Embodied Energy Versus Operational Energy in a Nearly Zero Energy Building Case Study

Currently in the NZEB energy demand calculation method the Embodied Energy is not included, despite the state-of-the-art recognizes a relevant energy impact caused by raw materials extraction as well as components manufacturing, product final assembly and transportation. Aim of this study was to assess the Embodied Energy in a NZEB case study along with the Operational Energy, pointing out the importance of taking into account both these aspects since the earliest design stage. Within the research activity here presented, for accounting the EE, a worksheet was developed and implemented with over 65 materials taken from a database carried out by the authors, in order to encourage designers to properly manage these issues

Existence and multiplicity of peaked bound states for nonlinear Schr\"odinger equations on metric graphs

We establish existence and multiplicity of one-peaked and multi-peaked positive bound states for nonlinear Schr\"odinger equations on general compact and noncompact metric graphs. Precisely, we construct solutions concentrating at every vertex of odd degree greater than or equal to $3$. We show that these solutions are not minimizers of the associated action and energy functionals. To the best of our knowledge, this is the first work exhibiting solutions concentrating at vertices with degree different than $1$. The proof is based on a suitable Ljapunov-Schmidt reduction.Comment: 25 pages, 2 figure

Energy Evaluation of a {PV}-Based Test Facility for Assessing Future Self-Sufficient Buildings

In recent years, investigations on advanced technological solutions aiming to achieve high-energy performance in buildings have been carried out by research centers and universities, in accordance with the reduction in buildings’ energy consumption required by European Union. However, even if the research and design of new technological solutions makes it possible to achieve the regulatory objectives, a building’s performance during operation deviates from simulations. To deepen this topic, interesting studies have focused on testing these solutions on full-scale facilities used for real-life activities. In this context, a test facility will be built in the university campus of Politecnico di Torino (Italy). The facility has been designed to be an all-electric nearly Zero Energy Building (nZEB), where heating and cooling demand will be fulfilled by an air-source heat pump and photovoltaic generators will meet the energy demand. In this paper, the facility energy performance is evaluated through a dynamic simulation model. To improve energy self-sufficiency, the integration of lithium-ion batteries in a HVAC system is investigated and their storage size is optimized. Moreover, the facility has been divided into three units equipped with independent electric systems with the aim of estimating the benefits of local energy sharing. The simulation results clarify that the facility meets the expected energy performance, and that it is consistent with a typical European nZEB. The results also demonstrate that the local use of photovoltaic energy can be enhanced thanks to batteries and local energy sharing, achieving a greater independence from the external electrical grid. Furthermore, the analysis of the impact of the local energy sharing makes the case study of particular interest, as it represents a simplified approach to the energy community concept. Thus, the results clarify the academic potential for this facility, in terms of both research and didactic purposes

Network Analysis of Microarray Data

DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.Peer reviewe

VOLTA : adVanced mOLecular neTwork Analysis

Motivation: Network analysis is a powerful approach to investigate biological systems. It is often applied to study gene co-expression patterns derived from transcriptomics experiments. Even though co-expression analysis is widely used, there is still a lack of tools that are open and customizable on the basis of different network types and analysis scenarios (e.g. through function accessibility), but are also suitable for novice users by providing complete analysis pipelines. Results: We developed VOLTA, a Python package suited for complex co-expression network analysis. VOLTA is designed to allow users direct access to the individual functions, while they are also provided with complete analysis pipelines. Moreover, VOLTA offers when possible multiple algorithms applicable to each analytical step (e.g. multiple community detection or clustering algorithms are provided), hence providing the user with the possibility to perform analysis tailored to their needs. This makes VOLTA highly suitable for experienced users who wish to build their own analysis pipelines for a wide range of networks as well as for novice users for which a 'plug and play' system is provided.Peer reviewe

Manually curated and harmonised transcriptomics datasets of psoriasis and atopic dermatitis patients

We present manually curated transcriptomics data of psoriasis and atopic dermatitis patients retrieved from the NCBI Gene Expression Omnibus and EBI ArrayExpress repositories. We collected 39 transcriptomics datasets, deriving from DNA microarrays and RNA-Sequencing technologies, for a total of 1677 samples. We provide quality-checked, homogenised and preprocessed gene expression matrices and their corresponding metadata tables along with the estimated surrogate variables. These data represent a ready-made valuable source of knowledge for translational researchers in the dermatology field.Peer reviewe

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an inte-grated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and infor-mativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creative-commons.org/licenses/by-nc-nd/4.0/).Peer reviewe

Unsupervised Algorithms for Microarray Sample Stratification

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.Peer reviewe

Supervised Methods for Biomarker Detection from Microarray Experiments

Biomarkers are valuable indicators of the state of a biological system. Microarray technology has been extensively used to identify biomarkers and build computational predictive models for disease prognosis, drug sensitivity and toxicity evaluations. Activation biomarkers can be used to understand the underlying signaling cascades, mechanisms of action and biological cross talk. Biomarker detection from microarray data requires several considerations both from the biological and computational points of view. In this chapter, we describe the main methodology used in biomarkers discovery and predictive modeling and we address some of the related challenges. Moreover, we discuss biomarker validation and give some insights into multiomics strategies for biomarker detection.Non peer reviewe