scholarly journals Detailed Analysis and Identification of Key Factors Resulting in Motor Accidents Across the UK

2021 ◽  
pp. 1-8
Author(s):  
Harshita Garg
Keyword(s):  
Data Analytics ◽  
Emergency Services ◽  
Weather Conditions ◽  
Classification Model ◽  
Insurance Companies ◽  
Key Factors ◽  
Spatial Features ◽  
The People ◽  
Temporal Features ◽  
The Uk

Motor accidents across the globe amount to a large number of deaths every year. The collisions result in not just the personal injury to people involved but also in the loss of money to the motor insurance companies, trauma to the people involved, and added pressure on the emergency services. With the help of data analytics techniques, this project aims to identify critical factors that might contribute to the accidents. Upon investigating the temporal features and geo-spatial features of the motor accident locations, we tried to establish a correlation between the accident intensity and its key factors. For this exploratory analysis, we also considered weather conditions and daily average traffic flow data. We then trained Supervised learning models on the data to find out the best performing multi-label classification model.

E-Insurance :A Platform For Insurance Penetration In India

2019 ◽  
Vol 118 (6) ◽  
pp. 90-93
Author(s):  
L. Terina Grazy ◽  
Dr.G. Parimalarani
Keyword(s):  
Insurance Industry ◽  
Cost Effective ◽  
Online Marketing ◽  
Insurance Companies ◽  
The Internet ◽  
Insurance Sector ◽  
The People ◽  
Cost Effective Approach ◽  
Time And Energy ◽  
Work Done

E-commerce is a part of Internet Marketing. The arrival of Internet made the world very simple and dynamic in all the areas. Internet is the growing business as a result most of the people are using it in their day to day life. E-commerce is attractive and efficient way for both buyers and sellesr as it reduce cost, time and energy for the buyer. No surprise the insurance sector has become quite active within the internet sphere. Most insurance companies are offering policies to be brought online and also the portals for paying premiums. It actually saves from hassles involved in going to an insurance office and spend hours to get the insurance work done. Insurance has become an important and crucial aspect of life. Online insurance is the best and most cost effective approach of taking the insurance deal. This paper focused on influence of online marketing on the insurance industry in India, usage of internet in India , the internet penetration in India and the online sale of insurance product by the insurance sector.

scholarly journals Rain Erosion Maps for Wind Turbines Based on Geographical Locations: A Case Study in Ireland and Britain

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
K. Pugh ◽  
M. M. Stack
Keyword(s):  
Wind Turbine ◽  
Western Europe ◽  
Meteorological Data ◽  
Turbine Blades ◽  
Weather Conditions ◽  
Annual Rainfall ◽  
Wind Turbine Blades ◽  
Weather Patterns ◽  
The Uk

AbstractErosion rates of wind turbine blades are not constant, and they depend on many external factors including meteorological differences relating to global weather patterns. In order to track the degradation of the turbine blades, it is important to analyse the distribution and change in weather conditions across the country. This case study addresses rainfall in Western Europe using the UK and Ireland data to create a relationship between the erosion rate of wind turbine blades and rainfall for both countries. In order to match the appropriate erosion data to the meteorological data, 2 months of the annual rainfall were chosen, and the differences were analysed. The month of highest rain, January and month of least rain, May were selected for the study. The two variables were then combined with other data including hailstorm events and locations of wind turbine farms to create a general overview of erosion with relation to wind turbine blades.

scholarly journals The role of action effects in motor sequence planning and execution: exploring the influence of temporal and spatial effect anticipation

2021 ◽  
Author(s):  
Rachel M. Brown ◽  
Erik Friedgen ◽  
Iring Koch
Keyword(s):  
Action Planning ◽  
Spatial Effects ◽  
Motor Sequence ◽  
Action Sequence ◽  
Sequential Action ◽  
Spatial Features ◽  
Temporal Features ◽  
Action Sequences ◽  
Temporal And Spatial ◽  
Anticipation Effect

AbstractActions we perform every day generate perceivable outcomes with both spatial and temporal features. According to the ideomotor principle, we plan our actions by anticipating the outcomes, but this principle does not directly address how sequential movements are influenced by different outcomes. We examined how sequential action planning is influenced by the anticipation of temporal and spatial features of action outcomes. We further explored the influence of action sequence switching. Participants performed cued sequences of button presses that generated visual effects which were either spatially compatible or incompatible with the sequences, and the spatial effects appeared after a short or long delay. The sequence cues switched or repeated across trials, and the predictability of action sequence switches was varied across groups. The results showed a delay-anticipation effect for sequential action, whereby a shorter anticipated delay between action sequences and their outcomes speeded initiation and execution of the cued action sequences. Delay anticipation was increased by predictable action switching, but it was not strongly modified by the spatial compatibility of the action outcomes. The results extend previous demonstrations of delay anticipation to the context of sequential action. The temporal delay between actions and their outcomes appears to be retrieved for sequential planning and influences both the initiation and the execution of actions.

scholarly journals Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects

2021 ◽  
Vol 13 (11) ◽  
pp. 6376
Author(s):  
Junseo Bae ◽  
Sang-Guk Yum ◽  
Ji-Myong Kim
Keyword(s):  
Machine Learning ◽  
Financial Management ◽  
Construction Projects ◽  
Transportation Infrastructure ◽  
Classification Model ◽  
Insurance Companies ◽  
Economic Damage ◽  
Infrastructure Projects ◽  
Machine Learning Classification ◽  
Financial Losses

Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects.

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 1327
Author(s):  
Rui Zhang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Siyang Zhou
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Parallel Structure ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Spatial Features ◽  
Temporal Features ◽  
Different Types ◽  
In Series ◽  
Parallel Network

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

Composite load modeling by spatial-temporal deep attention network based on wide-area monitoring systems

2021 ◽  
pp. 1-12
Author(s):  
Omid Izadi Ghafarokhi ◽  
Mazda Moattari ◽  
Ahmad Forouzantabar
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Wide Area ◽  
Load Modeling ◽  
Wide Area Monitoring ◽  
Training Performance ◽  
Spatial Features ◽  
Temporal Features ◽  
Frequency Domains ◽  
Area Monitoring

With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods.

scholarly journals EXPRESS: Unlearning and Consent in the UK Fire and Rescue Service

2021 ◽  
pp. 001872672110311
Author(s):  
James Brooks ◽  
Irena Grugulis ◽  
Hugh Cook
Keyword(s):  
Qualitative Study ◽  
Social Aspects ◽  
Successful Implementation ◽  
Human System ◽  
Rescue Service ◽  
Rescue Services ◽  
The People ◽  
The Social ◽  
New Knowledge ◽  
The Uk

Why does so much literature on unlearning ignore the people who do the unlearning? What would we understand differently if we focused on those people? Much of the existing literature argues that unlearning can only be achieved, and new knowledge acquired, if old knowledge is discarded: the clean slate approach. This might be a reasonable way of organising stock in a warehouse, where room needs to be created for new deliveries, but it is not an accurate description of a human system. This article draws on a detailed qualitative study of learning in the UK Fire and Rescue Services to challenge the clean slate approach and demonstrate that, not only did firefighters retain their old knowledge, they used it as a benchmark to assess new routines and practices. This meant that firefighters’ trust in, and consent to, innovation was key to successful implementation. In order to understand the social aspects of unlearning, this research focuses on the people involved as active agents, rather than passive recipients or discarders of knowledge.

scholarly journals Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

To bee or not to beet with neonicotinoids

2021 ◽  
Vol 23 (2) ◽  
pp. 103-109
Author(s):  
Lynda M. Warren
Keyword(s):  
Sugar Beet ◽  
Weather Conditions ◽  
The European Union ◽  
Environmental Standards ◽  
Economic Interests ◽  
Precautionary Approach ◽  
The United Kingdom ◽  
Commercial Sugar ◽  
The Uk ◽  
First Time

In January 2021 the UK government granted an application for authorisation to use thiamethoxam, a neonicotinoid pesticide, to protect commercial sugar beet crops from attack by viruses transmitted by aphids. This was the first time such an authorisation had been granted in the United Kingdom (UK) and there were concerns that it signalled a weakening of environmental standards now that the UK was no longer part of the European Union. In fact, similar authorisations had been granted by several European Member States in the last 2 years, despite the ban on the use of neonicotinoids introduced in 2018. Nevertheless, the reasons for granting the authorisation do suggest that the balance between adopting a precautionary approach to environmental protection and taking emergency action to protect economic interests may have shifted. It was acknowledged that the proposed mitigation to safeguard bees and other wildlife was not entirely satisfactory. In the end, due to unforeseen weather conditions it meant that the pesticide is not necessary, which in itself demonstrates that short-term emergency measures are unsuitable for dealing with the problem. If the sugar beet industry is to continue to prosper in the UK, it will need to be managed in a way that provides resistance to virus infection without the use of controversial chemicals.

Life Expectancy in Individuals With Type 2 Diabetes: Implications for Annuities

2009 ◽  
Vol 30 (3) ◽  
pp. 409-414 ◽  
Author(s):  
Hermione C. Price ◽  
Philip M. Clarke ◽  
Alastair M. Gray ◽  
Rury R. Holman
Keyword(s):  
Type 2 Diabetes ◽  
Life Expectancy ◽  
Cholesterol Level ◽  
Total Cholesterol Level ◽  
Insurance Companies ◽  
Age At Death ◽  
Life Annuity ◽  
Uk Government ◽  
The Uk

Background. Insurance companies often offer people with diabetes ‘‘enhanced impaired life annuity’’ at preferential rates, in view of their reduced life expectancy. Objective. To assess the appropriateness of ‘‘enhanced impaired life annuity’’ rates for individuals with type 2 diabetes. Patients. There were 4026 subjects with established type 2 diabetes (but not known cardiovascular or other life-threatening diseases) enrolled into the UK Lipids in Diabetes Study. Measurements. Estimated individual life expectancy using the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model. Results. Subjects were a mean (SD) age of 60.7 (8.6) years, had a blood pressure of 141/83 (17/10) mm Hg, total cholesterol level of 4.5 (0.75) mmol/L, HDL cholesterol level of 1.2 (0.29) mmol/L, with median (interquartile range [IQR]) known diabetes duration of 6 (3—11) years, and HbA1c of 8.0% (7.2—9.0). Sixty-five percent were male, 91% white, 4% Afro-Caribbean, 5% Indian-Asian, and 15% current smokers. The UKPDS Outcomes Model median (IQR) estimated age at death was 76.6 (73.8—79.5) years compared with 81.6 (79.4—83.2) years, estimated using the UK Government Actuary’s Department data for a general population of the same age and gender structure. The median (IQR) difference was 4.3 (2.8—6.1) years, a remaining life expectancy reduction of almost one quarter. The highest value annuity identified, which commences payments immediately for a 60-year-old man with insulin-treated type 2 diabetes investing 100,000, did not reflect this difference, offering 7.4K per year compared with 7.0K per year if not diabetic. Conclusions. The UK Government Actuary’s Department data overestimate likely age at death in individuals with type 2 diabetes, and at present, ‘‘enhanced impaired life annuity’’ rates do not provide equity for people with type 2 diabetes. Using a diabetes-specific model to estimate life expectancy could provide valuable information to the annuity industry and permit more equitable annuity rates for those with type 2 diabetes.

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