Evolving brand-name and generic drug competition may warrant a revision of the Hatch-Waxman Act.

The evolution of pharmaceutical competition since Congress passed the Hatch-Waxman Act in 1984 raises questions about whether the act's intended balance of incentives for cost savings and continued innovation has been achieved. Generic drug usage and challenges to brand-name drugs' patents have increased markedly, resulting in greatly increased cost savings but also potentially reduced incentives for innovators. Congress should review whether Hatch-Waxman is achieving its intended purpose of balancing incentives for generics and innovation. It also should consider whether the law should be amended so that some of its provisions are brought more in line with recently enacted legislation governing approval of so-called biosimilars, or the corollary for biologics of generic competition for small-molecule drugs.

Percutaneous Revision of a Testicular Prosthesis is Safe, Cost-effective, and Provides Good Patient Satisfaction.

Office-based percutaneous revision of a testicular prosthesis has never been reported. A patient received a testicular prosthesis but was dissatisfied with the firmness of the implant. In an office setting, the prosthesis was inflated with additional fluid via a percutaneous approach. Evaluated outcomes included patient satisfaction, prosthesis size, recovery time, and cost savings. The patient was satisfied, with no infection, leak, or complication after more than 1 year of follow-up, at significantly less cost than revision surgery. Percutaneous adjustment of testicular prosthesis fill-volume can be safe, inexpensive, and result in good patient satisfaction.

A novel neural/symbolic hybrid approach to heuristically optimized fuel allocation and automated revision of heuristics in nuclear engineering

FUELCON is an expert system for optimized refueling design in nuclear engineering. This task is crucial for keeping down operating costs at a plant without compromising safety. FUELCON proposes sets of alternative configurations of allocation of fuel assemblies that are each positioned in the planar grid of a horizontal section of a reactor core. Results are simulated, and an expert user can also use FUELCON to revise rulesets and improve on his or her heuristics. The successful completion of FUELCON led this research team into undertaking a panoply of sequel projects, of which we provide a meta-architectural comparative formal discussion. In this paper, we demonstrate a novel adaptive technique that learns the optimal allocation heuristic for the various cores. The algorithm is a hybrid of a fine-grained neural network and symbolic computation components. This hybrid architecture is sensitive enough to learn the particular characteristics of the ‘in-core fuel management problem’ at hand, and is powerful enough to use this information fully to automatically revise heuristics, thus improving upon those provided by a human expert.

Distributed belief revision as applied within a descriptive model of jury deliberations

Belief revision is a well-research topic within AI. We argue that the new model of distributed belief revision as discussed here is suitable for general modelling of judicial decision making, along with extant approach as known from jury research. The new approach to belief revision is of general interest, whenever attitudes to information are to be simulated within a multi-agent environment with agents holding local beliefs yet by interaction with, and influencing, other agents who are deliberating collectively. In the approach proposed, it's the entire group of agents, not an external supervisor, who integrate the different opinions. This is achieved through an election mechanism, The principle of "priority to the incoming information" as known from AI models of belief revision are problematic, when applied to factfinding by a jury. The present approach incorporates a computable model for local belief revision, such that a principle of recoverability is adopted. By this principle, any previously held belief must belong to the current cognitive state if consistent with it. For the purposes of jury simulation such a model calls for refinement. Yet we claim, it constitutes a valid basis for an open system where other AI functionalities (or outer stiumuli) could attempt to handle other aspects of the deliberation which are more specifi to legal narrative, to argumentation in court, and then to the debate among the jurors.

Belief revision as applied within a descriptive model of jury deliberations

Belief revision is a well-researched topic within Artificial Intelligence (AI). We argue that the new model of belief revision as discussed here is suitable for general modelling of judicial decision making, along with the extant approach as known from jury research. The new approach to belief revision is of general interest, whenever attitudes to information are to be simulated within a multi-agent environment with agents holding local beliefs yet by interacting with, and influencing, other agents who are deliberating collectively. The principle of 'priority to the incoming information', as known from AI models of belief revision, is problematic when applied to factfinding by a jury. The present approach incorporates a computable model for local belief revision, such that a principle of recoverability is adopted. By this principle, any previously held belief must belong to the current cognitive state if consistent with it. For the purposes of jury simulation such a model calls for refinement. Yet, we claim, it constitutes a valid basis for an open system where other AI functionalities (or outer stimuli) could attempt to handle other aspects of the deliberation which are more specific to legal narratives, to argumentation in court, and then to the debate among the jurors.

Numeracy skills for nurses: revision and review

Introduces a revision and working examples of the numeracy skills key to the role of nurses working with older people in nursing and residential homes.