Driven by the urgency of transitioning to a decarbonised energy sector, the confluence of Renewable Energy Sources (RES), Smart Grids (SG), and the widespread adoption of Electric Vehicles (EVs) has made power system planning and operation a focal point of global sustainability efforts. The rapid adoption of EVs has led to challenges related to charging infrastructure, grid demand, and scheduling management while also offering opportunities as a distributed energy storage solution, aiding peak load reduction and renewable energy integration. Enhanced accuracy in EV charging predictions could alleviate energy imbalances resulting from production and consumption disparities. Additionally, it supports SG architectural functions like demand response (DR) management. Compared to the initial baseline timeseries EV charging forecasting models, which performed poorly, the proposed strategy consists of a step-by-step forecasting methodology using data from predictions of road traffic, weather/seasonality, user charging information, and EV charging load. For each target, various novel implementations and models were conducted, including zero inflated (ZI) and a novel logarithmic ZI (LogZI) regression method. Results comparison indicated an increasing accuracy of EV charging demand forecasts from 78.1 to 88.7% regarding $$R^2$$ R 2 . It also highlighted a decreasing error from 1.75 to 1.05 kWh regarding MAE and from 2.48 to 1.77 kWh regarding RMSE (CVRMSE and NRMSE were also provided). This research contributes a novel one step ahead EV charging demand forecasting framework integrating traffic and user charging predictions with the proposed LogZI regression approach. The proposed, updated version of the ZI approach, methodology performs significantly better compared to the baseline models, aiming to enhance grid stability and optimize charging infrastructure.
A shared feature of B-cell receptors (BCRs) expressed on neoplastic B cells from patients with chronic lymphocytic leukaemia (CLL) concerns their continual activation of intracellular signalling without requiring external antigens. This autonomous signalling mechanism has previously been demonstrated to arise from BCR-BCR homotypic interactions in three distinct stereotyped CLL subsets (2, 4 and 169). CLL subset 1 is the second largest stereotyped subset, epitomizing unmutated CLL and known for a particularly aggressive clinical course. Here we show that, despite their significant sequence similarity, BCRs originating from three different subset 1 CLL cases exhibit variations in their combining site structures and associated physicochemical properties. Subset 1 BCRs are characterized by a common reactivity pattern towards various autoantigens, though they maintain distinct receptor-specific characteristics. Analysis of crystal structures of the respective BCR Fab fragments reveals lack of conservation in intermolecular crystal contacts, paralleled by no self-association in solution and incapacity to induce intracellular signalling when expressed in a model B-cell line. These findings suggest that cell-autonomous signalling may not be universally present across all CLL, implying the existence of other mechanisms in sustaining leukemic cell proliferation and CLL progression mediated by BCR signalling. It is generally acknowledged that pathological B-cell receptors drive chronic lymphocytic leukaemia (CLL) via continuous signalling emanating from BCR-BCR homotypic interactions, rather than external antigens. Here the authors show, by analysing the structure and function of three B-cell receptors from patients with stereotyped CLL subset 1 that homotypic interactions and consequential autonomous signalling is not universal and other mechanisms could play roles in leukemic proliferation of CLL cells.
The present research introduces GORGO, a unique penetration testing tool that utilizes Reinforcement Learning. It performs vulnerability assessments on 5G—and beyond—infrastructures against sophisticated Denial of Service (DoS) attacks driven by Artificial Intelligence (AI). GORGO consists of multiple Deep Q-Learning agents working in collaboration to assess the resilience of target infrastructure, making it the only penetration testing tool capable of performing such comprehensive evaluations. The objective is to construct the optimal attack strategy and provide the information to understand and mitigate AI-driven DoS attacks. The rationale is to provide detailed insights into the extent of Quality of Service degradation and to identify which strategic choices had the most significant impact on the target infrastructure. GORGO was evaluated in one real Software Defined Network (SDN)-based Cloud Native 5G infrastructure and one Beyond 5G (B5G) testbed. The experimental results demonstrate that GORGO is a highly efficient Reinforcement Learning model and successfully uncovered vulnerabilities in both testing environments.
In the light of the adverse effects of climate change, data analysis and Machine Learning (ML) techniques can provide accurate forecasts, which enable efficient scheduling and operation of energy usage. Especially in the built environment, Energy Load Forecasting (ELF) enables Distribution System Operators or Aggregators to accurately predict the energy demand and generation trade-offs. This paper focuses on developing and comparing predictive algorithms based on historical data from a near Zero Energy Building. This involves energy load, as well as temperature data, which are used to develop and evaluate various base ML algorithms and methodologies, including Artificial Neural Networks and Decision-trees, as well as their combination. Each algorithm is fine-tuned and tested, accounting for the unique data characteristics, such as the presence of photovoltaics, in order to produce a robust approach for One-Step-Ahead ELF. To this end, a novel hybrid model utilizing ensemble methods was developed. It combines multiple base ML algorithms the outputs of which are utilized to train a meta-model voting regressor. This hybrid model acts as a normalizer for any new data input. An experimental comparison of the model against unseen data and other ensemble approaches, showed promising forecasting results (mean absolute percentage error = 5.39%), particularly compared to the base algorithms.
In this study, we reported that the electroplating sludges, a solid waste of the electroplating process, could be employed as an effective and novel photocatalyst for removing methylene blue in an aqueous solution. The treated materials were comprehensively characterized by SEM, XRD, FTIR, and BET. The obtained results showed that the chemical composition of electroplating sludges contained a large amount of various metal oxides that would be beneficial for photocatalysis. As expected, the best photocatalytic degradation of methylene blue (removal 98.0
