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This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems., Peer Reviewed, Postprint (published version)

The ongoing monitoring of terrestrial carbon fluxes (TCF) goes hand in hand with progress in technical capacities, such as the next-generation Earth observation missions of the Copernicus initiative and advanced machine learning algorithms. Proceeding along this line, we present a physically-based data-driven workflow for quantifying gross primary productivity (GPP) and net primary productivity (NPP) at a global scale from the synergy of Copernicus’ Sentinel-3 (S3) Ocean and Land Color Instrument (OLCI) and the TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5 Precursor (S5P), along with meteorological variables from Copernicus ERA5-Land. Specifically, we created generic hybrid Gaussian process regression (GPR) retrieval models combining S3-OLCI-derived vegetation products with the TROPOMI solar-induced fluorescence (SIF) product to capture global GPP and NPP. First, the GPR algorithms were trained on theoretical simulations through the Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model, with the final retrieval models termed SCOPE-GPR-TCF. Second, the SCOPE-GPR-TCF models were integrated in Google Earth Engine (GEE) and fed with satellite data and products (coming from Sentinel 3 & 5P and ERA5-Land), producing global and regional (Iberian Peninsula) maps at spatial resolutions of 5 km and 300 m during the year 2019. Moderate relative uncertainties in the range between 10%–40% of the GPP and NPP estimates were achieved by the SCOPE-GPR-TCF models. Analysis of the driving variables revealed that the S3-OLCI vegetation products, i.e., leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), and SIF provided the highest prediction strengths. Validation of GPP temporal estimates from GPR against partitioned GPP estimates at 113 flux towers located in America and Europe highlighted a good overall consistency at the local scale, with performances varying depending on the site and vegetation type. The

This degree project undertakes a detailed examination of various algorithms used in Very Short-Term Load Forecasting (VSTLF) within network control systems, prioritizing forecasting accuracy and computational efficiency as critical evaluation criteria. The research comprehensively assesses a range of forecasting methods, including statistical models, machine learning algorithms, and advanced deep learning techniques, aiming to highlight their respective advantages, limitations, and suitability for different operational contexts. The study conducts a detailed analysis by comparing essential performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and execution time, before and after implementing adjustments to the formulations. This approach highlights how optimization strategies enhance the effectiveness of the models. Notably, the study identifies Support Vector Machine (SVM) and Multiple Linear Regression as frontrunners in terms of balancing accuracy with computational demand, making them particularly suitable for real-time forecasting needs. Meanwhile, Long Short-Term Memory (LSTM) networks demonstrate a commendable ability to capture complex, non-linear data patterns, albeit at a higher computational cost. The degree project further explores the sensitivity of these forecasting models to parameter adjustments, revealing a nuanced landscape where strategic modifications can significantly enhance model performance. This degree project not only contributes to the ongoing discourse on optimizing VSTLF algorithms but also provides actionable insights for stakeholders in the energy sector, aiming to facilitate the development of more reliable, efficient, and sustainable power system operations.

One of the leading global health issues is cancer. For women, by far the most pervasive variant is breast cancer. It was also one of the cancers with the highest number of new cases in 2020. To give individuals suffering from breast cancer the best chance possible to survive it is vital to detect the disease early. To do so, computer-aided diagnostics such as a machine learning model can be used to classify mammography images of the breast abnormality as either benign or malignant. In the patient’s journal, more data is available regarding the abnormality and the context of how the mammography scans were taken. This paper aims to examine how that additional data might affect the performance of a model classifying mammography images. Three different image model architectures were used, one quite simple and two utilizing transfer learning. They were then combined with a rather simple architecture used for the additional categorical data. The categorical data was divided into three classes: features describing how the images were taken (class 1), features describing the abnormality (class 2), and a combination of the two aforementioned classes (class 3). The results show that a significant increase was achieved when comparing the hybrid models to the image-only models. However, this increase seems to occur as a result of the image model’s poor performance, and the categorical data being favored by the models combining the two modalities. A slight synergy effect was observed for the hybrid models using the class 3 data. This was, however, too small for us to be able to make any general claims., En av de ledande globala hälsoproblemen är cancer. För kvinnor är den absolut mest förekommande varianten bröstcancer. Det var även en av varianterna av cancer med det högsta antalet nya fall 2020. För att ge individer drabbade av bröstcancer den bästa möjliga chansen att överleva är det avgörande att att upptäcka sjukdomen i ett tidigt stadie. För att göra det kan datorstödd diagnostik, som exempelvis en maskininlärningsmodel, användas för att classificera mammografibilder av bröstabnormiteter som elak- eller godartad. I patientjournalen finns det givetvis mer data som beskriver abnormiteten och den kontext i vilken bilderna togs. Den här rapporten har som mål att undersöka hur ytterligare data kan påverka prestandan hos en model som klassificerar mammografibilder. Tre olika arkitekturer för bildmodeller användes, en rätt så enkel och två som nyttjade transfer learning. Vardera arkitektur kombinerades med en ganska enkel arkitektur som användes för den ytterligare kategoriska datan. Den kategoriska datan delades in i tre klasser: attribut som beskriver hur bilderna togs (klass 1), attribut som beskriver abnormiteten (klass 2), och en kombination av de två redan nämnda klasserna (klass 3). Resultaten visar att en markant ökning uppnåddes när man jämför hybridmodellerna med modellerna som endast använder bilddata. Denna ökning verkar dock uppstå till följd av bildmodellernas låga prestanda och att den kategoriska datan föredras av modellerna som kombinerar de två modaliteterna. En viss synergieffekt observerades för kombinationsmodellerna som använde klass 3-data. Den var dock för liten för att vi ska kunna göra några generella påståenden.

It is nowadays a hot topic to apply machine learning (ML) algorithms to illustrate water temperature dynamics in lentic waters. Due to the limited amount of in-situ temperature measurements from traditional sampling programmes, most of the related studies, however, restricted their analysis within the surface and rarely checked the results for whole depth profiles. Moreover, capability of such methods in projecting thermal dynamics under future climate conditions is even less illustrated. To fill the gap, we collected an unparalleled huge database including 9 million water temperature measurements, from 2013 to 2022, in Rappbode Reservoir, Germany as well as the corresponding climatic and hydrological observations, to train three commonly used ML models (Random Forest, XGBoost, and Long Short-Term Memory) and comprehensively evaluate their performance in reproducing thremal structure within the whole water column. We also systemically compared such simulation results with a well-established process-driven model (CE-QUAL-W2). Going beyond a pure reproduction of observatiosn, we further evaluated the ability of CE-QUAL-W2 and ML models in projecting thermal dynamics under RCP8.5 climate scenario up to 2100. Our results suggested three ML methods yielded high accuracy in capturing water temperature dynamics from the surface (epilimnion) to bottom (hypolimnion), and also satisfactorily reproduced temporal development of the stratification pattern, with the results corresponding well with those from CE-QUAL-W2. By contrast, the projections by ML models were rather insensitive to future climate warming under RCP8.5 comparing with those by CE-QUAL-W2, pointing to an important risk whenever ML-based models are extrapolated beyond their training data range. Supported by millions of in situ measurements, our research clearly illustrated the opportunities (limits) of ML models in projecting thermal dynamics in deep waters, and the conclusion also provides key guidance for scie

[Abstract] Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user’s perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution.

Rainfall–runoff modeling has been the core of hydrological research studies for decades. To comprehend this phenomenon, many machine learning algorithms have been widely used. Nevertheless, a thorough comparison of machine learning algorithms and the effect of pre-processing on their performance is still lacking in the literature. Therefore, the major objective of this research is to simulate rainfall runoff using nine standalone and hybrid machine learning models. The conventional models include artificial neural networks, least squares support vector machines (LSSVMs), K-nearest neighbor (KNN), M5 model trees, random forests, multiple adaptive regression splines, and multivariate nonlinear regression. In contrast, the hybrid models comprise LSSVM and KNN coupled with a gorilla troop optimizer (GTO). Moreover, the present study introduces a new combination of the feature selection method, principal component analysis (PCA), and empirical mode decomposition (EMD). Mean absolute error (MAE), root mean squared error (RMSE), relative RMSE (RRMSE), person correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and Kling Gupta efficiency (KGE) metrics are used for assessing the performance of the developed models. The proposed models are applied to rainfall and runoff data collected in the Wadi Ouahrane basin, Algeria. According to the results, the KNN–GTO model exhibits the best performance (MAE = 0.1640, RMSE = 0.4741, RRMSE = 0.2979, R = 0.9607, NSE = 0.9088, and KGE = 0.7141). These statistical criteria outperform other developed models by 80%, 70%, 72%, 77%, 112%, and 136%, respectively. The LSSVM model provides the worst results without pre-processing the data. Moreover, the findings indicate that using feature selection, PCA, and EMD significantly improves the accuracy of rainfall–runoff modeling., Validerad;2023;Nivå 2;2023-10-13 (joosat);CC BY 4.0 License

This paper explores using and hybridizing simple prediction models to maximize the accuracy of local weather prediction while maintaining low computational effort and the need to process and acquire large volumes of data. A hybrid RF-LSTM model is proposed and evaluated in this research paper for the task of short-term local weather forecasting. The local weather stations are built within an acceptable radius of the measured area and are designed to provide a short period of forecasting - usually within one hour. The lack of local weather data might be problematic for an accurate short-term valuable prediction in sustainable applications like agriculture, transportation, energy management, and daily life. Weather forecasting is not trivial because of the non-linear nature of time series. Thus, traditional forecasting methods cannot predict the weather accurately. The advantage of the ARIMA model lies in forecasting the linear part, while the SVR model indicates the non-linear characteristic of the weather data. Both non-linear and linear approaches can represent the combined model. The hybrid ARIMA-SVR model strengthens the matched points of the ARIMA model and the SVR model in weather forecasting. The LSTM and random forest are both popular algorithms used for regression problems. LSTM is more suitable for tasks involving sequential data with long-term dependencies. Random Forest leverages the wisdom of crowds by combining multiple decision trees, providing robust predictions, and reducing overfitting. Hybrid Random forest-LSTM potentially leverages the robustness and feature importance of Random Forest along with the ability of LSTM to capture sequential dependencies. The comparison results show that the hybrid RF-LSTM model reduces the forecasting errors in metrics of MAE, R-squared, and RMSE. The proposed hybrid model can also capture the actual temperature trend in its prediction performance, which makes it even more relevant for many other possible decision-ma

The burgeoning field of machine learning has transformed numerous sectors, revolutionizing everything from image recognition to financial forecasting. However, classical machine learning algorithms often encounter limitations when dealing with complex, high-dimensional problems. This is where the nascent field of quantum machine learning (QML) emerges, offering a paradigm shift with its unique computational capabilities. By harnessing the principles of quantum mechanics, QML promises to solve problems intractable for classical methods, like simulating complex molecules or optimizing financial portfolios. However, current quantum hardware limitations necessitate a hybrid approach: Hybrid Quantum-Classical Machine Learning Models (HQCLML). The convergence of quantum computing and classical machine learning has sparked significant interest in the development of hybrid quantum-classical machine learning models. This research explores the synergy between quantum and classical paradigms, aiming to leverage the strengths of both to enhance the capabilities of machine learning algorithms. The paper provides an in-depth overview of quantum computing principles, classical machine learning models, and the foundational concepts that form the basis for hybrid models. Various approaches to integrating quantum computing into machine learning are discussed, emphasizing the potential advantages in solving complex problems, particularly those involving large-scale optimization or exponential search spaces. The study delves into quantum machine learning algorithms, showcasing examples such as Quantum Support Vector Machines and Quantum Neural Networks. Case studies and applications of hybrid models are presented to illustrate instances where quantum enhancements outperform classical counterparts. While highlighting the promising achievements, the paper also addresses the current challenges and limitations associated with hybrid models, including practical considerations, error rates

Time series forecasting and volatility forecasting is a particularly active research field within financial mathematics. More recent studies extend well-established forecasting methods with machine learning. This thesis will evaluate and compare the standard Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and some of its extensions to a proposed Long Short-Term Memory (LSTM) model on historic data from five Swedish stocks. It will also explore hybrid models that combine the two techniques to increase prediction accuracy over longer horizons. The results show that the predictability increases when switching from univariate GARCH and LSTM models to hybrid models combining them both. Combining GARCH, Glosten, Jagannathan, and Runkle GARCH (GJR-GARCH), and Fractionally Integrated GARCH (FIGARCH) yields the most accurate result with regards to mean absolute error and mean square error. The forecasting errors decreased with 10 to 50 percent using the hybrid models. Comparing standard GARCH to the hybrid models, the biggest gains were seen at the longest horizon, while comparing the LSTM to the hybrid models, the biggest gains were seen for the shorter horizons. In conclusion, the prediction ability increases using the hybrid models compared to the regular models., Tidsserieprognostisering, och volatilitetsprognostiering i synnerhet, är ett växande fält inom finansiell matamatik som kontinereligt står inför implementation av nya tekniker. Det som en gång startade med klassiksa tidsseriemodeller som ARCH har nu utvecklats till att dra fördel av maskininlärning och neurala nätverk. Detta examensarbetet uvärderar och jämför Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeller och några av dess vidare tillämpningar med Long Short-Term Memory (LSTM) modeller på fem svenska aktier. ARbetet kommer även gå närmare inpå hybridmodeller som kombinerar dessa två tekniker för att öka tillförlitlig prognostisering under längre tidshorisonter. Resultaten visar att förutsägbarheten ökar genom att byta envariata GARCH och LSTM modeller till hybridmodeller som kombinerar båda delarna. De mest korrekta resultaten kom från att kombinera GARCH, Glosten, Jagannathan, och Runkle GARCH (GJR-GARCH) och Fractionally Integrated GARCH (FIGARCH) modeller med ett LSTM nätverk. Prognostiseringsfelen minskade med 10 till 50 procent med hybridmodellerna. Specifikt, vid jämförelse av GARCH modellerna till hybridmodellerna sågs de största förbättringarna för de längre tidshorisonterna, medans jämförelse mellan LSTM och hybridmodellerna sågs den mesta förbättringen hos de kortare tidshorisonterna. Sammanfattningsvis öker prognostiseringsförmågan genom användning av hybridmodeller i jämförelse med standardmodellerna.

In order to respond to the changing demands of the business environment, organizations need to develop a higher degree of flexibility in relation to the market and prevailing competition. One way for organizations to become more adjustable is to adopt flexible ways of working that promote innovation. A dominant approach for this is agile methods. Although the basic values and principles of agile methods were initially formulated for work processes linked to software development, an interest in integrating the work method into other functions and industries beyond IT has emerged. An integration and usage of agile methods can, however, give rise to paradoxical tensions as it is challenging to combine organizational needs for stability with agile methods' emphasis on flexibility. The balance between stability and flexibility is highlighted as important and is described as organizational ambidexterity. This study aimed to investigate the integration and usage of agile methods in an organizational context and how to balance stability and flexibility to manage paradoxical tensions. To answer the study's purpose, a qualitative method has been conducted with semi-structured interviews within the chosen organization. The data has been analyzed with James March's (1991) theory Exploration and Exploitation in Organizational Learning and Tushman and O'reilly's (1996) theoretical framework Ambidextrous Organizations. The result shows that there are both external and internal motives for integrating agile methods. Furthermore, the study has found central challenges and opportunities linked to the integration and usage of agile methods. Prominent findings show the importance of reaching an organizational consensus and understanding when agile methods are being integrated, making a selection of what agile values and principles have the possibility to actually generate improvements and benefits, as well as applying traditional and agile methods in combination through hybrid models.

We provide a critique of mathematical biology in light of rapid developments in modern machine learning. We argue that out of the three modelling activities - (1) formulating models; (2) analysing models; and (3) fitting or comparing models to data - inherent to mathematical biology, researchers currently focus too much on activity (2) at the cost of (1). This trend, we propose, can be reversed by realising that any given biological phenomenon can be modelled in an infinite number of different ways, through the adoption of a pluralistic approach, where we view a system from multiple, different points of view. We explain this pluralistic approach using fish locomotion as a case study and illustrate some of the pitfalls - universalism, creating models of models, etc. - that hinder mathematical biology. We then ask how we might rediscover a lost art: that of creative mathematical modelling.

Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions., Validerad;2023;Nivå 2;2023-04-18 (joosat);Licens fulltext: CC BY License

Precipitation forecast is key for water resources management in semi-arid climates. The traditional hybrid models simulate linear and nonlinear components of precipitation series separately. But they do not still provide accurate forecasts. This research aims to improve hybrid models by using an ensemble of linear and nonlinear models. Preprocessing configurations and each of the Gene Expression Programming (GEP), Support Vector Regression (SVR), and Group Method of Data Handling (GMDH) models were used as in the traditional hybrid models. They were compared against the proposed hybrid models with a combination of all these three models. The performance of the hybrid models was improved by different methods. Two weather stations of Tabriz and Rasht in Iran with respectively annual and monthly time steps were selected to test the improved models. The results showed that Theil’s coefficient, which measures the inequality degree to which forecasts differ from observations, improved by 9% and 15% for SVR and GMDH relative to GEP for the Tabriz station. The applied error criteria indicated that the proposed hybrid models have a better representation of observations than the traditional hybrid models. Mean square error decreased by 67% and Nash Sutcliffe increased by 5% in the Rasht station when we combined the three machine learning models using genetic algorithm instead of SVR. Generally, the representation of the nonlinear models within the improved hybrid models showed better performance than the traditional hybrid models. The improved models have implications for modeling highly nonlinear systems using the full advantages of machine learning methods.

The prediction of ship motions and quiescent periods, is of paramount importance for the maritime industry. The capability to predict these events sufficiently in advance has the potential to improve the safety and efficiency of several marine operations, such as landing and take-off on aircraft carriers, transfer of cargo, and mating operations between ships. Several models have been proposed in the literature for the prediction of ship motions and quiescent period. This work will review them by first grouping them into three main categories (i.e., physical, data-driven, and hybrid models) and then by detailing the most recent and relevant ones describing the advantages and disadvantages of each approach. Review concludes with the open problems and future perspectives of this important field of research., Ship Design, Production and Operations

The role of meaning facets based on sensorimotor experiences is well investigated in comprehension but has received little attention in language production research. In two experiments, we investigated whether experiential traces of space influenced lexical choices when participants completed visually presented sentence fragments (e.g., “You are at the sea and you see a . . .”) with spoken nouns (e.g., “dolphin,” “palm tree”). The words were presented consecutively in an ascending or descending direction, starting from the centre of the screen. These physical spatial cues did not influence lexical choices. However, the produced nouns met the spatial characteristics of the broader sentence contexts such that the typical spatial locations of the produced noun referents were predicted by the location of the situations described by the sentence fragments (i.e., upper or lower sphere). By including distributional semantic similarity measures derived from computing cosine values between sentence nouns and produced nouns using a web-based text corpus, we show that the meaning dimension of “location in space” guides lexical selection during speaking. We discuss the relation of this spatial meaning dimension to accounts of experientially grounded and usage-based theories of language processing and their combination in hybrid approaches. In doing so, we contribute to a more comprehensive understanding of the many facets of meaning processing during language production and their impact on the words we select to express verbal messages., Peer Reviewed

The goal of this paper is the insight into the forecasting of Bitcoin price using machine learning models like AutoRegressive Integrated Moving Average (ARIMA), Support vector machines (SVM), hybrid ARIMA-SVM, and Long short-term memory (LSTM). Depending on the different types of data and the period, various models are used for prediction. A single model may be the best fit in the short term but may not be the best in long-term series data. Thus, using only a single model may not be suitable for forecasting time series data that depends on data sampling length and prediction time, and the type of specific applications. As a result, the ARIMA model produces better error results with a short prediction period or a small data set. In contrast, the Hybrid ARIMA-SVM model will help improve the performance of the ARIMA model when predicting over a long period, specifically 7 and 30 days for Bitcoin price prediction used in this research paper. The paper aims to compare traditional models such as the ARIMA, the Hybrid ARIMA-SVM, and deep learning models such as LSTM on a specific cryptocurrency prediction task using different scenarios.

For propeller-driven vessels, cavitation is the most dominant noise source producing both structure-borne and radiated noise impacting wildlife, passenger comfort, and underwater warfare. Physically plausible and accurate predictions of the underwater radiated noise at design stage, i.e., for previously untested geometries and operating conditions, are fundamental for designing silent and efficient propellers. State-of-the-art predictive models are based on physical, data-driven, and hybrid approaches. Physical models (PMs) meet the need for physically plausible predictions but are either too computationally demanding or not accurate enough at design stage. Data-driven models (DDMs) are computationally inexpensive ad accurate on average but sometimes produce physically implausible results. Hybrid models (HMs) combine PMs and DDMs trying to take advantage of their strengths while limiting their weaknesses but state-of-the-art hybridisation strategies do not actually blend them, failing to achieve the HMs full potential. In this work, for the first time, we propose a novel HM that recursively correct a state-of-the-art PM by means of a DDM which simultaneously exploits the prior physical knowledge in the definition of its feature set and the data coming from a vast experimental campaign at the Emerson Cavitation Tunnel on the Meridian standard propeller series behind different severities of the axial wake. Results in different extrapolating conditions, i.e., extrapolation with respect to propeller rotational speed, wakefield, and geometry, will support our proposal both in terms of accuracy and physical plausibility., Ship Design, Production and Operations

Renewable energies are currently experiencing promising growth as an alternative solution to minimize the emission of pollutant gases from the use of fossil fuels, which contribute to global warming. To integrate these renewable energies safely with the grid system and make the electric grid system more stable, it is vitally important to accurately forecast the amount of wind power generated at specific wind power generation sites and the timing of this generation. Deep learning approaches have shown good forecasting performance for complex and nonlinear problems, such as time series wind power data. However, further study is needed to optimize deep learning models by integrating multiple models with hyperparameter optimization, to attain optimal performance from these individual models. In this paper, we propose a hybrid CNN-LSTM model for wind power forecasting in Ethiopia. Bayesian optimization is applied to tune the hyperparameters of the individual learners, including 1D-CNN and LSTM models, before building the hybrid CNN-LSTM model. The proposed model is tested on three case study wind power datasets obtained from the Ethiopian Electric Power Corporation. According to the MAE, RMSE, and MAPE evaluation metrics, the hybrid model performs significantly better than benchmark models, including ANN, RNN, BiLSTM, CNN, and LSTM models, for all case study data.

CRUE-CSIC (Acuerdos Transformativos 2022), Gender-based crime is one of the most concerning scourges of contemporary society, and governments worldwide have invested lots of economic and human resources to foretell their occurrence and anticipate the aggressions. In this work, we propose to apply Machine Learning (ML) techniques to create models that accurately predict the recidivism risk of a gender-violence offender. We feed the model with data extracted from the official Spanish VioGen system and comprising more than 40,000 reports of gender violence. To evaluate the performance, two new quality measures are proposed to assess the effective police protection that a model supplies and the overload in the invested resources that it generates. The empirical results show a clear outperformance of the ML-centered approach, with an improvement of up to a 25% with respect to the preexisting risk assessment system. Additionally, we propose a hybrid model that combines the statistical prediction methods with the ML method, permitting authorities to implement a smooth transition from the preexisting model to the ML-based model. To the best of our knowledge, this is the first work that achieves an effective ML-based prediction for this type of crimes against an official dataset., Ministerio de Ciencia, Innovación y Universidades (España), Universidad Complutense de Madrid/Banco de Santander, Depto. de Análisis Matemático y Matemática Aplicada, Depto. de Álgebra, Geometría y Topología, Fac. de Ciencias Matemáticas, TRUE, pub

Monitoring and evaluating the biofouling state and its effects on the vessel's hull and propeller performance is a crucial problem that attracts the attention of both academy and industry. Effective and reliable tools to address this would allow a timely cleaning procedure able to trade off costs, efficiency, and environmental impacts. In this paper, the authors carry out a critical review, accompanied with summary tables, of the biofouling problem with a particular focus on the shipping industry and the state-of-the-art techniques for monitoring and evaluating the biofouling state and its effects on the vessel's hull and propeller performance. In particular, different techniques are grouped according to the three main families of numerical models that have been designed and exploited in the literature: Physical Models (i.e., models relying on the mechanistic knowledge of the phenomena), Data-Driven Models (i.e., models relying on historical data about the phenomena together with Artificial Intelligence), and Hybrid Models (i.e., a hybridisation between Physical and Data-Driven Models). A conclusion from the performed review, open problems, and future direction of this field of research is detailed at the end of the review., Ship Design, Production and Operations

Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models’ predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence travel time.

Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models., Validerad;2022;Nivå 2;2022-06-27 (joosat)

Digital Twins (DTs) are on the rise as innovative, powerful technologies to harness the power of digitalisation in the WRRF sector. The lack of consensus and understanding when it comes to the definition, perceived benefits and technological needs of DTs is hampering their widespread development and application. Transitioning from traditional WRRF modelling practice into DT applications raises a number of important questions: When is a model's predictive power acceptable for a DT? Which modelling frameworks are most suited for DT applications? Which data structures are needed to efficiently feed data to a DT? How do we keep the DT up to date and relevant? Who will be the main users of DTs and how to get them involved? How do DTs push the water sector to evolve? This paper provides an overview of the state-of-the-art, challenges, good practices, development needs and transformative capacity of DTs for WRRF applications.

Accurate and robust vehicle state estimation is important for proper operation of vehicle control systems. For lateral stability control accurate estimation of the Vehicle Sideslip Angle (VSA) is of utmost importance. This thesis aims to develop an accurate and highly robust model to estimate the VSA. Common vehicle sensors such as the Inertial Measurement Unit (IMU), Global Positioning System (GPS) and steering angle sensor are used to provide measurements of vehicle states that are relatively easy to directly measure. Additionally, wheel bearing strains measurements are collected by the Load-­Sensing Bearing (LSB), which can be mapped to the tire forces that are measured by wheel force transducers. Insight regarding the tire forces could be beneficial to VSA estimation. The focus lies upon neural network estimators and hybrid structures, which combine the neural network estimator with an observer model. A large-­scale experimental dataset composed of standardised vehicle manoeuvres is used to develop and evaluate different estimation architectures. The dataset consists of 216 manoeuvres, which corresponds to approximately two hours of driving time. Neural network estimators are data-­driven and therefore rely on high quality data. Various neural network architectures exist for the purpose of time series estimation. In this thesis the Feedforward Neural Network (FFNN), Recurrent Neural Network (RNN) and Transformer are examined in detail. The FFNN and RNN yield similar performance in terms of Root Mean Squared Error (RMSE) and Maximum Error (ME) on the test set. The Transformer is unable to match this performance level due to the absence of a proper positional encoding of the measurements. Due to the simpler structure and lower data requirement, the FFNN is selected for application in the hybrid structures. To create a hybrid estimator, the uncertainty level of the VSA estimate from the neural network is required. To obtain the uncertainty usin, Mechanical Engineering | Vehicle Engineering | Cognitive Robotics

With the embrace of the performance-based seismic design as the state-of-the-art design method, recent emphasis has been placed on eliminating its drawbacks and facilitating its application in practice. This study aims to propose an alternative design method: performance-based analytics-driven seismic design, which is applied to steel moment resisting frame buildings. First, the seismic performance of self-centering (with post-tensioned connections) and conventional moment resisting frames (with reduced-beam section connection) is comparatively assessed. The comparison indicates that the economic benefit for adopting the post-tensioned connection is not significant. Then, an end-to-end computational platform, which automates the seismic design, nonlinear structural model construction, and response simulation (static and dynamic) of steel moment resisting frames is developed. Using this platform, a comprehensive database is developed, which includes 621 special steel moment resisting frames designed in accordance with modern codes and standards and their corresponding nonlinear structural models and seismic responses (i.e., peak story drifts, peak floor accelerations, and residual story drifts). Using this database, the efficacy of mechanics-based, data-driven, and hybrid (combination of mechanics-based and data driven) approaches to estimating the seismic drift demand are evaluated. The evaluation results reveal that the hybrid approach has the best performance whereas the mechanics-based model has the lowest performance. Next, a set of non-parametric and parametric surrogate models are developed for estimating the engineering demand parameter distributions. A comparative assessment of the proposed surrogate models and the simplified analysis method proposed by FEMA P-58 is conducted to demonstrate the superior predictive performance of the former. Finally, the effect of various design variables on the collapse performance of steel moment resisting frames are evalua

Fast diesel engine models for real-time prediction in dynamic conditions are required to predict engine performance parameters, to identify emerging failures early on and to establish trends in performance reduction. In order to address these issues, two main alternatives exist: one is to exploit the physical knowledge of the problem, the other one is to exploit the historical data produced by the modern automation system. Unfortunately, the first approach often results in hard-to-tune and very computationally demanding models that are not suited for real-time prediction, while the second approach is often not trusted because of its questionable physical grounds. In this paper, the authors propose a novel hybrid model, which combines physical and data-driven models, to model diesel engine exhaust gas temperatures in operational conditions. Thanks to the combination of these two techniques, the authors were able to build a fast, accurate and physically grounded model that bridges the gap between the physical and data driven approaches. In order to support the proposal, the authors will show the performance of the different methods on real-world data collected from the Holland Class Oceangoing Patrol Vessel., Ship Design, Production and Operations

[Abstract] Soil Organic Carbon (SOC) content is a key element for soil fertility and productivity, nutrient availability and potentially represents a measurement of the sink for greenhouse gas abatement. Improving our knowledge on the spatial distribution of SOC is hence essential for sustainable nutrient management and carbon storage capacity. The objective of this study was to evaluate the performance of six tree-based machine-learning models when using environmental variables (i.e., remote sensing and terrain attributes - scenario 1), Fourier Transform Infrared Spectroscopy (FTIR) data (scenario 2) and combination of environmental variables and FTIR data (scenario 3) as predictors in prediction of SOC content. The models included Random Forest, Cubist, Conditional Inference Forest, Conditional Inference Trees, Extreme Gradient Boosting and Classification, Regression Trees. Furthermore, we explored if the Bat optimization algorithm can improve the prediction accuracy of the models. The study was conducted across a 7000 ha field in the Miandoab County, Northern Iran, with a total of 80 soil samples collected systematically in a regular grid (700 × 1000 m). According to Leave-One-Out Cross-Validation, the best prediction performance was achieved by the Cubist+Bat model when environmental variables and FTIR spectra (scenario 3) were used (Coefficient of determination = 0.73, Concordance Correlation Coefficient = 0.77, Root Mean Square Error = 0.36, Mean Absolute Error = 0.31, Median Absolute Error = 0.28). FTIR data had the highest influence on the prediction accuracy of SOC. Therefore, it can be concluded that the combination of environmental variables and FTIR data with Cubist+Bat model as a precise approach to monitor SOC in semi-arid soils of Iran. The final Digital Soil Map (DSM) of SOC revealed that improvements in prediction might be possible with the collection of more soil samples in areas where the land use and topography changed over short spatial scales.

The dramatic increase in Android-based smart devices has brought technological revolution to improve the overall quality of life and thus making it worth a billion-dollar market. Despite the huge hype surrounding Android market, the prevalent and potentially sophisticated malicious mobile malware has become a serious threat to the popular Android platform and an ideal target for varied cyber adversaries. Conversely, multivector malware efficient and timely detection is extremely challenging because it usually hides itself under legitimate third party software's and having the capability to be easily crafted on any executable file extension. To better streamline this complex issue of paramount concern, the authors propose a highly proficient hybrid deep learning (DL)-enabled intelligent multi-vector malware detection mechanism. The devised approach leverages Convolutional Neural Networks and Bidirectional Long Short-Term Memory (BiLSTM) to efficiently identify persistent malware. The proposed technique has been thoroughly evaluated with publicly available datasets, standard performance metrics, and state-of-the-art hybrid DL-driven architectures and benchmark DL algorithms. Besides, the proposed framework has been cross-validated and shows out performance both in terms of time efficiency and detection accuracy.

Ambient air pollution is among the top 10 health risk factors in the US. With increasing concerns about adverse health effects of ambient air pollution among stakeholders including environmental scientists, health professionals, urban planners and community residents, improving air quality is a crucial goal for developing healthy communities. The US Environmental Protection Agency (EPA) aims to reduce air pollution by regulating emissions and continuously monitoring air pollution levels. Local communities also benefit from crowd-sourced monitoring to measure air pollution, particularly with the help of rapidly developed low-cost sampling technologies. The shift from relying only on government-based regulatory monitoring to crowd-sourced effort has provided new opportunities for air quality data. In addition, the fast-growing data sciences (e.g., data mining) allow for leveraging open data from different sources to improve air pollution exposure assessment. My dissertation investigates how new data sources of air quality (e.g., community-based monitoring, low-cost sensor platform) and model predictor variables (e.g., non-government open data) based on emerging modeling approaches (e.g., machine learning [ML]) could be used to improve air quality models (i.e., land use regression [LUR]) at local, regional, and national levels for refined exposure assessment. LUR models are commonly used for predicting air pollution concentrations at locations without monitoring data based on neighboring land use and geographic variables. I explore the use of crowd-sourced low-cost monitoring data, new/open dataset from government and non-government sponsored platforms, and emerging modeling techniques to develop LUR models in the US. I focus on testing whether: (1) air quality data from community-based monitoring is feasible for developing LUR models, (2) air quality data from non-government crowd-sourced low-cost sensor platforms could supplement regulatory monitors for LUR developme

Product development has become essential in today’s technology driven marketplace. Currently, pointbased models such as the stage-gate model are applied in practice of several incumbent firms in the automotive industry. However, stage-gate models have been criticized for being too linear and plan focused as external pressures and internal complexity demand for faster and more flexible development, such as agile development. Although product development has a central role in the success of a firm, suppliers play a central role in helping firms achieve their product development goals. As a result, firms may undertake suppler development and integration initiatives to ensure quality and efficiency at the supplier. Utilizing supplier integration has shown positive effects on several performance outcomes. However, limited research on the effects of suppliers being integrated to deficient development practices has been published. Firms that integrate suppliers into point-based, documentation heavy processes could therefore potentially hinder internal development of flexible practices. Therefore, the aim of this study is to investigate how supplier integration challenges suppliers in adopting agile methods in product development and how these challenges can be addressed. This study is based a qualitative, exploratory case study approach, including 13 interviews with various stakeholders within the case company together with extensive data from observations. The empirical data in combination with theories on agile development, traditional development, and supplier integration resulted in a proposition for adopting agile development approaches for integrated suppliers being locked-in to traditional development models. The findings indicate that integrated suppliers may face challenges in adopting agile methods due to requirements from the integrating firm on using traditional development methods. However, a possibility to utilize agile practices on the microperspective whil, Produktutveckling har blivit väsentlig på dagens teknikdrivna marknadsplats. För närvarande är traditionella modeller som stage-gate vanligt förekommande i flera etablerade företag inom bilindustrin, även om dessa modeller har kritiserats för att vara för linjära och planfokuserade när omgivningen och intern komplexitet kräver snabbare och mer flexibel utveckling. Även om produktutveckling har en fundamental roll i framgången hos ett företag så är leverantörer centrala i att hjälpa företag att uppnå sina produktutvecklingsmål. Som ett resultat av ökad outsourcing kan företag vidta mer utvecklings och integrationsinitiativ för att säkerställa kvalitet och effektivitet hos leverantören. Att använda leverantörsintegration har visat positiva effekter på flera nyckeltal. Emellertid har begränsad forskning om effekterna av att integrera leverantörer i ineffektiva produktutvecklingsmetoder publicerats. Företag som integrerar leverantörer i linjära, dokumentationstunga processer kan därför potentiellt hindra den interna utvecklingen av flexibla tillvägagångssätt. Därmed är syftet med denna studie att undersöka hur leverantörsintegration kan försvåra tillämpning av agila metoder i produktutveckling för leverantörer och hur dessa utmaningar kan adresseras. Denna studie bygger på en kvalitativ, explorativ fallstudie som inkluderar 13 intervjuer med anställda inom det observerade företaget tillsammans med omfattande data från observationer. Empiriska data i kombination med teorier om agil utveckling, traditionell utveckling och leverantörsintegration resulterade i ett förslag till integrerade leverantörer, som är bundna till traditionella utvecklingsmodeller, att adoptera agila utvecklingsmetoder. Resultaten tyder på att integrerade leverantörer kan möta utmaningar när det gäller att använda agila metoder på grund av krav att använda traditionella utvecklingsmetoder från det integrerande företaget. Där emot finns möjlighet att tillämpa agila metoder i mikroperspektivet samtidig

This thesis captures the calibration of a FX hybrid model: The FX Black-Scholes Hull-White model. The main focus is on the calibration of the parameters in the Hull-White process: The mean reversion and the volatility parameter. The latter is commonly calibrated as a time-dependent parameter, whilst the mean reversion parameter is not. This thesis covers the calibration of the mean reversion as a time-dependent parameter. A known method from the Literature is researched, where we calibrate the mean reversion independently from the volatility parameter to the ratio of two swaptions with the same expiry but different tenor. In our research this method is extended to the negative interest rate environment by assuming that the swap rate follows a shifted lognormal distribution instead of a lognormal distribution. We show that a specific set of swaptions can be chosen, so that the calibration problem is simplified. This choice leads to sequential calibration of convex optimization problems. Numerical results of calibration to artificial and market data are presented, where we compare our method to picking the mean reversion parameter arbitrarily. The findings suggest that the choice of mean reversion parameter affects the calibration procedure. Therefore, we argue that a calibration method for the mean reversion would be appropriate. Besides the focus on the Hull-White process, the calibration of the volatility parameter in the FX Black-Scholes process of the hybrid model is investigated. For the calibration of the FX volatility parameter ATM options on FX rates are used. Numerical results of calibration to artificial and market data are shown. A typical problem of calibration in the industry is discussed: Precision for late maturities. The results in this thesis suggest that this problem could be solved. For research on homogeneity constraints on the time-dependent parameters, the performance of delta-hedging is investigated. The delta-hedging is performed in a simple B, Applied Mathematics

The pandemic caused by the virus responsible for COVID-19 has put educational systems around the world in check. Its unpredictable nature implied that virtual solutions had to be embraced in order to overcome moments of complete lockdown.This situation has evidenced all the different problems that afflict education and that these cannot be solved with the adopted emergency solutions. These are times to make the best of this crisis to reformulate many of the aspects of nowadays education systems to make them better and transform them if similar threats happen again. For this, technology must be an ally. This article reflects the need to design hybrid educational models that allow a fluent transition between face-to-face moments and virtual moments, especially if they are not programmed. Besides, a comprehensive vision of the school is necessary, including analog and digital contexts, suitable leadership for the digital age we are living in, and initial teacher training in the above-mentioned contexts., A pandemia causada pelo vírus responsável pela COVID-19 tem desafiado os sistemas educativos em todo o mundo. Sua natureza imprevista fez com que soluções virtuais fossem abraçadas para superar os momentos de confinamento total. Esta situação evidenciou diversos problemas que afligem a educação e que não podem ser resolvidos com as soluções emergenciais que foram adotadas. É hora de aproveitar a crise para repensar muitos aspectos da educação atual e transformar os nossos sistemas educativos aprimorando-os para que estejam em melhores condições caso uma ameaça semelhante se apresente novamente. Para isso, a tecnologia deve ser uma aliada.Este artigo propõe uma reflexão sobre a concepção necessária de modelos educacionais híbridos que permitam uma transição suave entre momentos presenciais e os não presenciais, especialmente se estes não forem programados. Além disso, é necessária uma visão abrangente da escola que inclua os contextos analógico e digital, uma liderança adequada para a era digital em que vivemos, e uma formação inicial de professores levada a cabo nos contextos mencionados., La pandemia por el virus responsable de la COVID-19 ha puesto en jaque a los sistemas educativos de todo el mundo. Su carácter imprevisto hizo que se tuvieran que abrazar soluciones virtuales para poder superar los momentos de confinamiento total. Esta situación ha puesto en evidencia diversos problemas que acucian a la educación y que no pueden ser resueltos con las soluciones de emergencia que se adoptaron. Es momento de aprovechar esta crisis para replantear muchos aspectos de la educación de hoy y transformar nuestros sistemas educativos para mejorarlos y para que estén en mejores condiciones si una amenaza similar vuelve a presentarse. Para ello, la tecnología debe ser una aliada.Este artículo plantea la reflexión sobre el necesario diseño de modelos educativos híbridos que permitan tran-sitar con fluidez entre momentos presenciales y momentos no presenciales, especialmente si estos no están programados. Además, es necesaria una visión integral de la escuela, que incluya tanto los contextos analógicos como los digitales, un liderazgo adecuado a la era digital que vivimos, y una formación inicial de los docentes que se lleva a cabo en los contextos citados

[EN] In this contribution, a nonlinear programming genetic hybrid algorithm for the optimal design of pressurized irrigation networks operating in shifts is presented, and an indicator is implemented to evaluate the flexibility of the resulting designs of the hybrid algorithm. The main objective is the minimization of the economic cost of the pipe network and the evaluation of the flexibility of the system designs. The research considers the allocation of shifts to the irrigation outlets (hydrants) and the sizing of the pipes as decision variables to reduce implementation costs. With the results of the optimization, the flexibility of the hydraulic network is analyzed based on the random reassignment of shifts to the hydrants, which simulates the modifications that may occur during network operation. To validate the algorithm, once calibrated, it was applied to the design of four real irrigation sectors of Ecuador and Spain. The results indicate that the designs obtained improve in cost and flexibility with respect to those found with the traditional algorithm of the Economic Series, and that the assignment of the shifts is of utmost importance to minimize investment costs in pipes. At the same time, it constitutes a determining factor in the flexibility of the system., [ES] En esta contribución se presenta un algoritmo híbrido genético-programación no lineal para el diseño óptimo de redes presurizadas de riego operando a turnos, y se implementa un indicador para evaluar la flexibilidad de los diseños resultantes del algoritmo híbrido. El objetivo principal es la minimización del costo económico de la red de tuberías y la evaluación de la flexibilidad de los diseños del sistema. La investigación considera como variables de decisión la asignación de turnos a las tomas de riego (hidrantes) y el dimensionado de las conducciones para reducir los costos de implementación. Con los resultados de la optimización se analiza la flexibilidad de la red hidráulica con base en la reasignación aleatoria de turno a los hidrantes, que simula las modificaciones susceptibles de ocurrir durante la explotación de la red. Para validar el algoritmo, una vez calibrado, éste fue aplicado al diseño de cuatro sectores de riego reales de Ecuador y España. Los resultados indican que los diseños obtenidos mejoran en costo y flexibilidad respecto a los encontrados con el algoritmo tradicional de la serie económica, y que la asignación de los turnos es de suma importancia para minimizar los costos de inversión en tuberías, lo que a la vez constituye un factor determinante en la flexibilidad del sistema.

Knowledge-intensive processes, such as those encountered in health care, finance and government, tend to allow a large degree of flexibility: there are many possible solutions towards a goal, and it is left to the expertise of knowledge workers to find the one most suitable for the particular case at hand. As a result, such processes usually exhibit more varied behaviour than traditional production processes. This poses a challenge for process discovery algorithms that return imperative, flow-based, models. The models tend to become highly complex when representing many alternative paths, and therefore, the miners need to either sacrifice on simplicity, fitness, or precision. It has been proposed that one should discover the constraints of the process instead, based on the assumption that such a constraint-based, declarative process model can describe highly varied behaviour more concisely. More recently, it has been observed that many processes do not neatly fall in one category or the other; instead, they contain both flexible and rigid parts. In such cases, it may be helpful to identify these parts and mine constraints for some and flow for others, resulting in a hybrid model. In this paper, we provide an overview of recent advances in both declarative and hybrid process discovery, discuss a number of open challenges that still remain, and propose directions for future research.

Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development., Validerad;2020;Nivå 2;2019-12-16 (johcin)

Dew point temperature (DPT) is known to fluctuate in space and time regardless of the climatic zone considered. The accurate estimation of the DPT is highly significant for various applications of hydro and agro–climatological researches. The current research investigated the hybridization of a multilayer perceptron (MLP) neural network with nature-inspired optimization algorithms (i.e., gravitational search (GSA) and firefly (FFA)) to model the DPT of two climatically contrasted (humid and semi-arid) regions in India. Daily time scale measured weather information, such as wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature, was used to build the proposed predictive models. The efficiencies of the proposed hybrid MLP networks (MLP–FFA and MLP–GSA) were authenticated against standard MLP tuned by a Levenberg–Marquardt back-propagation algorithm, extreme learning machine (ELM), and support vector machine (SVM) models. Statistical evaluation metrics such as Nash Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE) were used to validate the model efficiency. The proposed hybrid MLP models exhibited excellent estimation accuracy. The hybridization of MLP with nature-inspired optimization algorithms boosted the estimation accuracy that is clearly owing to the tuning robustness. In general, the applied methodology showed very convincing results for both inspected climate zones., Validerad;2019;Nivå 2;2019-04-15 (svasva)

Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

Process mining algorithms fall in two classes: imperative miners output flow diagrams, showing all possible paths, whereas declarative miners output constraints, showing the rules governing a process. But given a log, how do we know which of the two to apply? Assuming that logs exhibiting a large degree of variability are more suited for declarative miners, we can attempt to answer this question by defining a suitable measure of the variability of the log. This paper reports on an exploratory study into the use of entropy measures as metrics of variability. We survey notions of entropy used, e.g. in physics; we propose variant notions likely more suitable for the field of process mining; we provide an implementation of every entropy notion discussed; and we report entropy measures for a collection of both synthetic and real-life logs. Finally, based on anecdotal indications of which logs are better suited for declarative/imperative mining, we identify the most promising measures for future studies. For estimating overall entropy, global block and k-nearest neighbour estimators of entropy appear most promising and excel at identifying noise in logs. For estimating entropy rate we identify Lempel–Ziv and certain variants of k-block estimators performing well, and note that the former is more stable, but sensitive to noise, while the latter is less stable, being sensitive to cut-off constraints determining block size.

Separation of concerns has long been an important strategy to deal with complexity when developing a system. Some concerns (like security) are scattered through the whole system, and different modules are tangled to such concerns. These concerns are known as cross‐cutting concerns. When the system in question is a business process, cross‐cutting concerns are aimed at being encapsulated by Aspect‐Oriented Business Process Modeling. However, the state‐of‐the‐art techniques in this field lack efficient mechanisms that (1) support composition of cross‐cutting concerns that can be defined in parallel to (a part of) a process model and (2) enable specifying both mandatory and optional cross‐cutting concerns. To address these limitations, this paper proposes a new Aspect‐Oriented Business Process Modeling approach. The approach is hybrid since it is based on declarative rules to relate imperative cross‐cutting concerns and imperative business process models. The approach is explained, formally grounded with precise semantics, and used accordingly to implement the artifacts that support modeling and enactment of business processes in the proposed fashion as a proof of concept. In addition, the approach is evaluated on the basis of the Technology Acceptance Model during a workshop session. The result shows that participants perceived the approach usable and easy to use.

Rules, decisions, and workflows are intertwined components depicting the overall process. So far imperative workflow modelling languages have played the major role for the description and analysis of business processes. Despite their undoubted efficacy in representing sequential executions, they hide circumstantial information leading to the enactment of activities, and obscure the rationale behind the verification of requirements, dependencies, and goals. This workshop aimed at providing a platform for the discussion and introduction of new ideas related to the development of a holistic approach that encompasses all those aspects. The objective was to extend the reach of the business process management audience towards the decisions and rules community and increase the integration between different imperative, declarative and hybrid modelling perspectives. Out of the high-quality submitted manuscripts, three papers were accepted for publication, with an acceptance rate of 50%. They contributed to foster a fruitful discussion among the participants about the respective impact and the interplay of decision perspective and the process perspective.

Process mining algorithms can be partitioned by the type of model that they output: imperative miners output flow-diagrams showing all possible paths through a process, whereas declarative miners output constraints showing the rules governing a process. For processes with great variability, the latter approach tends to provide better results, because using an imperative miner would lead to so-called “spaghetti models” which attempt to show all possible paths and are impossible to read. However, studies have shown that one size does not fit all: many processes contain both structured and unstructured parts and therefore do not fit strictly in one category or the other. This has led to the recent introduction of hybrid miners, which aim to combine flow- and constraint-based models to provide the best possible representation of a log. In this paper we focus on a core question underlying the development of hybrid miners: given a log, can we determine a priori whether the log is best suited for imperative or declarative mining? We propose using the concept of entropy, commonly used in information theory. We consider different measures for entropy that could be applied and show through experimentation on both synthetic and real-life logs that these entropy measures do indeed give insights into the complexity of the log and can act as an indicator of which mining paradigm should be used.

To ease development and lower the entry barrier for new adopters many development environments offer visual means to edit complex data. Cyber-physical systems are a perfect candidate for such manipulations since they are usually described in the form of isolated, well defined components that can be manipulated individually. The physical parts of such systems often can be directly translated into real world objects and allowing the developer to interact with those in a familiar manner can greatly increase the usability and agility of the development process. In this thesis we focus on the exploration of interactive manipulation of hybrid system models. Our research examines a solution based on the Acumen simulation environment. We describe the tight integration between the textual model and 3D visualisation, go into detailed analysis of the implementation and use case-studies to illustrate concrete applications

This article presents the trend of hybrid models in the use of digital technologies, which includes the fusion of presential, e-learning and m-leaning models for the solution of learning problems in the disciplines that involve Computer Programming. It also shows how interdisciplinarity is intrinsic to the teaching-learning process of computer programming language. Based on hybrid models, a solution proposal for the learning difficulties in these disciplines is analyzed, with a model that involves the classroom, AVA, cloud computing and mobile devices, favoring a sustainability-based curriculum, considering that the practice of cloud computing is being considered a form of Green IT. In this article we do not intend to announce that hybrid models may be the solution to teaching computer programming, but it is said that they can be an outlet to minimize the high rate of disapproval and avoidance in this area of knowledge, therefore, deserves more studies. It was evident, after reviewing the literature that subsidized the reflections in this text, that the nature of the hybrid learning teaching systems that integrates as didactic strategies the classroom, e-learning and m-learnig requires an interdisciplinary knowledge., Este artigo apresenta a tendência de modelos híbridos no uso de tecnologias digitais, que compreende a fusão dos modelos presenciais, e-learning e m-leaning para a solução de problemas de aprendizagem nas disciplinas que envolvem a Programação de Computadores. Mostra também como a interdisciplinaridade está intrínseca ao processo de ensino-aprendizagem de linguagem de programação de computadores. Com base em modelos híbridos, analisa-se uma proposta de solução para as dificuldades de aprendizagem nestas disciplinas, com um modelo que envolve a sala de aula, AVA, Computação em nuvem e dispositivos móveis, favorecendo para um currículo baseado na sustentabilidade, considerando que a pratica de computação em nuvem vem sendo considerada uma forma de TI Verde. Neste artigo não se pretende anunciar que modelos híbridos podem ser a solução para o ensino de programação de computadores, mas afirma-se que eles podem ser uma saída para minimizar o alto índice de reprovação e evasão nesta área do conhecimento, portanto, merece mais estudos. Ficou evidente, após a revisão da literatura que subsidiou as reflexões neste texto, que a natureza dos sistemas híbridos de ensino aprendizagem que integra como estratégias didáticas a sala de aula, e-learning e m-learnig, requer um conhecimento interdisciplinar., Este artículo presenta la tendencia de modelos híbridos en el uso de tecnologías digitales, que comprende la fusión de los modelos presenciales, e-learning y m-leaning para la soluciónde problemas de aprendizaje en las disciplinas que involucran la Programación de Computadoras. También muestra cómo la interdisciplinaridad está intrínseca al proceso de enseñanza-aprendizaje del lenguaje de programación de computadoras. Con base en modelos híbridos, se analiza una propuesta de solución para las dificultades de aprendizaje en estas disciplinas, con un modelo que involucra el aula, AVA, Computación en nube y dispositivos móviles, favoreciendo para un currículo basado en la sostenibilidad, considerando que la práctica de computación en nube viene siendo considerada una forma de TI Verde. En este artículo no se pretende anunciar que modelos híbridos pueden ser la solución para la enseñanza de programación de computadoras, pero se afirma que pueden ser una salida para minimizar el alto índice de reprobación y evasión en esta área del conocimiento, por lo que merece más estudios. En el texto, la naturaleza de los sistemas híbridos de enseñanza aprendizaje que integra como estrategias didácticas al aula, e-learning y m-learning, requiere un conocimiento interdisciplinario.

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction.

Operational adaptive management, prediction, and prevention of risk events are significant for a successful concept of environmental capacity management. Therefore, it is necessary to develop an integrating hybrid model of environmental capacity management that would incorporate the existing models of environmental capacity management. The subject of this doctoral research is aimed towards developing a model of environmental capacity management and towards clearly and unequivocally proving the causal relationships between determinants of environmental vulnerability, resilience, and capacity. The research is focused on demonstrating the necessity of assessing environmental vulnerability, resilience, and capacity levels and on creating a new hybrid model of environmental capacity management that would help achieve proactivity in an environmental capacity management system and in environmental protection in general. The modelling of environmental capacity management processes and their performance will be based on quantitative and qualitative methods such as factor analysis, Turner's method, and fuzzy AHP. Incorporation of the hybrid model in a GIS environment will enable proper simulation analysis and verification of the model based on the DPSIR analytical framework and the relevant data available from the research sources. Definition of referential criteria and indicators of environmental vulnerability, resilience, and capacity constitutes the practical scientific contribution of this research. The results obtained in this research form a basis for the improvement of the following processes: identification of interdependence aspects in the society-environment system, ecological risk assessment, evaluation of vulnerability level of society as an environmental component, as well as design of advanced programs of environmental capacity management in a broader and narrower context and on different spatial and temporal levels. The results also raise the possibility of inv