The first of Calderón identities indicates that the operator of the electric field integral equation (EFIE) can potentially act as a preconditioner for itself. However, the discretization of this preconditioning operator via the boundary element method (BEM) requires constructing a dual space commonly defined on a barycentric refinement of the orginal mesh. This contribution introduces a novel Calderón strategy that leverages high-order quasi-Helmholtz projectors. Unlike the standard strategies, which are based on barycentric refinements, a dual space is obtained by combinating functions of higher order. This strategy allows the development of a discretization of the Calderón identity that is suitable for arbitrary order. The well-conditioning of the proposed formulation is investigated, and numerical results are presented to corroborate the theory.

This paper considers the software-based analysis of business process model size and complexity using the network connectivity measurement approach. Business process modeling is a key tool in business process management, software engineering, and information systems management. Business process models that are likely to have errors, may cause extra costs, time losses, or even more harmful impact on activities. Therefore, in this study we propose to improve the size and complexity efficiency of business process models based on their structural analysis using the Coefficient of Network Connectivity (CNC) measurement. The respective software tool is developed to calculate the CNC measure, determine quartile values and corresponding thresholds to classify business process models by their structural efficiency.

This study introduces a novel control framework for Human-Drone Interaction (HDI) in industrial warehouses, targeting pick-and-delivery operations. The goals are to enhance operator safety as well as well-being and, at the same time, to improve efficiency and reduce production costs. To these aims, the Speed and Separation Monitoring (SSM) operation method is employed for the first time in HDI, drawing an analogy to the safety requirements outlined in collaborative robots' ISO standards. The so-called protective separation distance is used to ensure the safety of operators engaged in collaborative tasks with drones. Additionally, we employ the Rapid Upper Limb Assessment (RULA) method to evaluate the ergonomic posture of operators during interactions with drones. To validate the proposed approach in a realistic industrial setting, a quadrotor is deployed for pick-and-delivery tasks along a predefined trajectory from the picking bay to the palletizing area, where the interaction between the drone and a moving operator takes place. The drone navigates towards the interaction space while avoiding collisions with shelves and other drones in motion. The control strategy for the drone cruise navigation integrates simultaneously the time-variant Artificial Potential Field (APF) technique for trajectory planning and the iterative Linear Quadratic Regulator (LQR) controller for trajectory tracking. Differently, in the descent phase, the receding horizon LQR algorithm is employed to follow a trajectory planned in accordance with the SSM, which starts from the approach point at the border of the interaction space and ends in the volume with the operator's minimum RULA. The presented control strategy facilitates drone management by adapting the drone's position to changes in the operator's position while satisfying HDI safety requirements. The results of the proposed HDI framework simulations for the case study demonstrate the effectiveness of the method in ensuring a safe and ergonomic HDI within industrial warehouses.

To emulate a quantum computation on a classical computer i.e. the evolution of the unitary operations on the wave function of the particle in quantum mechanics, we have to perform unitary matrix and normalized vector multiplications in the high-level programming languages of Python, C++, Java, etc. Quantum Libraries already available perform the matrix-vector multiplication in the backend using the numpy libraries of Python like Qiskit or use a C++ wrapper to further optimize the runtime it as in Qiskit-Aer Simulators. Since a fully functioning fault-tolerant computer is decades away, it is in our best interest to design new quantum algorithms and develop accelerator test beds for Quantum Emulations. All the quantum computer operations can be emulated on a classical computer, with the only downside being that the matrix multiplications scale up as 𝑂 (𝑁 3) in runtime. In contrast, the quantum computer scales it up as 𝑂 (𝑁 2 𝑙𝑜𝑔 2 𝑁), where N = 2 𝑛 , where N is the matrix dimension, where n is the number of qubits, so the runtime for quantum emulations on the classical computer increases exponentially with increase in number of qubits and increases linearly with increase in number of depths, complexity wise. Though it is not possible to change the exponential index, it is possible to reduce the runtime for quantum emulations on classical computers by use of GPU and Alveo Accelerator Cards, and also code optimization on the software side like using a C++ wrapper. In this paper, we will benchmark the matrix-matrix multiplications on HPC Accelerator Cards varying the qubit size and the depth of the quantum circuit and provide a universal mathematical equation for the runtime on the GPU and Alveo Vector Cards for two variables of qubit size and quantum circuit depth. So a theoretical limit on qubit size and qubit depth exactly can be established for quantum emulations on present classical supercomputers.

The rich-club phenomenon describes the tendency of well-connected nodes to form highly cohesive cores within networks, offering insights into their fundamental structure. This paper presents the Rich-Club Integrated (RCI) model, developed for predicting missing edges in large and complex bipartite networks, which is particularly relevant for product recommendation systems. By incorporating the rich-club coefficient, RCI demonstrates enhanced performance compared to models that do not account for this phenomenon. This study highlights the practical utility of the rich-club coefficient in enhancing recommendation systems and encourages further exploration of this approach in network analysis.

Passive synthetic aperture radar (SAR) has attracted research in recent years because of its advantages in exploiting opportunistic signals for imaging. Hence, it minimizes the need for active radar transmitters, significantly reducing system costs. In addition, the massive proliferation of telecommunication satellites in recent years motivates the idea of using the signal for the integrated sensing and communication (ISAC) concept. This paper explores the feasibility and opportunity of exploiting communication signals, specifically the common waveform of orthogonal frequency division multiplexing (OFDM), as a source of SAR illumination. The reflected signal is captured using a flying uncrewed aerial vehicle (UAV) at a much lower altitude than the satellite. To perform this evaluation, we developed an extensive signal-level emulator that considers many realistic aspects, such as orbital parameters, pulse repetition frequency (PRF), RF radio bandwidth, and antenna beamwidth. Point targets were placed within the imaging swath using regular spacing, and then the backscattered signal was collected using the UAV. Accordingly, the SAR image was formed using the range-Doppler (RDA) processing algorithm. The integrated sidelobe ratio (ISLR) was evaluated for ground reference point (GRP) cross-sections in order to provide a numerical assessment for performance. The results indicate the feasibility of exploring the direction of ISAC in passive SAR image formation.

Video Capsule Endoscopy (VCE) presents a significant challenge, particularly due to the vast and unstructured data generated through the GI tract which may lead to difficulties in realtime analysis and classification. To address these issues, this study presents a semi-supervised methodology for image classification that leverages texture-based features. This method initially addresses the imbalance in the provided dataset and utilizes advanced texture-based feature extraction techniques such as Gray Level Co-Occurrence Matrix, Local Binary Pattern, and deep features derived from VGG16. Then, a semi-supervised learning approach, Transductive Learning Algorithm has been carried out that strengthens the model's robustness and ability to classify the normal and abnormal classes, yielding improved accuracy. Additional classification models like Random Forest and K-Means Clustering are also carried out for comparison study. The Gray Level Co-Occurrence Matrix with transductive learning outperformed the other approaches in accurately classifying the images by achieving an effective accuracy of 95.14% with 0.99 mean AUC. In conclusion, our approach which has been carried out achieved 23 rd rank in the Capsule Vision 2024 Challenge, paves the way for enhancing diagnostic capabilities, ultimately contributing to more accurate and timely identification of gastrointestinal conditions.

With the growing need for accessible information across diverse linguistic backgrounds, text summarization in multilingual contexts has become increasingly essential. Text summarization is a crucial task in natural language processing, particularly in multilingual settings involving Indian languages. This paper presents our approach for the FIRE 2024 task, where we leverage large transformer-­based language models for summarizing Indian languages, addressing the linguistic diversity and frequent instances of code­mixing and script-­mixing unique to this context. Our methodology incorporates both extractive and abstractive summarization techniques, optimized for Indian languages through advanced fine­tuning of models like mT5, IndicBART, and BART. While prompt engineering has predominantly been applied to English tasks, we adapt it alongside fine­tuning to enhance summarization performance and computational efficiency. Our models achieved top results in five languages—Hindi, Gujarati, English, Tamil, and Bengali—and ranked second in Telugu. These results demonstrate substantial improvements in summarization accuracy, underscoring our approach’s efficacy in handling the complexities of Indian languages and advancing text processing in multilingual, mixed­-language environments.

In recent years, increasing model complexity and improvements in self-attention mechanisms have enabled language models to perform remarkably well on tasks involving human language understanding and generation. However, limitations in preserving context over long sequences continue to present challenges, as models often struggle to retain coherence across extended discourse. The introduction of the Trans-Contextual Embedding (TCE) Mechanism provides a novel approach to addressing these challenges through a context-aware embedding layer integrated into transformer architectures, uniquely designed to maintain semantic depth and logical continuity. Through incorporating TCE into the initial embedding layer, the model achieves improved contextual coherence by dynamically weighting contextual relevance, leading to more accurate representation of complex, interdependent text elements. A series of experiments evaluated the TCE Mechanism on various opensource models, with results indicating a marked improvement in context continuity, semantic density, and reduced error rates, suggesting the applicability of TCE in a range of complex language tasks. Furthermore, the model achieved these improvements with minimal increase in computational overhead, presenting TCE as a scalable option for enhancing LLM capabilities in real-world applications. These findings lay foundational insights into context preservation mechanisms, positioning TCE as a significant advancement for the development of sophisticated and contextually robust language models.

The ever increasing sophistication of ransomware attacks demands the development of advanced detection methodologies capable of identifying and mitigating such threats in real-time. The Adaptive Sequence Mapping Analysis (ASMA) framework introduces a novel approach that leverages sequential pattern recognition and entropy-based metrics to detect anomalous behaviors indicative of ransomware activity. Through comprehensive empirical evaluation, ASMA has demonstrated high detection accuracy across a diverse array of ransomware families, effectively distinguishing malicious actions from benign processes with minimal false positives. The framework's adaptability to zero-day attacks and scalability in high-throughput environments underline its potential for integration into existing cybersecurity infrastructures, providing a proactive defense mechanism against the evolving landscape of ransomware threats. The modular architecture of ASMA facilitates seamless integration with current security systems, enabling organizations to enhance their protective measures without necessitating extensive overhauls of existing infrastructures. The computational efficiency of the framework ensures real-time detection capabilities, maintaining system performance even under substantial network loads. These findings position ASMA as a valuable tool in the ongoing efforts to safeguard digital assets from malicious encryption-based attacks.

Large-scale virtual reality (VR) enables novel art forms in large physical environments. A key technical solution in such cases is to split the rendering process between a client and a streamer. In this article, we focused on the peculiarities of VR split rendering and particularly on the performance of Wi-Fi. First, we calculated the network delay budget and then conducted a set of experiments to evaluate the single-user and multi-user performance of VR split rendering systems over Wi-Fi in terms of latency. Our findings were summarized as recommendations on: i) the frame rate, ii) the encoding bitrate, iii) the signal strength, iv) the channel bandwidth, and v) the multiple access techniques. In particular, our multi-user tests highlighted cases where using the highest settings or latest features of Wi-Fi can compromise performance. Finally, we applied our recommendations to a large-scale multi-user case study that took place physically in the multi-purpose hall of our university campus and virtually in the Turret Singers environment of SteamVR Home. This article serves as a reference to the critical aspects of VR split rendering systems over Wi-Fi.

The capacity for sustained contextual coherence in language model outputs remains a critical factor in achieving natural, relevant, and semantically rich responses across extended interactions. Traditional context-handling methods, often limited to token-level attention and basic memory retention mechanisms, fall short when confronted with the intricate, hierarchical nature of real-world discourse. Multi-Stage Latent Contextual Synthesis presents a novel solution, employing a structured, multi-tiered synthesis approach to refine context integration within an LLM framework. Through a sequential layering process, latent knowledge is synthesized at progressively deeper levels, yielding a more nuanced understanding of semantic dependencies, thematic continuity, and response relevance across multi-turn conversations. The experimental results reveal substantial improvements in contextual retention, thematic alignment, and token stability, underscoring the method's efficacy in enhancing complex context management without adding prohibitive computational demands. Comprehensive evaluations demonstrated that Multi-Stage Latent Contextual Synthesis not only enhances memory efficiency and reduces thematic error rates but also promotes embedding stability, thereby supporting a more structured and responsive approach to contextual synthesis in high-stakes applications, including conversational AI and automated support systems. These findings illustrate the potential of hierarchical, latentcontextual strategies to redefine contextual processing standards within LLMs.

A general method to construct wavelet function on real number field is proposed in this article,which is based on finite length sequence.This finite length sequence is called the seed sequence, and the corresponding wavelet function is called the seed sequence wavelet function.The seed sequence wavelet function is continuous and energy concentrated in both time and frequency domains.That is, it has a finite support set in both time and frequency domains. It is proved that if and only if the seed sequence has 0 mean value, the interpolation function satisfies the admissible condition of wavelet function.The conditions corresponding to the higher order vanishing moment of the seed sequence wavelet function are also given in this article. On this basis, the concept of random wavelet function is proposed, and the condition of the regularity of random wavelet is discussed.

Transformers have emerged as the foundation of modern deep learning, achieving state-of-the-art performance across diverse domains such as natural language processing, computer vision, and multimodal tasks. However, their quadratic computational complexity and high memory requirements pose significant challenges, especially when scaling to long sequences or deploying on resource-constrained hardware. To address these limitations, the research community has proposed a variety of techniques to make Transformers more efficient. This survey provides a comprehensive overview of these advancements, categorizing them into five main strategies: sparsity-based methods, low-rank approximations, structured attention patterns, memory-efficient mechanisms, and hybrid approaches. For each category, we delve into the underlying principles, highlight representative methods, and analyze their strengths and limitations. We also discuss the trade-offs involved, including computational efficiency, memory savings, and model accuracy. Furthermore, we identify emerging trends and open challenges, such as unified frameworks, hardware-aware optimizations, and sustainable AI practices. By synthesizing the state of the art in efficient Transformers, this survey aims to guide future research and facilitate the adoption of these techniques in both academic and industrial settings.

The pharmaceutical industry faces significant cybersecurity challenges, increasing threats to sensitive data, intellectual property, and patient information. Traditional cybersecurity models, including signaturebased and threshold-based intrusion detection systems (IDS), often fail to address cyber threats' dynamic and evolving nature. This paper introduces a novel AI-driven cryptographic system designed to enhance cybersecurity in the pharmaceutical sector. The proposed model integrates advanced AI techniques, including reinforcement learning, game theory, and C*-algebra, to optimize resource allocation, improve threat detection, and enhance cryptographic protocols. Through a comprehensive evaluation, the AI model demonstrates superior performance over traditional methods, achieving a detection rate of 97.4%, significantly reducing false positive rates (2.6%) and response time (50ms), and achieving an F1-score of 0.95. Additionally, the AI model's ability to continuously adapt to emerging cyber threats makes it a future-proof solution for the pharmaceutical industry. The model's scalability ensures its applicability across various organizational sizes. Future research directions include the integration of blockchain, cloud computing, and post-quantum cryptography to strengthen the system's resilience further. This paper presents a scalable, adaptive, and efficient solution to address the growing cybersecurity challenges in the pharmaceutical industry, ensuring critical data protection in an increasingly complex threat landscape.

In this paper, an orthogonal mode decomposition method is proposed to decompose finite length real signals on both the real and imaginary axes of the complex plane. The interpolation function space of finite length discrete signal is constructed, and the relationship between the dimensionality of the interpolation function space and its subspaces and the band width of the interpolation function is analyzed. It is proved that the intrinsic mode is actually the narrow band signal whose intrinsic instantaneous frequency is always positive (or always negative). Thus, the eigenmode decomposition problem is transformed into the orthogonal projection problem of interpolation function space to its low frequency subspace or narrow band subspace. Different from the existing mode decomposition methods, the orthogonal modal decomposition is a local time-frequency domain algorithm. Each operation extracts a specific mode. The global decomposition results obtained under the precise definition of eigenmodes have uniqueness and orthogonality. The computational complexity of the orthogonal mode decomposition method is also much smaller than that of the existing mode decomposition methods.

This study presents a systematic scoping review of delegated voting in decentralized autonomous organizations (DAOs), focusing on its governance implications, implementation forms, and challenges. While delegated voting is often adopted to address low participation and mitigate the cognitive burden of direct involvement, the existing literature highlights its potential to exacerbate centralization, particularly when whales or influential networks are disproportionate. Various implementation models, including offchain platforms (e.g., Snapshot), hybrid governance architectures, and token-based delegation systems, exhibit distinct trade-offs in transparency, cost, and adaptability. Although innovations such as quadratic voting, weighted delegation constraints, and reputation-based governance show promise for improving fairness and accountability, they also face vulnerabilities, such as gaming, collusion, and high implementation complexity. Synthesizing insights from 13 publications, this review offers recommendations ranging from multi-tier governance structures to AI-driven support tools that aim to balance efficiency, inclusivity, and trust in DAO voting processes, thereby informing robust context-aware governance.

Public service motivation (PSM) has been defined as a somebody's desire to advance community principles over committing facilities to the governmental sector. Whereas prosocial motivation (PM) is the desire of the total community to support individuals, PSM is a definite kind of prosocial motivation connected to public provision. High public service motivation is significantly linked with high levels of prosocial behavior between staffs in the governmental sector. PSM and PM have been proposed to affect work results. Although there is noteworthy study on PSM and PM, there is a lack of significant study on these motivations through detailed orientation to non-Western countries. This study examines the impact of PSM and PM on engagement, commitment, and organizational citizenship behavior (OCB) in the governmental sector in the Kurdistan Region of Iraq via using machine learning techniques. Data were gathered from 418 public workers from different cities in the Kurdistan Region. Machine learning procedures, with support vector machines (SVM), k-nearest neighbors (KNN), linear regression, and logistic regression, were used to analyze the data. The outcomes discovered that PSM assumes employee engagement, organizational commitment, and OCB between staffs. Moreover, the connection between PM and these variables is also positive, mentioning that both PSM and PM have impact on the whole performances and attitudes of staffs in the public sector setting. This study has inferences for worker motivation in the public sector, particularly for human resource departments and employees. Furthermore, it informs current discussions on the usage of motivational systems or practices in the governmental sector and their association to administrative efficiency.