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In 1900, Paul Drude made a groundbreaking advancement with a model to describe the electrical conductivity of metals. Over 120 years later, his model remains widely used and featured in most introductory textbooks. Drude developed this model shortly after the discovery of the electron, during a time when the precise atomic structure was still debated, the Pauli exclusion principle was unknown, and well before Fermi-Dirac statistics. In this discussion, we focus on the assumptions underlying Drude’s model, mainly the choice of the electron mean collision time, analyze the physics it employs, and evaluate how well it fits experimental results of Ohm’s law, both for static and time-dependent fields. We also explore the insights it provides into thermal effects and briefly address how quantum effects can be incorporated. Overall, we highlight why Drude’s achievement is a remarkable success story that merits thorough examination and admiration.

Abstract Several highly effective Covid-19 vaccines are in emergency use, although more-infectious coronavirus strains, could delay the end of the pandemic even further. Because of this, it is highly desirable to develop fast antiviral drug treatments to accelerate the lasting immunity against the virus. From a theoretical perspective, computational approaches are useful tools for antiviral drug development based on the data analysis of gene expression, chemical structure, molecular pathway, and protein interaction mapping. This work studies the structural stability of virus–host interactome networks based on the graphical representation of virus–host protein interactions as vertices or nodes connected by commonly shared proteins. These graphical network visualization methods are analogous to those use in the design of artificial neural networks in neuromorphic computing. In standard protein-node-based network representation, virus–host interaction merges with virus–protein and host–protein networks, introducing redundant links associated with the internal virus and host networks. On the contrary, our approach provides a direct geometrical representation of viral infection structure and allows the effective and fast detection of the structural robustness of the virus–host network through proteins removal. This method was validated by applying it to H1N1 and HIV viruses, in which we were able to pinpoint the changes in the Interactome Network produced by known vaccines. The application of this method to the SARS-CoV-2 virus–host protein interactome implies that nonstructural proteins nsp4, nsp12, nsp16, the nuclear pore membrane glycoprotein NUP210, and ubiquitin specific peptidase USP54 play a crucial role in the viral infection, and their removal may provide an efficient therapy. This method may be extended to any new mutations or other viruses for which the Interactome Network is experimentally determined. Since time is of the essence, because of the impact of more-infectious strains on controlling the spread of the virus, this method may be a useful tool for novel antiviral therapies.

Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus.

The effects of the Dzyaloshinsky-Moriya interaction on finite size one-dimensional (1D) magnetic chains are investigated as a function of their length. The magnetic configuration the system adopts for varying boundary conditions are explored analytically, which leads to the appearance of chiral configurations that play a crucial role. The coercive and exchange bias fields show an unexpected chain length dependence, caused by the boundary conditions and by chiral symmetry breaking, which in turn leads to the breakdown of scale-invariance. Our treatment yields results in agreement with experimental evidence and ongoing research on phthalocyanine iron chains bonded to hydrogen.

A new type of collective excitations, due to the topology of a complex random network that can be characterized by a fractal dimension DF, is investigated. We show analytically that these excitations generate phase transitions due to the non-periodic topology of the DF > 1 complex network. An Ising system, with long range interactions, is studied in detail to support the claim. The analytic treatment is possible because the evaluation of the partition function can be decomposed into closed factor loops, in spite of the architectural complexity. The removal of the infrared divergences leads to an unconventional phase transition, with spin correlations that are robust against thermal fluctuations.