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Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Precursors are evoked upstream of the Capgras' syndrome. Then, an analogy is suggested between the need for prognostic classification linked to the saturation of the asylum population at the dawn of the 20th century, and the current overflow of the psychiatric healthcare system. The contemporary situation justifies the search for information useful to mitigate ill mental health in at-risk adolescents. The article presents recent research reports on adolescents at-risk of emotional dysregulation, stemming from a longitudinal cohort database of European adolescents. The database analyses have revealed new brain and psychometric predictors of emotional dysregulation in adolescents. New early indicators were derived from easy-to-administer questionnaires, exploring emotions, affective symptoms and traits, sleep, early adversity and stress, puberty. Findings suggest that the physiology and stages of brain development could be taken into account for decisions regarding Mental Health. Studies on adolescent brain development have implications for public health, in terms of the age of protection for adolescents, and targeted prevention upstream of care. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Experiential theories of cognition propose that the external environment shapes cognitive processing, shifting emphasis from internal mechanisms to the learning of environmental structure. Computational modelling, particularly distributional models of lexical semantics (e.g., Landauer & Dumais, 1997) and models of lexical organization (e.g., Johns, 2021a), exemplifies this, highlights the influence of language experience on cognitive representations. While these models have been successful, comparatively less attention has been paid to the training materials used to train these models. Recent research has explored the role of social/communicatively oriented training materials on models of lexical semantics and organization (Johns, 2021a, 2021b, 2023, 2024), introducing discourse- and user-centred text training materials. However, determining the optimal training materials for these two model types remains an open question. This article addresses this problem by using experiential optimization (Johns, Jones, & Mewhort, 2019), which selects the materials that maximize model performance. This study will use experiential optimization to compare user-based and discourse-based corpora in optimizing models of lexical organization and semantics, offering insight into pathways towards integrating cognitive models in these areas. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Condensation on surfaces in the presence of non-condensable gas (NCG) is a ubiquitous and critical phenomenon in many industrial fields. However, current empirical correlations for predicting condensation heat transfer coefficients in the presence of NCG may exhibit significant deviations from reality. Machine learning algorithms are now being dedicated to this field, but the models developed can only predict the condensation heat transfer coefficients of steam or vapor of a non-aqueous working fluid with similar physical properties to water in the presence of NCG. In the present study, a comprehensive theoretical analysis was conducted to investigate the total condensation heat transfer coefficients with NCG, and 16 dimensionless numbers were identified as input variables for machine learning models. Based on a filtered database consisting of 4377 data points extracted from 37 papers, the Spearman correlation coefficients were calculated to evaluate the relationship between the total heat transfer coefficients and the input variables, indicating the magnitude of the impact of the 16 dimensionless variables. Four machine learning models, namely Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost), Random Forest Regression (RFR), and Multilayer Perceptron (MLP), were developed to predict the total heat transfer coefficients for water and non-aqueous working fluids. The mean absolute percentage errors for the four models were 1.38%, 1.63%, 3.00%, and 4.42%, respectively, with the GBR model exhibiting the highest degree of accuracy. The determination of the application scope of these models was conducted by analyzing the value ranges for each dimensionless parameter and its corresponding frequency distribution. • Four machine-learning models were developed to predict condensation heat transfer. • Sixteen dimensionless variables were determined as input parameters for these models. • These models can predict condensation heat transfer under different working conditions. • Conditions were diverse in types of wall geometry, convection, working fluid and NCG. [ABSTRACT FROM AUTHOR]

Efficient detection of refrigerant leakage is of utmost importance for industrial refrigeration systems due to its potential to cause substantial impacts on system performance and the environment. Existing research on fault detection and diagnosis in refrigeration systems primarily revolves around solutions based on experimental or laboratory data. However, in the industrial use case, achieving accurate and early detection poses significant challenges. This paper reports on the development of a novel refrigerant leak detection method for industrial vapor compression refrigeration systems. Our method leverages real-world data obtained from operational installations, enabling us to assess its reliability and applicability. The proposed data-driven approach involves predicting the fault-free liquid level in the installation receiver and comparing the actual and predicted levels. In this work, we place emphasis on features and model selection. Dedicated metrics combined with a model comparison method are proposed to evaluate and compare the performance of commonly used regression models with two sets of features to determine the most effective one. Furthermore, we provide insights into the results obtained from the deployment of the proposed method in real-world industrial installations. • Refrigerant leak is detected through the receiver liquid level in the installation. • A regression model can be used to predict the fault-free liquid level. • System performance indicators were proven to be effective in long-term predictions. • Extremely randomized forest emerged as the top performer. [ABSTRACT FROM AUTHOR]

• Detailed energy and exergy analyses are conducted on the proposed ARC. • Explore the effects of varying refrigerant mass flow rate and ammonia concentration in strong and weak solutions on key performance parameters. • Employing the GBRT machine learning method and FHO approach in the ARC is highly recommended. • The GBRT model proves to be a reliable predictive tool with strong accuracy, offering valuable insights into system performance. • The generator is the main source of exergy destruction rate (50 %). A detailed analysis of energy and exergy is conducted on a single-effect solar ammonia–water (NH 3 –H 2 O) absorption refrigeration cycle (ARC) using TRNSYS and EES software. Considering the physical and chemical exergies, the exergy destruction rate (Ė D) in each component of the system is calculated, highlighting its contribution to the overall Ė D. The study explores the effects of varying refrigerant mass flow rate (ṁ ᵣ) and ammonia concentration in strong and weak solutions (X s and X w) on key performance parameters, including coefficient of performance (COP), exergy efficiency (Ė D), and overall Ė D across a range of generator temperatures (T g). In this study, a gradient boosting regression tree (GBRT) is employed as a supervised machine-learning technique for classification and regression problems, utilizing boosting to enhance conventional decision tree predictions. The Fire Hawk Optimizer (FHO) approach is also utilized to optimize performance parameters, maximizing COP and η η E while minimizing T g and Ė D. The GBRT models are developed using available experimental and simulation data, revealing relationships between variables (ṁ ᵣ, X s , X w , and T g) and outcomes (COP, Ė D , and overall Ė D). The results revealed that the generator exhibits considerable Ė D regardless of operating conditions, underscoring its pivotal role in the ARC. It emerges as the primary Ė D contributor (50 %), followed by the evaporator (17 %) and the absorber (15 %). However, Ė D associated with the recooler, pump, and expansion valves is negligible in comparison. Optimization results reveal that, when minimizing T g and Ė D , the highest COP and Ė D at T g of 373.15 K reach 0.8081 and 0.46, respectively. [ABSTRACT FROM AUTHOR]

Objective: This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD). Methods: A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening. Results: The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models. Conclusion: The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD. [ABSTRACT FROM AUTHOR]

Canola (Brassica napus L.) production is threatened by blackleg disease caused by Leptosphaeria maculans. Disease outcome is determined by interactions among pathogens, plants, farming practices, and environmental factors. Although the gene-for-gene interactions between the pathogen and its plant host are relatively clear, how precisely the pathogen interacts with the environment and farming practices is still poorly understood, making disease forecasting challenging for commercial farmlands. In recent years, artificial intelligence (AI) has been successful in forecasting disease risks based on environmental factors. In this study, we evaluated two AI methods and a data augmentation method to forecast disease risk using a dataset collected from 116 farmlands in Alberta in 2021 and 2022. We first assessed a machine learning model (support vector machine or SVM) and a deep-learning model (convolutional neural network or CNN) to predict blackleg severity based on five weather variables, flea beetle damage, root maggot damage, and crop-rotation variables. Both SVM and CNN predicted the disease risk with an accuracy of over 66%. The data augmentation method did not improve model performance. Flea beetle feeding and maggot damage contribute little to the model's performance, and omitting these data did not appear to affect the results. In contrast, crop rotation contributes substantially to model performance. The five weather variables contribute roughly equally to the model's performance, and removing any of the individual weather variables did not impact prediction ability for both models. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is omnipresent in our daily lives and generates questions about its future place in medicine, in general, and in hepatology in particular. Numerous articles have been published in recent years on the subject, highlighting the potential role of AI, particularly in radiology for the diagnosis of hepatocellular carcinoma and hepatic dysmorphia, as well as in pathology. The use of AI will probably expand and play an increasingly important role at different stages of patient care: screening for cirrhosis in general medicine, predicting the risk of developing advanced fibrosis or esophageal varices. AI could also help identify the ideal candidate for a liver transplant for cirrhosis linked to an alcohol use disorder and interfere in the doctor-patient relationship by responding in a "more empathetic" way to questions asked by patients. This mini-review presents some applications of AI in our specialty. [ABSTRACT FROM AUTHOR]

Copyright of Revue d'Orthopédie Dento-Faciale is the property of Parresia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The negative impact of stroke on society has led to a concerted effort to improve stroke management and diagnosis. Technological advances have led to the development of deep learning techniques that can be used to accurately detect and characterize brain damage caused by stroke. The ubiquitous growth of artificial intelligence and its medical applications has improved the efficiency of healthcare systems for patients requiring rapid care. Today, chronic diseases such as stroke are the leading cause of death worldwide. A stroke is a type of brain injury. Stroke lesions occur when a group of brain cells dies due to a lack of blood supply. Stroke damage can disrupt brain function, causing a wide range of symptoms such as weakness, disturbance of one or more senses and confusion. It is important to detect and characterize the damaged areas of brain tissue caused by stroke accurately, quickly and effectively in order to save the patient. Stroke diagnosis often relies on expensive imaging techniques such as CT and magnetic resonance imaging (MRI), which are expensive nevertheless CT is more available and accessible in some African hospitals in general and in Senegal in particular. We therefore use a CT dataset to automatically segment stroke lesions. We find that CNN combined with XGBoost can effectively detect, classify and characterize stroke-damaged areas. [ABSTRACT FROM AUTHOR]

In terms of party systems, Canada's system is an outlier. In our present work, we develop Richard Johnston's account of Canada's polarized pluralism in three ways. First, we link the literature on party systems to social identity theory. Second, we make an empirical contribution by directly testing Johnston's claim that intergroup affect plays a central role in shaping the dynamics of the party system. Using Canadian Election Study data from seven elections, we offer strong empirical support for the theory of polarized pluralism. Congruent with existing research, we find that the most important feature summarizing group-based affect in Canadian politics corresponds with the ideological left/right divide, but we also find that feelings toward groups on a second, uncorrelated axis (feelings toward Quebec and minority groups) shape vote choice. Yet our results show that fault lines in the polarized pluralist structure of the Canadian party system are emerging. [ABSTRACT FROM AUTHOR]

• A consolidated database is used to train four machine learning regression models. • The goal is to fix accuracy and generality in predicting heat transfer coefficient. • By sequentially excluding each database a new generality criterion is defined. • Generality of models is compared similar to the manner their accuracy is compared. This research concerns the development of universal models founded on machine learning to predict the condensation heat transfer coefficient. In this regard, a consolidated database has been employed encompassing 8340 data points which belong to the flow condensation inside mini/macro channels. The database consists of 25 working fluids with hydraulic diameters ranging from 0.42 to 20.8 mm, mass velocities ranging from 13.1 to 1400 Kg m −2 s −1 and reduced pressures ranging from 0.03 to 0.9. Four machine learning regression models namely artificial neural network (ANN), gradient-boosted regression (GBR), random forest regression (RFR) and support vector regression (SVR) are trained utilizing the database, and subsequently their accuracy and generality are compared. A multitude of dimensionless parameters are regarded as features, while three parameters specifically the heat transfer coefficient, Nusselt number and Nusselt number correction factor are each considered individually as targets. Within each model, the optimal values of the important hyper-parameters are tuned through appropriate search methods. In order to evaluate the generality of the models, the idea is to sequentially exclude each individual database from the consolidated database. The comparison of accuracy and generality of the models reveals that the RFR model using Nusselt number correction factor as target with a mean absolute relative deviation (MARD) of 3.01 % exhibits the highest level of accuracy, whereas the RFR model employing Nusselt number as target with a MARD for the predicted excluded values equal to 19.49 % demonstrates the superior generality. [ABSTRACT FROM AUTHOR]

Le présent article vise à proposer aux spécialistes en sciences humaines et sociales, en particulier à ceux qui traitent de corpus disposant de peu de données, des méthodes récemment développées en apprentissage machine, spécifiquement pour les besoins des sciences humaines. Nous nous attachons spécifiquement à la création d'espaces sémantiques vectoriels pour les langues anciennes.This article aims to provide humanists and social scientists, particularly those dealing with low-resource corpora, with recently developed machine learning methods specifically for the humanities. We focus specifically on the creation of vector semantic spaces for ancient languages.

• Energy and exergy analaysis of single and double-effect ammonia-water absorption refrigeration system powered by CPC collectors were invistigated. • Parametric optimzated based on particle swarm optimization and SVMR for different operating conditions were studied. • Machine learning was utilized to identify the optimal key performance parameters of the proposed system. • The optimization process findings showed that, at a Pe = 2.8 bar, Pg = 14.5 bar, Ta = 303.15 K, the maximum COP and exergy efficiency were 0.8483 and 0.3605, respectively, concerning the minimization of tg and te were 408 K and 267 K, respectively. • It is recommended to utilize various modeling tools, including EES and TRNSYS programs, as well as the SVMR approach as a machine learning algorithm to improve the performance of absorption chiller. The coefficient of performance (COP) and exergy efficiency of a single and double-effect ammonia-water absorption refrigeration system powered by compound parabolic concentrating collectors were analyzed under various operating situations. A novel method was proposed using support vector machine regression and particle swarm optimization to identify optimal operating parameters. The optimal pressure-temperature conditions, including evaporator pressure (Pe), generator pressure (Pg), and absorber temperature (Ta) that maximize the COP and exergy efficiency while minimizing generator temperature (Tg) and evaporator temperature (Te), were investigated. The generator temperature was the main independent variable, ranging from 370 to 470 K. The findings demonstrated that the gain in COP and exergy efficiency caused by raising the generator temperature to more than 430 K is not cost-effective. The COP increased when the evaporator temperature increased along the investigated range of generator temperatures but yielded lower exergy efficiency in all cases. The exergy destruction rate in condenser, pump, recooler, reheater, and expansion valves is insignificant compared to other components. The generator has the highest exergy destruction rate regardless of operating conditions, making it the most crucial component of the absorption system. The optimization process findings showed that, at Pe = 2.8 bar, Pg = 14.5 bar, and Ta = 303.15 K, the maximum COP and exergy efficiency were 0.8483 and 0.3605, respectively, concerning the minimization of Tg and Te, which were 408 and 267 K, respectively. The model produced an acceptable performance with a high prediction accuracy (coefficient of determination > 0.99 and mean square error < 0.0064). [ABSTRACT FROM AUTHOR]

• Precise predictive models for CO 2 frosting temperature were developed. • Machine learning algorithms of GPR and MLP were utilized for modeling. • The novel models showed excellent results for different natural gas mixtures. • The models provided excellent physical trends under different operating conditions. • The most effective operating factors on frosting temperature were identified. In this study, universal and precise predictive models for CO 2 frosting temperature in different natural gas mixtures were developed employing the machine learning algorithms of multilayer perceptron (MLP) and gaussian process regression (GPR). The models were verified by an extensive databank including 430 experimental samples collected from 7 published sources, enveloping a broad range of conditions in methane/CO 2 binary mixtures as well as the ternary mixtures of methane/CO 2 /ethane and methane/CO 2 /nitrogen. Both GPR and MLP models exhibited excellent predictions with total average absolute relative errors (AAREs) of 0.16% and 0.42%, and R2 values of 99.80% and 99.38%, respectively. The novel models use only 4 simple adjusted parameters, and they were found to be applicable for both binary and ternary mixtures with high precisions. In addition, the effects of each operating parameter on frosting temperature were studied, and the new models showed excellent physical trends. Subsequently, in order to provide more insight about the most effective factors on frosting temperature, a sensitivity analysis based on the present databank was performed. [ABSTRACT FROM AUTHOR]

Copyright of Romanian Journal of Intellectual Property Law / Revista Română de Dreptul Proprietăţii Intelectuale is the property of Universul Juridic Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cette étude propose à l’IMF ALPHA, ainsi surnommée pour des raisons d’anonymat et de respect de sa politique de confidentialité, une approche et un outil efficace pour fiabiliser davantage son processus d’octroi des prêts, jugé insuffisamment efficace.

Over the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy. [ABSTRACT FROM AUTHOR]

A classic goal in cognitive modelling is the integration of process and representation to form complete theories of human cognition (Estes, 1955). This goal is best encapsulated by the seminal work of Simon (1969) who proposed the parable of the ant to describe the importance of understanding the environment that a person is embedded within when constructing theories of cognition. However, typical assumptions in accounting for the role of representation in computational cognitive models do not accurately represent the contents of memory (Johns & Jones, 2010). Recent developments in machine learning and big data approaches to cognition, referred to as scaled cognitive modelling here, offer a potential solution to the integration of process and representation. This article will review standard practices and assumptions that take place in cognitive modelling, how new big data and machine learning approaches modify these practices, and the directions that future research should take. The goal of the article is to ground big data and machine learning approaches that are emerging in the cognitive sciences within classic cognitive theoretical principles to provide a constructive pathway towards the integration of cognitive theory with advanced computational methodology. [ABSTRACT FROM AUTHOR]

The creation of the relevant technology must be driven by educational demands. Even so, the human factor must always be taken into consideration. Exciting new technology and potent cures will continue to be produced by scientific research. The capacity of a digital computer, computer-controlled robot to carry out actions closely identified with intelligent beings is known as artificial intelligence (AI). The evolution of AI has been clearly accelerating over the past ten years, and dentistry has not been an exception. Dental AI is important for diagnosing patients, storing patient data, and evaluating genetic information to improve patient treatment. This applies particularly to oral medicine and radiography. A good understanding of technology adaption will not only contribute to better and more accurate patient care but will also lighten the workload of the physician. The need for sophisticated software to compute this data has arisen due to the massive growth in documented information as well as patient data. A new age in dentistry has emerged as a result of the convergence of artificial intelligence and digitization, and the field’s prospects for the future look quite bright. [ABSTRACT FROM AUTHOR]

• The performance of counter-flow Ranque-Hilsch Vortex Tube (RHVT) was modelled with respect to pressure, working fluid and nozzle specifications. • Linear Regression (LR), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Regression Trees (RT) and Ensembles of Trees (ET) prediction methods were used. • Optimizing the performance of counter-flow RHVT, it is aimed to fill the gap in the literature by using LR, SVM, GPR, RT and ET methods among the machine learning methods. This study planned to model a counter-flow Ranque-Hilsch Vortex Tube (RHVT) using compressed air and oxygen gas by machine learning to separate the thermal temperature. From within machine learning models, Linear Regression (LR), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Regression Trees (RT), and Ensemble of Trees (ET) were preferred. By leaving the outlet control valve on the hot fluid side fully open, data were received for each material and nozzle at RHVT with inlet pressure starting from 150 kPa and up to 700 kPa at 50 kPa intervals. In the counter flow RHVT, the lack in the literature has been tried to be eliminated by modeling the RHVT by finding the difference (ΔT) between the temperature of the cold flow exiting (T c) and the temperature of the leaving hot flow (T h). When analyzing each of the machine learning models in the study, 80% of all data was used as training data, 20% of all data was used for the test, 70% of all data was used as training data, and 30% of all data was used for the test. As a result of the analysis, when both air and oxygen fluids were used, the GPR method gave the best result with 0.99 among the machine learning models in two different test intervals of 70%–30% and 80%–20%. The success of other machine learning models differed according to the fluid and model used. [ABSTRACT FROM AUTHOR]

In low-and middle-income countries, the cold chain that supports vaccine storage and distribution is vulnerable due to insufficient infrastructure and interoperable data. To bolster these networks, we developed a convolutional neural network-based fault detection method for vaccine refrigerators using datasets synthetically generated by thermodynamic modeling. We demonstrate that these thermodynamic models can be calibrated to real cooling systems in order to identify system-specific faults under a diverse range of operating conditions. If implemented on a large scale, this portable, flexible approach has the potential to increase the fidelity and lower the cost of vaccine distribution in remote communities. • Thermodynamic simulations can be used to generate datasets of refrigeration faults. • These time-series datasets can be used to train convolutional neural networks. • This approach can support the cold chain for vaccine distribution. [ABSTRACT FROM AUTHOR]

• A transient model of frost growth on a flat plate was developed. • Five regression models (three traditional and two machine-learning) were used. • The frost thickness was predicted for different surface characteristics. • The model produced good predictions despite the nonlinear complex growth mechanism. A transient model of frost growth on a flat plate was developed, taking the surface characteristics of the plate into consideration. Five regression models were applied, three traditional (Multiple Linear, LASSO, Ridge) and two machine-learning (Artificial Neural Network, Support Vector Machine) regression models. The training database was established using data extracted from previously published experimental studies. The experimental data consisted of forced convection (1067 data points) and natural convection data (992 data points). The model outputs were then evaluated using common statistical indicators and the best performing model was selected. The highest R2 values for the artificial neural network were 0.9899 and 0.9944 for forced and natural convection, respectively, after removal of outliers. The frost thickness was predicted for various conditions, including different surface characteristics. The model produced good predictions despite the occurrence of nonlinear complex growth mechanism dependent on various conditions in the dataset obtained. [ABSTRACT FROM AUTHOR]

Copyright of Romanian Journal of Intellectual Property Law / Revista Română de Dreptul Proprietăţii Intelectuale is the property of Universul Juridic Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

L’usage d’outils intelligents d’aide à la conception redéfinit les pratiques en conception architecturale. Pour permettre aux non-experts d’appréhender les fonctions et les combinaisons potentielles de ces différentes formes d’intelligence artificielle, un modèle de tableau périodique et adaptatif des outils intelligents est proposé et discuté. Pour révéler la pertinence du modèle, différentes variétés algorithmiques sont présentées et placées dans le tableau.

• A novel IoT intelligent agent based cloud management system was proposed to improve energy efficiency and safety in building energy systems. • The residuals of actual condition and predicted fault-free condition were calculated as input of FDD model to improve generalization performance. • A k-fold cross validation method was presented to especially testify the generalization capacity in different operation conditions. • The diagnostic accuracy was over 99% and 71% in source conditions and cross conditions respectively. • The proposed smart cloud management framework can be applied in general energy systems. Collection of extensive sensor data from HVAC chillers has facilitated the development of smart cloud management systems to increase the energy efficiency of buildings. However, limited by computing resource and algorithm performance, smart energy management technique and its fault diagnosis method in the literature suffer from low response speed and generalization capacity. To this end, a novel IoT intelligent agent based cloud management system for HVAC systems to improve operational efficiency and safety. The smart cloud management system integrates fundamental framework with machine learning algorithm for fault detection and diagnosis. After preprocessing the data collected by IoT agents, an algorithm is constructed to predict virtual sensor values based on fault-free conditions. The calculated residuals of the actual values and virtual values on both normal and faulty conditions are used as inputs to an extreme gradient boosting algorithm that predicts the fault level. The diagnosis results are compared with other methods such as support vector machine, multi-layer perceptron and random forest. The k-fold cross validation indicated that the proposed methodology can achieve superior overall generalization performance with 67.8%, 70.5% and 71.6% while that of the conventional method were 59.4%, 63.9% and 68.3%. This study will contribute to the practical applications of smart cloud management system in building energy systems. [ABSTRACT FROM AUTHOR]

Linear model reduction techniques design offline low-dimensional subspaces that are tailored to the approximation of solutions to a parameterized partial differential equation, for the purpose of fast online numerical simulations. These methods, such as the Proper Orthogonal Decomposition (POD) or Reduced Basis (RB) methods, are very effective when the family of solutions has fast-decaying Karhunen-- Loeve eigenvalues or Kolmogorov widths, reflecting the approximability by finite-dimensional linear spaces. On the other hand, they become ineffective when these quantities have a slow decay, in particular for families of solutions to hyperbolic transport equations with parameter-dependent shock positions. The objective of this work is to explore the ability of nonlinear model reduction to circumvent this particular situation. To this end, we first describe particular notions of nonlinear widths that have a substantially faster decay for the aforementioned families. Then, we discuss a systematic approach for achieving better performance via a nonlinear reconstruction from the first coordinates of a linear reduced model approximation, thus allowing us to stay in the same "classical" framework of projection-based model reduction. We analyze the approach and report on its performance for a simple and yet instructive univariate test case. [ABSTRACT FROM AUTHOR]

On the one hand, technological advances and their enthusiastic uptake by government entities are seen as a push toward a Canadian dystopic state, with friendly bureaucrats being replaced by impassive machines. On the other hand, embracing technology is considered a confident move of the Canadian administrative state toward an utopian low-cost, high-impact decision making process. I will suggest in this paper that the truth--for the moment, at least--lies somewhere between the extremes of dystopia and utopia. In the federal public administration, technology is being deployed in a variety of areas, but rarely, if ever, displacing human decision making. Indeed, technology tends to be leveraged in areas of public policy that don't involve any settling of benefits, statuses, licenses, and so on. We are still a long way from sophisticated machine learning tools deciding whether marriages are genuine, whether taxpayers are compliant or whether nuclear facilities are safe. The reality is more down to earth. In this paper, I map out the uses of algorithms and machine learning in the federal public administration in Canada. I will briefly explain my methodology in Part I; in Part II, I identify seven different use cases, which I describe with the aid of representative examples, and offer some critical reflections. [ABSTRACT FROM AUTHOR]

Copyright of Épidémiologie et Santé Animale is the property of Association pour l'Etude de l'Epidemiologie des Maladies Animales (AEEMA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• FDD methods and process discussed in detail. • IoT and cloud technology for FDD is discussed. • Presents the detailed discussion on fault modeling. • Challenges and future prospects of FDD systems are discussed. This review study examines the latest research and developments in the fault detection and diagnostics of Heating Ventilation and Air Conditioning (HVAC) systems. This review describes the basics of Fault detection and diagnostics in the HVAC systems, and the methods developed for the FDD have been discussed in detail. Machine learning methods have become prevalent in the FDD. Supervised and unsupervised machine learning methods have been discussed. Data preprocessing and feature selection are the two essential steps of the FDD process using machine learning. Fault prognosis has also been discussed in brief. Further, fault modeling and its applications in the FDD have been covered. Various approaches have been used to model the different faults in HVAC systems. This paper reviews FDD systems based on four aspects, i.e., detection, diagnostics, prognostics, and modeling of faults. Then this review provides a comparative study of different FDD methods. Finally, the paper discusses future challenges for the more efficient FDD systems to reduce the energy consumption of the HVAC systems. [ABSTRACT FROM AUTHOR]

• Diagnosis of twenty faults in refrigeration systems by one classifier is proposed. • CNN, LDA, SVM, PCA-SVM, LDA-SVM classifiers are compared. • Variation of training data is very important to achieve better classification result. • CNN and PCA-SVM can not diagnose the faults satisfactorily. • SVM obtained the best verification result. The functionality of industrial refrigeration systems is important for environment-friendly companies and organizations, since faulty systems can impact human health by lowering food quality, cause pollution, and even lead to increased global warming. Therefore, in this industry, there is a high demand among manufacturers for early and automatic fault diagnosis. In this paper, different machine learning classifiers are tested to find the best solution for diagnosing twenty faults possibly encountered in such systems. All sensor faults and some relevant component faults are simulated in a high fidelity Matlab/Simscape model of the system, which has previously been used for controller development and verification. In this work, Convolutional Neural Networks, Support Vector Machines (SVM), Principal Components Analysis-SVM, Linear Discriminant Analysis-SVM, and Linear Discriminant Analysis classifiers are compared. The results indicate that the fault detection reliability of the algorithms highly depends on how well the training data covers the operation regime. Furthermore, it is found that a well-trained SVM can simultaneously classify twenty types of fault with 95% accuracy when the verification data is taken from different system configurations. [ABSTRACT FROM AUTHOR]

• Tailored method predicting properties of F/Cl-containing refrigerant is proposed. • The present models outperform the conventional methods in calculation. • An interpretable method was adopted to interpret the proposed models. In this work, molecular groups were used as the descriptor of molecular structures, combining with multi-layer perceptron algorithm to establish the prediction models of boiling point, critical temperature and critical pressure of fluorine/chlorine-containing refrigerants. The absolute average deviation values of the three prediction models between the calculated and experimental data in total datasets are 2.93%, 3.13% and 4.46%, respectively. Furthermore, in order to understand how the group features influence the prediction of properties, an interpretable method, Shapely Additive exPlanation (SHAP) was employed to interpret the proposed models, which computed the contributions of group features, and verified that the present models effectively building the relationship between the molecular groups and thermophyical properties. [ABSTRACT FROM AUTHOR]

During the first millennium ad, Europe saw much socio-environmental change, which is reflected in the archaeological and palaeoecological evidence. Using published and new isotope data from across western Europe, the author examines changing resource use from c.ad 350 to 1200. The geographical limits of millet and substantial marine consumption are identified and comparisons between childhood and adult diets made across regions. Cross-cultural interaction at a broad scale is emphasized and patterns within early medieval England form the subject of an in-depth case study. While doubt is cast onto the uptake of marine resource consumption in England following the Fish Event Horizon, changes in agricultural practices, the impact of Christianization, and the role of freshwater fish in diets are explored. The author's hierarchical meta-analytical approach enables identification of human–environment interactions, with significant implications for changing foodways in Europe during the first millennium ad. [ABSTRACT FROM AUTHOR]

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