Transformers for Multi-Horizon Forecasting in an Industry 4.0 Use Case.

Recently, a novel approach in the field of Industry 4.0 factory operations was proposed for a new generation of automated guided vehicles (AGVs) that are connected to a virtualized programmable logic controller (PLC) via a 5G multi-access edge-computing (MEC) platform to enable remote control. However, this approach faces a critical challenge as the 5G network may encounter communication disruptions that can lead to AGV deviations and, with this, potential safety risks and workplace issues.

Chlorophyll soft-sensor based on machine learning models for algal bloom predictions.

Harmful algal blooms (HABs) are a growing concern to public health and aquatic ecosystems. Long-term water monitoring conducted by hand poses several limitations to the proper implementation of water safety plans. This work combines automatic high-frequency monitoring (AFHM) systems with machine learning (ML) techniques to build a data-driven chlorophyll-a (Chl-a) soft-sensor.

B5GEMINI: AI-Driven Network Digital Twin.

Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. There is broad consensus that Artificial Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology.

Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks.

Due to the growing rise of cyber attacks in the Internet, the demand of accurate intrusion detection systems (IDS) to prevent these vulnerabilities is increasing. To this aim, Machine Learning (ML) components have been proposed as an efficient and effective solution. However, its applicability scope is limited by two important issues: (i) the shortage of network traffic data datasets for attack analysis, and (ii) the data privacy constraints of the data to be used.

Forecasting short-term data center network traffic load with convolutional neural networks.

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly.

Network time and frequency transfer with GNSS receivers located in time laboratories.

In this paper we investigate a possible network solution, similar to the IGS analysis center solutions, that can be easily managed by a network of timing institutes to solve for all the clock differences (in addition to other quantities) in a unique system to understand the feasibility and the advantages of this approach in time and frequency transfer. The investigation is based on a suite of global navigation satellite system (GNSS) software products that allows the users to perform a wide range of calculations and analyses related to GNSS, from the evaluation of performances at the user level to the computation of precise GNSS orbits and clocks, including the calculation of precise receiver coordinates. The time and frequency transfer capabilities of the network solution (named ODTS) are evaluated and compared with PPP solutions as well as to other time transfer results.

[Pallidotomy in the treatment of complicated Parkinson's disease: clinical results at two years and analysis of prognostic factors].

Background: There is a renewed interest in pallidotomy as a treatment for complicated Parkinson's disease (PD).

Aim: To present the clinical results as well as the analysis of prognostic factors obtained in 28 patients with PD and motor complications submitted to pallidotomy and followed by one year (n = 28) and 2 years (n = 12).

Results: The total motor score (Unified Parkinson's Disease Rating Scale [UPDRS]) in the off state improved by 33 and 23% at one and two years, respectively (p < 0.

On the interactions of histone H4 and H4 peptides with DNA. Electrooptical, hydrodynamic and electron microscopy studies.

The interactions of DNA with histone H4 and with its fragments N-H4 (1-84) and C-H4 (85-102) have been studied by using electrooptical techniques, viscosity and electron microscopy. Electron microscopy reveals that histone H4 induces a large folding of DNA molecules : this is in agreement with electrooptical measurements which indicate that, with the increase of their ratio, H4/DNA complexes undergo a gradual process of condensation. Viscosity measurements show that complexes at ratios up to 0.

Filament interaction in intact muscle fibers monitored by light scattering.

Measurement of changes in optical properties of intact muscle fibers during contraction has proven to be difficult or, in some cases, impossible due to movement of the muscle relative to the incident beam. In this paper we describe a technique for immobilizing single fibers in clear gelatin, which permits measurement of light scattering signals undistorted by movement artifacts. We also describe the phase and amplitude relationship between changes in intensity of light scattering (at 90 degrees to incident beam) and tensions induced by electrically activating single fibers.