Sequential Deliberation for Social Choice

In large scale collective decision making, social choice is a normative study of how one ought to design a protocol for reaching consensus. However, in instances where the underlying decision space is too large or complex for ordinal voting, standard voting methods of social choice may be impractical. How then can we design a mechanism - preferably decentralized, simple, scalable, and not requiring any special knowledge of the decision space - to reach consensus? We propose sequential deliberation as a natural solution to this problem. In this iterative method, successive pairs of agents bargain over the decision space using the previous decision as a disagreement alternative. We describe the general method and analyze the quality of its outcome when the space of preferences define a median graph. We show that sequential deliberation finds a 1.208- approximation to the optimal social cost on such graphs, coming very close to this value with only a small constant number of agents sampled from the population. We also show lower bounds on simpler classes of mechanisms to justify our design choices. We further show that sequential deliberation is ex-post Pareto efficient and has truthful reporting as an equilibrium of the induced extensive form game. We finally show that for general metric spaces, the second moment of of the distribution of social cost of the outcomes produced by sequential deliberation is also bounded

Rationalisation of Profiles of Abstract Argumentation Frameworks

Different agents may have different points of view. This can be modelled using different abstract argumentation frameworks, each consisting of a set of arguments and a binary attack-relation between them. A question arising in this context is whether the diversity of views observed in such a profile of argumentation frameworks is consistent with the assumption that every individual argumentation framework is induced by a combination of, first, some basic factual attack-relation between the arguments and, second, the personal preferences of the agent concerned. We treat this question of rationalisability of a profile as an algorithmic problem and identify tractable and intractable cases. This is useful for understanding what types of profiles can reasonably be expected to come up in a multiagent system

Method Development and Validation of an Inline Process Analytical Technology Method for Blend Monitoring in the Tablet Feed Frame Using Raman Spectroscopy

Inline process analytical technology sensors are the key elements to enable continuous manufacturing. They facilitate real-time monitoring of critical quality attributes of both intermediate materials and finished products. The aim of this study was to demonstrate method development and validation for inline and offline calibration strategies to determine the blend content during tablet compression via Raman spectroscopy. An inline principal component regression model was developed from Raman spectra collected in the feed frame. At the same time, an offline study was conducted over a small amount of the calibration blends using an in-house moving powder setup to simulate the environment of the feed frame. The model developed offline was able to predict the active ingredient content after a bias correction and used only a fraction of the material. The offline method can serve as a simple method to facilitate calibration development when the time and access to the press is limited. The study takes into consideration, the necessary components of method development and offers perspectives on the validation of an inline process analytics method. Method testing and validation was performed for the inline process analytical technology method. The established Raman method was demonstrated as suitable for the determination of bulk assay of the active ingredient in powders inside the feed frame for use during batch and continuous manufacturing processes

Fabric Softener–Cellulose Nanocrystal Interaction: A Model for Assessing Surfactant Deposition on Cotton

There is currently a renewed interest for improving household and personal-care formulations to provide more environment-friendly products. Fabric conditioners used as softeners have to fulfill a number of stability and biodegradability requirements. They should also display significant adsorption on cotton under the conditions of use. The quantification of surfactant adsorption remains however difficult because the fabric-woven structure is complex and deposited amounts are generally small. Here, we propose a method to evaluate cellulose–surfactant interactions with increased detection sensitivity. The method is based on the use of cellulose nanocrystals (CNCs) in lieu of micron-sized fibers or yarns, combined with different techniques, including light scattering, optical and electron microscopy, and electrophoretic mobility. CNCs are rod-shaped nanoparticles in the form of 200 nm laths that are negatively charged and can be dispersed in bulk solutions. In this work, we use a double-tailed cationic surfactant present in fabric softener. Results show that the surfactants self-assemble into unilamellar, multivesicular, and multilamellar vesicles, and the interaction with CNCs is driven by electrostatics. Mutual interactions are strong and lead to the formation of large-scale aggregates, where the vesicles remain intact at the cellulose surface. The technique developed here could be exploited to rapidly assess the fabric conditioner efficiency obtained by varying the nature and content of their chemical additives

Fabric Softener–Cellulose Nanocrystal Interaction: A Model for Assessing Surfactant Deposition on Cotton

There is currently a renewed interest for improving household and personal-care formulations to provide more environment-friendly products. Fabric conditioners used as softeners have to fulfill a number of stability and biodegradability requirements. They should also display significant adsorption on cotton under the conditions of use. The quantification of surfactant adsorption remains however difficult because the fabric-woven structure is complex and deposited amounts are generally small. Here, we propose a method to evaluate cellulose–surfactant interactions with increased detection sensitivity. The method is based on the use of cellulose nanocrystals (CNCs) in lieu of micron-sized fibers or yarns, combined with different techniques, including light scattering, optical and electron microscopy, and electrophoretic mobility. CNCs are rod-shaped nanoparticles in the form of 200 nm laths that are negatively charged and can be dispersed in bulk solutions. In this work, we use a double-tailed cationic surfactant present in fabric softener. Results show that the surfactants self-assemble into unilamellar, multivesicular, and multilamellar vesicles, and the interaction with CNCs is driven by electrostatics. Mutual interactions are strong and lead to the formation of large-scale aggregates, where the vesicles remain intact at the cellulose surface. The technique developed here could be exploited to rapidly assess the fabric conditioner efficiency obtained by varying the nature and content of their chemical additives