An ontology-based semantic building post-occupancy evaluation framework and its application

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonCatering to sustainable development in Architecture, Engineering and Construction (AEC) industry, many building performance evaluation (BPE) schemas have been developed to support building assessment and aim to narrow down the performance gap. Post-Occupancy Evaluation (POE), viewed as a sub-process of BPE, is a systematic method to obtain feedback on building performance in use. However, building evaluation is a complex and knowledge-intensive process with scattered and fragmented knowledge, it is time-consuming and error-prone to acquire explicit knowledge. Benefiting from the advantages of Semantic Web technology in knowledge conceptualization, ontology, as the core of the Semantic Web, has been widely taken as an effective method for knowledge management, information representation and extraction, and logical inference in the AEC industry, especially in the BPE field. However, most of the existing ontologies in the AEC industry are lightweight ontologies that mainly focus on building a structured system to represent the specific domain knowledge or information, without developing formal axioms and constraints to provide higher expressivity. Moreover, the research focus of ontology in building assessment is mainly on energy-related fields, and there is not a comprehensive POE ontology yet, especially with the focus on building occupant satisfaction, which is the starting point of this research. This research develops an ontology-based post-occupancy evaluation framework dedicated to building performance assessment, with the ultimate aim of optimizing building operation and improving building occupants' use experience quality and well-being. In the developed framework, a heavyweight ontology is developed to structure the fragmented building performance assessment knowledge in the POE domain. In POE ontology, the building occupants' needs for building performance are generalized and classified, and the corresponded building performance assessment knowledge is formalized. In addition, a set of SWRL (Semantic Web Rule Language) rules and SQWRL (Semantic Query-Enhanced Web Rule Language) query rules are developed based on the benchmarking evaluation axioms to enable automatic rule-based reasoning and query in different identified application scenarios. This ontology model enables effective POE-related knowledge retrieving and sharing, and promotes its implementation in the POE domain. To validate the developed framework, a case study is carried out facilitated by the Building Use Studies (BUS) Methodology to illustrate its feasibility and effectiveness in different application scenarios. This research concludes that the proposed ontology-based POE framework has the capability to conduct a multi-objective and multi-criteria POE assessment at the building operation stage and provide a multi-criteria optimised solution

Triple Regression for Camera Agnostic Sim2Real Robot Grasping and Manipulation Tasks

Sim2Real (Simulation to Reality) techniques have gained prominence in robotic manipulation and motion planning due to their ability to enhance success rates by enabling agents to test and evaluate various policies and trajectories. In this paper, we investigate the advantages of integrating Sim2Real into robotic frameworks. We introduce the Triple Regression Sim2Real framework, which constructs a real-time digital twin. This twin serves as a replica of reality to simulate and evaluate multiple plans before their execution in real-world scenarios. Our triple regression approach addresses the reality gap by: (1) mitigating projection errors between real and simulated camera perspectives through the first two regression models, and (2) detecting discrepancies in robot control using the third regression model. Experiments on 6-DoF grasp and manipulation tasks (where the gripper can approach from any direction) highlight the effectiveness of our framework. Remarkably, with only RGB input images, our method achieves state-of-the-art success rates. This research advances efficient robot training methods and sets the stage for rapid advancements in robotics and automation

Influence of Media Richness of E-WOM on Consumers\u27 Mental Imagery and Attitudes

E-WOM has become a hot research topic. Based on the theory of media richness, this paper divides online reviews into three types according to the degree of e-WOM richness from low to high - text , text + image and text + image + video . From the perspective of mental imagery, this paper explores the consumer attitudes of these three kinds of reviews with different richness. The Experimental data analysis shows that compared with text reviews, text + image reviews have more significant impact on consumers\u27 metal imagery and attitudes; as a form of high richness e-WOM, text + image + video has the most significant impact on consumers\u27 metal imagery and attitudes. This discovery points out several meaningful directions for the further study from the perspective of media richness

Atmospheric nitrogen deposition in the Yangtze River basin: spatial pattern and source attribution

The Yangtze River basin is one of the world's hotspots for nitrogen (N) deposition and likely plays an important role in China's riverine N output. Here we constructed a basin-scale total dissolved inorganic N (DIN) deposition (bulk plus dry) pattern based on published data at 100 observational sites between 2000 and 2014, and assessed the relative contributions of different reactive N (Nr) emission sectors to total DIN deposition using the GEOS-Chem model. Our results show a significant spatial variation in total DIN deposition across the Yangtze River basin (33.2 kg N ha−1 yr−1 on average), with the highest fluxes occurring mainly in the central basin (e.g., Sichuan, Hubei and Hunan provinces, and Chongqing municipality). This indicates that controlling N deposition should build on mitigation strategies according to local conditions, namely, implementation of stricter control of Nr emissions in N deposition hotspots but moderate control in the areas with low N deposition levels. Total DIN deposition in approximately 82% of the basin area exceeded the critical load of N deposition for semi-natural ecosystems along the basin. On the basin scale, the dominant source of DIN deposition is fertilizer use (40%) relative to livestock (11%), industry (13%), power plant (9%), transportation (9%), and others (18%, which is the sum of contributions from human waste, residential activities, soil, lighting and biomass burning), suggesting that reducing NH3 emissions from improper fertilizer (including chemical and organic fertilizer) application should be a priority in curbing N deposition. This, together with distinct spatial variations in emission sector contributions to total DIN deposition also suggest that, in addition to fertilizer, major emission sectors in different regions of the basin should be considered when developing synergistic control measures

Influences of hydroxyl radicals (OH) on top-down estimates of the global and regional methane budgets

The hydroxyl radical (OH), which is the dominant sink of methane (CH4), plays a key role in closing the global methane budget. Current top-down estimates of the global and regional CH4 budget using 3D models usually apply prescribed OH fields and attribute model–observation mismatches almost exclusively to CH4 emissions, leaving the uncertainties due to prescribed OH fields less quantified. Here, using a variational Bayesian inversion framework and the 3D chemical transport model LMDz, combined with 10 different OH fields derived from chemistry–climate models (Chemistry–Climate Model Initiative, or CCMI, experiment), we evaluate the influence of OH burden, spatial distribution, and temporal variations on the global and regional CH4 budget. The global tropospheric mean CH4-reaction-weighted [OH] ([OH]GM−CH4) ranges 10.3–16.3×105 molec cm−3 across 10 OH fields during the early 2000s, resulting in inversion-based global CH4 emissions between 518 and 757  Tg yr−1. The uncertainties in CH4 inversions induced by the different OH fields are similar to the CH4 emission range estimated by previous bottom-up syntheses and larger than the range reported by the top-down studies. The uncertainties in emissions induced by OH are largest over South America, corresponding to large inter-model differences of [OH] in this region. From the early to the late 2000s, the optimized CH4 emissions increased by 22±6  Tg yr−1 (17–30  Tg yr−1), of which ∼25  % (on average) offsets the 0.7  % (on average) increase in OH burden. If the CCMI models represent the OH trend properly over the 2000s, our results show that a higher increasing trend of CH4 emissions is needed to match the CH4 observations compared to the CH4 emission trend derived using constant OH. This study strengthens the importance of reaching a better representation of OH burden and of OH spatial and temporal distributions to reduce the uncertainties in the global and regional CH4 budgets

Ammonia mitigation campaign with smallholder farmers improves air quality while ensuring high cereal production

Reducing cropland ammonia (NH3) emissions while improving air quality and food supply is a challenge, particularly in China with millions of smallholder farmers. We tested the effectiveness of a tailored nitrogen (N) management strategy applied to wheat-maize cropping systems in “Demonstration Squares” across Quzhou county in North China Plain. The N management techniques included optimal N rates, deep fertilizer placement and application of urease inhibitors, implemented through cooperation between government, researchers, businesses and smallholders. Compared with conventional local smallholder practice, our NH3 mitigation campaign reduced NH3 volatilization from wheat and maize by 49% and 39%, and increased N use efficiency by 28% and 40% and farmers’ profitability by 25% and 19%, respectively, with no detriment to crop yields. County-wide atmospheric NH3 and PM2.5 concentrations decreased by 40% and 8%, respectively. County-wide net benefits were estimated at $7.0 million. Our “Demonstration Square” approach demonstrates that cropland NH3 mitigation and improved air quality and farm profitability can be achieved simultaneously by coordinated actions at county level

Effects of elevated ozone concentration and nitrogen addition on ammonia stomatal compensation point in a poplar clone

The stomatal compensation point of ammonia (χs) is a key factor controlling plant-atmosphere NH3 exchange, which is dependent on the nitrogen (N) supply and varies among plant species. However, knowledge gaps remain concerning the effects of elevated atmospheric N deposition and ozone (O3) on χs for forest species, resulting in large uncertainties in the parameterizations of NH3 incorporated into atmospheric chemistry and transport models (CTMs). Here, we present leaf-scale measurements of χs for hybrid poplar clone ‘546’ (Populusdeltoides cv. 55/56 x P. deltoides cv. Imperial) growing in two N treatments (N0, no N added; N50, 50 kg N ha−1 yr−1 urea fertilizer added) and two O3 treatments (CF, charcoal-filtered air; E-O3, non-filtered air plus 40 ppb) for 105 days. Our results showed that χs was significantly reduced by E-O3 (41%) and elevated N (19%). The interaction of N and O3 was significant, and N can mitigate the negative effects of O3 on χs. Elevated O3 significantly reduced the light-saturated photosynthetic rate (Asat) and chlorophyll (Chl) content and significantly increased intercellular CO2 concentrations (Ci), but had no significant effect on stomatal conductance (gs). By contrast, elevated N did not significantly affect all measured photosynthetic parameters. Overall, χs was significantly and positively correlated with Asat, gs and Chl, whereas a significant and negative relationship was observed between χs and Ci. Our results suggest that O3-induced changes in Asat, Ci and Chl may affect χs. Our findings provide a scientific basis for optimizing parameterizations of χs in CTMs in response to environmental change factors (i.e., elevated N deposition and/or O3) in the future

Source attribution of particulate matter pollution over North China with the adjoint method

We quantify the source contributions to surface PM2.5 (fine particulate matter) pollution over North China from January 2013 to 2015 using the GEOS-Chem chemical transport model and its adjoint with improved model horizontal resolution (1/4 degrees x 5/16 degrees) and aqueous-phase chemistry for sulfate production. The adjoint method attributes the PM2.5 pollution to emissions from different source sectors and chemical species at the model resolution. Wintertime surface PM2.5 over Beijing is contributed by emissions of organic carbon (27% of the total source contribution), anthropogenic fine dust (27%), and SO2 (14%), which are mainly from residential and industrial sources, followed by NH3 (13%) primarily from agricultural activities. About half of the Beijing pollution originates from sources outside of the city municipality. Adjoint analyses for other cities in North China all show significant regional pollution transport, supporting a joint regional control policy for effectively mitigating the PM2.5 air pollution.China's National Basic Research Program [2014CB441303]; National Natural Science Foundation of China [41205103, 41475112]SCI(E)[email protected]

Optimization of NiFe2O4/rGO composite electrode for lithium-ion batteries

The combination of carbon compositing and the proper choice of binders in one system offer an effective strategy for improving electrode performance for lithium ion batteries (LIBs). Here, we focus on the optimization of reduced graphene oxide content in NiFeO/reduced graphene oxide (abbreviated to NiFeO/rGO) composites and the proper choice of binders to enhance the cycling stability of the NiFeO electrode. The NiFeO/rGO composites were fabricated by a hydrothermal-annealing method, in which the mean size of spinel NiFeO nanoparticles was approximately 20\ua0nm. When tested as anode materials for LIBs, the NiFeO/rGO electrodes with carboxymethylcellulose (CMC) binder exhibited excellent lithium-storage performance including high reversible capacity, good cycling durability and high-rate capability. The capacity could be retained as high as 1105\ua0mAh\ua0g at a current density of 100\ua0mA\ua0g for over 50 cycles, even cycled at higher current density of 1000\ua0mA\ua0g, a capacity of 800\ua0mAh\ua0gcan be obtained, whereas the electrode with the polyvinylidene fluoride (PVDF) binder suffered from rapid capacity decay under the same test conditions. As a result, the NiFeO/rGO composites with CMC binder electrode in this work are promising as anodes for high-performance LIBs, resulting from the synergistic effect of optimal graphene content and proper choice of binder