In this article, we have studied the device modeling and optimization of perovskite solar cell with Formamidinium Bismuth Iodide (FA)3Bi2I9 as the absorber layer, PEDOT: PSS (poly (3,4-ethylenedioxythiophene) polystyrene sulfonate) as the hole transport material and C60 (Fullerene) as the electron transport material. Rigorous theoretical investigation has been performed for the device parameter optimization especially the ETL, HTL and active layers. Different aspects of design strategies like temperature, carrier density, series and shunt resistance have been analyzed. The optimized device structure shows maximum ETA of 30.81% with the FF of 83.53% for (FA)3Bi2I9 based perovskite solar cell.

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications.

To support the development of wearable medical devices for remote monitoring and treatment of cardiovascular diseases, we tackle the data scarcity problem that hinders the application of machine learning methods. We propose a self-supervised approach applied to cardiological signals, which benefits from existing datasets despite differences between them and inconsistencies within them. We develop a specific implementation: a diffusion autoencoder with a semantic encoder based on linear recurrent units, trained on ECG signals (various leads mixed together) without any annotations. The semantic encoder is evaluated as a feature extractor by measuring classification metrics of a logistic regression on a dataset not included in the self-supervised training. We obtain promising results and propose future directions.

This paper explores enhancements in Adaptive Cruise Control (ACC) using Frequency Modulated Continuous Wave (FMCW) radar to optimize performance in varied environmental clutter conditions, including rain, fog, and insect interference. By implementing Constant False Alarm Rate (CFAR) algorithms, the study reduces clutter impact, enhancing radar accuracy for both range and velocity measurements of multiple targets. Results demonstrate that CFAR integration mitigates errors in velocity and distance estimation, particularly under adverse conditions, supporting ACC reliability over a range of up to 200 meters. This study underscores the effectiveness of advanced clutter rejection in improving safety and functionality in ACC systems for real-world applications.

The discrete complex image method (DCIM) is applied to compute the complete set of electric, magnetic, and potential Green functions (or integral equation kernels) for planar multilayers with uniaxial anisotropy. It is assumed that the layered medium is of infinite lateral extent and the optic axis is normal to the stratification. Sample results are presented and are shown to be in good agreement with reference data obtained by a rigorous integration method.

This paper presents an efficient beam pattern modeling method for reflector antennas where the performance of a specific feed antenna, as a function of frequency and geometric variations of the antenna, is known on a different (but similar) reflector antenna. Instead of modeling patterns for the desired reflector directly, the difference patterns between the desired and previously modeled reflectors are employed. Scaling and normalization measures are applied to the patterns to simplify their modeling as Characteristic Basis Function Patterns (CBFPs), which can be accurately interpolated over the desired design parameter space. The CBFP coefficients drive an adaptive sampling algorithm that aims to accurately obtain the antenna's performance across the entire parameter space with as few electromagnetic simulations as possible. Two examples are presented using basic reflector optics and a simple corrugated horn and demonstrate the advantage of using a reference design compared to directly modeling the reflector patterns, showing that fewer simulations are required to obtain a certain level of accuracy.

The advent of Recursive Token Integration (RTI) introduces an innovative paradigm in the architecture of large language models, facilitating dynamic contextual expansion that transcends the constraints imposed by traditional fixed-length context windows. Through the strategic implementation of RTI, models exhibit marked enhancements in performance across a diverse array of natural language processing tasks, encompassing language modeling, text generation, and comprehension. Empirical analyses reveal that RTI effectively enables models to assimilate extended contextual information, thereby producing outputs characterized by heightened coherence and contextual relevance. This advancement not only addresses existing limitations in context management but also paves the way for future explorations into recursive mechanisms within language model architectures. The integration of RTI signifies a pivotal step toward the development of more sophisticated and adaptable language models, capable of navigating the complexities inherent in human language with greater proficiency. As the field progresses, the principles underlying RTI may serve as a foundation for subsequent innovations aimed at further augmenting the capabilities of large language models, ultimately contributing to the evolution of natural language processing technologies.

5G-based mmWave wireless positioning has emerged as a promising solution for autonomous vehicle (AV) positioning in recent years. Previous studies have highlighted the benefits of fusing line-of-sight (LoS) 5G signals with an Inertial Navigation System (INS) for an improved positioning solution. However, the highly dynamic environment of urban areas, where AVs are expected to operate, poses a challenge, as non-line-of-sight (NLoS) communication can deteriorate the 5G mmWave positioning solution and lead to erroneous corrections to the INS. To address this challenge, we exploit 5G single-bounce reflections (SBRs) and LoS signals to improve positioning performance in dense urban environments. In addition, we integrate the proposed 5G-based positioning with a low-cost inertial measurement unit (IMU) and a wheel encoder. Moreover, the integration is realized using an unscented Kalman filter (UKF) as an alternative to the widely utilized extended Kalman filter (EKF) within the 5G-based positioning research community. We performed two test trajectories in the dense urban environment of downtown Toronto, Canada. For each trajectory, quasi-real 5G measurements were generated using a ray-tracing tool incorporating 3D map scans of real-world buildings, allowing for realistic NLoS and multipath scenarios. For the same trajectories, real motion data were collected from two different low-cost IMUs. Our integrated positioning solution was capable of maintaining a level of accuracy below 30 cm for approximately 97% of the time, which is superior to the accuracy level achieved when SBR signals are not considered, which is only around 92% of the time.

Object sorting is a problem of separating objects of different kinds based on shape, size, or any other feature into separate stacks to process those separately. In the food processing industry, object sorting is used to separate fruits of different grades. Separating fruits into different grades is essential for quality control. Manual sorting is slower and costs more than automated sorting. This paper presents a novel solution for the object sorting problem using YOLOv8x and various optimization algorithms (ILP, GA and PSO). Here, the object sorting problem is solved by modelling it as a TSP. Through experiments, it was observed that a PSO-based approach could solve the object-sorting problem efficiently. The PSO method obtained a near-optimal solution with 42% less processing time than those obtained from the ILP method, which would enhance the productivity and performance of the industrial robot.

The integration of Neural Pathway Embedding within large language models has led to significant advancements in language generation quality. By incorporating hierarchical interchange networks, the model effectively captures complex linguistic patterns and semantic relationships, resulting in more coherent and contextually appropriate text. The observed enhancements in BLEU scores, reduced syntax error rates, and increased lexical diversity demonstrate the efficacy of the proposed approach. While certain limitations, such as increased computational requirements and the need for further evaluation across diverse languages, have been identified, the overall findings validate the potential of Neural Pathway Embedding to elevate the performance of large language models in various natural language processing tasks. Future research directions could involve optimizing the model architecture to balance performance improvements with computational efficiency. Investigating the model's adaptability to multilingual contexts would provide insights into its versatility and potential for global applications. Additionally, exploring the integration of Neural Pathway Embedding with other advanced techniques, such as reinforcement learning or transfer learning, may further enhance the model's capabilities. Continuous refinement and comprehensive evaluation across diverse linguistic tasks and datasets will be pivotal in advancing the field of large language models.

In microwave remote sensing of vegetated land surfaces, there are volume scattering from the vegetation and rough-surface scattering of the interface between the vegetation and the ground. However, the coupling of volume scattering and surface scattering and its impact on microwave emission have not been fully studied, particularly when multiple scattering effects are to be considered as in forested scenarios. Earlier treatment with the Kirchhoff approximation neglects the incoherent bistatic scattering components from the rough soil interface and thus overestimates the transmitted upward emission from the soil half space and underestimate the reflected upward emission from the downward-going intensity streams arising from the vegetation layer in non-specular directions. To address this issue, bistatic scattering coefficients are introduced into the boundary conditions and an interpolation-accelerated numerical iterative method is employed to solve the passive radiative transfer equation to rigorously couple the volumesurface scattering interactions at the vegetation/soil interface. In this paper, the rough-surface bistatic scattering is modeled using the advanced integral equation method (AIEM). The volume scattering model is derived from a radiative transfer based multiple scattering model that accounts for the vertical profile of the vegetation structure. The SMAPVEX12 forest dataset is utilized to validate the proposed model, encompassing L-band radiometric brightness temperature observations corresponding to extensive variations in soil moisture. Comparisons are made between the brightness temperatures simulated by the model with and without considering the incoherent bistatic scattering coefficients, under diverse vegetation water contents (VWCs) and varying soil roughness conditions. Notable features are observed in the angular pattern of the vegetated surface emission by fully accounting for the multiple scattering effects and the incoherent bistatic scattering effects of the rough soil. These features become more evident at smaller observation angles, lower VWCs, and greater soil roughness. The findings reveal that the model incorporating the bistatic scattering coefficient of rough surfaces exhibits improved agreement with the measured brightness temperatures. Furthermore, the proposed model is parameterized by matching the high-order solutions to the RT equation to the widely adopted albedo-tau formalism, i.e., the zero-order solution to the passive radiative transfer equation with a flat lower boundary. The resulting equivalent optical thickness and the equivalent scattering albedo incorporates the multiple scattering effects within the vegetation layer and they are solely associated with the geometries and the electromagnetic properties of the vegetation layer. Additionally, the equivalent reflectivity of the soil is proposed to characterize the scattering properties of rough surfaces and the scattering coupling between the rough surface and the vegetation layer. The new model developments will significantly enhance the prediction and interpretation of vegetated land surface emission characteristics and thus improve the remote sensing of the vegetation and the underlying soil parameters.

How to build the arithmetic finding 3-uncolorable vertices in polynomial time in 3-coloring is studied. For planar graph G[V,E] the diagrammatic arithmetic computing jammed configurationwith the independent clip (DJC) is built in the color space according to the Grötzseh theorem. Each DJC corresponds a triplet in G[V,E] , which is the invariant set of three sets of monochromatic vertices with different colors under coloring transformation. The algorithmcancomplete the computation to found all DJC in 8 O(V ) . The vertex adjoins vertices of three monochromatic sets is 3-uncolorable. They are only the part of 3-uncolorable vertices. So, 3-coloring has 3-uncolorable vertices can be recognized in polynomial time.

Optimization techniques are pivotal in neural network training, shaping both predictive performance and convergence efficiency. This study introduces Foxtsage, a novel hybrid optimisation approach that integrates the Hybrid FOX-TSA with Stochastic Gradient Descent for training Multi-Layer Perceptron models. The proposed Foxtsage method is benchmarked against the widely adopted Adam optimizer across multiple standard datasets, focusing on key performance metrics such as training loss, accuracy, precision, recall, F1-score, and computational time. Experimental results demonstrate that Foxtsage achieves a 42.03% reduction in loss mean (Foxtsage: 9.508, Adam: 16.402) and a 42.19% improvement in loss standard deviation (Foxtsage: 20.86, Adam: 36.085), reflecting enhanced consistency and robustness. Modest improvements in accuracy mean (0.78%), precision mean (0.91%), recall mean (1.02%), and F1-score mean (0.89%) further underscore its predictive performance. However, these gains are accompanied by an increased computational cost, with a 330.87% rise in time mean (Foxtsage: 39.541 seconds, Adam: 9.177 seconds). By effectively combining the global search capabilities of FOX-TSA with the stability and adaptability of SGD, Foxtsage presents itself as a robust and viable alternative for neural network optimization tasks.

This paper revisits the bicycle model for robust lateral control of autonomous front-steered vehicles to account for uncertainty in mass and inertia resulting from loading the vehicle. Unlike previous studies that tacitly assume no displacement of the center of mass due to loading, we provide a model which represents the uncertainty due to variation of loads as a positive semi-definite additive perturbation to the mass matrix. The question of how stabilizable the open-loop vehicle dynamics are, naturally follows because of additional loading. We address this problem as by determining the worst case loading distribution subject to a constraint on the maximum load added. We provide a bound on the minimum load that can be added that alter the handling characteristic of the unladen vehicle (i.e., from being an understeered vehicle to an oversteered vehicle). The purpose of lateral control is to enable the vehicle to track a specified trajectory with available information, namely, cross-track error, heading error and its rate with respect to the specified trajectory. We provide a robust fixed-structure controller that uses available feedback information and accommodates the bounded uncertainty in mass matrix. We also provide a numerical corroboration of the effectiveness of our approach.

Iterative thresholding-type algorithm, as one of the typical methods of compressed sensing (CS) theory, is widely used in sparse recovery field, because of its simple computational process. However, the estimation accuracy and convergence speed achieved by this type of algorithm with a nonconvex regularization, e.g., iterative half thresholding (IHalfT) algorithm, are not satisfactory, which limits its practical application. To improve the performance, a modified algorithm is proposed in this paper. Firstly, a novel non-negative expression is introduced in the algorithm to reduce the gap between the relaxation function and the objective function, which can bring tens of dB estimation accuracy improvement, and the convergence of the modified algorithm is verified. Secondly, the fundamental reasons for the remarkable improvement of performance are discussed and analyzed through theoretical derivation. Thirdly, the applicable conditions are elaborated for the modified algorithm. Finally, extensive experimental results demonstrate the effectiveness of the modified iterative thresholding-type algorithm with nonconvex regularization. Keywords-compressed sensing (CS), iterative hard thresholding (IHT), iterative soft thresholding (IST), iterative half thresholding (IHalfT), fast iterative shrinkage-thresholding algorithm (FISTA), normalized iterative hard thresholding (NIHT), alternating direction method of multipliers (ADMM).

The escalating sophistication of cyber threats necessitates innovative detection mechanisms to safeguard digital infrastructures. Dynamic Enclave Partitioning (DEP) emerges as a novel approach, offering real-time isolation and monitoring of suspicious code activities within virtualized environments. DEP's adaptive segmentation enables continuous adjustment of enclave boundaries based on observed behavioral patterns, effectively distinguishing between benign and malicious processes. This methodology enhances detection accuracy by minimizing false positives and provides a robust defense against evolving ransomware tactics. The modular design of DEP facilitates seamless integration with existing security frameworks, extending its applicability beyond ransomware to a broader spectrum of malware threats. Comprehensive evaluation demonstrates DEP's efficacy in identifying and isolating novel ransomware behaviors, showing its potential as a significant component in contemporary cybersecurity strategies.

This paper introduces Convergent Hierarchical Reasoning (CHR), a unified artificial intelligence (AI) framework that integrates four advanced reasoning paradigms-Chain-of-Thought (CoT), Forest-of-Thought (FoT), Trilevel Reasoning Graph (TRG), and Logarithmic Memory Networks (LMNs)-into a single, plug-and-play module. CHR offers end-to-end interpretability, multi-tree exploration, multi-tiered decision-making, and efficient long-sequence handling, making it especially suitable for high-stakes government applications. Agencies often struggle with strict compliance requirements, data sensitivity, and resource constraints when implementing AI for large-scale training, simulation, and decision support. By merging CoT, FoT, TRG, and LMNs into a seamless system, CHR alleviates the complexity and security risks associated with manually integrating multiple modules. Its architecture facilitates one-stop deployment within FedRAMP-approved cloud environments, incorporating real-time monitoring and automated checks aligned with NIST and FIPS standards. CHR A Preprint Early evaluations indicate significant improvements in both interpretability and computational efficiency, vital for applications ranging from military readiness to cybersecurity threat analysis. With parallel reasoning trees (FoT) and stepwise clarity (CoT) operating under a tiered decision-making structure (TRG), CHR ensures that high-level goals inform operational tactics. Meanwhile, LMNs efficiently manage extended historical data, minimizing computational overhead for long-sequence tasks. Although CHR is primarily designed for government and defense settings, its modular and transparent nature makes it equally valuable in industry and academia. By unifying advanced reasoning with compliance-focused MLOps, CHR demonstrates a scalable path for adopting next-generation AI, offering robust oversight and clear accountability in mission-critical scenarios.

Network Function Virtualization (NFV) enables the implementation of Service Function Chains (SFCs), where a set of ordered Virtual Network Functions (VNFs) support diverse network services over resources. However, deploying SFCs across multiple domains faces challenges due to resource limitations and the confidentiality of domain-specific data. Achieving lowlatency SFC placement, followed by the deployed network service performance guarantee, is under research. Maintaining low endto-end (E2E) latency of network service delivery, while preserving the privacy of configuration details among Network Function Providers (NFPs) is also challenging the cross-domain SFC deployment. This paper focuses on reducing the E2E latency of cross-domain SFCs deployment while considering VNF scaling with data privacy. First, a Federated Learning with Graph Neural Network (FLGNN) approach is introduced among different NFPs to optimise VNF placement and SFC deployment while preserving data privacy. Then, a Viterbi-based SFC partitioning (VSFCP) method is proposed, which segments the SFC into sub chains based on FLGNN predictions, optimising E2E latency. After deploying the cross-domain SFC, a VNF auto-scaling method with transfer learning (TLVS) is presented to handle dynamic service demands. Results show that cross-domain SFC deployment latency decreases by up to 30.8%. VNF resource prediction error reduces by 39.3% to avoid service latency.