Local structure in nematic and isotropic liquid crystals

By computer simulations of systems of ellipsoids, we study the influence of the isotropic/nematic phase transition on the direct correlation functions (DCF) in anisotropic fluids. The DCF is determined from the pair distribution function by solving the full Ornstein-Zernike equation, without any approximations. Using a suitable molecular-fixed reference frame, we can distinguish between two qualitatively different contributions to the DCF: One which preserves rotational invariance, and one which breaks it and vanishes in the isotropic phase. We find that the symmetry preserving contribution is barely affected by the phase transition. However, symmetry breaking contributions emerge in the nematic phase and may become quite substantial. Thus the DCF in a nematic fluid is not rotationally invariant. In the isotropic fluid, the DCF is in good agreement with the prediction of the Percus-Yevick theory.Comment: to appear in J. Chem. Phy

Energy Disaggregation for Real-Time Building Flexibility Detection

Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy.Comment: To appear in IEEE PES General Meeting, 2016, Boston, US

Engineering education for sustainable development in Vietnamese universities: building culturally appropriate strategies for transforming the engineering curriculum towards sustainable development

The main goal of this study was to improve the contribution of higher education to sustainable development in Vietnam, specifically in the area of engineering education. The study mapped the current scenario of sustainable development and engineering in higher education in Vietnam as well as investigated how a cultural perspective may influence change strategies in higher education for sustainable development. This study addressed the need for empirical research on the education for sustainable development experience in Vietnam. It argued for and contributed to an emerging international dialogue about how to accelerate progress towards engineering curriculum transformation for sustainable development in different cultural contexts. Located in the interpretivist tradition, the study utilised a wide range of qualitative research techniques to collect and validate data including open-ended questionnaires, interviews, group discussions, participant observation and documentary review. Empirical data was generated between May 2010 and August 2012 in both Vietnam and the UK through three research stages. The first stage was informed by a qualitative survey which captured baseline data collected through a large group of stakeholders from different sectors and various levels of governance. The study mapped the current responses to sustainable development in Vietnam, and confirmed the need and expectation for change in Vietnamese engineering education towards sustainable development. Case study research was carried out at three Vietnamese engineering universities during stage two. The focus was on understanding the current processes and opportunities for curriculum change for sustainable development, as well as investigating how the specific contextual and cultural factors might influence the desired change. The study found evidence of issues which hampered the current efforts in education for sustainable development in the engineering universities in Vietnam. The analysis also provided insights into the Vietnamese values, attitudes and expectancies, and behavioural preferences which contributed to explaining why these issues existed

AI-Based Estimation of Available Flexibility at Individual House Level

This paper develops a data-driven model for assessing the availability of flexibility from individual household devices, at house level. The model predicts the potential shift, increase or decrease of the energy consumption of a particular type of device at a given time, in response to a price signal, and for each house separately. Therefore, the location of the flexibility source is known with accuracy. The model has an Auto-Encoder architecture based on Convolution Neural Network. The augmented model demonstrates good performance in terms of predicting the time-shift in load.</p

Advancing Wound Filling Extraction on 3D Faces: A Auto-Segmentation and Wound Face Regeneration Approach

Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions. To achieve accurate segmentation, we conducted thorough experiments and selected a high-performing model from the trained models. The selected model demonstrates exceptional segmentation performance for complex 3D facial wounds. Furthermore, based on the segmentation model, we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study. Our method achieved a remarkable accuracy of 0.9999986\% on the test suite, surpassing the performance of the previous method. From this result, we use 3D printing technology to illustrate the shape of the wound filling. The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design. By automating facial wound segmentation and improving the accuracy of wound-filling extraction, our approach can assist in carefully assessing and optimizing interventions, leading to enhanced patient outcomes. Additionally, it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants. Our source code is available at \url{https://github.com/SIMOGroup/WoundFilling3D}