HOTSPOT: An ad hoc teamwork platform for mixed human-robot teams.

Ad hoc teamwork is a research topic in multi-agent systems whereby an agent (the "ad hoc agent") must successfully collaborate with a set of unknown agents (the "teammates") without any prior coordination or communication protocol. However, research in ad hoc teamwork is predominantly focused on agent-only teams, but not on agent-human teams, which we believe is an exciting research avenue and has enormous application potential in human-robot teams. This paper will tap into this potential by proposing HOTSPOT, the first framework for ad hoc teamwork in human-robot teams. Our framework comprises two main modules, addressing the two key challenges in the interaction between a robot acting as the ad hoc agent and human teammates. First, a decision-theoretic module that is responsible for all task-related decision-making (task identification, teammate identification, and planning). Second, a communication module that uses natural language processing to parse all communication between the robot and the human. To evaluate our framework, we use a task where a mobile robot and a human cooperatively collect objects in an open space, illustrating the main features of our framework in a real-world task.

Machine Learning Determination of New Hammett's Constants for meta- and para-Substituted Benzoic Acid Derivatives Employing Quantum Chemical Atomic Charge Methods.

Hammett's constants σ quantify the electron donor or electron acceptor power of a chemical group bonded to an aromatic ring. Their experimental values have been successfully used in many applications, but some are inconsistent or not measured. Therefore, developing an accurate and consistent set of Hammett's values is paramount. In this work,we employed different types of machine learning (ML) algorithms combined with quantum chemical calculations of atomic charges to predict theoretically new Hammett's constants σm, σp, σm0, σp0, σp+, σp-, σR, and σI for 90 chemical donor or acceptor groups. New σ values (219), including previously unknown 92, are proposed. The substituent groups were bonded to benzene and meta- and para-substituted benzoic acid derivatives. Among the charge methods (Mulliken, Löwdin, Hirshfeld, and ChelpG), Hirshfeld showed the best agreement for most kinds of σ values. For each type of Hammett constant, linear expressions depending on carbon charges were obtained. The ML approach overall showed very close predictions to the original experimental values, with meta- and para-substituted benzoic acid derivative values showing the most accurate values. A new consistent set of Hammett's constants is presented, as well as simple equations for predicting new values for groups not included in the original set of 90.

Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives.

We decomposed density functional theory charge densities of 53 nitroaromatic molecules into atom-centered electric multipoles using the distributed multipole analysis that provides a detailed picture of the molecular electronic structure. Three electric multipoles, (the charge of the nitro groups), (the total dipole, i.e., polarization, of the nitro groups), (the total electron delocalization of the C ring atoms), and the number of explosophore groups (#NO2) were selected as features for a comprehensive machine learning (ML) investigation. The target property was the impact sensitivity h50 (cm) values quantified by drop-weight measurements, with a large h50 (e.g., 150 cm) indicating that an explosive is insensitive and vice versa. After a preliminary screening of 42 ML algorithms, four were selected based on the lowest root mean square errors: Extra Trees, Random Forests, Gradient Boosting, and AdaBoost. Compared to experimental data, the predicted h50 values of molecules having very different sensitivities for the four algorithms have differences in the range 19-28%. The most important properties for predicting h50 are the electron delocalization in the ring atoms and the polarization of the nitro groups with averaged weights of 39% and 35%, followed by the charge (16%) and number (10%) of nitro groups. A significant result is how the contribution of these properties to h50 depends on their actual sensitivities: for the most sensitive explosives (h50 up to ∼50 cm), the four properties contribute to reducing h50, and for intermediate ones (∼50 cm ≲ h50 ≲ 100 cm) #NO2 and contribute to increasing it and the other two properties to reducing it. For highly insensitive explosives (h50 ≳ 200 cm), all four properties essentially contribute to increasing it. These results furnish a consistent molecular basis of the sensitivities of known explosives that also can be used for developing safer new ones.

Interfacial Stress and Bond Strength of Bulk-Fill or Conventional Composite Resins to Dentin in Class II Restorations.

The aim was to evaluate the microtensile bond strength (mTBS) to dentin and interfacial stress in a class II cavity restored with bulk-fill or conventional composite resins and the margin interfaces. Vertical slot class II cavities in the mesial face, with the gingival end in dentin, were prepared in 72 third molars, being divided into groups (n=24): G1-Tetric N-Ceram; G2-Tetric N-Ceram Bulk-Fill; G3-SonicFill. Clearfil SE Bond adhesive system was used in all groups. Half of the teeth in each group (n=12) were submitted to thermo-mechanical cycling (TMC). Restored teeth (n=9) were cut perpendicular to obtain beams, which were submitted to a mTBS test in an EMIC machine. The cervical margins in dentin of the restored teeth (n=3) were assessed using SEM through epoxy resin replicas as well as the section of the restoration. Interfacial stresses after load application were calculated by 2D finite element analysis. The mTBS means-MPa followed by different letters represent statistical difference by ANOVA and Games-Howell's test (p<0.05): Without TMC: G1-15.68±6.10a; G2-10.08±5.21ab; G3-7.98±3.76b. With TMC: G1-9.70±5.52a; G2-5.79±1.42a; G3-4.37±1.87a. Interfacial stress (MPa) was 4.4 for SonicFill, 3.9 for Tetric N-Ceram, and 3.5 for Tetric N-Ceram Bulk-Fill. SEM images showed continuous margins for all composite resin restorations. It was possible to conclude that SonicFill obtained a slightly higher interfacial stress and lower bond strength to dentin in comparison with Tetric N-Ceram and Tetric N-Ceram Bulk-Fill. Continuous margin interfaces were obtained for Tetric N-Ceram, Tetric N-Ceram Bulk-Fill, and SonicFill. However, voids were observed in the SonicFill restorations.

Condrosarcoma con componente mesequimal adicional

Reportamos el caso de um paciente masculino de 32 años de edad a quien se le diagnosticó condrosarcoma com componente mesenquimal adicional (CCMA). Revisamos la literatura sobre dicha neoplasia haciendo énfasis en el diagnóstico diferencial con otras neoplasias, debido a su importância pronóstica