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The Widmark formula has been widely adopted in forensic applications to drink driving cases for the last 70 years. It is known that the amount of alcohol consumed and the body weight of the drinkers are important information for the estimation of blood alcohol concentration (BAC). However, the direct application of the Widmark factors derived from Caucasian to the calculation of BAC for the Chinese population often encounters serious challenges. Owing to this inherent weakness, a thorough analysis to determine the theoretical Widmark factors for the Chinese population, r(0) at the start of drinking and the practical factors, r(peak), at peak BAC was conducted. In the present study, other factors such as gender, stomach condition and other physiological conditions are taken into account. The determined theoretical Widmark factors, r(0,) for local Chinese male and female are 0.68 and 0.59 (with BAC in the units of weight/volume), respectively, demonstrating the applicability of the Widmark formula to the Chinese population. The practical factors at peak BAC, r(peak), were also determined to serve the forensic purpose of refuting the "hip-flask" defence in drink driving cases. Findings show that gender and stomach condition are the key factors that could statistically explain the variability of both r(0) and r(peak).

PolyU Library Call No.: [THS] LG51 .H577P AP 2017 Qian ; xiv, 148 pages :color illustrations ; Due to the increasing energy requirements of the portable electronic devices, backup power sources and radio frequency identification (RFID) tags, there is a growing need of high performance energy storage devices, mainly including supercapacitors and lithium ion batteries. Particularly, a simple and large scale production of high performance electrode materials is highly sought. By now, many novel nanostructured materials, including carbon nanotubes, graphene, metal oxides, transition metal dichalcogenides (TMDs) and black phosphorus have been reported to be the promising candidates as electrodes for energy storage devices. Among them, graphene and manganese dioxide (MnO₂) have attracted much attention. On one hand, graphene performs unique electrical, thermal and mechanical properties, especially outstanding electrical conductivity (106 S·cm⁻¹) and large specific surface area (~2630 m2·g⁻¹), but suffers from a relatively low theoretical capacity (550 F·g⁻¹ for supercapacitors and 1116 mAh·g⁻¹ for Li ion batteries). On the other hand, MnO₂ exhibits a high theoretical capacity (1370 F·g⁻¹ for supercapacitors and 1232 mAh·g⁻¹ for Li ion batteries) but suffers from poor electrical conductivity (10⁻⁶-10⁻⁷ S·cm⁻¹). Hence, the MnO₂/graphene composites are expected as the promising candidates of the electrode materials with improved electrochemical performances for both rechargeable batteries and supercapacitors. In this work, the synthesis of MnO₂/graphene composites with different morphologies from zero- to three-dimensional were investigated. The electrochemical performances of as-prepared composites for lithium-ion batteries and supercapacitors were tested. MnO₂/graphene composite aerogel (MnGA) was prepared via a fast and simple wet chemical process. The as-prepared composite aerogel exhibits 3D rigid graphene networks embedded by MnO₂ nanoparticles. A maximum specific capacitance of 534 F·g⁻¹ at 1 mV·s⁻¹ is achieved by 1.33 wt% MnGA electrode due to the synergistic effect. The aerogel could be a promising candidate for large-scale production of energy storage devices. MnO₂/graphene composite ink (MnGI) was prepared via a facile synthetic path. The MnGI is formed by 2D hexagonal MnO₂ nanosheets and reduced graphene oxide (rGO) sheets. The ink could be printed on different substrates and suitable for mass production in industry. A maximum specific capacitance of 648 F·g⁻¹ at 5 mV·s-1 is achieved. All these merits allow the MnGI to be a promising candidate for high performance energy storage devices. ; A free standing, compact and robust paper comprised of reduced graphene oxide (rGO), multi-walled carbon nanotubes (MCNTs) and manganese dioxide nanowires (MnNWs) was prepared via a simple strategy. The combination of rGO, MCNTs and MnNWs exhibits high packing density but hierarchical porous structure, which facilitates the energy capacity, rate capability and long term stability. The rGO/MCNTs/MnNWs (GMM) paper electrode retains a specific capacitance of 48 mF·cm⁻² (28 F·cm⁻³) at a high current density of 20 mA·cm⁻² (11,765 mA·cm⁻³). A symmetric capacitor assembled by two GMM paper electrodes was investigated. The as-prepared device achieves a maximum specific energy and power densities of 7.96 mWh·cm⁻³ and 10,470 mW·cm⁻³ respectively, with a capacitance retention of 99% after 5,000 cycles at 1,176 mA·cm⁻³. All the results indicate that the free standing GMM paper electrode has a potential for the large scale production, low cost, environmental friendly and high performance capacitive energy storage devices. Furthermore, the synthesis strategy may be extendable to other composite materials for batteries, solar cells, fuel cells or other related fields. MnO₂/graphene quantum dots composite (MnGQDs) were synthesized via a low-temperature chemical reaction process. The MnO₂ quantum dots (MnQDs) were well mixed with graphene quantum dots (GQDs) to form a homogeneous quantum dots composite powder. To investigate the electrochemical performances, the as-obtained MnGQDs served as the anode materials for lithium ion batteries. An initial charge and discharge specific capacity of 662 and 1444 mAh·g⁻¹ are achieved respectively. Besides, a high coulombic efficiency of nearly 100% and good rate capability were measured. The discharge specific capacity remains at 246 mAh·g⁻¹ after 50 cycles. The results show that the MnGQDs anode could be an ideal candidate for lithium ion batteries with high efficiency and rate capability. ; Department of Applied Physics ; Ph.D., Department of Applied Physics, The Hong Kong Polytechnic University, 2017 ; Doctorate ; published_final

This paper aims to develop a green building meta-model for a representative passively designed high-rise residential building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic building model and provide reliable building performance predictions. Input parameters are selected from different passive design strategies including the building layout, envelop thermophysics, building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust building performance predictions associated with the green building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.

Metal object detection (MOD) is very important to guarantee the thermal safety of wireless charging systems for electric vehicles (EVs). Regarding the MOD methods based on passive sensing coils, most previous designs are targeted at the systems employing unpolarized coils, and the detection criteria are voltage-difference-based (VDB). Unfortunately, they are unsuitable for those employing DD coils due to the fundamental difference of field pattern. In addition, the VDB method has an inherent problem of low sensitivity. To fix these two problems, in this work, a set of field-oriented sensing coils is devised, which successfully realizes blind-zone elimination and super-high sensitivity. The arrangement of sensing coils accords with the pole-to-pole field distribution of DD coils to highlight the influence of MOs. The blind zone caused by the axial symmetry of the coupling field is removed by a non-symmetric patch coil. Moreover, a MOD mechanism with new criteria for MO identification is developed, which is the core of super-high sensitivity. Tested by ten positions where MOs might intrude using a 3 kW prototype, the optimized sensing coils can entirely remove the y-axis blind zones. The average sensitivity for the charging area reaches up to 16.

We are now living in the big data era, where firms can improve their decision makings by adopting big data technology to utilize mass information. To explore the effects of the big data technology, we build an analytical model to study the sustainable investment in a supply chain, consisting of one manufacturer and one retailer, by using Bayesian information updating approach. We derive the optimal sustainable investment level for the manufacturer and the optimal order quantity for the retailer. Comparing the results with and without the big data technology, we find that whether the manufacturer should make more sustainable investment when the retailer adopts the big data technology depends on the service level at the retailer side. Interestingly, it is not always optimal for the retailer to adopt the big data technology. We identify the conditions under which the manufacturer and retailer are better off with the big data technology. In addition, we investigate the impact of the number of observations regarding the market information and find that the optimal decisions and profits increase in the number of the observations, if and only if the service level is low.

Abstract This paper presents full field offshoresurfactant-polymer (SP) floodingapplied in JZ 9–3 Oilfield, Bohai Bay, China. Followed by 3-year polymer flooding which produced 4% incremental oil, the field turned to surfactant-polymer flooding. In the first stage, SP flooding was implemented in 4 injection wells. Then full field SP flooding was conducted in 8 injectors and 23 correspondent producers were involved. The injection scheme is designed as a slug of 0.23 pore volume (PV) of polymer solution at 0.120% polyacrylamide and second slug of 0.3 PV mixture of 0.20–0.24% of HDS surfactant and 0.12–0.15% of 3640C polymer and third slug of 0.035 PV of 0.12% 3640C polymer solution. By monitoring injection characteristics, oil increasing and water content decreasing, and development index, the results show significant effect in incremental oil recovery compared to polymer flooding since 2010 when the SP flooding started.By 1st, Dec, 2014, the SP flooding has been improving oil production for 42 million tons with composite water cut 73.5% which declined by about 6.5%. There are 11 wells in 23 producers has obviously responded. The daily oil production of the center correspondent producer was doubled and water content decreased from 81% to 42%. The SP flooding effects lasted for about 2 years and could be extended further. This paper presents the feasibility of SP flooding in offshore environment.

ABSTRACT : Dynamic Voltage Restorer (DVR) has become very popular in recent years for compensation of voltage sag and swell. The voltage sag and swell is very severe problem of power quality for an industrial customer which needs urgent attention for its compensation. There are various methods for the compensation of voltage sag and swell. One of the most popular methods of sag and swell compensation is Dynamic Voltage Restorer (DVR), which is used in both low voltage and medium voltage applications. In this work, our main focus is on DVR. DVR compensate the voltage sag by injecting voltage as well as power into the system. The compensation capability of this is mainly influenced by the various load conditions and voltage dip to be compensated. In this work the Dynamic Voltage Restorer is designed and simulated with the help of Matlab Simulink for sag compensation. Efficient control technique (Park’s Transformations) is used for mitigation of voltage sag through which optimized performance of DVR is obtained. The performance of DVR is analyzed on various conditions of active and reactive power of load at a particular level of dc energy storage. Parameters of load are varied and the results are analyzed on the basis of output voltages.

The construction sector continues to experience significant challenges brought by new techniques and technologies. Hence, there is a dire need for construction companies to address critical issues concerning changing environmental conditions, construction innovations, market globalization and many other aspects, thereby enhancing their competitive edge. Thus, the primary goal for this research is to develop a multi-criteria decision making model that would consider and evaluate all essential factors in determining the competitiveness index of construction companies. In the developed model, three new pillars (3P) for competitiveness are introduced: (1) non-financial internal pillar; (2) non-financial external pillar; and (3) financial pillar. The 3P includes 6 categories and 26 factors that are defined and incorporated in the developed assessment model for the purpose of measuring the companies’ competitiveness. The weights for the identified factors are computed using fuzzy analytical network process (FANP) to diminish the uncertainty inherited within the judgment of the respondents. The weight of factors and their affiliated performance scores are used as an input for the preference ranking organization method for enrichment evaluation (PROMETHEE II) technique. In this regard, PROMETHEE II is undertaken as a ranking technique to prioritize any given construction company by determining its respective competitiveness index. The developed model is validated through five cases studies that reveal its potential of illustrating detailed analysis with respect to the competitive ability of construction companies. A sensitivity analysis is carried out to determine the most influential factors that affect the competitiveness of construction companies. It is anticipated that the developed evaluation model can be used in the decision-making process by all parties involved in construction projects. For instance, contractors can leverage the evaluation model in taking better decisions pertinent to the markup values. In addition, it can benefit employers in the evaluation process of contractors.