Hydrogen is a promising energy vector for achieving renewable integration into the grid, thus fostering the decarbonization of the energy sector. This paper presents the control platform architecture of a real hydrogen-based energy production, storage, and re-electrification system (HESS) paired to a wind farm located in north Norway and connected to the main grid. The HESS consists of an electrolyser, a hydrogen tank, and a fuel cell. The control platform includes the management software, the control algorithms, and the automation technologies operating the HESS in order to address the three use cases (electricity storage, mini-grid, and fuel production) identified in the IEA-HIA Task24 final report, that promote the integration of wind energy into the main grid. The control algorithms have been already developed by the same authors in other papers using mixed-logical dynamical modeling, and implemented via a two-layer model predictive control scheme for each use case, and are quickly introduced in order to make evident their integration into the presented architecture. Simulation test runs with real equipment data, wind generation, load profiles, and market prices are also reported so as to highlight the control platform performances.Note to Practitioners—The paper develops the integration between the management platform of a HESS, paired to a real wind farm in northern Norway, and the control algorithms aimed at scheduling hydrogen production and re-electrification on the basis of several forecast streams about exogenous conditions and different possible operating modes of the wind-hydrogen system. The control algorithms address the three use cases identified by the IEA-HIA in the final report of Task 24 about the integration of wind energy into the grid, namely i) electricity storage, where the HESS is operated in order to enable the wind farm to power smoothing; ii) mini-grid, where the wind farm and the HESS form a mini-grid with a local load (small town) and the HESS is therefore operated in order to fulfill it without and with grid support (in this case buying and selling electricity to the market is also handled); and iii) fuel production, where the HESS is operated in order to fulfill a hydrogen demand (e.g., due to fuel cell vehicles). In addition to the specific objectives of each use case, the developed control algorithms also optimize the HESS operating costs and typically address two time-scale behaviors to appropriately handle corresponding long and short terms dynamics. The management platform of the HESS is arranged in three layers (physical, control, and supervision layers), and located in the cloud. The physical layer targets the physical components, sensors, and actuators. The automation layer includes all local controllers and modules used for measurement, and several servers for interactions between the higher and lower layers of the control architecture and databases. In the supervision layer, the execution of control algorithms and clients for remote diagnoses, monitoring, and top-management activities are located. Since each layer performs specific functionalities, a multi-tier architecture is implemented and the communications among the layers occur through services and microservices.

Mini-drones have a wide range of applications, including weather monitoring, parcel delivery, search and rescue, and entertainment. As their functionality, safety, and performance heavily relies on ubiquitously reliable positioning and navigation, their applications are relatively limited to outdoor environments where Global Positioning System (GPS) and/or similar are available. Indoor localization is improving, e.g, using Visual Simultaneous Localization and Mapping (V-SLAM). However, for the applications of mini-drone navigation with a higher safety requirements, further improvements are still needed. This paper proposes a novel approach to improve the localization performance for mini-drone indoor navigation. The proposed approach enhances V-SLAM techniques by combining Oriented Rotated Brief SLAM (ORB-SLAM2) and Semi-direct monocular Visual Odometry (SVO) algorithms along with an Adaptive Complementary Filter (ACF). The results show that the proposed approach improvement in position estimation in the different conditions (low light, low texture and dynamic environments) in comparison with other commonly used indoor positioning approaches.

MOTIVATION: The assessment of protein structure prediction techniques requires objective criteria to measure the similarity between a computational model and the experimentally determined reference structure. Conventional similarity measures based on a global superposition of carbon α atoms are strongly influenced by domain motions and do not assess the accuracy of local atomic details in the model. RESULTS: The Local Distance Difference Test (lDDT) is a superposition-free score that evaluates local distance differences of all atoms in a model, including validation of stereochemical plausibility. The reference can be a single structure, or an ensemble of equivalent structures. We demonstrate that lDDT is well suited to assess local model quality, even in the presence of domain movements, while maintaining good correlation with global measures. These properties make lDDT a robust tool for the automated assessment of structure prediction servers without manual intervention. AVAILABILITY AND IMPLEMENTATION: Source code, binaries for Linux and MacOSX, and an interactive web server are available at http://swissmodel.expasy.org/lddt. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Developers of new methods in computational structural biology are often hampered in their research by incompatible software tools and non-standardized data formats. To address this problem, we have developed OpenStructure as a modular open source platform to provide a powerful, yet flexible general working environment for structural bioinformatics. OpenStructure consists primarily of a set of libraries written in C++ with a cleanly designed application programmer interface. All functionality can be accessed directly in C++ or in a Python layer, meeting both the requirements for high efficiency and ease of use. Powerful selection queries and the notion of entity views to represent these selections greatly facilitate the development and implementation of algorithms on structural data. The modular integration of computational core methods with powerful visualization tools makes OpenStructure an ideal working and development environment. Several applications, such as the latest versions of IPLT and QMean, have been implemented based on OpenStructure-demonstrating its value for the development of next-generation structural biology algorithms. AVAILABILITY: Source code licensed under the GNU lesser general public license and binaries for MacOS X, Linux and Windows are available for download at http://www.openstructure.org. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.