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Land degradation and air pollution are primarily caused by the salinization of soil and desertification that occurs from the drying of salinity lakes and the release of dust into the atmosphere because of their dried bottom. The complete drying up of a lake has caused a community environmental catastrophe. In this study, we presented an optimization problem to determine the total surface runoff to maintain the level of salinity lake (Urmia Lake). The proposed process has two key stages: identifying the influential factors in determining the lake water level using sensitivity analysis approaches based upon historical data and optimizing the effective variable to stabilize the lake water level under changing design variables. Based upon the Sobol'-Jansen and Morris techniques, the groundwater level and total surface runoff flow are highly effective with nonlinear and interacting impacts of the lake water level. As a result of the sensitivity analysis, we found that it may be possible to effectively manage lake levels by adjusting total surface runoff. We used genetic algorithms, non-linear optimization, and pattern search techniques to solve the optimization problem. Furthermore, the lake level constraint is established based on a pattern as a constant number every month. In order to maintain a consistent pattern of lake levels, it is necessary to increase surface runoff by approximately 8.7 times during filling season. It is necessary to increase this quantity by 33.5 times during the draining season. In the future, the results may serve as a guide for the rehabilitation of the lake.

In this work we revisit the topic of existence of hydrodynamic shock waves in a cold weakly collisional plasma. For this purpose we consider the well established Adlam-Allen model with the addition of a dashpot term associated with the dissipation of the motion of the electrons relative to the ions. We establish the connection between this model and the Korteweg-de-Vries Burgers equation via an asymptotic multiscale analysis. This fact suggests the possibility that this system may support shock wave solutions. Accordingly, by considering a corresponding dynamical system arising through a co-traveling frame reduction, we identify such orbits via a phase-plane analysis. We then leverage such initial conditions within systematic simulations of the original modified Adlam-Allen model, revealing a variety of supported robust wavefronts depending on the magnitude of the dissipation considered.

The mathematical complexity and high dimensionality of neural networks hinder the training and deployment of machine learning (ML) systems while also requiring substantial computational resources. This fundamental limitation drives ML research, particularly in the exploration of alternative neural network architectures that integrate novel building blocks, such as advanced activation functions. Tunnel diodes are well-known electronic components that utilise the physical effect of quantum tunnelling (QT). Here, we propose using the current voltage characteristic of a tunnel diode as a novel, physics-based activation function for neural networks. We demonstrate that the tunnel-diode activation function (TDAF) outperforms traditional activation functions in terms of accuracy and loss during both training and evaluation. We also highlight its potential for implementation in electronic circuits suited to developing neuromorphic, quantum-inspired AI systems capable of operating in environments not suitable for qubit-based quantum computing hardware., Comment: 8 pages, 6 figures

We introduce the notion of a gauge and of a tagged partition (subordinate to a given gauge) by intersections of open and closed sets of a compact metric space extending the corresponding notions in Henstock-Kurzweil integration of real-valued functions with respect to the Lebesgue measure on the unit interval. We show that, for the integration of bounded functions with respect to a normalised Borel measure $\mu$ on a compact metric space, the notion of a gauge and an associated tagged partition, arise naturally from a normalised simple valuation way-below the Borel measure. Then we consider the integration of unbounded functions with respect to a normalised Borel measure on a compact metric space, for which the Lebesgue integral may fail to exist. A pair of a tagged partition and a gauge defines a simple valuation and we introduce a partial order on these pairs, emulating the partial order of simple valuations in the probabilistic power domain. We define the $D_\mu$-integral of a real-valued function with respect to a Borel measure using the limit of the net of the integrals of the simple valuations induced by pairs of tagged partitions and gauges for the function. The $D_\mu$-integral of functions on a compact metric space with respect to a normalised Borel measure satisfies the basic properties of an integral and generalises the Henstock-Kurzweil integral. We show that when the Lebesgue integral of the function exists then the $D_\mu$-integral also exists and they have the same value. We provide a family of real-valued functions on the Cantor space that are $D_\mu$-integrable but not Lebesgue integrable., Comment: 22 pages

We introduce a family of random matrices where correlations between matrix elements are induced via interaction-derived Boltzmann factors. Varying these yields access to different ensembles. We find a universal scaling behavior of the finite-size statistics characterized by a heavy-tailed eigenvalue distribution whose extremes are governed by the Fr\'echet extreme value distribution for the case corresponding to a ferromagnetic Ising transition. The introduction of a finite density of nonlocal interactions restores standard random-matrix behavior. Suitably rescaled average extremes, playing a physical role as an order parameter, can thus discriminate aspects of the interaction structure; they also yield further nonuniversal information. In particular, the link between maximum eigenvalues and order parameters offers a potential route to resolving long-standing problems in statistical physics, such as deriving the exact magnetization scaling function in the two-dimensional Ising model at criticality., Comment: 4 figures

Accurate prediction of long-term CO2 plume migration beneath seals is crucial for the viability of CO2 storage in deep saline aquifers. Groundwater counterflow and chemical reactions between CO2, brine, and rock significantly influence plume dynamics, dispersion, and boundary evolution. This study develops a novel analytical framework by deriving governing equations for gravity-driven gas migration and obtaining exact self-similar solutions for both pulse and continuous injection. The approach explicitly incorporates chemical reactions and water counterflow, addressing factors often neglected in existing analytical models. A sensitivity analysis is conducted to evaluate the effects of key parameters on CO2 migration. The results show that increasing the chemical reaction coefficient delays the first arrival of CO2 while accelerating its final arrival, highlighting the impact of geochemical reactions on plume migration. Additionally, higher up-dip water velocity shortens the accumulation period, thereby reducing the time required for CO2 to become trapped in geological formations. These findings improve predictive capabilities for long-term CO2 storage and provide insights for optimising sequestration strategies.

Low-Altitude Economy Networks (LAENets) have emerged as significant enablers of social activities, offering low-altitude services such as the transportation of packages, groceries, and medical supplies. Unlike traditional terrestrial networks, LAENets are characterized by control mechanisms and ever-changing operational factors, which make them more complex and susceptible to vulnerabilities. As applications of LAENet continue to expand, robustness of these systems becomes crucial. In this paper, we investigate a novel application of Generative Artificial Intelligence (GenAI) to improve the robustness of LAENets. We conduct a systematic analysis of robustness requirements for LAENets, complemented by a comprehensive review of robust Quality of Service (QoS) metrics from the wireless physical layer perspective. We then investigate existing GenAI-enabled approaches for robustness enhancement. This leads to our proposal of a novel diffusion-based optimization framework with a Mixture of Expert (MoE)-transformer actor network. In the robust beamforming case study, the proposed framework demonstrates its effectiveness by optimizing beamforming under uncertainties, achieving a more than 44% increase in the worst-case achievable secrecy rate. These findings highlight the significant potential of GenAI in strengthening LAENet robustness., Comment: 7 pages, 4 figures

Fines migration during subterranean CO2 storage is considered as one of major contributors to formation damage and injectivity decline. The main formation damage mechanism by migrating fines is rock clogging by straining of particles in thin pore throats. The main mobilisation mechanism of attached natural reservoir fines is their detachment by capillary forces exerting from the advancing water-gas menisci. The mathematical model for displacement of water by injected CO2 from layer-cake reservoirs under fines mobilisation, migration and straining extends the classical vertical capillary-gravity equilibrium model, where hydrostatic pressure gradient is assumed in each phase. The analytical model generalises classical Buckley-Leverett problem, where the extended fractional flow accounts for permeability damage due to fines migration. We show the significant effect of fines migration on water displacement - while it decreases well injectivity, fines migration simultaneously increases reservoir sweep, increasing CO2 storage capacity.

This paper introduces a novel framework for graph sparsification that preserves the essential learning attributes of original graphs, improving computational efficiency and reducing complexity in learning algorithms. We refer to these sparse graphs as "learning backbones". Our approach leverages the zero-forcing (ZF) phenomenon, a dynamic process on graphs with applications in network control. The key idea is to generate a tree from the original graph that retains critical dynamical properties. By correlating these properties with learning attributes, we construct effective learning backbones. We evaluate the performance of our ZF-based backbones in graph classification tasks across eight datasets and six baseline models. The results demonstrate that our method outperforms existing techniques. Additionally, we explore extensions using node distance metrics to further enhance the framework's utility., Comment: 13th International Conference on Complex Networks and their Applications

For practical superconducting quantum processors, orders of magnitude improvement in coherence is required, motivating efforts to optimize hardware design and explore new materials. Among the latter, the coherence of superconducting transmon qubits has been shown to improve by forming the qubit capacitor pads from $\alpha$-tantalum, avoiding the meta-stable $\beta$-phase that forms when depositing tantalum at room temperature, and has been previously identified to be a source of microwave losses. In this work, we show lumped element resonators containing $\beta$-phase tantalum in the form of inclusions near the metal-substrate interface with internal quality factors ($Q_\text{i}$) up to $(5.0 \pm 2.5) \times 10^6$ in the single photon regime. They outperform resonators with no sign of the $\beta$-phase in x-ray diffraction and thermal quasi-particle loss. Our results indicate that small concentrations of $\beta$-phase can be beneficial, enhancing critical magnetic fields and potentially, for improving coherence in tantalum based superconducting circuits.

Super-resolution Structured Illumination Microscopy (SR-SIM) enables fluorescence microscopy beyond the diffraction limit at high frame rates. Compared to other super-resolution microscopy techniques, the low photon fluence used in SR-SIM makes it readily compatible with live-cell imaging. Here, we combine SR-SIM with electro-optic fluorescence lifetime imaging (EOFLIM), adding the capability of monitoring physicochemical parameters with 156 nm spatial resolution at high frame rate for live-cell imaging. We demonstrate that our new SIMFLIM technique enables super-resolved multiplexed imaging of spectrally overlapping fluorophores, environmental sensing, and live-cell imaging.

Accurate prediction of neural dynamics in the brain's reward circuitry is crucial for elucidating how natural and pharmacological rewards influence neural activity and connectivity. Traditional linear models, such as autoregressive (AR) and vector autoregressive (VAR), often inadequately capture the inherent nonlinear interactions in neural data. This study develops and benchmarks both linear and advanced deep learning models for predicting local field potentials (LFPs) in the rat hippocampus (HIP) and nucleus accumbens (NAc) across morphine, food, and saline conditions. We compared AR, VAR, long short-term memory (LSTM), and wavelet-based deep learning model (WCLSA). Additionally, a novel wavelet coherence-enhanced model (WCOH CLSA) was introduced to capture cross-region connectivity. Results indicate that WCLSA achieves superior predictive accuracy (up to 0.97 for HIP in food, 0.96 for NAc in morphine), while VAR performs competitively in the food group due to significant HIP-NAc correlation. Wavelet coherence analysis reveals robust connectivity in natural reward contexts and disrupted or nonlinear relationships under pharmacological influence. These findings highlight the differential engagement of HIP and NAc in reward processing and underscore the importance of advanced nonlinear models for capturing complex neural dynamics. The study provides a robust framework for predictive neuroscience and elucidates functional interactions within the reward circuitry.

Precise tracking and measurement of the energy carried by the individual magnetohydrodynamic (MHD) modes has important implications and utility in astrophysical and laboratory plasmas. Previously, this was only achievable in limited linear MHD cases in the $\beta \ll 1$ or $\beta \gg 1$ regimes. In a series of papers, of which this is the third, we introduced the Eigenenergy Decomposition Method (EEDM) and derived exact analytical expressions for the modal energy components--called eigenenergies--of nonlinear 3D disturbances governed by the homogeneous ideal-MHD equations. Here, we extend the method to inhomogeneous ideal-MHD by introducing a source term accounting for gravity, and provide detailed guidelines for applying the decomposition scheme to any general inhomogeneous quasi-linear PDEs that possess a globally conserved quantity, beyond the realm of MHD. Furthermore, we show that the eigenenergies can be used to locate and measure nonlinear mode conversions, which is an additional feature of the method. Finally, we provide well-categorized context for the application of the method to simulations and discuss the possible numerical inaccuracies that may inevitably arise due to discretization. This paper provides a more mature description of the method and its interpretation, and is recommended as the starting point for readers unfamiliar with the method.

We generalize the enhanced power graph by replacing elements with classes under automorphisms. We show that the connectivity and diameter of this graph is similar to that of the enhanced power graph. We consider the universal vertices of this graph and when this graph is a complete graph. Finally, we classify when this graph is the empty graph.

This study investigates the multi-label classification of Local Field Potential (LFP) data from the hippocampus (HIP) and nucleus accumbens (NAc) in the rat brain, focusing on reward responses using the Conditioned Place Preference (CPP) paradigm. Rats were conditioned with saline, morphine, and food rewards, and LFP recordings were conducted from both HIP and NAc during pre- and post-tests. The LFP data were classified into four categories: treatment types, test phases, recording channels, and chamber positions within the CPP setup. Features were extracted using Continuous Wavelet Transform (CWT), Wavelet Coherence, and Wavelet Scattering. Classification was performed via Decision Trees, Multilayer Perceptrons, and Support Vector Machines. Notably, in the Food group, HIP and combined HIP-NAc features yielded the highest classification accuracy for CPP chambers, whereas NAc features excelled in the Morphine group. Employing wavelet scattering, an 80% classification accuracy was achieved across treatment groups, test phases, and channels. Exceptionally high classification accuracies were observed for Food-post-test-HIP (99.75%) and Morphine-post-test-NAc (99.58%). The study reveals that NAc activity is pivotal for morphine-induced CPP, whereas HIP and HIP-NAc connectivity are crucial for food-induced CPP. The proposed methodology provides a novel avenue for precisely classifying LFP data, shedding light on neural circuit activities underlying behavioral responses.

Modern organizations frequently rely on chat-based platforms (e.g., Slack, Microsoft Teams, and Discord) for day-to-day communication and decision-making. As conversations evolve, organizational knowledge can get buried, prompting repeated searches and discussions. While maintaining shared documents, such as Wiki articles for the organization, offers a partial solution, it requires manual and timely efforts to keep it up to date, and it may not effectively preserve the social and contextual aspect of prior discussions. Moreover, reaching a consensus on document updates with relevant stakeholders can be time-consuming and complex. To address these challenges, we introduce CHOIR (Chat-based Helper for Organizational Intelligence Repository), a chatbot that integrates seamlessly with chat platforms. CHOIR automatically identifies and proposes edits to related documents, initiates discussions with relevant team members, and preserves contextual revision histories. By embedding knowledge management directly into chat environments and leveraging LLMs, CHOIR simplifies manual updates and supports consensus-driven editing based on maintained context with revision histories. We plan to design, deploy, and evaluate CHOIR in the context of maintaining an organizational memory for a research lab. We describe the chatbot's motivation, design, and early implementation to show how CHOIR streamlines collaborative document management., Comment: 4 pages, CHI2025 Workshop on Tools for Thought

We investigate the nuclear Stark effect induced in hydrogen-like atomic nuclei under super-intense laser fields. Since laser wavelengths are generally larger than nuclear dimensions, direct laser-nucleus interaction is unfeasible. Instead, this effect is induced indirectly through electron oscillations in the laser field, which produce a periodic electric field that shifts the nuclear energy levels. Using perturbation theory, we derive an expression for the energy shift and dynamic polarizability of the nucleus as a function of laser parameters. Our findings reveal that the Nuclear Stark effect can be controlled by adjusting the laser frequency and intensity, potentially enabling applications in nuclear and quantum optical systems., Comment: 4 Pages, 3 Figures

We address the computational challenges of solving parametric PDEs with non parametrized geometric variations and non-reducible problems, such as those involving shocks and discontinuities of variable positions. Traditional dimensionality reduction methods like POD struggle with these scenarios due to slowly decaying Kolmogorov widths. To overcome this, we propose a novel non-linear dimensionality reduction technique to reduce the required modes for representation. The non-linear reduction is obtained through a POD after applying a transformation on the fields, which we call optimal mappings, and is a solution to an optimization problem in infinite dimension. The proposed learning framework combines morphing techniques, non-linear dimensionality reduction, and Gaussian Process Regression (GPR). The problem is reformulated on a reference geometry before applying the dimensionality reduction. Our method learns both the optimal mapping, and the solution fields, using a series of GPR models, enabling efficient and accurate modeling of complex parametric PDEs with geometrical variability. The results obtained concur with current state-of-the-art models. We mainly compare our method with the winning solution of the ML4CFD NeurIPS 2024 competition.

Let $(\tilde{\Sigma},h_{ab},K_{ab})$ be an initial data set and let $x^a$ be a symmetry vector of $\tilde{\Sigma}$. Consider a MOTS $\mathcal{S}$ in $\tilde{\Sigma}$ and let the symmetry vector be decomposable along the unit normal to $\mathcal{S}$ in $\tilde{\Sigma}$, and along $\mathcal{S}$. In this note we present some basic results with regards to the stability of $\mathcal{S}$. The vector decomposition allows us to characterize the instability of $\mathcal{S}$ by the nature of the zero set of the normal component to $\mathcal{S}$ and the divergence of the component along $\mathcal{S}$. Further observations are made under the assumption of $\mathcal{S}$ having a constant mean curvature, and $\tilde{\Sigma}$ being an Einstein manifold., Comment: 7 2-column pages, submitted to CQG as a Note, comments are welcome

The global electronic properties of solid-state devices are strongly affected by the microscopic spatial paths of charge carriers. Visualising these paths in novel devices produced by scalable processes would provide a quality assessment method that can propel the device performance metrics towards commercial use. Here, we use high-resolution nitrogen-vacancy (NV) magnetometry to visualise the charge flow in gold-contacted, single-layer graphene devices produced by scalable methods. Modulating the majority carrier type via field effect reveals a strong asymmetry between the spatial current distributions in the electron and hole regimes that we attribute to an inhomogeneous microscopic potential landscape, inaccessible to conventional measurement techniques. In addition, we observe large, unexpected, differences in charge flow through nominally identical gold-graphene contacts. Moreover, we find that the current transfer into the graphene occurs several microns before the metal contact edge. Our findings establish high-resolution NV-magnetometry as a key tool for characterizing scalable 2D material based devices, uncovering quality deficits of the material, substrate, and electrical contacts that are invisible to conventional methods.

This study evaluates the effectiveness of different feature extraction techniques and classification algorithms in detecting spam messages within SMS data. We analyzed six classifiers Naive Bayes, K-Nearest Neighbors, Support Vector Machines, Linear Discriminant Analysis, Decision Trees, and Deep Neural Networks using two feature extraction methods: bag-of-words and TF-IDF. The primary objective was to determine the most effective classifier-feature combination for SMS spam detection. Our research offers two main contributions: first, by systematically examining various classifier and feature extraction pairings, and second, by empirically evaluating their ability to distinguish spam messages. Our results demonstrate that the TF-IDF method consistently outperforms the bag-of-words approach across all six classifiers. Specifically, Naive Bayes with TF-IDF achieved the highest accuracy of 96.2%, with a precision of 0.976 for non-spam and 0.754 for spam messages. Similarly, Support Vector Machines with TF-IDF exhibited an accuracy of 94.5%, with a precision of 0.926 for non-spam and 0.891 for spam. Deep Neural Networks using TF-IDF yielded an accuracy of 91.0%, with a recall of 0.991 for non-spam and 0.415 for spam messages. In contrast, classifiers such as K-Nearest Neighbors, Linear Discriminant Analysis, and Decision Trees showed weaker performance, regardless of the feature extraction method employed. Furthermore, we observed substantial variability in classifier effectiveness depending on the chosen feature extraction technique. Our findings emphasize the significance of feature selection in SMS spam detection and suggest that TF-IDF, when paired with Naive Bayes, Support Vector Machines, or Deep Neural Networks, provides the most reliable performance. These insights provide a foundation for improving SMS spam detection through optimized feature extraction and classification methods.

The primary objective of this study is to demonstrate the impact of data augmentation using ChatGPT-4o-mini on food hazard and product analysis. The augmented data is generated using ChatGPT-4o-mini and subsequently used to train two large language models: RoBERTa-base and Flan-T5-base. The models are evaluated on test sets. The results indicate that using augmented data helped improve model performance across key metrics, including recall, F1 score, precision, and accuracy, compared to using only the provided dataset. The full code, including model training and the augmented dataset, can be found in this repository: https://github.com/AREEG94FAHAD/food-hazard-prdouct-cls

As AI systems integrate into critical infrastructure, security gaps in AI compliance frameworks demand urgent attention. This paper audits and quantifies security risks in three major AI governance standards: NIST AI RMF 1.0, UK's AI and Data Protection Risk Toolkit, and the EU's ALTAI. Using a novel risk assessment methodology, we develop four key metrics: Risk Severity Index (RSI), Attack Potential Index (AVPI), Compliance-Security Gap Percentage (CSGP), and Root Cause Vulnerability Score (RCVS). Our analysis identifies 136 concerns across the frameworks, exposing significant gaps. NIST fails to address 69.23 percent of identified risks, ALTAI has the highest attack vector vulnerability (AVPI = 0.51) and the ICO Toolkit has the largest compliance-security gap, with 80.00 percent of high-risk concerns remaining unresolved. Root cause analysis highlights under-defined processes (ALTAI RCVS = 033) and weak implementation guidance (NIST and ICO RCVS = 0.25) as critical weaknesses. These findings emphasize the need for stronger, enforceable security controls in AI compliance. We offer targeted recommendations to enhance security posture and bridge the gap between compliance and real-world AI risks.

Plasmonic polarization conversion offers significant advantages over conventional methods, including a smaller device footprint and easier integration into photonic circuits. In this work, we numerically and experimentally investigate the polarization conversion properties of a plasmonic double-hole structure surrounded by circular nanograting, i.e., a bull's eye antenna. Using a combination of polarimetric imaging via back focal plane (BFP) microscopy and the Stokes parameter analysis, we demonstrate the functionality of our structure as a miniature on chip polarization converter. Our results show that this nanostructure enables complex polarization transformations, including converting linear to circular polarization and vice versa. The polarization conversion efficiency is found to be dependent on the periodicity of the circular gratings and is particularly pronounced in the central region of the Fourier space. Moreover, the strong asymmetric scattering leads to distinctive patterns in the Stokes parameters across various incident polarization states. This work provides insights into the plasmonic manipulation of light polarization at the nanoscale, with potential applications in miniature on-chip polarization convertors, polarization-controlled emitters, and advanced sensing technologies.

All data modalities are not created equal, even when the signal they measure comes from the same source. In the case of the brain, two of the most important data modalities are the scalp electroencephalogram (EEG), and the intracranial electroencephalogram (iEEG). They are used by human experts, supported by deep learning (DL) models, to accomplish a variety of tasks, such as seizure detection and motor imagery classification. Although the differences between EEG and iEEG are well understood by human experts, the performance of DL models across these two modalities remains under-explored. To help characterize the importance of clean data on the performance of DL models, we propose BrainCodec, a high-fidelity EEG and iEEG neural compressor. We find that training BrainCodec on iEEG and then transferring to EEG yields higher reconstruction quality than training on EEG directly. In addition, we also find that training BrainCodec on both EEG and iEEG improves fidelity when reconstructing EEG. Our work indicates that data sources with higher SNR, such as iEEG, provide better performance across the board also in the medical time-series domain. BrainCodec also achieves up to a 64x compression on iEEG and EEG without a notable decrease in quality. BrainCodec markedly surpasses current state-of-the-art compression models both in final compression ratio and in reconstruction fidelity. We also evaluate the fidelity of the compressed signals objectively on a seizure detection and a motor imagery task performed by standard DL models. Here, we find that BrainCodec achieves a reconstruction fidelity high enough to ensure no performance degradation on the downstream tasks. Finally, we collect the subjective assessment of an expert neurologist, that confirms the high reconstruction quality of BrainCodec in a realistic scenario. The code is available at https://github.com/IBM/eeg-ieeg-brain-compressor., Comment: Published at ICLR 2025, see https://openreview.net/forum?id=b57IG6N20B. Code is available at https://github.com/IBM/eeg-ieeg-brain-compressor

This paper presents an integrated multiphysics simulation approach of piezoelectric micropumps. Micropumps and micro blowers are essential devices in various cutting-edge industries like laboratory equipment, medical devices, and fuel cells. A piezoelectric micropump involves complex physics including microfluidics, flow-structure interaction, electricity, and piezoelectric material. Hence, a comprehensive analysis of the interactions between different physical phenomena, would be essential for the effective design and optimization of these micropumps. Prior studies on piezoelectric micropump were mainly focused on isolated physical aspects of these pumps, such as piezoelectric mechanics, fluid dynamics, electrical properties, and also fluid-structure Interactions. The present paper fills this gap by integrating these aspects into a holistic simulation and design approach, introducing a new methodology for micropump analysis. Advanced simulation and design tools like COMSOL and SolidWorks were employed in accordance. A brief review of piezoelectric materials, and an exploration of different types of micropumps and their operating principles is discussed. Also, a comparison of various piezoelectric materials, including their properties and applications is investigated. Further, the paper discusses the simulation process of the micropumps, using COMSOL software, and presents an in-depth analysis of the simulation results. This structured approach provides a comprehensive understanding of piezoelectric micropumps, from theoretical underpinnings to practical design considerations. ..

This paper presents an analysis of Model Predictive Control (MPC) and Reinforcement Learning (RL) approaches for active flow control over a NACA (National Advisory Committee for Aeronautics) 4412 airfoil around the static stall condition at a Reynolds number of 4*10^5. The Reynolds Averaged Navier-Stokes (RANS) equations with the Scale-Adaptive Simulation (SAS) turbulence model are utilized for the numerical simulations. The dielectric barrier discharge (DBD) plasma actuators were employed in dual excitation mode for flow separation control. The study systematically evaluates adaptive MPC, temporal difference reinforcement learning (TDRL), and deep Q-learning (DQL) based on optimizing the excitation frequency and expediting the time to identify stable conditions. Moreover, an integrated approach that combines signal processing with DQL is examined. The results demonstrate that while MPC and RL significantly improve flow control, RL approaches offer superior adaptability and performance. In optimal conditions, a lift coefficient of around 1.619 was achieved within less than 2.5 seconds with an excitation frequency of 100 or 200 Hz. This research highlights that RL-based approaches could perform better in flow control applications than MPC.

The advancements in autonomous driving technology, coupled with the growing interest from automotive manufacturers and tech companies, suggest a rising adoption of Connected Autonomous Vehicles (CAVs) in the near future. Despite some evidence of higher accident rates in AVs, these incidents tend to result in less severe injuries compared to traditional vehicles due to cooperative safety measures. However, the increased complexity of CAV systems exposes them to significant security vulnerabilities, potentially compromising their performance and communication integrity. This paper contributes by presenting a detailed analysis of existing security frameworks and protocols, focusing on intra- and inter-vehicle communications. We systematically evaluate the effectiveness of these frameworks in addressing known vulnerabilities and propose a set of best practices for enhancing CAV communication security. The paper also provides a comprehensive taxonomy of attack vectors in CAV ecosystems and suggests future research directions for designing more robust security mechanisms. Our key contributions include the development of a new classification system for CAV security threats, the proposal of practical security protocols, and the introduction of use cases that demonstrate how these protocols can be integrated into real-world CAV applications. These insights are crucial for advancing secure CAV adoption and ensuring the safe integration of autonomous vehicles into intelligent transportation systems.

Distinguishing in- and out-of-distribution (OOD) inputs is crucial for reliable deployment of classification systems. However, OOD data is typically unavailable or difficult to collect, posing a significant challenge for accurate OOD detection. In this work, we present a method that harnesses the generative capabilities of Large Language Models (LLMs) to create high-quality synthetic OOD proxies, eliminating the dependency on any external OOD data source. We study the efficacy of our method on classical text classification tasks such as toxicity detection and sentiment classification as well as classification tasks arising in LLM development and deployment, such as training a reward model for RLHF and detecting misaligned generations. Extensive experiments on nine InD-OOD dataset pairs and various model sizes show that our approach dramatically lowers false positive rates (achieving a perfect zero in some cases) while maintaining high accuracy on in-distribution tasks, outperforming baseline methods by a significant margin.

The IoT facilitates a connected, intelligent, and sustainable society; therefore, it is imperative to protect the IoT ecosystem. The IoT-based 5G and 6G will leverage the use of machine learning and artificial intelligence (ML/AI) more to pave the way for autonomous and collaborative secure IoT networks. Zero-touch, zero-trust IoT security with AI and machine learning (ML) enablement frameworks offers a powerful approach to securing the expanding landscape of Internet of Things (IoT) devices. This paper presents a novel framework based on the integration of Zero Trust, Zero Touch, and AI/ML powered for the detection, mitigation, and prevention of DDoS attacks in modern IoT ecosystems. The focus will be on the new integrated framework by establishing zero trust for all IoT traffic, fixed and mobile 5G/6G IoT network traffic, and data security (quarantine-zero touch and dynamic policy enforcement). We perform a comparative analysis of five machine learning models, namely, XGBoost, Random Forest, K-Nearest Neighbors, Stochastic Gradient Descent, and Native Bayes, by comparing these models based on accuracy, precision, recall, F1-score, and ROC-AUC. Results show that the best performance in detecting and mitigating different DDoS vectors comes from the ensemble-based approaches.

The number of applications in Google Play has increased dramatically in recent years. On Google Play, users can write detailed reviews and rate apps, with these ratings significantly influencing app success and download numbers. Reviews often include notable information like feature requests, which are valuable for software maintenance. Users can update their reviews and ratings anytime. Studies indicate that apps with ratings below three stars are typically avoided by potential users. Since 2013, Google Play has allowed developers to respond to user reviews, helping resolve issues and potentially boosting overall ratings and download rates. However, responding to reviews is time-consuming, and only 13% to 18% of developers engage in this practice. To address this challenge, we propose a method to prioritize reviews based on response priority. We collected and preprocessed review data, extracted both textual and semantic features, and assessed their impact on the importance of responses. We labelled reviews as requiring a response or not and trained four different machine learning models to prioritize them. We evaluated the models performance using metrics such as F1-Score, Accuracy, Precision, and Recall. Our findings indicate that the XGBoost model is the most effective for prioritizing reviews needing a response., Comment: 8 pages, 1 figure, 5 tables