THE IMPACT OF OIL PRICE ON BANK PROFITABILITY IN CANADA

Using the ordinary least squares estimation, this paper analyzes the impact of oil price on bank profitability in Canada. We use data on 10 public banks from 1995 to 2015. Our profitability determinants include bank-specific characteristics and macroeconomic factors. We separately consider how banks react to two dramatically drops during these 20 years.   We find that there is a significantly positive relationship between the oil price and bank profitability in the early period, but no evidence shows that they have relationship in recent years. This evidence that Canadian banks have taken action to immunize from the risk of oil price drop

Long-Term Seepage Assessment Using Numerical Modeling for Upstream-Type Tailings Dams

A tailings dam, as part of a tailings storage facility, is typically constructed using waste rock or mine tailings, which are the waste created during extraction of ore from mining or by-products from mineral beneficiation and manufacturing. These dams are often raised continuously, as the tailings facility is expanding. The holding capacity of these facilities is needed to accommodate large volumes of tailings with the increasing demand placed on the mining industry. Thus, the topic of focus in this thesis is to investigate the seepage conditions that can ensure that the embankment can be safely constructed and operated during the raising stage and that it remains safe afterwards, beyond closure of the mine. However, according to statistics, a tailings dam must be protected against failures caused by various reasons even during its construction stage. Structural stability is the most important aspect and it should to be considered when addressing the problem of "safety" for tailings dams, which are threatened by seismic liquefaction, slope instability, overtopping and seepage. The seepage conditions of upstream-type tailings dams are the main topic of this research, which is associated with knowing the position of phreatic surface. The amount of pore water below the phreatic surface affects the stability of a tailings dam by reducing the shear strength of soil. The purpose of this thesis is to develop a seepage analysis model using a numerical modeling technique and to investigate potential means of phreatic surface control. Parameters like beach width, permeability anisotropy, raising rate of embankment and slope inclination will be investigated to identify factors that may have a significant influence on the long-term evolution of phreatic surface within the tailings dam during the mine’s life. This research will develop an uncoupled hydro-mechanical model using finite elements in which the whole process of construction is simulated in stages of embankment raising and filling of the tailings pond. The finite element model will be built using RS2, which is a comprehensive two-dimensional finite element program for soil and rock applications. It can model a wide range of engineering projects including excavation design, slope stability analysis, groundwater seepage, probabilistic analysis, and dynamic analysis. RS2 is able to carry out a finite element groundwater seepage analysis, with due consideration of both saturated and unsaturated soil states, in both steady-state and transient groundwater seepage formulations through both homogeneous and heterogeneous dams, dikes and other embankment types. However, basic finite element analysis principles will also be presented in this thesis, to aid the comprehension of the models developed. Based on the results of modeling, each identified parameter was assessed and guidelines were given regarding its contribution in the development of seepage face breakout on the downstream face of tailings embankment dams. These guidelines can serve practicing engineers in their design and evaluation of tailings dams to ensure safe and economical operation of tailings facilities

Exploring passenger assessments of bus service quality using Bayesian networks

Studies on public transit have emphasized the role of passenger satisfaction with service quality in travel choice decisions and indicated that satisfaction depends on various service attributes. Few studies have, however, systematically examined the underlying relationships among service attributes to assess their influence on passenger overall satisfaction. Therefore, to contribute to this rapidly-emerging literature, this paper applies Bayesian networks to quantify the influence of each service aspect on passenger overall satisfaction with regular bus service quality. This analysis involved 609 passengers who participated in a 2013 regular bus service survey in Nanjing, China. The derived Bayesian network shows the relationships among service attributes and passenger overall satisfaction graphically. In particular, service aspects such as running on schedule, acceptable waiting time, available seats, clean onboard environment, pleasant environment at stations, convenient design for transfers, and air-conditioning were the key determinants of overall satisfaction with bus service

DIF statistical inference without knowing anchoring items

Establishing the invariance property of an instrument (e.g., a questionnaire or test) is a key step for establishing its measurement validity. Measurement invariance is typically assessed by differential item functioning (DIF) analysis, i.e., detecting DIF items whose response distribution depends not only on the latent trait measured by the instrument but also on the group membership. DIF analysis is confounded by the group difference in the latent trait distributions. Many DIF analyses require knowing several anchor items that are DIF-free in order to draw inferences on whether each of the rest is a DIF item, where the anchor items are used to identify the latent trait distributions. When no prior information on anchor items is available, or some anchor items are misspecified, item purification methods and regularized estimation methods can be used. The former iteratively purifies the anchor set by a stepwise model selection procedure, and the latter selects the DIF-free items by a LASSO-type regularization approach. Unfortunately, unlike the methods based on a correctly specified anchor set, these methods are not guaranteed to provide valid statistical inference (e.g., confidence intervals and p-values). In this paper, we propose a new method for DIF analysis under a multiple indicators and multiple causes (MIMIC) model for DIF. This method adopts a minimal L 1 norm condition for identifying the latent trait distributions. Without requiring prior knowledge about an anchor set, it can accurately estimate the DIF effects of individual items and further draw valid statistical inferences for quantifying the uncertainty. Specifically, the inference results allow us to control the type-I error for DIF detection, which may not be possible with item purification and regularized estimation methods. We conduct simulation studies to evaluate the performance of the proposed method and compare it with the anchor-set-based likelihood ratio test approach and the LASSO approach. The proposed method is applied to analysing the three personality scales of the Eysenck personality questionnaire-revised (EPQ-R)

Towards efficiently provisioning 5G core network slice based on resource and topology attributes

Efficient provisioning of 5G network slices is a major challenge for 5G network slicing technology. Previous slice provisioning methods have only considered network resource attributes and ignored network topology attributes. These methods may result in a decrease in the slice acceptance ratio and the slice provisioning revenue. To address these issues, we propose a two-stage heuristic slice provisioning algorithm, called RT-CSP, for the 5G core network by jointly considering network resource attributes and topology attributes in this paper. The first stage of our method is called the slice node provisioning stage, in which we propose an approach to scoring and ranking nodes using network resource attributes (i.e., CPU capacity and bandwidth) and topology attributes (i.e., degree centrality and closeness centrality). Slice nodes are then provisioned according to the node ranking results. In the second stage, called the slice link provisioning stage, the k-shortest path algorithm is implemented to provision slice links. To further improve the performance of RT-CSP, we propose RT-CSP+, which uses our designed strategy, called minMaxBWUtilHops, to select the best physical path to host the slice link. The strategy minimizes the product of the maximum link bandwidth utilization of the candidate physical path and the number of hops in it to avoid creating bottlenecks in the physical path and reduce the bandwidth cost. Using extensive simulations, we compared our results with those of the state-of-the-art algorithms. The experimental results show that our algorithms increase slice acceptance ratio and improve the provisioning revenue-to-cost ratio

Are LLMs Effective Backbones for Fine-tuning? An Experimental Investigation of Supervised LLMs on Chinese Short Text Matching

The recent success of Large Language Models (LLMs) has garnered significant attention in both academia and industry. Prior research on LLMs has primarily focused on enhancing or leveraging their generalization capabilities in zero- and few-shot settings. However, there has been limited investigation into effectively fine-tuning LLMs for a specific natural language understanding task in supervised settings. In this study, we conduct an experimental analysis by fine-tuning LLMs for the task of Chinese short text matching. We explore various factors that influence performance when fine-tuning LLMs, including task modeling methods, prompt formats, and output formats