Sporadic, late-onset, and multistage diseases

Somatic mutations can cause cancer and have recently been linked with a range of non-malignant diseases. Multistage models can characterise how mutations lead to cancer, and may also be applicable to these other diseases. Here we found the incidence of over 60% of common diseases in UK Biobank were consistent with a multistage model with an ordered sequence of stages, as approximated by a Weibull distribution, with the log of incidence linearly related to the log of age and the slope often interpreted as the number of stages. A model where the stages can occur in any order was also explored, as was stratification by smoking and diabetes status. Most importantly, we find that many diseases are low risk when young but then become inevitable in old age, but many other diseases do not, being more sporadic with a modest and modifiable risk that slowly increases with age.

Prediction of Soybean Price Trend via a Synthesis Method With Multistage Model

Soybean is an important crop, so it is very important to forecast soybean price trend, which can stabilize the market. This paper presents a Synthesis Method with Multistage Model (SMwMM) in order to identify and forecast soybean price trend in China. In the previous work,Toeplitz Inverse Covariance-based Clustering(TICC) has been applied to cluster the prices of four variables. The research have found that there are four patterns in soybean market price, which could be explained by economic theory. This paper consider four patterns as market risk levels. Based on the clustering results, we used Long short-term memory(LSTM) to forecast the prices of these four variables. Multivariate long short-term memory(MLSTM) is then used to classify soybean price to determine level of risk . Experimental results show that :(1)The LSTM model has achieved great fitting effect and high prediction accuracy;(2) The performance of MLSTM-FCN and MALSTM-FCN is better than that of LSTM-FCN and ALSTM-FCN. Furthermore,MALSTM-FCN had the higher accuracy than MLSTM-FCN, which reached 76.39%.

Prediction of Soybean Price Trend via a Synthesis Method with Multistage Model

Soybean is an important crop, so it is very important to forecast soybean price trend, which can stabilize the market. This paper presents a Synthesis Method with Multistage Model (SMwMM) in order to identify and forecast soybean price trend in China. In the previous work,Toeplitz Inverse Covariance-based Clustering(TICC) has been applied to cluster the prices of four variables. The research have found that there are four patterns in soybean market price, which could be explained by economic theory. This paper consider four patterns as market risk levels. Based on the clustering results, we used Long short-term memory(LSTM) to forecast the prices of these four variables. Multivariate long short-term memory(MLSTM) is then used to classify soybean price to determine level of risk . Experimental results show that :(1)The LSTM model has achieved great fitting effect and high prediction accuracy;(2) The performance of MLSTM-FCN and MALSTM-FCN is better than that of LSTM-FCN and ALSTM-FCN. Furthermore,MALSTM-FCN had the higher accuracy than MLSTM-FCN, which reached 76.39%.

Optimizing University Acceleration Programs. The Case of NKUA’s Multistage Model

Purpose: Business accelerators have rapidly emerged as prominent players in the entrepreneurial ecosystem. A key strategic decision in designing acceleration programs is whether to customize or standardize the new venture development program (Cohen et al., 2019). Recognizing a trade-off between customization and standardization, the paper presents a multistage acceleration model aiming to harvest benefits of standardization while keeping several advantages found in tailor-made acceleration programs.

AIRLINE PILOT SITUATION AWARENESS: PRESENTING A CONCEPTUAL MODEL FOR META-COGNITION, REFLECTION AND EDUCATION

The dissertation research summarized here, utilized the Grounded Theory Method to develop a conceptual model of pilot situation awareness from 223 Aviation Safety Reporting System (ASRS) narratives. The application of Latent Semantic Analysis aided the theoretical sampling of ASRS reports. A multistage model was developed involving attention, perception, interpretation, decision making, and action in support of goal-driven behavior. Narrative report coding identified several categories of situation awareness elements that pilots direct their attention to in building and maintaining situation awareness. Internal to the aircraft, flight crews directed their attention to the aircraft’s flight state and automation state. They also directed their attention to the condition of the aircraft, the functioning of the crew, and the status of the cabin. External to the aircraft, flight crews directed their attention to airport conditions, air traffic control, terrain, traffic, and weather. Pilots were also aware of the passage of time. Twelve characteristics of situation awareness were identified from narrative report coding which were subsequently compared with existing theoretical perspectives of situation awareness.

Model-Based Design of a Pseudo-Cogenerative Heating System for e-Boat Battery Cold Start

Full-electric boats are an expression of recent advancements in the area of vessel electrification. The installed batteries can suffer from poor cold-start performance, especially in the frigid season and at higher latitudes, leading to driving power limitations immediately after startup. At state, the leading solution is to adopt a dedicated heater placed on the common cooling/heating circuit; this implies poor volume, weight, and cost figures, given the very limited duty cycle of such a part. The Heater-in-Converter (HiC) technology allows removing this specialized component, exploiting the power electronics converters already available on board: HiC modulates their efficiency to produce valuable heat (pseudo-cogeneration). In this work, we use the model-based approach to design this system, which requires heating power minimization to fulfill power electronics limitations, while guaranteeing the user-expected startup time to full power. A multistage model is used to get the yearly vessel temperature distribution from latitude information and some additional data. Then, a lumped parameter for the cooling/heating circuit is used to determine the minimum required power as a function of the properties of the thermal interface material used for the battery coupling. The design is validated on a 1:5 test bench (battery power and energy), which demonstrates how the technology can be to scaled up to also fit different boats and battery sizes.

Augmentation in Healthcare: Augmented Biosignal Using Deep Learning and Tensor Representation

In healthcare applications, deep learning is a highly valuable tool. It extracts features from raw data to save time and effort for health practitioners. A deep learning model is capable of learning and extracting the features from raw data by itself without any external intervention. On the other hand, shallow learning feature extraction techniques depend on user experience in selecting a powerful feature extraction algorithm. In this article, we proposed a multistage model that is based on the spectrogram of biosignal. The proposed model provides an appropriate representation of the input raw biosignal that boosts the accuracy of training and testing dataset. In the next stage, smaller datasets are augmented as larger data sets to enhance the accuracy of the classification for biosignal datasets. After that, the augmented dataset is represented in the TensorFlow that provides more services and functionalities, which give more flexibility. The proposed model was compared with different approaches. The results show that the proposed approach is better in terms of testing and training accuracy.