Named Entity Recognition (NER) is a crucial component in extracting structured information from unstructured text across various domains. A novel approach has been developed to address the variability in domain-specific annotations through the integration of a unified label schema, significantly enhancing cross-domain NER performance. The study involved comprehensive modifications to the Mistral Large model, including adjustments to its architecture, output layer, and loss function, to incorporate the aligned label schema effectively. The methodology encompassed rigorous data collection, preprocessing, and evaluation processes, ensuring robust model training and validation. Evaluation metrics such as precision, recall, F1-score, and accuracy demonstrated substantial improvements, validating the efficacy of the label alignment algorithm. The research highlights the model's ability to generalize entity recognition capabilities across diverse domains, making it adaptable to various linguistic and contextual details. The implications extend to numerous applications reliant on accurate entity recognition, including information retrieval, question answering, and knowledge base population, demonstrating the broader impact of the findings. Through these significant advancements, the study contributes to the development of more intelligent and adaptive NER systems capable of handling the complexities of diverse and evolving textual environments.

Microscopic images of cells often contain multiple objects with similar properties. In this study, we collected videos of blood microcirculation in nailfold capillaries to identify correlations between video data and indicators of complete blood count tests. The specific features of these videos include a high number of capillaries and a high variance in their visual quality. Therefore, we proposed a novel loss function and a video processing framework that combines metric learning and classification with uncertainty estimation. We also applied test-time augmentation to enhance the confidence of the predictions. During simulation experiments, we used shallow and deep learning models to compare a set of classifiers for the collected metadata. Both types of models demonstrated good results, with an accuracy of up to 80% in gender classification. Deep learning models outperformed shallow models and also classified the age of the volunteers with an accuracy of 74%. Subsequently, we addressed a series of binary classification tasks to identify conditionally high or low levels of the main complete blood count indicators. The obtained accuracy of the models reached 63%. Although the accuracy of the blood test classifiers is not yet sufficient for clinical purposes, we have demonstrated that video data correlates with some of the indicators.

This study considers a virtual multiuser multipleinput multiple-output system with PSK modulation realized via the reconfigurable intelligent surface-based passive transmitter setup. With a discrete phase-shift reconfigurable intelligent surface model the study proposes a power minimization problem under quality-of-service constraints. The problem formulation is divided into two scenarios. First, for the case of QPSK user's data, the symbol-error probability is utilized as the quality-of-service criterion. Then, for the general case of 𝑀-PSK modulation, the union-bound symbol-error probability is utilized for the qualityof-service constraints. Based on the considered formulations a branch-and-bound approach is developed which improves on full branch-and-bound methods in the sense of allowing for favorable complexity performance trade-offs. Finally, for the special case of high-resolution reconfigurable intelligent surfaces, the study proposes a low-complexity approach based on the approximation of the discrete phase shift set by its continuous counterpart. Based on this, the previously proposed power minimization problems are reformulated as constrained optimizations on an oblique manifold, and solved via the proposed bisection method. Numerical results demonstrate the effectiveness of the proposed approaches in minimizing the transmit power for different symbol-error probability requirements.

In this paper, an analysis and performance review of a unique hybrid high-power lithium-iron phosphate cell (HP-LFP) with a high cycle life and fast charge/discharge rate is presented. The new hybrid cell has been developed under the framework of the EU-funded project Hybrid Energy Storage Station (HEROES). The proposed HP-LFP cell simultaneously achieves an optimized energy and power throughput making it useful for applications where both power and energy are demanded such as different types of electric vehicles (EVs), microgrids, and charge stations. The cathode of the hybrid HP-LFP cell is similar to the LFP batteries while the supercapacitorlike anode is based on hard carbon. Thus, the HP-LFP cell has some similarities to the recent lithium-ion capacitors (LiCs) but it behaves differently than the existing LFP batteries and supercapacitors. To distinguish the differences, HP-LFP cell samples are tested, characterized, and analyzed to review their performance compared to the existing technologies. Different standard tests including the open-circuit test, hybrid pulse power characteristics test, and capacity test are fulfilled. The test data is used for model parameterization and parameter sensitivity analysis with respect to the cell's state of charge, temperature, and charge rate. The cell performance has been validated through mission profile testing in an EV fast charge station. Based on the analysis, the paper provides a comparison between different energy storage cells.

Treap is a balanced search tree that relies on a random distribution of keys to keep the tree balanced. This paper introduces a novel data structure that expands the concept of binary treap to N-ary trees. We outline the structural characteristics, including the parent-child relationship, and discuss the relative arrangement of elements with different priorities within this tree data structure. Furthermore, we analyze algorithms for performing operations such as searching, inserting, and deleting. Additionally, to evaluate the runtime of the operations mentioned above, we implemented the proposed data structure in C programming language and conducted several experiments. The proposed data structure achieves insertion and deletion operations in expected logarithmic time in average case. Furthermore, the suggested data structure is much simpler to implement than other n-ary trees like B-Trees [2]. This paper makes a valuable contribution to the field by offering a new approach to N-ary trees, which may be beneficial in the development of storage engines.

Airlines must manage flight schedules adequately to maintain consumer satisfaction and profitability in operations. This study aims to create a prediction model for aeroplane delays by applying machine learning techniques. This research aims to evaluate the potential of multiple machine learning algorithms in predicting flight delays using a large dataset that includes precise aircraft specifications, airline operating data, and records of previous delays. Throughout-depth pretreatment techniques are used in the study to ensure data accuracy, and feature selection is carried out to pinpoint important delay variables. A variety of measures are used to assess the model, and the decision tree regression model’s evaluation metrics show overall high performance. The R-squared score of 0.933 indicates that the features of the model account for around 93.3% of the variance in the target variable, exhibiting an outstanding capacity to capture data variability. The low values comprising a Mean Squared Error of 102.71 are also observed. 

The fifth generation (5G) communication, which is capable of realizing wireless communication with a extremely low latency and high data rate, has been put into commercial use widely. The propagation channel is of great significance for design, performance evaluation, and improvement of 5G system. As to investigate the wireless channel of 5G network, a channel measurement system based on passive sounding method is proposed in this paper. By utilizing the 5G downlink signal, e.g., synchronization signal block (SSB) and channel state information reference signal (CSI-RS), the proposed system is able to extract the multipath parameters of 5G propagation environment. Moreover, a measurement campaign is conducted at an urban macro-cellular (uMa) environment with the passive sounding system. The measured results is briefly analysed and compared with the 3rd generation partnership project (3GPP) results.

Secure software development is essential for safeguarding systems against an increasing array of cyber threats. Evaluating the capabilities of advanced language models in providing secure coding advice is crucial for understanding their potential as reliable tools for developers. The study benchmarks ChatGPT and Google Gemini across metrics such as accuracy, relevance, comprehensiveness, and adherence to best practices, offering a detailed comparative analysis of their performance. Google Gemini demonstrated superior accuracy and adherence to secure coding standards, while ChatGPT excelled in generating contextually relevant and comprehensive responses. The findings highlight the strengths and weaknesses of each model, providing insights into their application in secure software development. The study's results inform developers on leveraging these models to enhance their coding practices, contributing to the creation of more secure and resilient software systems.

The focus of this paper is to analyze the changes in the security of pacemaker IoMT (Internet of Medical Things) When its uses 5G networks connections compared with 4G network connection. This study compares the thread model of both connections and assesses the risk mitigation when using the 5G. The research indicates that while 5G offers enhanced security features, the inherent vulnerabilities of pacemakers remain significant and require further hardware-level security improvements.

This paper presents novel observations on the impact of ground-bounce noise (GBN) on Logic-HIGH levels of the output response of CMOS inverter circuits. While the impact of power supply noise on Logic-HIGH is very obvious as well as comprehensively studied in the literature, the impact of ground bounce is perceived and the analysis is majorly focused on logic-LOW. This work demonstrates the significant impact of GBN on Logic-HIGH and the observations are supported by a theoretical analysis using a semi-analytical approach. Several examples are considered to validate the novel observations considering both simulation as well as measurement-based case studies. The simulation based case studies are performed using a 40 nm technology of United Microelectronics Corporation (UMC), and measurement based case studies are performed using standard HEX inverter Integrated Circuits (ICs). There is a close correlation observed between the practical observations and its theoretical foundation.

The paper presents relatively simple formulations of the problem of acoustic scattering by elastic shells immersed in fluids, which can serve as a basis for efficient numerical models. The full rigorous formulation of the problem, which involves the Helmholtz equations for acoustic pressures in the fluids and the Navier equation for 3D displacements in the elastic material, is reduced to a boundary value problem only for the Helmholtz equations with effective boundary conditions relating the boundary pressures and normal displacements on both sides of the shell. To that end, the thin elastic shell is regarded as a neighborhood of its midsurface and the boundary values of the elastic quantities (displacements and stresses) are expressed via their values at the midsurface. The shell thickness is considered a small parameter. Special consideration is given to the cases of flooded and hollow shells. Under certain conditions, the problem is reduced to a system of hypersingular integral equations, appropriate for solving by the Boundary Element Method (BEM). The resulting numerical models are validated by the comparison with the exact solutions for the case of spherical elastic shells. In particular, the BEM numerical solutions reproduce the resonant peaks and dips of the exact solutions.

Snow over sea ice affects the overall radiative balance of the polar region by modulating the planetary albedo and influencing sea ice growth. In satellite altimetry, snow is a major source of uncertainty in sea ice thickness estimates, especially with Ku-band radar altimeters. This is because conventional algorithms assume the primary scattering occurs at the snow/ice interface which may not hold for all snowpacks. In particular, complex snow morphological features, including wet snow and/or ice lenses, can shift the main scattering horizon toward the snow surface. These snow features may be particularly prominent over Antarctic sea ice. This study investigates how the properties of the snowpack in the Weddell Sea influence Ku-and Ka-band altimeter waveforms, particularly in the context of extreme conditions and complex snow stratigraphy. Using an adapted waveform model, we deconvolute radar echoes into contributions from the snow surface, snow volume, and ice surface, employing both least-squares fitting and Convolutional Neural Networks (CNN). Our findings challenge previous default assumptions, revealing that snow-volume scattering is as significant as that from the ice surface. To better characterize the snow scattering effects, we propose an adapted threshold first-maximum retracker algorithm (TFMRA) for CryoSat-2 radar freeboard retrieval, identifying a 70% threshold for ice floe retracking as the most accurate based on Operation IceBridge data. The algorithm, applied in CRYO2ICE campaigns and validated against snow buoy measurements, highlights the critical role of snow in altimeter retrievals. The result is particularly important given the recent unprecedented Antarctic sea ice loss and its application for future satellite missions like ESA's CRISTAL.

This case study examines the integration of Lean Supply Chain Management (LSCM) and Industry 4.0 technologies in European manufacturing companies. The study aims to understand how digital technologies can enhance lean practices and improve supply chain performance. Key digital tools implemented include IoT sensors, data analytics, and cloud computing, which were used to monitor and optimize supply chain operations. Employee training and real-time monitoring systems were also critical components of the implementation process.The results indicate significant cost savings due to improved operational efficiency and reduced waste. Additionally, the integration led to environmental benefits, such as a reduction in carbon emissions and waste. Enhanced visibility and control over supply chain operations improved load stability and reduced product damage.The discussion highlights the practical benefits of integrating LSCM and Industry 4.0 technologies, including enhanced efficiency, environmental impact, and operational control. The study also provides insights into strategic integration, continuous improvement, and operational synergy. Current market trends indicate a synergistic effect between LSCM and Industry 4.0, aligning with the growing emphasis on green and sustainable processes.This case study offers valuable insights for industry practitioners and policymakers on the practical benefits and challenges of integrating LSCM and Industry 4.0 technologies, supporting the feasibility and proportional benefits of this integration for optimizing supply chain performance.

In the field of information technology, cybersecurity is essential and one of the main concerns of today is information security. Ransomware is one of the more concerning cyber threats that is always evolving. This type of cyberattack entails encrypting the data or files belonging to the victim and requesting payment for the decryption process. Ransomware occurrences continue to increase in frequency, inflicting substantial disruption and financial loss despite the various cybersecurity precautions implemented by governments and organizations. This study does a thorough analysis of the literature on the creation and application of ransomware exploits. It looks at the techniques used to make ransomware, the weaknesses that are exploited, and how ransomware strategies have changed over time. This paper attempts to highlight areas for further research and uncover common trends in the evolution of ransomware exploits by synthesizing findings from multiple investigations. The assessment also covers defense tactics, new difficulties in battling this constantly changing danger, and the life cycle of ransomware attacks. This thorough investigation aims to advance our knowledge of ransomware exploit techniques and support the creation of stronger cybersecurity defenses.

High dynamic range (HDR) imaging enhances visual realism by capturing a wide luminance range, but displaying HDR images on devices with limited dynamic range requires effective tone mapping operators (TMOs). We propose a novel two-stage tone mapping network (TSTMNet) based on attention mechanisms to address this challenge. The first stage utilizes adaptive luminance modulation blocks based on channel attention to dynamically adjust global luminance, achieving adaptive luminance modification. The second stage integrates local enhancement transformer blocks, leveraging self-attention from Transformers to enhance local details. This combination allows the TSTMNet to utilize the strengths of both CNNs and Transformers, overcoming their individual limitations in modeling long-range dependencies and preserving local details. Extensive experiments demonstrate that TSTMNet significantly outperforms the state-of-the-art TMOs in both quantitative metrics and qualitative visual quality, achieving superior dynamic range compression and detail preservation. Our method offers a robust and efficient solution to the tone mapping problem.

Modern real-time systems often involve numerous computational tasks characterized by intricate dependency relationships. Within these systems, data propagate through cause-effect chains from one task to another, making it imperative to minimize end-to-end latency to ensure system safety and reliability. In this paper, we introduce innovative non-preemptive scheduling techniques designed to reduce the worst-case end-to-end latency and/or time disparity for task sets modeled with directed acyclic graphs (DAGs). This is challenging because of the non-continuous and non-convex characteristics of the objective functions, hindering the direct application of standard optimization frameworks. Customized optimization frameworks aiming at achieving optimal solutions may suffer from scalability issues, while general heuristic algorithms often lack theoretical performance guarantees. To address this challenge, we incorporate the ''1-opt" concept from the optimization literature (Essentially, 1-opt means that the quality of a solution cannot be improved if only one single variable can be changed) into the design of our algorithm. We propose a novel optimization algorithm that effectively balances the trade-off between theoretical guarantees and algorithm scalability. By demonstrating its theoretical performance guarantees, we establish that the algorithm produces 1-opt solutions while maintaining polynomial run-time complexity. Through extensive large-scale experiments, we demonstrate that our algorithm can effectively reduce the latency metrics by 20% to 40%, compared to state-of-the-art methods.

This letter introduces Time-to-Exit (TTE), a soft mobility robustness optimization (MRO) parameter for handovers in 5G and beyond networks. The deployment of small cells to meet higher capacity demands complicates mobility management, leading to frequent handovers, increased ping-pong effects, unnecessary handovers, and handover failures due to premature, delayed, or incorrect cell transitions. We propose an instantaneous TTE estimation model that uses location measurements, moving speed, Reference Signal Received Power (RSRP), and quality thresholds (Q_rxlevmin and REFSENS) to optimize handover initiation before the user exits the serving cell. This method aims to reduce handover rates, ping-pong handovers, unnecessary handovers, and handover failures.

To achieve Artificial intelligence technological singularity(AITS), the most important task is to explain the job for the computer, so that it will understand easily and train the computer ways to accomplish the job successfully and generate and execute the beast result. To accomplish that task, the most important thing to do is to breakdown a big job into bite size pieces, so at the end, combining all that small jobs result will the accomplishment of the big job. In this paper, we will establish a framework that artificial intelligence can break down a big job into small bite size pieces task and at the end, it will combine all the bite size pieces task result and accomplish the big job successfully.