A combined experimental and computational study of the pressure dependence of the vibrational spectrum of solid picene C_22H_14

We present high-quality optical data and density functional perturbation theory calculations for the vibrational spectrum of solid picene (C$_{22}$H$_{14}$) under pressure up to 8 GPa. First-principles calculations reproduce with a remarkable accuracy the pressure effects on both frequency and intensities of the phonon peaks experimentally observed . Through a detailed analysis of the phonon eigenvectors, We use the projection on molecular eigenmodes to unambiguously fit the experimental spectra, resolving complicated spectral structures, in a system with hundreds of phonon modes. With these projections, we can also quantify the loss of molecular character under pressure. Our results indicate that picene, despite a \sim 20 % compression of the unit cell, remains substantially a molecular solid up to 8 GPa, with phonon modes displaying a smooth and uniform hardening with pressure. The Grueneisen parameter of the 1380 cm^{-1} a_1 Raman peak ($\gamma_p=0.1$) is much lower than the effective value ($\gamma_d=0.8$) due to K doping. This is an indication that the phonon softening in K doped samples is mainly due to charge transfer and electron-phonon coupling.Comment: Replaced with final version (PRB

Ground truth deficiencies in software engineering: when codifying the past can be counterproductive

Many software engineering tools build and evaluate their models based on historical data to support development and process decisions. These models help us answer numerous interesting questions, but have their own caveats. In a real-life setting, the objective function of human decision-makers for a given task might be influenced by a whole host of factors that stem from their cognitive biases, subverting the ideal objective function required for an optimally functioning system. Relying on this data as ground truth may give rise to systems that end up automating software engineering decisions by mimicking past sub-optimal behaviour. We illustrate this phenomenon and suggest mitigation strategies to raise awareness

Vibrational spectrum of solid picene (C_22H_14)

Recently, Mitsuhashi et al., have observed superconductivity with transition temperature up to 18 K in potassium doped picene (C22H14), a polycyclic aromatic hydrocarbon compound [Nature 464 (2010) 76]. Theoretical analysis indicate the importance of electron-phonon coupling in the superconducting mechanisms of these systems, with different emphasis on inter- and intra-molecular vibrations, depending on the approximations used. Here we present a combined experimental and ab-initio study of the Raman and infrared spectrum of undoped solid picene, which allows us to unanbiguously assign the vibrational modes. This combined study enables the identification of the modes which couple strongly to electrons and hence can play an important role in the superconducting properties of the doped samples

Analysis of Circular Economy Research and Innovation (R&I) intensity for critical products in the supply chains of strategic technologies.

To develop renewable energy, digital, space and defence technologies, the European Union (EU) needs access to critical raw materials of which a large share is currently imported from third countries. To mitigate the risk of supply disruptions, the Critical Raw Materials Act proposes to diversify sources of imports, while increasing domestic extraction, processing, and recycling. The circular economy is therefore positioned as a key element of the EU strategy to deploy strategic technologies for navigating the sustainability transition in a complex geopolitical landscape. In line with this position, the present study analyses the intensity of circular economy research and innovation (R&I) in the supply chains of strategic technologies. The focus is placed on four critical products containing raw materials having high supply risks: lithium-ion battery cells; neodymium-iron-boron permanent magnets; photovoltaic cells; hydrogen electrolysers and fuel-cells. The R&I analysis is based on the identification of scientific articles, patents, and innovation projects on the subject, with a global scope, in the period between 2014 and 2022. The analysis is enriched by connecting to parallel work on the subject, conducted by Joint Research Centre (JRC) as well as academic institutions, industry, and policy stakeholders. This is functional to provide insight into: where circularity efforts R&I have been placed in terms of different products and supply chains; which countries are undertaking these efforts; how the EU is positioned and how much funding was deployed so far; what are the current gaps and trends going forward. Main insights include the following: 1) circularity R&I for critical products is not balanced, with a prominent focus placed on Li-ion cells on a global level 2) the EU has followed this trend in terms of number of innovation projects and public spending; 3) Next to EU efforts, China and the USA focus intensely on circular economy R&I as well. This study contributes with evidence to advance scientific research and policymaking on the role of a circular economy to achieve open strategic autonomy and climate neutrality in the EU

Neuro-evolution Methods for Designing Emergent Specialization

This research applies the Collective Specialization Neuro-Evolution (CONE) method to the problem of evolving neural controllers in a simulated multi-robot system. The multi-robot system consists of multiple pursuer (predator) robots, and a single evader (prey) robot. The CONE method is designed to facilitate behavioral specialization in order to increase task performance in collective behavior solutions. Pursuit-Evasion is a task that benefits from behavioral specialization. The performance of prey-capture strategies derived by the CONE method, are compared to those derived by the Enforced Sub-Populations (ESP) method. Results indicate that the CONE method effectively facilitates behavioral specialization in the team of pursuer robots. This specialization aids in the derivation of robust prey-capture strategies. Comparatively, ESP was found to be not as appropriate for facilitating behavioral specialization and effective prey-capture behaviors

A two-step learning approach for solving full and almost full cold start problems in dyadic prediction

Dyadic prediction methods operate on pairs of objects (dyads), aiming to infer labels for out-of-sample dyads. We consider the full and almost full cold start problem in dyadic prediction, a setting that occurs when both objects in an out-of-sample dyad have not been observed during training, or if one of them has been observed, but very few times. A popular approach for addressing this problem is to train a model that makes predictions based on a pairwise feature representation of the dyads, or, in case of kernel methods, based on a tensor product pairwise kernel. As an alternative to such a kernel approach, we introduce a novel two-step learning algorithm that borrows ideas from the fields of pairwise learning and spectral filtering. We show theoretically that the two-step method is very closely related to the tensor product kernel approach, and experimentally that it yields a slightly better predictive performance. Moreover, unlike existing tensor product kernel methods, the two-step method allows closed-form solutions for training and parameter selection via cross-validation estimates both in the full and almost full cold start settings, making the approach much more efficient and straightforward to implement

Barrett’s esophagus: results from an Italian cohort with tight endoscopic surveillance

Background and aim: Barrett’s Esophagus represent a condition that predisposes to the development of esophageal adenocarcinoma. The aim of the present study was to analyze the demographic and clinical characteristics of patients with BE, to establish the presence of risk factors for this condition, and to determine the frequency of dysplastic lesions as well as the evolution towards adenocarcinoma under tight endoscopic control. Methods: In this study, we retrospectively collected and analyzed data from a cohort of patients with Barrett’s Esophagus identified through endoscopic records of ULSS7 in Northern Italy, who underwent upper esophago-gastroduodenoscopy over a 10-year period from July 2008 to December 2020. Results: A total of 264 patients were identified as having BE and included in the study. Mean follow-up was 6.7 years (range: 3 months-13 years). Demographic characteristics of the study population included mean age of 62.7 years (range 33-90 years), with 62.5% of the study population being aged 60 or older, and a male predominance. Females were significantly older than males (65.7 years, range 37-90 vs 61.9 years, range 33-87, p=0.043, respectively). Conclusions: The present study confirms the importance of tight endoscopic control in the management of BE, favoring early detection of BE degeneration towards high grade dysplasia or adenocarcinoma. In a subset of patients with high-risk factors including male sex, cigarette smoking and heavy alcohol intake, it may be worthwhile to consider endoscopic control over time in order to detect the development of BE. (www.actabiomedica.it)