The lambda point experiment in microgravity

The motivation and potential for performing very high resolution measurements of the heat capacity singularity at the lambda point of helium in microgravity conditions was briefly discussed. It is clear that tests extending deep into the asymptotic region can be performed, where the theoretical predictions take on their simplest form. This advantageous situation should lead to a major improvement in the understanding of the range of applicability of current theoretical ideas in this field. The lambda transition holds out the prospect of giving the maximum advance of any system, and with the application of cryogenic techniques, the potential of this system can be realized. The technology for the initial experiments is already developed, and results could be obtained in 1990

A New Limit on Signals of Lorentz Violation in Electrodynamics

We describe the results of an experiment to test for spacetime anisotropy terms that might exist from Lorentz violations. The apparatus consists of a pair of cylindrical superconducting cavity-stabilized oscillators operating in the TM_{010} mode with one axis east-west and the other vertical. Spatial anisotropy is detected by monitoring the beat frequency at the sidereal rate and its first harmonic. We see no anisotropy to a part in 10^{13}. This puts a comparable bound on four linear combinations of parameters in the general Standard Model extension, and a weaker bound of <4 x 10^{-9} on three others.Comment: 4 pages, 3 figures, 2 table

Unlocking Knowledge to Benefit the Patient : How Connecting KM and QRM Can Strengthen Science and Risk-Based Decision Making

This thesis explored knowledge management effectiveness in the pharmaceutical sector and included an examination of the critical relationship between knowledge management (KM) and quality risk management (QRM) as the dual enablers of an effective pharmaceutical quality system. The primary research objectives were to improve understanding and effectiveness of the interdependency between KM and QRM and to improve knowledge management across the pharmaceutical product lifecycle, starting with a focus on knowledge transfer during technology transfer. The thesis explored how improved KM across the product lifecycle coupled with thoughtful and intentional connectivity between KM and QRM as defined by this study could lead to more informed risk-based decision making and ultimately help benefit patients. This research study employed a variety of methods, including literature review, expert interviews, philosophical dialogue, focus groups, and case studies as a means to include a large number of stakeholders across the pharmaceutical sector. The study progress was disseminated through a variety of methods and channels including several peer-reviewed papers and conference presentations as a means to solicit input and feedback. The research findings verify that while KM and QRM are considered highly interdependent in theory, in practice they are – at best – partially integrated. This suggests the industry is not leveraging the best knowledge available to inform QRM, leading to sub-optimal risk-based decision making. Furthermore, knowledge created during QRM activities may not be effectively managed. When considering technology transfer, the study found that while knowledge transfer is considered critically important, knowledge transfer is only marginally effective for explicit knowledge and somewhat ineffective for tacit knowledge. This lack of effective knowledge transfer poses a risk to successful technology transfer and the goals of ICH Q10. In response to these findings, the research generated a variety of outputs, many of which have already demonstrated outcomes and impacts on the sector and have the potential for seminal importance. These outputs include a Knowledge Management Process Model to define the process of knowledge management, the Risk-Knowledge Infinity Cycle (RKI Cycle) as a framework to unite KM and QRM, a framework for knowledge transfer enhancement (KTE Framework) during technology transfer, and a variety of case studies to demonstrate the impact of these outputs and their applicability across the product lifecycle. These outputs can be immediately applied to the benefit of the pharmaceutical sector. Areas of future study include additional assets such as training and application materials to accelerate application of these outputs. Additional opportunity also exists to better define knowledge transfer toolkits, create knowledge management frameworks for other phases of the product lifecycle, and to better define the relationship between data analytics, knowledge management and risk management

Interpretation of experimental data near lambda-transition point in liquid helium

The recently published experimental data for specific heat C_p of liquid helium in zero gravity conditions very close to the lambda-transition have been discussed. We have shown that these data allow different interpretations. They can be well interpreted within the perturbative RG approach and within our recently developed theory, as well. Allowing the logarithmic correction, the corresponding fits lie almost on top of each other over the whole range of the reduced temperatures t (for bin averaged data) 6.3 x 10^{-10} < t < 8.8 x 10^{-3}. However, the plot of the effective exponent alpha_eff(t) suggests that the behaviour of C_p, probably, changes very close to the lambda-transition temperature. To clarify this question, we need more accurate data for t<10^{-7}. In addition, we show that the experimental data for superfluid fraction of liquid helium close to the critical point within 3 x 10^{-7} < t < 10^{-4} can be better fit by our exponents nu=9/13, Delta=5/13 than by the RG exponents (nu approximately 0.6705 and Delta about 0.5). The latter ones are preferable to fit the whole measured range 3 x 10^{-7} < t < 10^{-2} where, however, remarkable systematic deviations appear. Our estimated value 0.694 +/- 0.017 of the asymptotic exponent nu well agrees with the theoretical prediction nu=9/13.Comment: 9 pages, 4 figures. The first version was a preliminary one. Now it is substentially extended and coincides with the published pape

Steps Beyond Risk Assessment in QRM: RBDM, The next horizon

The topic of Risk-based Decision Making (RBDM) was highlighted as one of these areas requiring clarification in the International Committee on Harmonisation (ICH) concept paper for the revision of Q9. This paper examines 5 key areas on RBDM identified in the concept paper, focusing on: What good risk-based decision making actually means, How QRM may improve decision-making and How risk-based decisions might be achieved. This was executed by reviewing peer-reviewed published research literature and examining best practices in other industries, with a view to initiating a dialogue which could help advance the questions posed. Based on this analysis, a list of 21 attributes commonly applied to RBDM were identified. These criteria were sorted under the headers of Governance, Process (each QRM and KM) and People

Effective Knowledge Transfer during Biopharmaceutical Technology Transfer - How Well Do We Do It?

Author’s Note: This is the author’s submitted manuscript which has subsequently been published in the Journal of Validation Technology (www.ivtnetwork.com, Volume 25, Issue 4 – August 2019). While knowledge management (KM) has been widely applied in other sectors, the international biopharmaceutical sector has struggled with the meaningful and sustained application of effective KM practices. This is evident even though KM has been highlighted in regulatory guidance for over 10 years, and the positive business impact of KM is well recognized in other sectors. This paper focuses on the topic of KM as applied to biopharmaceutical technology transfer, introducing new research that explores the importance and effectiveness of knowledge transfer as an integral component of a biopharmaceutical product technology transfer. Results from multiple sources explored in this paper are well aligned in recognizing that knowledge transfer is very important to enable technology transfer, yet the biopharmaceutical sector is not very effective at this knowledge transfer. This is especially true of tacit knowledge transfer which is often reported to be ineffective. Additional research will further define the barriers to improve knowledge transfer effectiveness and how the biopharmaceutical sector might improve in this area

Knowledge as the Currency of Managing Risk: a Novel Framework to Unite Quality Risk Management and Knowledge Management

In a manner of speaking, knowledge is the currency of managing risk and an organization that is risk-focused will want to apply the best of what it knows to assess those risks, identify appropriate risk controls and evaluate the performance of those controls. An organization that effectively manages knowledge should be able to recognize and proactively apply new learnings to better anticipate risks. This is particularly important in the manufacture of medicinal products. Since the publication of ICH Q10 in 2010, QRM and KM have been positioned as coenablers to the Pharmaceutical Quality System. However, in practice these two disciplines have remained largely distinct and disconnected. This paper presents a novel way to consider Quality Risk Management (QRM) and Knowledge Management (KM) which represents their true interdependencies, and which has the potential to deliver more effective and risk-based control strategies in a more synergistic and effective manner. This paper advocates for the need to strengthen the relationship between QRM and KM. In order to better understand the synergistic relationship between QRM and KM, a Knowledge Management process model is first proposed to envision KM akin to the familiar representation of QRM in ICH Q9 . Following this model, a framework is presented in the form of a Risk-Knowledge Infinity Cycle which serves to visualise and understand the QRM-KM relationship. It is the authors belief that treating QRM and KM in this way has a variety of potential benefits for biopharmaceutical companies, including improved risk-based decision making, facilitating evidence-based risk reduction and increased process knowledge, leading to less uncertainty and subjectivity in QRM outputs. This should ultimately result in more effective risk-based control strategies and more reliable manufacturing processes, which potentially lead to increased protection – and other benefits including product availability and value – for patients

Introducing a Model and a Framework to Unify the Pharmaceutical Quality System Enablers Quality Risk Management & Knowledge Management

An organization that effectively manages knowledge should be able to recognize and proactively apply new learnings to better anticipate risks. This is particularly important in the manufacture of medicinal products. Since the publication of ICH Q10 in 2010, Quality Risk Management (QRM) and Knowledge Management (KM) have been positioned as co-enablers to the Pharmaceutical Quality System. The authors of this paper present a Knowledge Management process model to foster greater practical understanding of the practice of knowledge management. This model when joined with the familiar ICH Q9 process model for QRM, should enable a company to better manage their knowledge and risk. In addition, a framework, the Risk-Knowledge Infinity Cycle is presented to better link these two disciplines at large and specifically as the dual enablers to ICH Q10. It is the authors belief that treating QRM and KM in this way will have a variety of potential benefits for biopharmaceutical companies, including improved risk-based decision making, facilitating evidence-based risk reduction and increased process knowledge, leading to less uncertainty and subjectivity in QRM outputs. This should ultimately result in more effective risk-based control strategies and more reliable manufacturing processes, which potentially lead to increased protection - and other benefits including product availability and value - for patients. This paper presents the Knowledge Management process model, and the Risk-Knowledge Infinity Cycle and an example of the application of the Risk-Knowledge Infinity Cycle