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Though ubiquitous in many engineering applications, including drug delivery, the compound droplet hydrodynamics in confined geometries have been barely surveyed. For the first time, this study thoroughly investigates the hydrodynamics of a ferrofluid compound droplet (FCD) during its migration in a microchannel under the presence of a pressure-driven flow and a uniform external magnetic field (UEMF) to manipulate its morphology and retard its breakup. Finite difference and phase-field multiple-relaxation time lattice Boltzmann approaches are coupled to determine the magnetic field and ternary flow system, respectively. Firstly, the influence of the magnetic Bond number (Bo) on the FCD morphology is explored depending on whether the core or shell is ferrofluid when the UEMF is applied along {\alpha}=0{\deg} and {\alpha}=90{\deg} relative to the fluid flow. It is ascertained that imposing the UEMF at {\alpha}=0{\deg} when the shell is ferrofluid can postpone the breakup. Intriguingly, when the core is ferrofluid, strengthening the UEMF enlarges the shell deformation. Afterward, the effects of the Capillary number (Ca), density ratio, viscosity ratio, radius ratio, and surface tension coefficients are scrutinized on the FCD deformation and breakup. The results indicate that augmenting the core-to-shell viscosity and density ratios accelerates the breakup process. Additionally, surface tension between the core and shell suppresses the core deformation. Moreover, increasing the Ca number intensifies the viscous drag force exerted on the shell, flattening its rear side, which causes a triangular-like configuration. Ultimately, by varying Bo and Ca numbers, five distinguished regimes are observed, whose regime map is established., Comment: 63 pages, 26 figures, 3 tables

Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materials and Methods: We analyzed data from 9,134 COVID-19 patients collected across four hospitals. Seven CML models, including XGBoost and random forest (RF), were trained and evaluated. The structured data was converted into text for zero-shot classification by eight LLMs, including GPT-4 and Mistral-7b. Additionally, Mistral-7b was fine-tuned using the QLoRA approach to enhance its predictive capabilities. Results: Among the CML models, XGBoost and RF achieved the highest accuracy, with F1 scores of 0.87 for internal validation and 0.83 for external validation. In the LLM category, GPT-4 was the top performer with an F1 score of 0.43. Fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, resulting in an F1 score of 0.74, which was stable during external validation. Conclusion: While LLMs show moderate performance in zero-shot classification, fine-tuning can significantly enhance their effectiveness, potentially aligning them closer to CML models. However, CMLs still outperform LLMs in high-dimensional tabular data tasks., Comment: Code is available at: https://github.com/mohammad-gh009/Large-Language-Models-vs-Classical-Machine-learning and https://github.com/Sdamirsa/Tehran_COVID_Cohort. The datasets are available from the corresponding author on reasonable request (sdamirsa@ymail.com)

In the rapidly evolving field of business process management, there is a growing need for analytical tools that can transform complex data into actionable insights. This research introduces a novel approach by integrating Large Language Models (LLMs), such as ChatGPT, into process mining tools, making process analytics more accessible to a wider audience. The study aims to investigate how ChatGPT enhances analytical capabilities, improves user experience, increases accessibility, and optimizes the architectural frameworks of process mining tools. The key innovation of this research lies in developing a tailored prompt engineering strategy for each process mining submodule, ensuring that the AI-generated outputs are accurate and relevant to the context. The integration architecture follows an Extract, Transform, Load (ETL) process, which includes various process mining engine modules and utilizes zero-shot and optimized prompt engineering techniques. ChatGPT is connected via APIs and receives structured outputs from the process mining modules, enabling conversational interactions. To validate the effectiveness of this approach, the researchers used data from 17 companies that employ BehfaLab's Process Mining Tool. The results showed significant improvements in user experience, with an expert panel rating 72% of the results as "Good". This research contributes to the advancement of business process analysis methodologies by combining process mining with artificial intelligence. Future research directions include further optimization of prompt engineering, exploration of integration with other AI technologies, and assessment of scalability across various business environments. This study paves the way for continuous innovation at the intersection of process mining and artificial intelligence, promising to revolutionize the way businesses analyze and optimize their processes.

Polynomial multiplication stands out as a highly demanding arithmetic process in the development of post-quantum cryptosystems. The importance of the number-theoretic transform (NTT) extends beyond post-quantum cryptosystems, proving valuable in enhancing existing security protocols such as digital signature schemes and hash functions. CRYSTALS-KYBER stands out as the sole public key encryption (PKE) algorithm chosen by the National Institute of Standards and Technology (NIST) in its third round selection, making it highly regarded as a leading post-quantum cryptography (PQC) solution. Due to the potential for errors to significantly disrupt the operation of secure, cryptographically-protected systems, compromising data integrity, and safeguarding against side-channel attacks initiated through faults it is essential to incorporate mitigating error detection schemes. This paper introduces algorithm level fault detection schemes in the NTT multiplication using Negative Wrapped Convolution and the NTT tailored for Kyber Round 3, representing a significant enhancement compared to previous research. We evaluate this through the simulation of a fault model, ensuring that the conducted assessments accurately mirror the obtained results. Consequently, we attain a notably comprehensive coverage of errors. Furthermore, we assess the performance of our efficient error detection scheme for Negative Wrapped Convolution on FPGAs to showcase its implementation and resource requirements. Through implementation of our error detection approach on Xilinx/AMD Zynq Ultrascale+ and Artix-7, we achieve a comparable throughput with just a 9% increase in area and 13% increase in latency compared to the original hardware implementations. Finally, we attained an error detection ratio of nearly 100% for the NTT operation in Kyber Round 3, with a clock cycle overhead of 16% on the Cortex-A72 processor.

Whether stemming from malicious intent or natural occurrences, faults and errors can significantly undermine the reliability of any architecture. In response to this challenge, fault detection assumes a pivotal role in ensuring the secure deployment of cryptosystems. Even when a cryptosystem boasts mathematical security, its practical implementation may remain susceptible to exploitation through side-channel attacks. In this paper, we propose a lightweight fault detection architecture tailored for modular exponentiation, a building block of numerous cryptographic applications spanning from classical cryptography to post quantum cryptography. Based on our simulation and implementation results on ARM Cortex-A72 processor, and AMD/Xilinx Zynq Ultrascale+, and Artix-7 FPGAs, our approach achieves an error detection rate close to 100%, all while introducing a modest computational overhead of approximately 7% and area overhead of less than 1% compared to the unprotected architecture. To the best of our knowledge, such an approach benchmarked on ARM processor and FPGA has not been proposed and assessed to date., Comment: 5 pages, 2 figures

A Dielectric Barrier Discharge (DBD) plasma actuator for controlling airflow is proposed. It consists of diverging and converging nozzles, two concentric cylinders and an actuator mounted in-between the two cylinders. The actuator employs electrohydrodynamic (EHD) body force to induce an air jet within the air gap between the two cylinders, effectively creating a suction area while passing through the diverging nozzle, due to the Coanda effect. While merging with the air stream inside the inner cylinder, the Coanda jet effectively enhances amplification of the airflow. The outflow rate is measured by a velocity sensor at the outlet and controlled by the plasma actuator. The control strategy is based on the Active Disturbance Rejection Control (ADRC) and compared to the baseline PID controller. The actuator was modelled by seamlessly linking two modeling platforms for a co-simulation study. The CFD simulation of the plasma and airflow was carried out in the COMSOL multi-physics commercial software, and the control was implemented in the Simulink. The DBD plasma model was based on the two-species model of discharge, and the electric body force, calculated from the plasma simulation, was used in the Navier-Stokes equation for the turbulent flow simulation. The plasma-air flow system was analyzed using the input (the actuator voltage) and output (the outlet flow rate) data for the control design. Finally, the performance of the system of air flow control device was tested and discussed in the co-simulation process.

The rise of quantum computers exposes vulnerabilities in current public key cryptographic protocols, necessitating the development of secure post-quantum (PQ) schemes. Hence, we conduct a comprehensive study on various PQ approaches, covering the constructional design, structural vulnerabilities, and offer security assessments, implementation evaluations, and a particular focus on side-channel attacks. We analyze global standardization processes, evaluate their metrics in relation to real-world applications, and primarily focus on standardized PQ schemes, selected additional signature competition candidates, and PQ-secure cutting-edge schemes beyond standardization. Finally, we present visions and potential future directions for a seamless transition to the PQ era.

Technology has become an indispensable competitive tool as science and technology have progressed throughout history. Organizations can compete on an equal footing by implementing technology appropriately. Technology trends or technology lifecycles begin during the initiation phase. Finally, it reaches saturation after entering the maturity phase. As technology reaches saturation, it will be removed or replaced by another. This makes investing in technologies during this phase unjustifiable. Technology forecasting is a critical tool for research and development to determine the future direction of technology. Based on registered patents, this study examined the trends of IOT technologies. A total of 3697 patents related to the Internet of Things from the last six years of patenting have been gathered using lens.org for this purpose. The main people and companies were identified through the creation of the IOT patent registration cooperation network, and the main groups active in patent registration were identified by the community detection technique. The patents were then divided into six technology categories: Safety and Security, Information Services, Public Safety and Environment Monitoring, Collaborative Aware Systems, Smart Homes/Buildings, and Smart Grid. And their technical maturity was identified and examined using the Sigma Plot program. Based on the findings, information services technologies are in the saturation stage, while both smart homes/buildings, and smart grid technologies are in the saturation stage. Three technologies, Safety and Security, Public Safety and Environment Monitoring, and Collaborative Aware Systems are in the maturity stage.