Abstract
Density functional calculation of electronic structures of materials is one of the most used techniques in theoretical solid state physics. These calculations retrieve single electron wavefunctions and their eigenenergies. The berry suite of programs amplifies the usefulness of DFT by ordering the eigenstates in analytic bands, allowing the differentiation of the wavefunctions in reciprocal space. It can then calculate Berry connections and curvatures and the second harmonic generation conductivity. The berry software is implemented for two dimensional materials and was tested in hBN and InSe. In the near future, more properties and functionalities are expected to be added.Program summary Program Title: berry CPC Library link to program files: https://doi .org /10 .17632 /mpbbksz2t7 .1 Developer's repository link: https://github .com /ricardoribeiro -2020 /berry Licensing provisions: MIT Programming language: Python3 Nature of problem: Differentiation of Bloch wavefunctions in reciprocal space, numerically obtained from a DFT software, applied to two dimensional materials. This enables the numeric calculation of material's properties such as Berry geometries and Second Harmonic conductivity. Solution method: Extracts Kohn-Sham functions from a DFT calculation, orders them by analytic bands using graph and AI methods and calculates the gradient of the wavefunctions along an electronic band. Additional comments including restrictions and unusual features: Applies only to two dimensional materials, and only imports Kohn-Sham functions from Quantum Espresso package.

Abstract
This research is inserted in the context of cybersecurity and specifically in the attribution of Advanced Persistent Threats (APT). The investigation that gave rise to the article studies the MICTIC Framework, validating it and proposing an extension to facilitate the assignment of APTs. In this research, we present the motivation for this proposal and its validation. Also, the MICTIC is presented layer by layer and the extended version is submitted for validation through a survey of around 50 university professors and researchers. Due to the fact the MICTIC by itself has not been validated, we decided to do that in conjunction with the extension proposal. Attribution is very important because lets you know who promoted or who carried out an APT-type attack. On the other hand, just the fact that there are sophisticated Attribution mechanisms can act as a deterrent to future attacks. This research contributes to greater ease in obtaining the Assignment of APTs and consequently in understanding how this type of cybercrime works. so much so that there are few studies on the Assignment of APTs. This study objectively contributes to achieving the APT attribution by combining technological and non-technological techniques. It contributes to achieving computer security environments since an APT Attribution is a high deterrent to an APT group getting uncovered and an Attribution being assigned to it. Typically, cybercriminals who have been identified have stopped operating, whereas the opposite is not true; unidentified actors persist with attacks for a long time. Thus, this study also contributes to the overall maintenance of cybersecurity.

Abstract
This work provides a method that combines graphene oxide coating and self-image theory to improve the sensitivity of optical sensors. The sensor is designed specifically to measure the amount of glucose present quantitatively in aqueous solutions that replicate the range of glucose concentrations found in human saliva. COMSOL Multiphysics 6.0 was used to simulate the self-imaging phenomenon using a coreless silica fiber (CSF). For high-quality self-imaging, the second and fourth self-imaging points are usually preferred because of their higher coupling efficiency, which increases the sensor sensitivity. However, managing the fourth self-image is more difficult because it calls for a longer CSF length. As a result, the first and second self-image points were the focus of the simulation in this work. After the simulation, using the Layerby-Layer method, the sensor was constructed to a length that matched the second self-image point (29.12 mm) and coated with an 80 mu m/mL graphene oxide layer. When comparing uncoated and graphene oxide-covered sensors to measure glucose in liquids ranging from 25 to 200 mg/dL, one bilayer of polyethyleneimine/graphene demonstrated an eight-fold improvement in sensitivity. The final sensor, built on graphene oxide, showed stability with a low standard deviation of 0.6 pm/min. It also showed sensitivity at 10.403 +/- 0.004 pm/(mg/dL) with a limit of detection of 9.15 mg/dL.

Abstract
In 2021, the energy sector was put at risk by extreme weather in many different ways: North America and Spain suffered heavy winter storms that led to the collapse of the electricity network; California speci fi cally experienced heavy droughts and heat -wave conditions, causing the operations of hydropower stations to halt; fl oods caused substantial damage to energy infrastructure in central Europe, Australia, and China throughout the year, and unusual wind drought conditions decreased wind power production in the United Kingdom by almost 40% during summer. The total economic impacts of these extreme weather events are estimated at billions of U.S. dollars. Here we review and assess in some detail the main extreme weather events that impacted the energy sector in 2021 worldwide, discussing some of the most relevant case studies and the meteorological conditions that led to them. We provide a perspective on their impacts on electricity generation, transmission, and consumption, and summarize estimations of economic losses.

Abstract

Abstract
The Electroencephalogram (EEG) is a medical tool that captures, in a non-invasive way, electrical signals from the brain activities performed by neurons. EEG signals have been the target of study as a biomarker of Parkinsons disease (PD), where several methods of analysis are applied. The present work aims to evaluate features extracted from EEG signals, through methodologies such as HOS, Haralick descriptors, and Fractal Features, as new biomarkers for PD identification. Data from 50 individuals, available at the Open Neuro repository, who underwent an attentional cognitive task were analyzed. RF and SVM algorithms were employed for the classification of the extracted features. The best accuracy achieved was 79.49% in differentiating between Parkinsons subjects and control subjects using Haralick descriptors and RF classifier, suggesting that these features can identify activations in brain areas caused by dopaminergic medication.