitude $t$ between NN sites, and the other two are the NN pseudo-spin exchange interaction $J$ and the one that measures anisotropy of pseudo-spin interactions. We investigate the system by means of the Monte-Carlo simulations and clarify the low-temperature phase diagram. In particular, we are interested in how the competing orders, i.e., AF order and superfluidity, are realized, and also whether supersolid forms as a result of hole doping into the state of the $\sqrt{3}\times \sqrt{3}$ pseudo-spin pattern with the $120^o$ structure.Comment: 18 pages, 17 figures, Version to appear in J.Phys.Soc.Jp