Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.

geophysical monitoring --- data scarcity --- missing data --- climate extreme indices (CEIs) --- rule extraction --- Dataset Licensedatabase --- data assimilation --- data imputation --- support vector machines --- environmental observations --- multi-class classification --- earth-science data --- remote sensing --- magnetotelluric monitoring --- soil texture calculator --- machine learning --- ClimPACT --- invasive species --- species distribution modeling --- 3D-Var --- ensemble learning --- data quality --- water quality --- microhabitat --- k-Nearest Neighbors --- Expert Team on Climate Change Detection and Indices (ETCCDI) --- decision trees --- processing --- attribute reduction --- Expert Team on Sector-specific Climate Indices (ET-SCI) --- core attribute --- rough set theory --- GLDAS --- arthropod vector --- environmental modeling --- statistical methods

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy.

ISA 701 --- audit expectation gap --- key audit matters --- materiality --- Poland --- corporate bankruptcy --- forecasting --- fuzzy sets --- artificial neural networks --- decision trees --- bankruptcy prediction --- tax arrears --- payment defaults --- financial ratios --- failure --- bankruptcy --- chapter 11 --- regression count --- meta-analysis --- literature review --- manufacturing insolvency --- prediction --- citation mining --- classification --- credit risk modelling --- corporate failure --- rating systems --- ensemble classifiers --- boosting --- bagging --- stacking --- scoring models --- insolvency --- financial distress --- default --- forecasting methods --- models predicting financial distress --- phases of economic cycle --- Czech Republic --- European large companies --- bankruptcy risk --- company performance --- Principal Component Analysis --- neural networks --- support vector machine --- bankruptcy model

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Within the idea and objective of bringing together original studies dealing with the continuum aspects of meat, i.e., from farm to fork, this book grouped papers on the study of the nutritional, sensory, and technological aspects of carcass, muscle, meat, and meat-product qualities. This nook highlights a great part of the research activities in the field of meat science. A total of 14 original studies and one comprehensive review were edited within five main topics: (i) production systems and rearing practices, (ii) prediction of meat quality, (iii) statistical approaches for meat quality prediction/management, (iv) muscle biochemistry and proteomics techniques, and (v) consumer acceptability, development, and characterization of meat products.

meat quality --- sensory and technological quality --- serratus ventralis muscle --- modeling --- suckling cattle --- immunohistology --- pig --- carcass --- LC-MS/MS --- calpain --- sensorial and technological quality --- on-line --- infraspinatus muscle --- statistical tools for meat quality prediction --- malondialdehyde --- skeletal muscle --- chuck sale section --- male and female turkeys --- rearing managements --- dry-cured belly --- meat science --- breeders --- carcass characteristics --- production systems --- castration --- muscle biochemistry --- biomarker --- monitoring --- sample preparation --- quality --- bovine proteomics --- h2afx --- prediction --- broilers --- nutritional --- phenol --- shear force --- beef performances --- fetus --- complexus muscle --- near-infrared spectroscopy --- phenotypic model --- sensory acceptability --- cull cow --- trade-off --- meat sensory properties --- one-dimensional electrophoresis --- rhomboideus muscle --- boar taint --- OMICs tools --- pancetta --- longissimus muscle --- spectroscopy --- carcass and meat qualities --- finishing practices --- pre-weaning period --- grass-fed --- nutritional quality --- pasture --- consumer --- tenderness --- predictive model --- intramuscular fat --- fattening period --- rearing surveys --- rearing practices --- cows --- young bulls --- machine learning --- breed --- farm survey --- beef quality --- biological marker --- Folin-Ciocalteu --- decision trees --- natural antioxidant --- meat --- color attributes --- bovine --- processed meat --- meat rheological properties --- growth period --- muscle proteins --- extracellular matrix --- beef tenderness --- farm-to-fork

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.

model-based design --- FPGA --- HDL code generation --- wearable sensors --- embedded devices --- face recognition --- face verification --- biometric sensors --- deep learning --- distillation --- convolutional neural networks --- spatial transformer network --- video coding --- discrete cosine transform --- directional transform --- VLSI --- alternative representations to float numbers --- posit arithmetic --- Deep Neural Networks (DNNs) --- neural network activation functions --- surface electromyography --- event-driven --- functional electrical stimulation --- embedded system --- resampling --- interpolating polynomial --- polyphase filter --- digital circuit design --- ASIC --- bitmap indexing --- processing in memory --- memory wall --- big data --- internet of things --- intelligent sensors --- autonomous driving --- cyber security --- HW accelerator --- on-chip random number generator (RNG) --- SHA2 --- ASIC standard-cell --- machine learning --- edge computing --- edge analytics --- ANN --- k-NN --- SVM --- decision trees --- ARM --- X-Cube-AI --- STM32 Nucleo --- rad-hard --- PLL (phase-locked loop) --- SEE (single event effects) --- Spacefibre --- TID (total ionization dose) --- charge pump --- phase/frequency detector --- frequency divider --- ring oscillator --- LC-tank oscillator --- SpaceFibre --- rad-hard circuits --- radiation effects --- high-speed data transfer --- support attitude --- inertial measurement unit --- coal mining --- unscented Kalman filter --- quaternion --- gradient descent --- research data collection and sharing --- connected and automated driving --- deployment and field testing --- vehicular sensors --- impact assessment --- knowledge management --- collaborative project methodology --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling.

Quality check --- bike frame --- mathematical model --- graphical user interface --- risk management --- safety assurance --- medical parallel robot --- robotic assisted cancer treatment --- risk performance reasoning --- hidden Markov model --- Handy bauxite carrier --- process safety --- performance evaluation --- safety --- coupling correlation --- risk assessment --- multi-dimensional theory --- precision steel tape --- tape transportation --- roller-tape interactions --- roller-tape contact pair --- reliability --- truck unloading system --- petroleum equipment --- preventive maintenance --- cork–rubber composites --- compression --- apparent compression modulus --- Young’s modulus --- bonded condition --- importance measure --- cost --- inventory systems --- air compression system --- nitrogen generation system --- utility module --- availability --- sensitivity analysis --- predictive maintenance --- Industry 4.0 --- Internet of Things --- artificial intelligence --- machine learning --- maintenance --- predictive scheduling --- flow shop --- job shop --- ant colony optimisation --- on-line monitoring --- collaborative robots --- human robot collaboration --- time between failure (TBF) --- common beta hypothesis (CBH) test --- meta-analysis --- level of heterogeneity --- mean time between failure (MTBF) --- text mining --- network-based distributed manufacturing systems --- moth flame optimization algorithm --- support vector machines --- Naive Bayes --- random forest --- decision trees --- supplier classification --- machining centre --- DSM --- Copula function --- fault propagation intensity --- fault propagation behaviour --- lubricity --- gear oil --- wear --- operational reliability --- n/a --- cork-rubber composites --- Young's modulus