Real Platform Research Articles

Making Formulog Fast: An Argument for Unconventional Datalog Evaluation

With its combination of Datalog, SMT solving, and functional programming, the language Formulog provides an appealing mix of features for implementing SMT-based static analyses (e.g., refinement type checking, symbolic execution) in a natural, declarative way. At the same time, the performance of its custom Datalog solver can be an impediment to using Formulog beyond prototyping—a common problem for Datalog variants that aspire to solve large problem instances. In this work we speed up Formulog evaluation, with some surprising results: while 2.2× speedups can be obtained by using the conventional techniques for high-performance Datalog (e.g., compilation, specialized data structures), the big wins come by abandoning the central assumption in modern performant Datalog engines, semi-naive Datalog evaluation. In the place of semi-naive evaluation, we develop eager evaluation, a concurrent Datalog evaluation algorithm that explores the logical inference space via a depth-first traversal order. In practice, eager evaluation leads to an advantageous distribution of Formulog’s SMT workload to external SMT solvers and improved SMT solving times: our eager evaluation extensions to the Formulog interpreter and Soufflé’s code generator achieve mean 5.2× and 7.6× speedups, respectively, over the optimized code generated by off-the-shelf Soufflé on SMT-heavy Formulog benchmarks. All in all, using compilation and eager evaluation (as appropriate), Formulog implementations of refinement type checking, bottom-up pointer analysis, and symbolic execution achieve speedups on 20 out of 23 benchmarks over previously published, hand-tuned analyses written in F ♯ , Java, and C++, providing strong evidence that Formulog can be the basis of a realistic platform for SMT-based static analysis. Moreover, our experience adds nuance to the conventional wisdom that traditional semi-naive evaluation is the one-size-fits-all best Datalog evaluation algorithm for static analysis workloads.

An Extra-High Voltage Test System for Transmission Expansion Planning Studies Considering Single Contingency Conditions

This paper presents an extra-high voltage synthetic test system that consists of 500 kV and 765 kV voltage levels, specifically designed for transmission expansion planning (TEP) studies. The test network includes long transmission lines whose series impedance and shunt admittance are calculated using the equivalent π circuit model, accurately reflecting the distributed nature of the line parameters. The proposed test system offers technically feasible steady-state operation under normal and all single contingency conditions. By incorporating accurate modeling for long transmission lines and EHV voltage levels, the test system provides a realistic platform for validating models and theories prior to their application in actual power systems. It supports testing new algorithms, control strategies, and grid management techniques, aids in transmission expansion planning and investment decisions, and facilitates comprehensive grid evaluations. Moreover, a TEP study is conducted on this test system and various scenarios are evaluated and compared economically.

Direct Observations of Spontaneous In-Plane Electronic Polarization in 2D Te Films.

Single-element polarization in low dimensions is fascinating for constructing next-generation nanoelectronics with multiple functionalities, yet remains difficult to access with satisfactory performance. Here, spectroscopic evidences are presented for the spontaneous electronic polarization in tellurium (Te) films thinned down to bilayer, characterized by low-temperature scanning tunneling microscopy/spectroscopy. The unique chiral structure and centrosymmetry-breaking character in 2DTe gives rise to sizable in-plane polarization with accumulated charges, which is demonstrated by the reversed band-bending trends at opposite polarization edges in spatially resolved spectra and conductance mappings. The polarity of charges exhibits intriguing influence on imaging the moiré superlattice at the Te-graphene interface. Moreover, the plain spontaneous polarization robustly exists for various film thicknesses, and can universally preserve against different epitaxial substrates. The experimental validations of considerable electronic polarization in Te multilayers thus provide a realistic platform for promisingly facilitating reliable applications in microelectronic devices.

Balance Controller Design for Inverted Pendulum Considering Detail Reward Function and Two-Phase Learning Protocol

As a complex nonlinear system, the inverted pendulum (IP) system has the characteristics of asymmetry and instability. In this paper, the IP system is controlled by a learned deep neural network (DNN) that directly maps the system states to control commands in an end-to-end style. On the basis of deep reinforcement learning (DRL), the detail reward function (DRF) is designed to guide the DNN learning control strategy, which greatly enhances the pertinence and flexibility of the control. Moreover, a two-phase learning protocol (offline learning phase and online learning phase) is proposed to solve the “real gap” problem of the IP system. Firstly, the DNN learns the offline control strategy based on a simplified IP dynamic model and DRF. Then, a security controller is designed and used on the IP platform to optimize the DNN online. The experimental results demonstrate that the DNN has good robustness to model errors after secondary learning on the platform. When the length of the pendulum is reduced by 25% or increased by 25%, the steady-state error of the pendulum angle is less than 0.05 rad. The error is within the allowable range. The DNN is robust to changes in the length of the pendulum. The DRF and the two-phase learning protocol improve the adaptability of the controller to the complex and variable characteristics of the real platform and provide reference for other learning-based robot control problems.

An Innovative Bio-Vehicle for Resveratrol and Tocopherol Based on Quinoa 11S Globulin-Nanocomplex Design and Characterization.

Nanocomplexes, which possess immense potential to function as nanovehicles, can link diverse ligand compounds. The objective of the present study was to design and characterize resveratrol (RSV)- and tocopherol (TOC)-loaded 11S quinoa seed protein nanocomplexes. Firstly, molecular docking was performed to describe the probable binding sites between protein and ligands, and binding energies of -5.6 and -6.2 kcal/mol were found for RSV and TOC, respectively. Isothermal titration calorimetry allowed us to obtain the thermodynamic parameters that described the molecular interactions between RSV or TOC with the protein, finding the complexation process to be exothermic and spontaneous. 11S globulin intrinsic fluorescence spectra showed quenching effects exerted by RSV and TOC, demonstrating protein-bioactive compound interactions. The application of Stern-Volmer, Scatchard, and Förster resonance energy transfer models confirmed static quenching and allowed us to obtain parameters that described the 11S-RSV and 11S-TOC complexation processes. RSV has a higher tendency to bind 11S globulin according to ITC and fluorescence analysis. Secondly, the protein aggregation induced by bioactive compound interactions was confirmed by dynamic light scattering and atomic force microscopy, with diameters <150 nm detected by both techniques. Finally, it was found that the antioxidant capacity of a single 11S globulin did not decrease; meanwhile, it was additive for 11S-RSV. These nanocomplexes could constitute a real platform for the design of nutraceutical products.

Online task allocation and scheduling in multi-manipulator system considering collision constraints and unknown tasks

Compared to a single robot, multi-robot systems (MRS) offer several advantages in complex multi-task scenarios. The overall efficiency of MRS relies heavily on an efficient task allocation and scheduling process. Multi-robot task allocation (MRTA) is often formulated as a multiple traveling salesman problem, which is NP-hard and typically addressed offline. This paper specifically addresses the online allocation problem in multi-manipulator systems within multi-task scenarios. The tasks are initially pre-allocated to alleviate the computational burden of online allocation. Subsequently, considering collision constraints, we search for the current feasible set of manipulators and employ greedy algorithms to achieve local optima as the online allocation result within this set. Our method can handle the online addition of new, unknown tasks to the task list. Moreover, we demonstrate the feasibility of our approach through simulations and on a realistic platform, where multiple manipulators are tasked with polishing the white body of automobile parts. The results demonstrate that our method is effective and efficient for online allocation and scheduling scenarios.

Correlation driven a novel spin-polarized hourglass loop in monolayer VCl2

Hourglass loop in two-dimensional (2D) systems is typically vulnerable against spin–orbit coupling (SOC). Here, we explore 2D systems with a type of spin-polarized nodal loop that is robust under SOC and characteristic of an hourglass-type dispersion. Through first-principles calculations, we identify the monolayer VCl2 materials as a realistic material platform to realize an hourglass loop. There exist three phases, all of which are dynamically stable. For the γ-structure, as a new single spin hourglass loop material. It shows semiconducting and gapless properties in spin down and spin up channels, respectively. Moreover, it always exhibits an hourglass loop property in the absence and presence of SOC. It indicates that the hourglass has strong against SOC. Our work suggests a realistic material platform for investigating the novel physics associated with band crossings in 2D systems.

Advanced co-culture 3D breast cancer model to study cell death and nanodrug sensitivity of tumor spheroids

The interaction between tumor cells and other cells in the tumor microenvironment (TME) dramatically affects the behavior and progression of the disease. Fibroblasts, as a major component of TME, play a role in cancer development and drug resistance. The current study demonstrates a microfluidic-based co-culture 3D breast cancer spheroids with simplified on-chip testing. To better mimic the breast TME, we have successfully generated a 3D stroma-rich in vitro model of breast cancer using fibroblasts and breast cancer cells. Using fluorescence imaging and histology techniques, the developed spheroids were characterized. MDA-MB-231 cells were successfully grown into 3D spheroids. Upon co-culturing the cells, a decrease in spheroid diameter was detected. Furthermore, tumor spheroids' overall viability was increased when co-cultured with fibroblasts over 5 days. The results demonstrated an increase in ECM content in co-culture spheroids (heterotypic spheroids) compared to mono-culture spheroids (homotypic spheroids), as demonstrated by the augmentation in the quantity of blue stain all over the spheroids. When exposed to liposomal doxorubicin nanodrug, a survival advantage was noticed in heterotypic spheroids compared to mono-culture spheroids (91 % vs. 82 %). The results highlighted the significance of using 3D tumor models rather than 2D systems as a realistic platform for proper evaluation of therapeutics efficacy. The co-culture tumor spheroids in proposed microwell-based microfluidic devices may be valuable to investigate TME and drug screening.

A patient-based realistic simulation platform to train future electrophysiologists to complex ablation procedures

Abstract Title An immersive catheter ablation platform based on patients’ 3D heart model for the simulation and training of future electrophysiologists. Background Arrhythmias are on the rise worldwide due to the ageing of the population. Projections urge to train the next generation of electrophysiologists (EPs) to perform catheter (CATH) ablation (CA) of arrhythmias. Training of future EPs relies mostly on a companionship, where teaching of CATH steerability is made on patients (pts) referred for procedures. While several other domains have established the utility of simulators to speed up learning process, increase safety and establish procedure road maps, dedicated simulators for EPs based on pts’ real anatomy do not exist yet. Purpose To speed up the handling of steerable CATH on MRI-based 3D heart models to optimize ablation road maps in pts referred for complex arrhythmias. Methods ARTS is an Artificial Intelligence and Augmented Reality-based platform comprising two key components: HeARTS and ARTSim. HeARTS is designed to automatically generate 3D heart models from pts’ MRI scans. ARTSim is a CA simulator that utilizes the 3D heart models of the pts for the planning and simulation of CA procedures. This simulator incorporates innovative techniques for digitizing and tracking the movements of a physical catheter in real time, and enabling its navigation within a completely virtual heart. With this technology, the CATH can be precisely steered to various heart locations, including the right and left (LA) atrium, the coronary sinus, and the right and left (LV) ventricles. Results Panel A of the figure shows the mannequin used for the simulator, where a real ablation CATH is introduced within the pt’s heart through a venous introducer. Panel B shows right anterior oblique (RAO) and modified RAO views of a pt suffering from a typical right atrial flutter. Note the presence of yellow tags positioned along the cavotricuspid isthmus (CTI) that the trainee must reach for a programmable duration using the shaft and the handle of the CATH shown in panel A. Panel C shows the virtual ablation CATH in green positioned on the target dots of the CTI. The pink color indicates that the trainee reached the desired target, which became red when the CATH remained stable at the same location for a duration of 5 sec (i.e. stability) with a force &amp;gt;5g (i.e. efficacy) and &amp;lt;20g (i.e. safety). Conclusions Herein, we present ARTS, a realistic CA simulation platform for the training of future EPs, that offers all the typical characteristics of 3D navigation including steerability, stability measurements and the force applied. Importantly, ARTS is based on the true anatomy of the pts, hence might be used to establish and discuss roadmaps to optimize procedures efficacy and safety. Version 2.0, foreseen early 2024, will include 3D late gadolinium enhancement and transseptal puncture for virtual LA and LV procedures, and later on activation times and myocardial voltage.Figure

Self-assembled dipeptide grafted nanohybrid material strips for reliable spectroscopic and electrochemical recognition of tryptamine

According to the World Health Organization (WHO), foods high in tryptamine may cause foodborne diseases and abnormal activity in the central nervous system (CNS). The real-time monitoring of tryptamine levels is an emphasized topic among scientific community. In the present work, we report a cost-effective fluorescent paper strips-based methodology impregnated with self-assembled dipeptide-modified (DM1) zinc oxide (ZnO) i.e.DM1@ZnO for the real-time monitoring of tryptamine levels. It is based on an organic-inorganic nanohybrid material of self-assembled N-functionalised dipeptide molecule coated over the surface of ZnO thus tailoring its properties for the detection of tryptamine employing spectroscopic and electrochemical methods. The designed material exhibited a noteworthy response towards tryptamine irrespective of the presence of other biogenic amines (BAs). It selectively displayed blue fluorescence under the 365 nm UV light. On top of that, the detection of tryptamine was also corroborated by its transition to 2-oxytryptamine, as deduced from its electrochemical route. Thus, the proposed paper strip-based methodology unbolts a realistic platform for the efficient recognition of varying levels of tryptamine thus tackling the prima facie reasons for foodborne diseases.

Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention

In the scheduling of hard real-time systems on multicore platforms, significant unpredictability arises from interference caused by shared hardware resources. The objective of this paper is to offer a schedulability analysis for such systems by assuming a general model that introduces interference as a time parameter for each task. The analysis assumes constrained deadlines and is provided for fixed priorities. It is based on worst-case response time analysis, which exists in the literature for monocore systems. We demonstrate that the worst-case response time is an upper bound, and we evaluate our proposal with synthetic loads and execution on a real platform.

Serious game for radiotherapy training

BackgroundCancer patients are often treated with radiation, therefore increasing their exposure to high energy emissions. In such cases, medical errors may be threatening or fatal, inducing the need to innovate new methods for maximum reduction of irreversible events. Training is an efficient and methodical tool to subject professionals to the real world and heavily educate them on how to perform with minimal errors. An evolving technique for this is Serious Gaming that can fulfill this purpose, especially with the rise of COVID-19 and the shift to the online world, by realistic and visual simulations built to present engaging scenarios. This paper presents the first Serious Game for Lung Cancer Radiotherapy training that embodies Biomedical Engineering principles and clinical experience to create a realistic and precise platform for coherent training.MethodsTo develop the game, thorough 3D modeling, animation, and gaming fundamentals were utilized to represent the whole clinical process of treatment, along with the scores and progress of every player. The model’s goal is to output coherency and organization for students’ ease of use and progress tracking, and to provide a beneficial educational experience supplementary to the users’ training. It aims to also expand their knowledge and use of skills in critical cases where they must perform crucial decision-making and procedures on patients of different cases.ResultsAt the end of this research, one of the accomplished goals consists of building a realistic model of the different equipment and tools accompanied with the radiotherapy process received by the patient on Maya 2018, including the true beam table, gantry, X-ray tube, CT Scanner, and so on. The serious game itself was then implemented on Unity Scenes with the built models to create a gamified authentic environment that incorporates the 5 main series of steps; Screening, Contouring, External Beam Planning, Plan Evaluation, Treatment, to simulate the practical workflow of an actual Oncology treatment delivery for lung cancer patients.ConclusionThis serious game provides an educational and empirical space for training and practice that can be used by students, trainees, and professionals to expand their knowledge and skills in the aim of reducing potential errors.

Differential osteo-specific invasion of patient-derived cancer cells in a microfluidic co-culture model

Cancer cell invasion to the surrounding extracellular matrix contributes to distant metastasis. There are no predictive models that focus on the accuracy of secondary site-infiltrative pattern. The degree of cancer invasion is regulated by receptive pre-metastatic niche. Herein, we show the feasibility of real time three-dimensional (3D) tissue-specific invasion of cancer stem like-cells (CSCs) from two different primary tumors employing a microfluidic device. An osteo-specific matrix (Osteomatrix-Collagen I, Os-Col) allows 3D osteogenic culture proven by differentiation of human adipose derived mesenchymal cells (ADSCs) into an osteoblastic lineage. In vitro enriched CSC-like cells (EpCAM+/CD44+/CD24− phenotype) derived from primary culture of oral squamous carcinoma and breast adenocarcinoma were used to demonstrate the differential strategy adopted for tumor progression. Morphological and viability parameters of the CSCs were monitored to assess the robustness as a realistic platform for in vitro osteotropic assay. Spatio-temporal elevated invasion into the osteo-specific matrix was evident in Os-Col compared to conventional collagen gel. Drug screening on co-cultures of CSC-like cells and osteoblasts exhibited drug resistance. Overall, this work demonstrates that the proposed microfluidic chip with bone-specific ECM can efficiently replicate in vitro biochemical conditions for distinguishing tissue-specific invasive phenotype and screening drug resistance. This may serve as a tool to systematically examine the contribution of the bone microenvironment on the progression of various primary cancers to predict its invasive form, advancing personalized therapies.

Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles

Optimization studies for the energy management systems of hybrid electric powertrains have critical importance as an effective measure for vehicle manufacturers to reduce greenhouse gas emissions and fuel consumption due to increasingly stringent emission regulations in the automotive industry, strict fuel economy legislation, continuously rising oil prices, and increasing consumer awareness of global warming and environmental pollution. In this study, firstly, the mathematical model of the powertrain and the rule-based energy management system of the vehicle with a power-split hybrid electric vehicle configuration are developed in the Matlab/Simulink environment and verified with real test data from the vehicle dynamometer for the UDDS drive cycle. In this way, a realistic virtual test platform has been developed where the simulation results of the energy management systems based on discrete dynamic programming and Pontryagin’s minimum principle optimization can be used to train the artificial neural network-based energy management algorithms for hybrid electric vehicles. The average fuel consumption in relation to the break specific fuel consumption of the internal combustion engine and the total electrical energy consumption of the battery in relation to the operating efficiency of the electrical machines, obtained by comparing the simulation results at the initial battery charging conditions of the vehicle using different driving cycles, will be analyzed and the advantages of the different energy management techniques used will be evaluated.

Abstract 4199: Comprehensive 3D liver cancer model to study the tumor microenvironment for therapy development

Abstract Liver cancer, or hepatocellular carcinoma (HCC), is a formidable malignancy characterized by the uncontrolled growth of cells within the liver tissue. It represents a global health concern, with risk factors including chronic viral infections (such as hepatitis B and C), excessive alcohol consumption, fatty liver disease, and certain genetic conditions. The silent progression of HCC complicates early detection, emphasizing the need for a deeper understanding of the liver tumor microenvironment, which plays a crucial role in disease development. The liver model presented here incorporates a wide array of relevant liver cell types, including hepatocytes, endothelial cells, stellate cells, and resident immune cells. This is complemented by a complete vascularization system, allowing for fluid flow across a micro vascular network that faithfully models the dimensions of liver sinusoids. All these elements are seamlessly integrated into an automated and high throughput platform known as the OrganoPlate®. To model the liver tumor microenvironment, hepatocellular carcinoma cells were integrated into healthy liver tissue model. The resulting model allowed for the observation of integrated cancer colonies, providing a more realistic platform for therapy development compared to monocultures. Simultaneously, the liver's particle clearance mechanisms were investigated using fluorescent imaging. Endothelial cell phagocytosis of E. coli particles and resident macrophages' internalization of dextran molecules were studied to explore hepatic delivery of macromolecules and nanoparticles via the systemic circulation. The integration of hepatocellular carcinoma cells within healthy liver tissue revealed the formation of integrated cancer colonies, offering insights into the complex interactions within the liver tumor microenvironment. Fluorescent imaging showcased endothelial cell phagocytosis of E. coli particles and resident macrophages' internalization of fluorescent molecules. These findings elucidate the liver's particle clearance mechanisms, providing a foundation for understanding immunological processes and designing efficient drug delivery systems. Understanding the liver tumor microenvironment is critical for developing effective therapies for HCC. The integration of hepatocellular carcinoma cells within healthy liver tissue offers a more realistic platform for therapy development compared to traditional monocultures. We believe this system holds the potential for a groundbreaking progress in liver modelling which, besides including functional hepatic cells, also reflects the cellular organization and interactions found within the liver lobule. In conjunction with its high throughput capability, this has the potential to revolutionize drug discovery and develop therapies for complex liver diseases. Citation Format: Flavio Bonanini, Roelof Dinkelberg, Vincent van Duinen, Jeroen Heijmans, Desiree Gouber, Tessa Hagens, Nienke Kortekaas, Manuel Caro Torregrosa, Dorota Kurek, Paul Vulto, Kristin Bircsak. Comprehensive 3D liver cancer model to study the tumor microenvironment for therapy development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4199.

Realistic 3D Phantoms for Validation of Microwave Sensing in Health Monitoring Applications.

The development of new medical-monitoring applications requires precise modeling of effects on the human body as well as the simulation and the emulation of realistic scenarios and conditions. The first aim of this paper is to develop realistic and adjustable 3D human-body emulation platforms that could be used for evaluating emerging microwave-based medical monitoring/sensing applications such as the detection of brain tumors, strokes, and breast cancers, as well as for capsule endoscopy studies. New phantom recipes are developed for microwave ranges for phantom molds with realistic shapes. The second aim is to validate the feasibility and reliability of using the phantoms for practical scenarios with electromagnetic simulations using tissue-layer models and biomedical antennas. The third aim is to investigate the impact of the water temperature in the phantom-cooking phase on the dielectric properties of the stabilized phantom. The evaluations show that the dielectric properties of the developed phantoms correspond closely to those of real human tissue. The error in dielectric properties varies between 0.5-8%. In the practical-scenario simulations, the differences obtained with phantoms-based simulations in S21 parameters are 0.1-13 dB. However, the differences are smaller in the frequency ranges used for medical applications.

The effect of covid policy restrictions on donations during the sustainable and entrepreneurial context

Using real platform data, this paper assesses donors’ reactions to a rare event; specifically, we demonstrate how donors were able to compensate for (or stabilize) healthcare deficits and policy restrictions used to mitigate the spread of COVID-19. Based on a sample of 20,784 covid-related donation campaigns promoted on GoFundMe platform from January 30, 2020 to November 23, 2021, the paper shows that donors decided to donate based on campaign purpose and reliability: specifically, campaigns with the purpose to support hospitals or to buy healthcare materials in countries more affected by the pandemic received more funds. Moreover, the paper provides evidence of the impact of different government policy restrictions on campaign outcome in terms of amount raised and number of campaigns promoted in six European countries. As donations during COVID-19 were valuable (in particular, for public institutions) in terms of amount and timeliness, our findings offer valuable insights into how governments encouraged donations via fiscal incentives and policy restrictions.

A step-by-step guide to include key electroencephalography (EEG) parameters in the study of human performance applied to air traffic control

The study of human performance of air traffic controllers (ATCOs) is an interesting line of research to improve operational safety. In recent years, there has been an increase in the number of techniques available to develop this research based on massive data analysis. This study presents the use of certain electroencephalography (EEG) parameters to study the human performance of ATCOs. Although software and applications are now available to calculate these parameters, there are often problems in understanding the detailed process used to calculate them. The parameters presented in this study are intended to overcome this limitation and are applicable in real air traffic control (ATC) situations. Six parameters are analysed: excitement, stress, relaxation, boredom, engagement, and attention. As an application case for the parameters, a total of 50 data samples obtained during the development of real-time simulations on a highly realistic ATC platform are analysed. From these data, the above-mentioned EEG parameters and their trends are calculated. In addition, the evolution of these parameters is studied in relation to two other variables that characterise the operational situation of the sector during the simulations: the taskload based on ATC events and the number of simultaneous aircraft in the sector per minute.

CSC-Net: Cross-Color Spatial Co-Occurrence Matrix Network for Detecting Synthesized Fake Images

Recently, the Generative Adversarial Networks (GAN) generated images have been spread over the social networks, which brings the new challenge in the the community of media forensics. Although some reliable forensic tools have advanced the study of detecting GAN generated images, while the detection accuracy cannot be guaranteed when facing the malicious post-processing attacks, especially in the practical social network scenario. Thus, in this context, we propose a novel well-designed deep neural network equipped with handed-crafted features for dealing with this problem. In particular, relying on the Cross-color Spatial Co-occurrence Matrix (CSCM), the discriminative features are extracted after carefully analyzing and selecting the most effective color channels. Next, the fused features are fed into the deep neutral network for training a high-efficient forensic detector. Extensive experimental results empirically verify that in most detection scenarios, our proposed detector performs superiorly to the prior arts, especially in the case of post-processing attacks. Moreover, we also highlight the relevance of the proposed detector over the realistic social network platforms, and its generalization capability in three different scenarios.

A digital twin model-based approach to cost optimization of residential community microgrids

A digital twin model-based approach to cost optimization of residential community microgrids