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Background The association between psoriasis and some diseases has become relevant in recent years. Providing appropriate management of psoriasis from an early stage requires prompt diagnosis and treatment of concomitant dis- eases and to prevent any potential comorbidity. This approach should consider the adverse events of the drugs used to treat psoriasis potentially related to the onset of comorbidities. Objective To provide the dermatologist with an accurate and friendly tool for systematizing the diagnosis of psoriasis- associated comorbidities, which generally escapes the scope of the dermatology setting, and to facilitate decision-mak- ing about the referral and treatment of patients with comorbidities. Methods These position statement recommendations were developed by a working group composed of ten experts (four dermatologists, one cardiologist, one rheumatologist, one gastroenterologist, one nephrologist, one endocrinologist and one psychiatrist) and two health services researchers. The expert group selected the psoriasis comorbidities considered according to their relevance in the dermatology setting. The recommendations on diagnostic criteria are based on the cur- rent clinical practice guidelines for each of the comorbidities. The information regarding the repercussion of psoriasis medi- cal treatments on associated comorbid diseases was obtained from the summary of product characteristics of each drug. Results Recommendations were developed to detect and refer the following psoriasis comorbidities: psoriatic arthritis, cardiovascular risk factors (diabetes, dyslipidaemia, obesity, hypertension and metabolic syndrome), non-alcoholic fatty liver disease, inflammatory bowel disease, kidney disease and psychological disorders (anxiety and depression). In addi- tion, alcohol consumption and tobacco consumption were included. The tables and figures are precise, easy-to-use tools to systematize the diagnosis of comorbidities in patients with psoriasis and fac, Fundación Investigación Clinico-Dermatológica, Instituto de Salud Carlos III, Depto. de Medicina Legal, Psiquiatría y Patología, Fac. de Medicina, TRUE, pub

Solar photocatalytic H₂ production has drawn an increasing amount of attention from the scientific community, industry, and society due to its use of green solar energy and a photocatalyst (semiconductor material) to produce green H₂. Cu-based semiconductors are interesting as photocatalysts for H₂ production because Cu is earth-abundant, cheap, and the synthesis of its copper-containing semiconductors is straightforward. Moreover, Cu-based semiconductors absorb visible light and present an adequate redox potential to perform water splitting reaction. Nevertheless, pristine Cu-based semiconductors exhibit low photoactivity due to the rapid recombination of photo-induced electron-hole (e⁻-h⁺) pairs and are subject to photo corrosion. To remedy these pitfalls, the Cu semiconductor-based Z-scheme family (Z-schemes and S-schemes) presents great interest due to the charge carrier mechanism involved. Due to the interest of Z-scheme photocatalysts in this issue, the basic concepts of the Z-scheme focusing on Cu-based semiconductors are addressed to obtain novel systems with high H₂ photo-catalytic activity. Focusing on H₂ production using Cu-based Z-schemes photocatalyst, the most representative examples are included in the main text. To conclude, an outlook on the future challenges of this topic is addressed.

To date, vibrational simulation results constitute more of an experimental support than a predictive tool, as the simulated vibrational modes are discrete due to quantization. This is different from what is obtained experimentally. Here, we propose a way to combine outputs such as the phonon density of states surrogate and peak intensities obtained from ab initio simulations to allow comparison with experimental data by using machine learning. This work is paving the way for using simulated vibrational spectra as a tool to identify materials with defined stoichiometry, enabling the separation of genuine vibrational features of pure phases from morphological and defect-induced signals.

A theoretical study is conducted with three MX₃ monolayer 2D materials (AlI₃, AsI₃, and IrBr₃) on their electronic properties and how a halogen monovacancy affects their thermostability. Density functional theory (DFT) calculations are run to obtain the band structures and phonon dispersions for both pristine and defective structures. It is shown that AlI₃ and AsI₃ have indirect bandgaps of 2.40 and 2.23 eV, respectively. IrBr₃ has a direct bandgap of 1.65 eV. Phonon dispersions indicate that they are all thermodynamically stable in pristine state, but their defective counterparts are not. Ab-initio molecular dynamics (AIMD) simulations are conducted for defective ones to further investigate their stability. It is found that AlI₃ and AsI₃ layers are decomposed while IrBr3 layer is bent. Further investigations are conducted by analyzing the bond energies and bond lengths of the three materials. It shows that AlI₃ and AsI₃ have lower bond energy and longer bond length, which makes them dissociated at the ambient temperature while higher bond energy and shorter bond length keep IrBr₃ stabilized and enable its displacive phase transition in displacive limit.

Two-dimensional (2D) semiconductors are central to many scientific fields. The combination of two semiconductors (heterostructure) is a good way to lift many technological deadlocks. Although ab initio calculations are useful to study physical properties of these composites, their application is limited to few heterostructure samples. Herein, we use machine learning to predict key characteristics of 2D materials to select relevant candidates for heterostructure building. First, a label space is created with engineered labels relating to atomic charge and ion spatial distribution. Then, a meta-estimator is designed to predict label values of heterostructure samples having a defined band alignment (descriptor). To this end, independently trained k-nearest neighbors (KNN) regression models are combined to boost the regression. Then, swarm intelligence principles are used, along with the boosted estimator’s results, to further refine the regression. This new “swarm smart” algorithm is a powerful and versatile tool to select, among experimentally existing, computationally studied, and not yet discovered van der Waals heterostructures, the most likely candidate materials to face the scientific challenges ahead.

Material synthesis is time- and chemicals-consuming due to the traditional (“brute force”) methodology. For instance, Ni₃TeO₆ (NTO) is a multiferroic material relevant in different applications. Herein, we used an active learning scheme to explore the different phases obtained using a complex hydrothermal synthesis procedure instead of a solid-state methodology. Different from conventional ML prediction requiring a large dataset, we show that with only 9 data points obtained through experimental endeavor, 87% of the experimental condition space is predicted. The predicted phase configuration is verified with the sample in a new synthetic work. Beside exploring the NTO species, scheme developed herein constitute a powerful tool for experimental condition optimization.

A large number of novel two-dimensional (2D) materials are constantly being discovered and deposited in databases. Consolidated implementation of machine learning algorithms and density functional theory (DFT)-based predictions have allowed the creation of several databases containing an unimaginable number of 2D samples. As the next step in this chain, the investigation leads to a comprehensive study of the functionality of the invented materials. In this work, a family of transition metal dichlorides have been screened out for systematic investigation of their structural stability, fundamental properties, structural defects, and environmental stability via DFT-based calculations. The work highlights the importance of using the potential of the invented materials and proposes a comprehensive characterization of a new family of 2D materials.

This paper describes the development of novel/state-of-art computational framework to accurately predict the degree of binder activity of a reclaimed asphalt pavement sample as a percentage of the indirect tensile strength (ITS) using a reduced number of input variables that are relatively easy to obtain, namely compaction temperature, air voids and ITS. Different machine learning (ML) techniques were applied to obtain the most accurate data representation model. Specifically, three ML techniques were applied: 6th-degree multivariate polynomial regression with regularization, artificial neural network and random forest regression. The three techniques produced models with very similar precision, reporting a mean absolute error ranging from 12.2 to 12.8% of maximum ITS on the test data set. The work presented in this paper is an evolution in terms of data analysis of the results obtained within the interlaboratory tests conducted by Task Group 5 of the RILEM Technical Committee 264 on Reclaimed Asphalt Pavement. Hence, despite it has strong bonds with this framework, this work was developed independently and can be considered as a natural follow-up.