This paper addresses the claim that by enabling intelligent, flexible, and autonomous decision-making at the local scale, MAS can substantially strengthen the performance, reliability, and durability of smart grid operations. We discuss the design and implementation of a MAS for smart grid management through an in-depth case study, emphasizing on particular design decisions, difficulties faced, and results achieved. Among MAS's main benefits are its capacity to guarantee grid stability, reduce overload, and maximize the utilization of resources.

The calculation of Tumor Stroma Ratio (TSR) is a challenging medical issue that could improve predictions of neoadjuvant chemotherapy benefits and patient prognoses. Although several studies on breast cancer and deep learning methods have achieved promising results, the drawbacks that pixel-level semantic segmentation processes could not extract core tumor regions containing both tumor pixels and stroma pixels make it difficult to accurately calculate TSR. In this paper, we propose a Vague-Segment Technique (VST) consisting of a designed SwinV2UNet module and a modified Suzuki algorithm. Specifically, the SwinV2UNet identifies tumor pixels and generate pixel-level classification results, based on which the modified Suzuki algorithm extracts the contour of core tumor regions in terms of cosine angle. Through this way, VST obtains vaguely segmentation results of core tumor regions containing both tumor pixels and stroma pixels, where the TSR could be calculated by the formula of Intersection over Union (IOU). For the training and evaluation, we utilize the well-known The Cancer Genome Atlas (TCGA) database to create an annotated dataset, while 150 images with TSR annotations from real cases are also collected. The experimental results illustrate that the proposed VST could generate better tumor identification results compared with state-of-the-art methods, where the extracted core tumor regions lead to more consistencies of calculated TSR with senior experts compared to junior pathologists. The experimental results demonstrate the superiority of our proposed pipeline, which has promise for future clinical application.

Cyber security for power systems is becoming a concern for many researchers. However, the impact of cyberattacks on actual power systems is unclear. This paper discusses analytically the significance of cyber security for power systems and evaluates whether cyber-attacks are a real threat, or are just a hypothetical concept. First, this paper categorizes the different types of power system attacks and then systematically reviews the cyber-attack modeling, detection, and mitigation approaches for bulk power systems. This paper also discusses prominent cyber and physical attacks on actual power systems and draws a comparison among them based on economical and societal impacts. Based on the facts uncovered in the paper, when compared to physical attacks, cyber-attacks on power systems have had negligible economic and societal impacts. In fact, there is only one documented case (the Ukrainian cyber-attack by Russia) that successfully caused customer disconnections.

This letter analyses the achievable performance of Low Earth Orbit (LEO) Position-Navigation-Timing (PNT) using the novel Orthogonal Time Frequency Space (OTFS) modulation. While originally designed for communication purposes, we show the advantages this modulation may bring to the PNT domain, in particular for doubly-selective channels exhibiting both delay and Doppler spreads. We show how the LEO channel fits this description due to its fast dynamics, and we present the improvements of using OTFS with respect to the standard Binary Phase Shift Keying (BPSK) modulation in terms of delay-Doppler estimation accuracy, in addition to a comparison of their respective Multipath Error Envelopes.

Rice’s characteristic function method of analysis for combinations of signals and noise in a memoryless nonlinear system is extended in this paper to greatly facilitate numerical methods of solutions. This improvement is made by describing the nonlinearity as a Fourier series instead of a Fourier transform, as in the original method, thereby producing expressions for calculating the desired output correlation functions as summations rather than double or triple improper integrals that must be estimated. Even with modern computing tools, it has been found that estimating the values of the former improper integrals can be problematic for SNR conditions of particular interest that motivate the use of the characteristic function method, producing unreliable results and prompting researchers to look elsewhere for a solution. The modified method presented in this paper makes this powerful analytical tool more accessible to researchers and engineers, particularly as new semiconductor devices are under development requiring noise analysis. The extended method retains the useful properties of the original, including applicability to one or more signals (sine waves) and to any noise distribution since its characteristic function always exists, particularly Gaussian or random-telegraph shot noise. An alternative fundamental formula for the characteristic function method is presented expressed in terms of the Fourier coefficients and a discrete parameterization of the general characteristic function. New equations are presented and some numerical results for an example nonlinearity with a sinusoid plus Gaussian noise. The new procedure is concisely summarized for application to other problems.

Convolutional neural networks (CNNs) models play a vital role in achieving state-of-the-art performances in various technological fields. CNNs are not limited to Natural Language Processing (NLP) or Computer Vision (CV) but also have substantial applications in other technological domains, particularly in cybersecurity. The reliability of CNN's models can be compromised because of their susceptibility to adversarial attacks, which can be generated effortlessly, easily applied, and transferred in real-world scenarios. In this paper, we present a novel and comprehensive method to improve the strength of attacks and assess the transferability of adversarial examples in CNNs when such strength changes, as well as whether the transferability property issue exists in computer network applications. In the context of our study, we initially examined six distinct modes of attack: the Carlini and Wagner (C&W), Fast Gradient Sign Method (FGSM), Iterative Fast Gradient Sign Method (I-FGSM), Jacobian-based Saliency Map (JSMA), Limited-memory Broyden fletcher Goldfarb Shanno (L-BFGS), and Projected Gradient Descent (PGD) attack. We applied these attack techniques on two popular datasets: the CIC and UNSW datasets. The outcomes of our experiment demonstrate that an improvement in transferability occurs in the targeted scenarios for FGSM, JSMA, LBFGS, and other attacks. Our findings further indicate that the threats to security posed by adversarial examples, even in computer network applications, necessitate the development of novel defense mechanisms to enhance the security of DL-based techniques.

Residual generation and hypothesis test are two important components in fault detection techniques. Recent studies mainly focused on enhancing residual generation algorithms, but often overlook the Gaussian distribution assumption that necessary for hypothesis test. Based on the conditional invertible neural network (CINN), this study proposes a novel approach for mapping residual signals into near-Gaussian distributed latent variables, thereby enhancing the reliability and effectiveness of the hypothesis test approaches used for fault detection. With the specially designed architecture using CINN, the proposed mapping from residual signals to latent variables has no information loss, thus guaranteeing the accuracy of the proposed fault detection method. The main contributions of this study are two-fold: 1) To ensure that the latent variables are distributed similarly to an ideal Gaussian distribution, a novel CINN training approach is proposed; 2) historical process information is incorporated into the residual-to-latent variable mapping, dynamically refining the mapping procedures in response to the system behavior. This approach is primarily used to tackle the challenges posed by non-additive and non-Gaussian noises in industrial systems. A DC speed control system and a waste water treatment system are adopted to verify the effectiveness of the proposed fault detection approach.

Food freshness classification is a growing concern in the food industry, mainly to protect consumer health and prevent illness and poisoning from consuming spoiled food. Intending to take a significant step towards improving food safety and quality control measures in the industry, this study presents two models based on deep learning for the classification of fruit and vegetable freshness: a robust model and an efficient model. Models' performance evaluation shows remarkable results; in terms of accuracy, the robust model and the efficient model achieve 97.6% and 94.0% respectively, while in terms of AUC score, both models achieve more than 99%, with the difference in inference time between each model over 844 images being 14 seconds.

The aim of our research is to establish relationship between linearly independent vectors and the R3 Space The means for that are going to be taken from Balanced Ternary analysis e.g. triplets with entries (-1,0,1) This is not going to be any specific vector, for instance (-1,0,1) It’s going to be rather a Coordinate system with 8 quadrants and directional vectors The novelty of the proposed research lies in the fact that unlike traditional Cartesian system-vector basis our system uses 8 vectors which are at the same time a polyvector (Boas) We may define it as a multiple of the type:  ∑ (i=-1)^1∑ (j=-1)^1 ∑ (k=-1)^1〖(i,j,k)〗  

Driver emotions play an important role in driving behaviour. Understanding how human emotions affect driving behavior is essential to enhance modern driver support systems, which aim at adjusting the mental and emotional states of a driver in order to improve road safety. In this paper, we first discuss the latest work on how to enhance driver emotions and how to improve driving habits with human emotion regulation, through the analysis of facial expressions, emotions, and driving behaviours of the subjects during different driving situations. Then, we study various driving support systems, and look at current findings and into potential applications of artificial intelligence for road safety via driver emotion regulation. Finally, further research directions are identified, and conclusions are drawn.

The reachable and feasible sets of spacecraft are important tools for many areas of astrodynamics such as mission design, space situational awareness, and assessment of potential threats. Whilst these may be obtained by solving many optimal control problems in the indirect formulation, this process is slow and can suffer from convergence issues with a lack of a good initial guess. This work presents an analytical method of rapidly estimating the reachable and feasible sets utilizing the D matrix, an error state transition matrix under linearity assumptions for Keplerian motion which employs time as a state variable and true anomaly as the reference variable. Analytical expressions for the control laws are derived to produce the maximum/minimum change in orbital radius, time-offlight, and out-of-plane height. Moreover, an analytical expression for the control efficiency is derived to allow thrust activation only when it is efficient to do so, allowing a target fuel consumption to be attained. By mixing the contributions of these controls, the complete reachable or feasible sets can then be rapidly swept over with minimal effort, which is demonstrated in a series of numerical simulations. The results show that the optimal control problem solution presents only minor improvements, highlighting the capacity of the D matrix to provide marginally sub-optimal results within a fraction of the computational time.

In the computer world, ransomware continues to evolve as a significant threat to cybersecurity, employing increasingly sophisticated techniques to evade detection and cause widespread disruption across industries. The novel approach presented in this research leverages opcode sequences combined with machine learning to address the limitations of traditional static analysis methods, offering a more robust and accurate detection mechanism. Through the use of t-SNE for dimensionality reduction and SVM for classification, this method efficiently handles high-dimensional opcode data, allowing for precise identification of ransomware samples. The experimental results show substantial improvements in detection accuracy, precision, and F1-score compared to baseline models, while demonstrating the importance of feature reduction in optimizing performance. Despite the computational overhead introduced through t-SNE, the overall findings indicate that the proposed framework enhances the static detection of ransomware, making it a valuable tool for cybersecurity efforts aimed at mitigating evolving threats.

The 3D point cloud plays a crucial role in deep learning-based vision tasks by providing precise spatial and depth information, leading to its increasing importance in various applications. However, the sparse nature of 3D point clouds poses computational challenges. Researchers have explored the Submanifold Sparse Convolutional Network (SSCN) for processing point cloud data while preserving sparsity. Nevertheless, existing Convolutional Neural Network (CNN) accelerators encounter difficulties in effectively handling SSCNs, prompting recent studies to focus on developing dedicated accelerators for point cloud networks to improve processing performance. This brief presents a specialized hardware architecture designed for SSCNs to address the challenges of effectively processing sparse 3D point clouds. The proposed accelerator achieves a significant 2.51× improvement in throughput density compared to previous works, highlighting its effectiveness in point cloud processing.

Serverless enables users to execute the code and shift the management problem towards Cloud Service Providers. Resource management in serverless computing environments presents unique challenges and opportunities due to the fundamentally different operational model compared to traditional server-based architectures. Serverless computing abstracts the underlying infrastructure from developers, allowing them to focus on writing code rather than managing servers. This abstraction, however, introduces complexities in resource management, necessitating innovative approaches to optimize performance, cost, and scalability. In serverless environments, resources such as CPU, memory, and storage are dynamically allocated and deallocated by the cloud provider based on the workload. This dynamic nature can lead to unpredictable performance characteristics and cost implications, as the resource usage scales with the number of function invocations. Whenever a function is invoked it must be served by a virtual machine. The major objective behind the function execution is to schedule the incoming function to the most suitable VM so that the VM resources can be utilized efficiently. It offers real-time scheduling of function instances that need to be executed to obtain the best performance and significantly lowers energy consumption. The study provides an extensive overview of resource management strategies in a serverless setting within this framework. Regarding cloud computing, a conceptual framework for workload prediction and resource management, classification of current machine learning-based resource allocation techniques, and key issues with inefficient physical resource distribution are covered. After that, a thorough analysis of current methods supporting machine learning-based methods in serverless resource management is provided. Ultimately, the study examines and summarises several recently identified issues as well as potential future research areas related to elastic resource management in serverless computing environments.

The reactive Power Take Off (PTO) force is the key to maximizing mechanical power absorption and electric power generation of Wave Energy Converters (WECs) from ocean waves of variable frequencies and amplitudes, but its study is limited due to its difficulty in physical realization. This paper presents a simple yet effective RLC-tuned WEC that generates a tunable reactive PTO force from tunable resistor R, inductor L and capacitor C in the WEC. A complete closed loop system model of the WEC is derived first, then five quantitative rules are obtained from analyzing the model. These rules are used to tune the RLC network and hence the reactive PTO force for the WEC to resonate with the regular wave input force and generate maximal electric power over a range of wave frequencies, or to significantly enhance electric power generation from irregular wave input force. Mathematical analysis of the WEC and tuning rules reveals the analytical and quantitative descriptions of mechanical power absorption, active and reactive electric power generation and power factor, optimal electric load R, and generator and LC capacity requirements of the WEC. Simulation studies verify the analysis results and show the effectiveness and advantages of the proposed WEC and its robustness to imprecise estimate of wave frequency.

The lack of explainability using relevant clinical knowledge hinders the adoption of Artificial Intelligence-powered analysis of unstructured clinical dialogue. A wealth of relevant, untapped Mental Health (MH) data is available in online communities, providing the opportunity to address the explainability problem with substantial potential impact as a screening tool for both online and offline applications. We develop a method to enhance attention in popular transformer models and generate clinician-understandable explanations for classification by incorporating external clinical knowledge. Inspired by how clinicians rely on their expertise when interacting with patients, we leverage relevant clinical knowledge to model patient inputs, providing meaningful explanations for classification. This will save manual review time and engender trust. We develop such a system in the context of MH using clinical practice guidelines (CPG) for diagnosing depression, a mental health disorder of global concern. We propose an application-specific language model called ProcesS knowledge-infused cross ATtention (PSAT), which incorporates CPGs when computing attention. Through rigorous evaluation on three expert-curated datasets related to depression, we demonstrate application-relevant explainability of PSAT. PSAT also surpasses the performance of nine baseline models and can provide explanations where other baselines fall short. We transform a CPG resource focused on depression, such as the Patient Health Questionnaire (e.g. PHQ-9) and related questions, into a machine-readable ontology using SNOMED-CT. With this resource, PSAT enhances the ability of models like GPT-3.5 to generate application-relevant explanations.

This paper presents the design of an open-API-based explainable AI (XAI) service to provide feature-contribution explanations for cloud AI services. Cloud AI services have broad usage in developing domain-specific applications with learning precision metrics. However, the underlying AI models remain opaque on how the model produces the prediction. We argue that XAI operations are accessible as open APIs to enable the consolidation of the XAI operations into the cloud AI services assessment. We propose a design using a microservice architecture that offers feature contribution explanations for cloud AI services without unfolding the network structure of the cloud AI services. We can also utilize this architecture to evaluate the performance and XAI consistency metrics showing cloud AI services' trustworthiness. We collect provenance data from XAI operations to enable traceability within the XAI service. Furthermore, we present the discovery scenarios for the experimental tests regarding performance and XAI consistency metrics for the leading cloud AI services for computer vision. The results confirm the open-API-based architecture cloud-agnostic. Additionally, data augmentation has a marked improvement in XAI consistency metrics for the cloud AI services.

The Einstein's theory of special relativity is based on his two postulates. The first is that the laws of physics are the same in all inertial reference frames. The second is that the velocity of light in the vacuum is the same in all inertial frames. The theory of special relativity is considered to be supported by a large number of experiments. This paper revisits the two postulates according to the new interpretations to the exact solutions of moving sources in the laboratory frame. The exact solutions are obtained using the classic Maxwell's theory, which clearly show that the propagation velocity of the electromagnetic waves of moving sources in the vacuum is not isotropic; the propagation velocity of the electromagnetic waves and the moving velocity of the sources cannot be added like vectors; the transverse Doppler effect is intrinsically included in the fields of the moving sources. The electromagnetic sources are subject to the Newtonian mechanics, while the electromagnetic fields are subject to the Maxwell's theory. We argue that since their behaviors are quite different, it is not a best choice to try to bind them together and force them to undergo the same coordinate transformations as a whole, like that in the Lorentz transformations. Furthermore, the Maxwell's theory does not impose any limitations on the velocity of the electromagnetic waves. To assume that all objects cannot move faster than the light in the vacuum need more examinations. We have carefully checked the main experiment results that were considered as supporting the special relativity. Unfortunately, we found that the experimental results may have been misinterpreted. We here propose a Galilean-Newtonian-Maxwellian relativity, which can give the same or even better explanations to those experimental results.