A critical look at power law modelling of the Internet

This paper takes a critical look at the usefulness of power law models of the Internet. The twin focuses of the paper are Internet traffic and topology generation. The aim of the paper is twofold. Firstly it summarises the state of the art in power law modelling particularly giving attention to existing open research questions. Secondly it provides insight into the failings of such models and where progress needs to be made for power law research to feed through to actual improvements in network performance.Comment: To appear Computer Communication

Automobile maintenance modelling using gcForest

Automobile maintenance has gained increasing attention in recent years. If the failure time of an automobile can be predicted, it can bring tangible benefits to automobile fleet management. The Multi-Grained Cascade Forest (gcForest) is a tree-based deep learning algorithm, which was originally developed for image classification, but is potentially a helpful tool in automobile maintenance. This study aims to introduce the gcForest into automobile maintenance based on historical maintenance data and geographical information system (GIS) data. The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost

Reliability analysis for automobile engines: conditional inference trees

The reliability model with covariates for machinery parts has been extensively studied by the proportional hazards model (PHM) and its variants. However, it is not straightforward to provide business recommendations based on the results of the PHM. We use a novel method, namely the Conditional Inference Tree, to conduct the reliability analysis for the automobile engines data, provided by a UK fleet company. We find that the reliability of automobile engines is significantly related to the vehicle age, early failure, and repair history. Our tree-structured model can be easily interpreted, and tangible business recommendations are provided for the fleet management and maintenance

Understanding freight drivers’ behavior and the impact on vehicles’ fuel consumption and CO2e emissions

Despite the significant impact of driver behavior on fuel consumption and carbon dioxide equivalent (CO2e) emissions, this phenomenon is often overlooked in road freight transportation research. We review the relevant literature and seek to provide a deeper understanding of the relationship between freight drivers’ behavior and fuel consumption. This study utilizes a real-life dataset of over 4000 driving records from the freight logistics sector to examine the effects of specific behaviors on fuel consumption. Analyzed behaviors include harsh acceleration/deceleration/cornering, over-revving, excessive revolutions per minute (RPM), and non-adherence to legal speed limits ranging from 20 to 70 miles per hour (mph). Our findings confirm existing literature by demonstrating the significant impact of certain driving characteristics, particularly harsh acceleration/cornering, on fuel consumption. Moreover, our research contributes new insights into the field, notably highlighting the substantial influence of non-adherence to the legal speed limits of 20 and 30 mph on fuel consumption, an aspect not extensively studied in previous research. We subsequently introduce an advanced fuel consumption model that takes into account these identified driver behaviors. This model not only advances academic understanding of fuel consumption determinants in road freight transportation, but also equips practitioners with practical insights to optimize fuel efficiency and reduce environmental impacts

An integrated deep learning-based approach for automobile maintenance prediction with GIS data

Predictive maintenance (PdM) can be beneficial to the industry in terms of lowering maintenance cost and improve productivity. Remaining useful life (RUL) prediction is an important task in PdM. The RUL of an automobile can be impacted by various surrounding factors such as weather, traffic and terrain, which can be captured by the geographical information system (GIS). Recently, most researchers have conducted studies of RUL modelling based on sensor data. Owing to the fact that the collection of sensor data is expensive, while maintenance data is relatively easy to obtain. This study aims to establish an automobile RUL prediction model with GIS data through a data-driven approach. In this approach, firstly, due to the data type and sampling rate of the maintenance data and GIS data are different, a data integration scheme was researched. Secondly, the Cox proportional hazard model (Cox PHM) was introduced to construct the health index (HI) for the integrated data. Then, a deep learning structure called M-LSTM (Merged-long-short term memory) network was designed for HI modelling based on the integrated data which contains both sequential data and ordinary numeric data. Finally, the RUL was mapped by predicted HI and the Cox PHM. An experimental study using a sizable real-world fleet maintenance dataset provided by a UK fleet company revealed the effectiveness of the proposed approach and the impact of the GIS factors on the automobiles under investigation

All-optical header processing in a 42.6Gb/s optoelectronic firewall

A novel architecture to enable future network security systems to provide effective protection in the context of continued traffic growth and the need to minimise energy consumption is proposed. It makes use of an all-optical pre-filtering stage operating at the line rate under software control to distribute incoming packets to specialised electronic processors. An experimental system that integrates software controls and electronic interfaces with an all-optical pattern recognition system has demonstrated the key functions required by the new architecture. As an example, the ability to sort packets arriving in a 42.6Gb/s data stream according to their service type was shown experimentally

Improving automotive garage operations by categorical forecasts using a large number of variables

Cost effective job scheduling for garage management relies upon assigning repair times into appropriate categories rather than using exact repair time lengths. In this paper, we employ an ordinal logit model with least absolute shrinkage and selection operator (LASSO) to forecast such repair time categories for automotive engines. Our study is based on a unique dataset of maintenance records from the network of 64 UK garages of BT Fleet Solutions, and we consider a large number of predictor variables, with condition, manufacturing, geographical, and calendar-related information. The application of LASSO enables the identification of relevant predictor variables for forecasting purposes. Based on the Brier score and ranked probability score (and their skill scores), we document substantial predictive ability of our method which outperforms five benchmarks, including the method used by the company. More importantly, we demonstrate explicitly how to associate the predicted probabilities with a loss function in order to make operational decisions in garages. We find that the best choice of job scheduling does not always correspond to the predicted categories, especially when the loss function is asymmetric. We show that scheduling jobs on the basis of our method can help the company reduce loss value. Finally, we identify opportunities for further improvements in the operations of the company and for garage maintenance operations in general

A Sequence-Based Metaheuristics for Tactical Distribution Problems in Closed-Loop Supply Chains

International audienc