Miracle-dz/UniFAR-FTUs huggingface dataset:
Potentials of radar sensor detecting the presence of an imitated user for optimising short-range presence-sensing lighting in homes

Current presence-sensing technologies for energy-efficient lighting control and building optimisation are (i) catered to commercial and institutional environments, and (ii) focused on lamp technology and occupancy detection. They often ignore user behaviour characteristics, which significantly influence energy consumption. Therefore, this study aims to identify alternative sensing techniques as part of a lighting control system that can energy-efficiently support user’s behavioural needs in mixed-function residential spaces. An exploratory study investigated the optimal placement of a non-wearable radar sensor to detect an imitated user’s breathing frequency at varying pre-set horizontal distance positions, and the sensor’s performance was validated with a spirometer. The procedure measured a balloon’s radar-detected distance, radar-detected breathing frequency, and spirometer-registered breathing frequency at each pre-set position. The radar sensor detected all simulated breathing frequencies with almost 100% data accuracy but was not comparable in detecting all distances. The radar offers a less intrusive short-range presence-sensing for homes, accurately detecting breathing frequencies in a contactless way between 0.2m to 0.8m. Further investigations are recommended to develop radar sensing that could predict lighting options based on user’s objective feedback.
PixelFrame: A reconfigurable, precast, post-tensioned concrete structural system for a circular building economy

The demand to build for a growing urban population conflicts with the need to reduce the AEC (Architecture, Engineering and Construction) sector’s contributions to global CO2 emissions. Structural systems are a key factor in this challenge, as they contribute substantially to a building’s mass and are typically overdesigned. In this paper, we present PixelFrame, a new precast concrete structural system that is algorithmically designed to use less carbon upfront and be reused across multiple lifecycles. We achieve a high reuse potential for structural elements through building load demand analysis, a segmental externally post-tensioned design, and an integrated optimal assignment strategy. In addition to improving reuse potential, a segmental optimization strategy also yields higher efficiency in comparison to traditional prismatic concrete structural elements. The detailing of concrete mixes is informed by a statistical analysis of anticipated building load demands and a custom clustering asymmetric distance function and clustering algorithm. Compared to previous work in this area, our contribution is original in its bidirectional algorithmic approach; both the target building structure and the circular material inventory are computationally generated to achieve versatility while minimizing emissions.
Experimental study to understand the thermal environment of an office cooled by radiant ceiling panels and dedicated outdoor air system

This research aims to better understand the radiant environment of an interior space located in the tropical climate that is cooled by Radiant Ceiling Panels (RCPs). To do so, we have designed a highly instrumented 177m2 office testbed in Singapore installed with RCPs, Dedicated Outdoor Air System (DOAS) and Fan Coil Units (FCUs). An 11-day experiment is designed to monitor the radiant environment when being cooled by 1) RCPs + DOAS and 2) FCUs + DOAS with a fixed internal load of 2kW and humidity load of 8g/hr. We alternate between these two cooling modes, with variations in panels’ water supply temperature and window blinds. We expect to see a separation of Mean Radiant Temperature (MRT) and air temperature in mode 1 with the use of improved radiant sensors. However, results showed only minor differences in the radiant environment between the two modes of cooling. There was no significant MRT and air temperature separation (<1°C). These insights provide a better understanding of our radiant environment, which can potentially improve the way we control radiant systems. With better understanding, we can support high thermal comfort performance of a radiant systems as compared to a more common air-based system.
Embodied net-zero compatible buildings? They already exist!

This paper identifies buildings on pathway to meet carbon targets for embodied emissions aligned with global carbon budgets and mitigation pathways. A simplified bottom-up model is used, assessing multiple variations of a new construction archetype to identify the main strategies to achieve the targets. The model estimates the quantities of the main components with a few input geometry parameters. Life cycle emissions are then computed based on predefined building components. The reference building is representative of a typical new construction with standard operational values and massive construction. Strategies evaluate design optimization measures, construction techniques, and materials variations. Results show that (1) characteristics and volume of the building play a determining role. The existence and size of underground floors can determine the achievement of todays and future targets. (2) Construction choices can half emissions just by switching from concrete to wood and using natural insulation. (3) Future improvements in the supply chain of materials do not follow the required reduction pathway determined by the Swiss climate strategy. Net-zero compatible buildings are already possible, it is just a matter of making the right choices.
A Robust Localization Approach Based on Point Cloud Descriptor and SLAM in Urban Environment

The importance of autonomous driving localization to autonomous driving systems cannot be overstated, reliable and high-precision localization is the focus of research in autonomous driving. Many studies at this stage use multiple sensors to generate high-definition maps for matching and localization. The labor and economic costs required to maintain such maps are often high. This paper proposes a fusion localization framework for simultaneous localization and mapping (SLAM) and matching localization. It could afford high-precision localization results of autonomous vehicles using only light detection and ranging (LiDAR) sensors. Concretely, a novel global point cloud descriptor, named binary scan context (BSC) is proposed, for matching localization. It encodes the structural features in the z-height direction in the point cloud space in a binary way. An efficient two-stage matching strategy is proposed to improve the efficiency of matching. Experiments on public datasets and vehicles have demonstrated the validation and precision of the method.
Model predictive control of heating in a low energy single-family house

This paper presents a control scheme that uses a Model Predictive Control (MPC) approach to manage the heating system of a low-energy single-family house. The house is equipped with an air-to-water Heat Pump (HP) and individually controlled hydronic underfloor heating circuits for each of its 11 heating zones. The MPC scheme is designed to maintain individual room comfort levels in each zone, while incorporating weather forecasts and following a heating reference to allow for load shifting for periods with low energy prices and high Photovoltaic (PV) production calculated by an upper level in a hierarchical control scheme. The focus of the design has been the model structure that allows for fast solutions to the MPC optimisation problem, while still capturing the high complexity, non-linear dynamics of the building. The controller is tested on a high-fidelity simulation model of the house, achieving disturbance rejection and system stabilisation. The rapid solving time makes repeated experiments and longer simulations feasible.
Sensitivity analysis of fifth generation district heating and cooling coupled with borehole thermal energy storage with respect to cooling adoption

Fifth generation district heating and cooling (5GDHC) is a promising solution for decarbonising building thermal demands, as it allows for simultaneous heating and cooling through a bi-directional network. This paper aims to investigate the general performance of 5GDHC under uncertainty. Initially, an uncertainty analysis is performed on a 5GDHC model in a case study, revealing significant variation in economic, technical, and environmental performance due to uncertain parameters. Subsequently, variance-based global sensitivity analysis is used to rank the uncertain parameters based on their impact on the economic performance of 5GDHC. The results identify cooling adoption rate, borehole pricing, and discount rate as the most influential parameters. Lastly, factors mapping demonstrates that cooling adoption rate within 0.6-0.9, low borehole costs, low discount rates, and high gas prices will drive the switch to 5GDHC. Overall, this study emphasizes the general applicability of 5GDHC.
Automated data labeling of building automation systems using time series data and conditional probabilities

As energy efficiency demands increase, buildings get smarter, and the amount of data to analyze grows, where each building device may generate multiple data streams. These extensive quantities of monitoring data serve as a great opportunity for detecting anomalies in building automation systems and for optimizing their control. However, each building usually uses a custom format for data labels, therefore requiring an individual data label analysis per building. This makes the conceptually manageable task of detecting energy systems from the raw data increasingly complex and error-prone, which is a further hurdle that any building operation optimizer must resolve. This paper presents a methodology for automatically categorizing and labeling raw monitoring data from building automation systems. Using statistical features of the data, the method checks which data streams follow which known building operation rules and patterns. Therefore, an initial labeling of the data streams takes place. Furthermore, examining the correlation between the data streams indicates possible related system components using the concept of conditional probability. As a use case for the methodology, unlabeled data from a real building automation system are examined. The results show that, using unlabeled time series, data types from certain sensors and actuators can be reliably identified. The proposed methodology could therefore simplify the implementation of energy applications such as operation optimization and fault detection of building automation systems
A review on Energy Storage Systems

The urgent need to address global warming and the energy crisis, caused by dependence on fossil fuels, has led to enhanced research for sustainable energy sources. The adoption of renewable energy alternatives has been swift, but the intermittent nature of these sources makes consistent power production challenging. To address this, various techniques are used to store energy from renewable sources, which can then be used in a controlled manner to meet rising energy demands while reducing global CO2 emissions. This article analyzes the state of the art of energy storage technologies, focusing on their characteristics, classifications, applications, comparisons, and limitations. The study also includes recent research on new energy storage types, as well as significant advances and developments.
Towards healthy and energy-efficient buildings in the context of Egypt: Modelling demand-controlled ventilation to improve the indoor air quality in a generic office space in Cairo

Cairo is characterised by high concentrations of ambient air pollutants, especially air particulate matter of diameter less than 2.5 micrometre (PM2.5). Many studies have emphasized the impact of PM2.5 on people’s health and wellbeing including a World Bank report that has attributed 12% of the total annual deaths in Cairo to the exposure to ambient PM2.5. On one hand. improving the energy efficiency of buildings may involve implementing energy efficiency measures that aim to achieve indoor thermal comfort by maximizing the use of natural ventilation and minimizing mechanical air-conditioning. However, while natural ventilation can help reduce CO2 levels, it can also potentially lead to an increase in indoor PM2.5 levels. This study aims to investigate the impact of multiple air filtration scenarios on the energy consumption and the indoor air quality for a shoebox model that aims to represent generic offices Cairo. The study uses EnergyPlus simulations that leverage an Energy Management System script to model the demand-controlled ventilation, apply air filters when required, and simulate the increase in energy use due to the relevant pressure drops in the air system. The results for the scenarios investigated in the study highlighted that air filters can reduce the average indoor PM2.5 levels by nearly 40% during occupancy hours while causing an estimated increase of around 2-7% in the total operational energy. Given data and assumptions relevant to the study context, it was found that filtering the recirculated air while minimizing the introduction outdoor fresh air can be sufficient to minimize indoor PM2.5 levels.
Optimizing SIFT algorithm parameters for better matching UAV and satellite images

Image registration has been increasingly employed in various applications such as target identification, 3D mapping, and motion tracking. The main idea of Image registration is aligning two or more images of the same scene captured from different viewpoints, at different times. Scale-invariant feature transform, SIFT, is considered one of the most robust algorithms used in image registration for extracting and matching features under different conditions. Using SIFT algorithm default parameters in Matching UAV and satellite Images provides unreliable results due to the nature of aerial images because the dynamic range is quite low. The number of extracted features depends on the image content and the selected parameters. In this paper we tuned SIFT parameters to get the best performance with aerial images, to increase the number of features (SM) and the correct match rate (CMR) which increases the efficiency of the process of registration. The algorithm is validated by matching a large number of aerial images taken by mini-UAV with satellite images for the same region.
Analysis of the Correlation between Genetic Traits and Pathogenic Sites Based on Multiple Linear Regression Model

Current research shows that many phenotypic traits, susceptibility to diseases, and drugs of the human body are related to some pathogenic sites. Locating the position of loci associated with traits or diseases in chromosomes or genes helps researchers understand disease traits and genetic mechanisms and prevent the occurrence of certain genetic diseases. Select 1000 data sets of two types of sample (disease and health) information, including disease information of the sample, 9445 site coding information and gene information of these sites. Two types of methods are used to analyze the correlation between the pathogenic sites and related traits, the chi-square test method is used to find the sites with the highest probability of pathogenicity between single disease and pathogenic sites, and the multiple regression analysis model is used to determine the most likely pathogenic sites between multiple phenotypic traits and sites. Use statistical tools and software to solve the model, and use the error detection rate (FDR) method to conduct multiple comparisons and corrections for each regression parameter. The conclusion of the modeling analysis is consistent with the standard conclusion provided by the 17th Organizing Committee for Mathematical Modeling of Postgraduates. This shows that the comprehensive method of chi-square test and multiple linear regression modeling is reliable, and has universal and reference significance for finding the pathogenic sites of a certain genetic disease and locating the pathogenic genes.
ALTERNATIVE INVESTMENT RESEARCH CENTRE WORKING PAPER SERIES Working Paper # 0001 H EDGE F UND P ERFORMANCE 1990-2000

We investigate the claim that hedge funds offer investors a superior risk-return tradeoff. We do so using a continuous time version of Dybvig’s (1988a, 1988b) payoff distribution pricing model. The evaluation model, which does not require any assumptions with regard to the return distribution of the funds to be evaluated, is applied to the monthly returns of 77 hedge funds and 13 hedge fund indices over the period May 1990 – April 2000. The results show that as a stand-alone investment hedge funds do not offer a superior risk-return profile. We find 12 indices and 72 individual funds to be inefficient, with the average efficiency loss amounting to 2.76% per annum for indices and 6.42% for individual funds. Part of the inefficiency cost of individual funds can be diversified away. Funds of funds, however, are not the preferred vehicle for this as their performance appears to suffer badly from their double fee structure. Looking at hedge funds in a portfolio context results in a marked improvement in the evaluation outcomes. Seven of the 12 hedge fund indices and 58 of the 72 individual funds classified as inefficient on a stand-alone basis are capable of producing an efficient payoff profile when mixed with the S&P 500. The best results are obtained when 10-20% of the portfolio value is invested in hedge funds.
Validation of SARC-F-Proxy for the Screening of Sarcopenia in Older Patients with Cognitive Impairment

Abstract Objectives: The SARC-F is a validated questionnaire for the screening of sarcopenia in an older population. However, the clinical relevance of this self-reported questionnaire in patients with cognitive problems is questionable. This study aims to validate the SARC-F-Proxy as an alternative screening tool for sarcopenia in patients with cognitive impairment. Methods: This cross-sectional study included hospitalised community-dwelling older adults aged 60 years or older with confirmed cognitive impairment. Three SARC-F questionnaires were completed: one by patients, one by informal caregivers and one by formal caregivers. Muscle strength, mass and physical performance were measured by handgrip strength, anthropometric measurements, and gait speed respectively. The recently updated EWGSOP2 diagnostic criteria were used as the “gold standard” for diagnosis of sarcopenia. Results: The prevalence of sarcopenia using SARC-F-Proxy was 75.4% for SARC-F-Proxy-Formal caregiver and 66% for SARC-F-Proxy-Informal caregiver. SARC-F-Proxy had high sensitivity (85.9% for SARC-F-Proxy-Formal caregiver and 77% for SARC-F-proxy-informal caregiver) and low specificity (46.5% for SARC-F-Proxy-Formal caregiver and 54.7% for SARC-F-Proxy-Informal caregiver). Conclusions: the proxy-reported SARC-F questionnaire can be applied as a surrogate for the SARC-F in the screening of sarcopenia in hospitalised community-dwelling older people with known or suspected cognitive impairment. Second, the results in this study suggest a higher reliability when the proxy-reported questionnaire is performed by the formal caregiver.
Evaluating the effect of digital technology on the learning of orthodontic cephalometric measurement [version 2; peer review: 1 approved, 1 approved with reservations]

Background: This study aimed to evaluate the effect of applying digital technology in cephalometric measurement teaching and students’ acceptance towards it. Methods: In total, 94 undergraduates of stomatology were recruited and randomly allocated to two groups. According to the cross-over design, both groups completed cephalometric measurements through the traditional hand-drawn method and digital technology (the Dolphin software) in different orders. By traditional hand-drawn method, students need to depict the outlines of the craniofacial anatomical structures on the sulfuric transfer paper first, then marked the measurement points and completed the measurement of line spacings and angles. By digital technology, they should mark the points
Novel brain-penetrant inhibitor of G9a methylase blocks Alzheimer’s disease proteopathology for precision medication

Current amyloid beta-targeting approaches for Alzheimer’s disease (AD) therapeutics only slow cognitive decline for small numbers of patients. This limited efficacy exists because AD is a multifactorial disease whose pathological mechanism(s) and diagnostic biomarkers are largely unknown. Here we report a new mechanism of AD pathogenesis in which the histone methyltransferase G9a noncanonically regulates translation of a hippocampal proteome that defines the proteopathic nature of AD. Accordingly, we developed a novel brain-penetrant inhibitor of G9a, MS1262, across the blood-brain barrier to block this G9a-regulated, proteopathologic mechanism. Intermittent MS1262 treatment of multiple AD mouse models consistently restored both cognitive and noncognitive functions to healthy levels. Comparison of proteomic/phosphoproteomic analyses of MS1262-treated AD mice with human AD patient data identified multiple pathological brain pathways that elaborate amyloid beta and neurofibrillary tangles as well as blood coagulation, from which biomarkers of early stage of AD including SMOC1 were found to be affected by MS1262 treatment. Notably, these results indicated that MS1262 treatment may reduce or avoid the risk of blood clot burst for brain bleeding or a stroke. This mouse-to-human conservation of G9a-translated AD proteopathology suggests that the global, multifaceted effects of MS1262 in mice could extend to relieve all symptoms of AD patients with minimum side effect. In addition, our mechanistically derived biomarkers can be used for stage-specific AD diagnosis and companion diagnosis of individualized drug effects.
Caution on the Use of 68Ga-DOTATATE for the Diagnosis of Pheochromocytoma: A Report of 2 Cases

Abstract Pheochromocytomas are intra-adrenal sympathetic neuroendocrine tumors that arise from chromaffin cells. Paragangliomas similarly arise from chromaffin cells, although at extra-adrenal sites such as sympathetic paraganglia in the abdomen/thorax, or parasympathetic paraganglia in the head/neck. Collectively, pheochromocytomas and paragangliomas are important to diagnose and resect because they may secrete harmful levels of catecholamines, have mass effects, hemorrhage, and/or metastasize. Anatomic imaging of pheochromocytomas is usually completed with computed tomography or magnetic resonance imaging; however, functional imaging may be used to provide additional localization, staging, and/or biologic information. Accordingly, selection of the proper functional imaging modality can be critical to developing the optimal therapeutic strategy. 68Gallium- and 64Copper-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)-octreotate positron emission tomography computed tomography (68Ga- and 64Cu-DOTATATE) are widely used in evaluating pheochromocytomas and paragangliomas, although data regarding the sensitivity for diagnosing pheochromocytoma are limited. We report 2 cases of pheochromocytoma that showed nondiagnostic 68Ga-DOTATATE uptake but were subsequently visualized using alternative functional imaging modalities. Additionally, we provide a review of the literature to highlight the underappreciated limitations of functional adrenal imaging with somatostatin-based compounds.