System f2lp – computing answer sets of first-order formulas

Abstract. We present an implementation of the general language of stable models proposed by Ferraris, Lee and Lifschitz. Under certain conditions, system f2lp turns a first-order theory under the stable model semantics into an answer set program, so that existing answer set solvers can be used for computing the general language. Quantifiers are first eliminated and then the resulting quantifier-free formulas are turned into rules. Based on the relationship between stable models and circumscription, f2lp can also serve as a reasoning engine for general circumscriptive theories. We illustrate how to use f2lp to compute the circumscriptive event calculus.

M3DISEEN: A Novel Machine Learning Approach for Predicting the 3D Printability of Medicines

Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) 3-dimensional printing (3DP) has made significant advancements in the field of oral drug delivery with personalized drug-loaded formulations being designed, developed and dispensed for the needs of the patient. However, the optimization of the fabrication parameters is a time-consuming, empirical trial approach, requiring expert knowledge. Here, M3DISEEN, a web-based pharmaceutical software, was developed to accelerate FDM 3D printing, which includes producing filaments by hot melt extrusion (HME), using AI machine learning techniques (MLTs). In total, 614 drug-loaded formulations were designed from a comprehensive list of 145 different pharmaceutical excipients, 3D printed and assessed in-house. To build the predictive tool, a dataset was constructed and models were trained and tested at a ratio of 75:25. Significantly, the AI models predicted key fabrication parameters with accuracies of 76% and 67% for the printability and the filament characteristics, respectively. Furthermore, the AI models predicted the HME and FDM processing temperatures with a mean absolute error of 8.9 °C and 8.3 °C, respectively. Strikingly, the AI models achieved high levels of accuracy by solely inputting the pharmaceutical excipient trade names. Therefore, AI provides an effective holistic modeling technology and software to streamline and advance 3DP as a significant technology within drug development. M3DISEEN is available at (http://m3diseen.com/predictions/)

Machine learning predicts 3D printing performance of over 900 drug delivery systems

Three-dimensional printing (3DP) is a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy trial-and-error approach in optimization. Artificial intelligence (AI) is a technology that could revolutionize pharmaceutical 3DP through analyzing large datasets. Herein, literature-mined data for developing AI machine learning (ML) models was used to predict key aspects of the 3DP formulation pipeline and in vitro dissolution properties. A total of 968 formulations were mined and assessed from 114 articles. The ML techniques explored were able to learn and provide accuracies as high as 93% for values in the filament hot melt extrusion process. In addition, ML algorithms were able to use data from the composition of the formulations with additional input features to predict the drug release of 3D printed medicines. The best prediction was obtained by an artificial neural network that was able to predict drug release times of a formulation with a mean error of ±24.29 min. In addition, the most important variables were revealed, which could be leveraged in formulation development. Thus, it was concluded that ML proved to be a suitable approach to modelling the 3D printing workflow

Predicting pharmaceutical inkjet printing outcomes using machine learning

Inkjet printing has been extensively explored in recent years to produce personalised medicines due to its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films to complex polydrug implants. However, the multi-factorial nature of the inkjet printing process makes formulation (e.g., composition, surface tension, and viscosity) and printing parameter optimization (e.g., nozzle diameter, peak voltage, and drop spacing) an empirical and time-consuming endeavour. Instead, given the wealth of publicly available data on pharmaceutical inkjet printing, there is potential for a predictive model for inkjet printing outcomes to be developed. In this study, machine learning (ML) models (random forest, multilayer perceptron, and support vector machine) to predict printability and drug dose were developed using a dataset of 687 formulations, consolidated from in-house and literature-mined data on inkjet-printed formulations. The optimized ML models predicted the printability of formulations with an accuracy of 97.22%, and predicted the quality of the prints with an accuracy of 97.14%. This study demonstrates that ML models can feasibly provide predictive insights to inkjet printing outcomes prior to formulation preparation, affording resource- and time-savings

Axiomatic systems and topological semantics for intuitionistic temporal logic

We propose four axiomatic systems for intuitionistic linear temporal logic and show that each of these systems is sound for a class of structures based either on Kripke frames or on dynamic topological systems. Our topological semantics features a new interpretation for the `henceforth' modality that is a natural intuitionistic variant of the classical one. Using the soundness results, we show that the four logics obtained from the axiomatic systems are distinct. Finally, we show that when the language is restricted to the `henceforth'-free fragment, the set of valid formulas for the relational and topological semantics coincide

Editor's Note

Artificial Intelligence has become nowadays one of the main relevant technologies that is driven us to a new revolution, a change in society, just as well as other human inventions, such as navigation, steam machines, or electricity did in our past. There are several ways in which AI might be developed, and the European Union has chosen a path, a way to transit through this revolution, in which Artificial Intelligence will be a tool at the service of Humanity. That was precisely the motto of the 2020 European Conference on Artificial Intelligence (“Paving the way towards Human-Centric AI”), of which these special issue is a selection of the best papers selected by the organizers of some of the Workshops in ECAI 2020

Correlações dos teores de fósforo nos solos com respostas de micro-parcelas de milho, na zona cacaueira da Bahia

Correlations were made between soil phosphorus content and yield of corn on soils with and without exchangeable aluminum. Phosphorus levels were established by applications on microplots from which corn yields were measured. The correlations describe critical levels of soil phosphorus in relation to effect on corn yield.Foram estudadas correlações, por equações lineares e logarítmicas, entre os teores de fósforo extraído por diferentes métodos em solos que apresentam e não apresentam alumínio trocável, na zona cacaueira da Bahia, com as respostas relativas de microparcelas de milho à fertilização fosfatada. O maior coeficiente de correlação encontrado foi 0,700, significativo ao nível de 0,1% de probabilidades. Indicam níveis críticos de fósforo para diferentes respostas relativas

Forgetting in Answer Set Programming with Anonymous Cycles

FORGET (PTDC/CCI-INF/32219/2017). NOVA LINCS (UID/CEC/04516/2019).It is now widely accepted that the operation of forgetting in the context of Answer Set Programming [10, 18] is best characterized by the so-called strong persistence, a property that requires that all existing relations between the atoms not to be forgotten be preserved. However, it has been shown that strong persistence cannot always be satisfied. What happens if we must nevertheless forget? One possibility that has been explored before is to consider weaker versions of strong persistence, although not without a cost: some relations between the atoms not to be forgotten are broken in the process. A different alternative is to enhance the logical language so that all such relations can be maintained after the forgetting operation. In this paper, we borrow from the recently introduced notion of fork [1] – a conservative extension of Equilibrium Logic and its monotonic basis, the logic of Here-and-There – which has been shown to be sufficient to overcome the problems related to satisfying strong persistence. We map this notion into the language of logic programs, enhancing it with so-called anonymous cycles, and we introduce a concrete syntactical forgetting operator over this enhanced language that we show to always obey strong persistence.publishe