Computational Power Research Articles

Advanced real-time monitoring techniques for high-dimensional data streams in industrial two-sample analysis

Abstract High-dimensional data, characterized by a greater number of variables than observations, is increasingly relevant in industrial applications due to advancements in computational power and data storage. Developing control charts for such data poses challenges in statistical process control, particularly for two-sample cases where traditional feature reduction methods are insufficient. Therefore, two-sample means tests such as Srivastava and Du (SD), Dempster (DR), and Bai and Saranadasa (BS) tests effectively address high-dimensional challenges, such as the curse of dimensionality and unreliable covariance matrix estimation. The SD test modifies Hotelling’s $${T}^{2}$$ T 2 test by assuming a diagonal covariance structure, the BS test replaces the covariance matrix with a scaled identity matrix, and the DR test employs a pseudo-inverse covariance matrix to address singularity issues. These tests are scalable, robust, and theoretically sound, outperforming traditional methods. While Shewhart control charts based on these tests detect large shifts in location parameters, they are less effective for small shifts. To overcome this, exponentially weighted moving average (EWMA) charts named SDEWMA, DREWMA, and BSEWMA were developed. Simulations and real-world high-dimensional data, such as wind turbine bearing grease damage, demonstrate the improved performance of proposed charts in detecting small shifts compared to traditional memoryless charts.

Implementasi Asimetris Kuantum dalam Kriptografi Modern untuk Enkripsi Data Satu Arah

In a company, data security is very important. Data security and integrity issues must be considered to prevent information from falling into the hands of unauthorized persons. Therefore, a good mechanism is needed to secure data, which can be done through various cryptographic methods, which are generally divided into two categories: symmetric and asymmetric. Currently, the development of quantum computing shows extraordinary potential, especially in the field of cryptography. The phenomenon of quantum supremacy, where quantum computers can solve problems that cannot be solved by classical computers, is increasingly being felt. This raises uncertainty in the existing data security system, because various cryptographic algorithms and protocols are predicted to be quickly solved by sufficiently powerful quantum computers. To overcome this potential insecurity, the field of cryptography must adapt, one of which is through the concept of quantum key distribution which offers a new standard in secure communication. In symmetric key cryptography, a secure communication channel is required for key delivery, and the concept of quantum key distribution is a promising alternative to overcome this problem. This study aims to explore the implementation of quantum asymmetric cryptography in one-way data encryption, as well as assess its effectiveness in improving information security.

Empowering Human-AI Collaboration: Enterprise Technology Platforms and Human Expertise Synergy in Healthcare, Finance, and Scientific Research

The relationship between professionals and intelligent machines has taken an unexpected turn. Rather than the wholesale job displacement many predicted, a more nuanced reality emerged—one where artificial intelligence becomes a collaborative partner in complex decision-making. A multinational technology corporation exemplifies this shift through platforms that transform how doctors diagnose diseases, bankers assess risk, and scientists make discoveries. Real-world deployments tell compelling stories. Radiologists working with AI catch tumors too small for the human eye alone, yet clinical judgment determines treatment paths. Trading desks employ algorithms that process market data in milliseconds, while portfolio managers apply wisdom no machine possesses about human psychology and market irrationality. Experimental laboratories accelerate discovery through computational analysis of massive datasets, but breakthrough insights still require human creativity and intuition. This technology corporation took a different path when designing its tools. Azure AI offers massive computing power but lets users decide how to apply it. Copilot understands plain English requests rather than forcing people to learn programming languages. Power Platform turns business experts into app developers without writing code. Each choice reflects the same bet: professionals know their work better than any algorithm. The technology should adapt to them, not vice versa. Ethics weren't an afterthought either—built-in safeguards prevent discriminatory outcomes, protect privacy, and explain AI decisions in terms humans understand. Early results validate the approach. Healthcare institutions report improvement in diagnostic accuracy and reduction in physician burnout. Financial firms detect fraud patterns more effectively while maintaining customer relationships that require human empathy. Investigation teams tackle previously impossible problems by combining computational power with scientific creativity. Challenges remain substantial. Privacy regulations constrain healthcare applications. Financial compliance grows more complex as AI systems require new oversight frameworks. Scientific reproducibility demands careful documentation of algorithmic processes. Yet organizations navigating these challenges successfully demonstrate that human-AI collaboration represents not just a technological shift but a fundamental reimagining of professional work itself. The most effective implementations recognize that optimal outcomes emerge when each partner—human and machine—contributes their distinctive strengths to solving problems neither could address alone.

Abstract A019: A fully transparent and automatable form of AI for biomarker and new target discovery using diverse multi-omics data

Abstract Modern chemical biology and multi-omics approaches capture far more data than can be digested in human readable form, creating an opportunity for machine-based methods to significantly expand the boundaries of scientific knowledge. Artificial intelligence (AI) methods promise to fill this gap, however significant challenges remain to adapt machine learning (ML) based approaches to data that is noisy, complex, and bespoke while still preserving the transparency required for scientific and regulatory decision making. Herein we describe a novel approach combining knowledge graphs and centrality algorithms, allowing the construction of systems that are scalable, robust to noise, concise, and perhaps most importantly, highly transparent. The central construct, which we have termed a “focal graph”, can seamlessly integrate information across multiple, diverse, complex data sets, including large-scale chemical biology and multi-omics data. Focal graphs can be combined with agentic large language models (LLMs) as part of retrieval-augmented generation (RAG) strategies to build intelligent, autonomous workflows for discovery of new targets, biomarkers, and combination treatment strategies. Importantly, unlike ML methods, our focal graph approach preserves the provenance of the underlying experimental data, allowing the processes and conclusions to be examined and evaluated in tremendous detail by human or machine-based methods. Using focal graphs combined with agentic LLMs and workflow automation, we can plan and execute research programs that lead to the discovery of entirely novel biological insights, including new potential biomarkers, original drug targets, drug repurposing strategies, and combination therapy hypotheses. Here we present the theoretical underpinnings of focal graphs and their automation, as well as sharing selected findings related to novel disease therapeutic and biomarker strategies. These results derive their support from multiple, diverse, large-scale experimental data sets, with full transparency with respect to the experimental basis of the discoveries. As these autonomous systems become more sophisticated and are given access to more data and computational power, they can be expected to generate insights with increasing levels of novelty and experimental support. More information is available at www.plexresearch.com. Citation Format: Jedidiah Gaetz, Timothy R. Wall, Eleni S. Stylianou, Ehab Khalil, Oren Levy, Douglas W. Selinger. A fully transparent and automatable form of AI for biomarker and new target discovery using diverse multi-omics data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A019.

PeGAS: A Versatile Bioinformatics Pipeline for AMR, Virulence and Pangenome Analysis

Abstract Motivation Antimicrobial resistance is increasingly recognized as one of the most significant global health threats, with profound implications for human, animal, and environmental health. Genome analysis represents a very useful tool that provides accurate and reproducible results allowing for the advancement of knowledge regarding antimicrobial resistance diagnosis, therapeutics, surveillance, transmission and evolution. However, due to increasing complexity of bacterial genome analysis and computational power required for genomic approaches, there is a continuous need for comprehensive, user-friendly tools for data analysis. We developed Pangenome and Genomic Analysis Suite—PeGAS, to address some of these challenges by offering an all-in-one pipeline that performs a range of analyses. Results PeGAS integrates key genomic analysis features of bacteria whole genome sequencing, including the prediction of antimicrobial resistance profiles, sorted by various categories of antibiotics, VF detection and plasmid replicon assignment. The pipeline also performs pangenome analysis, multilocus sequence typing, genome assembly quality control (by reporting statistics such as GC content, contig length, the number of contigs, as well as variation from certain GC thresholds) providing a comprehensive genomic overview. PeGAS also offers the ability to restart seamlessly from any sporadic interruptions that might occur during long or resource-intensive runs. Availability and implementation PeGAS is available at: https://github.com/liviurotiul/PeGAS

Biophysical and computational insights from modeling human cortical pyramidal neurons

The human brain’s remarkable computational power enables parallel processing of vast information, integrating sensory inputs, memories, and emotions for rapid learning, adaptability, and creativity – far surpassing present-day artificial systems. These capabilities likely arise, in part, from the distinct properties of human neurons, which have only recently been elucidated through collaborative efforts among neurosurgeons, experimental, and theoretical neuroscientists. This effort has yielded unprecedented morphological and biophysical data on human neurons obtained during epilepsy or tumor surgeries. To integrate and interpret this diverse data, two complementary modeling approaches have emerged: detailed biophysical models, unraveling how morpho-electrical properties shape signal processing in human neurons, and machine learning models, which leverage the biophysical models to uncover hidden structure–function relationships. A major focus has been the disproportionately expanded layers 2/3 of the human cortex, where the large L2/3 pyramidal neurons (HL2/3 PNs) can track high-frequency input modulations, exhibit enhanced dendritic signaling, maintain numerous functional dendritic compartments, and display unique dendritic excitability. More recent efforts extend to modeling human hippocampal, cerebellar, and inhibitory cortical neurons. This review synthesizes key theoretical insights from biophysical and machine-learning models of HL2/3 PNs, and explores their implications for understanding “what makes us human.”

Spike-Driven Channel-Temporal Attention Network with Multi-Scale Convolution for Energy-Efficient Bearing Fault Detection

Real-time bearing fault diagnosis necessitates highly accurate, computationally efficient, and energy-conserving models suitable for deployment on resource-constrained edge devices. To address these demanding requirements, we propose the Spike Convolutional Attention Network (SpikeCAN), a novel spike-driven neural architecture tailored explicitly for real-time industrial diagnostics. SpikeCAN utilizes the inherent sparsity and event-driven processing capabilities of spiking neural networks (SNNs), significantly minimizing both computational load and power consumption. The SpikeCAN integrates a multi-dilated receptive field (MDRF) block and a convolution-based spike attention module. The MDRF module effectively captures extensive temporal dependencies from signals across various scales. Simultaneously, the spike-based attention mechanism dynamically extracts spatial-temporal patterns, substantially improving diagnostic accuracy and reliability. We validate SpikeCAN on two public bearing fault datasets: the Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT). The proposed model achieves 99.86% accuracy on the four-class CWRU dataset through five-fold cross-validation and 99.88% accuracy with a conventional 70:30 train–test random split. For the more challenging ten-class classification task on the same dataset, it achieves 97.80% accuracy under five-fold cross-validation. Furthermore, SpikeCAN attains a state-of-the-art accuracy of 96.31% on the fifteen-class MFPT dataset, surpassing existing benchmarks. These findings underscore a significant advancement in fault diagnosis technology, demonstrating the considerable practical potential of spike-driven neural networks in real-time, energy-efficient industrial diagnostic applications.

Recent Advancements in the Field of Nanotechnology Using Artificial Intelligence, Data Science and Machine Learning

Rapidly developing technologies like nanotechnology and machine learning (ML) have many practical uses in data processing, computer engineering, materials research, and medicine. By making data processing easier in the synthesis of nanomaterials, characterisation, and optimization of nanoscale features, machine learning (ML) advances nanotechnology. ML algorithms, on the other hand, depend on faster and more efficient processing power, which nanotechnology enhances. A survey of the academic literature offers insights into the fascinating horizons of both nanotechnology and machine learning, despite the fact that both areas’ capabilities are still in their infancy. It also suggests that their integration can have transformational effects. Creating instruments for working with nanomaterials and making sure that data collecting for ML models is impartial and moral are two areas of future research. Furthermore, nanochips can boost the performance and power of contemporary computers, which could cut down on the time and expense required to train intricate machine learning models. The goal is to draw attention to how important it is for these two domains to work together, since this can improve computing power, optimize model training, and hasten nanotechnology research and nanomachine development. It is anticipated that a deeper comprehension of this cross-collaboration will enhance computer capabilities to maximize model training, accelerate nanotechnology research, and guide future nanomachines. Major Findings: This information provides a broad overview of nanotechnology, emphasising both the worldwide concerns regarding their application and the importance of these fields in the development of new systems and technologies. These instances highlight machine learning’s revolutionary potential in finding nanoparticles with desired characteristics and streamlining time-consuming procedures in nanotechnology

A Residual Optronic Convolutional Neural Network for SAR Target Recognition

Deep learning (DL) has shown great capability in remote sensing and automatic target recognition (ATR). However, huge computational costs and power consumption are challenging the development of current DL methods. Optical neural networks have recently been proposed to provide a new mode to replace artificial neural networks. Here, we develop a residual optronic convolutional neural network (res-OPCNN) for synthetic aperture radar (SAR) recognition tasks. We implement almost all computational operations in optics and significantly decrease the network computational costs. Compared with digital DL methods, res-OPCNN offers ultra-fast speed, low computation complexity, and low power consumption. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the lightweight nature of the optronic method and its feasibility for SAR target recognition.

Service-Chain-Driven Communication and Computing Integration Networking: A Case Study of Levee Piping Hazard Inspection via Remote Sensing

Computing power network (CPN) is designed to utilize multi-dimensional resources to complete computing tasks. However, in practical applications, the CPN architecture has difficulty in coordinating cross-domain heterogeneous resources, making it impossible to achieve the real-time and high scalability requirements of computationally intensive and time-sensitive tasks such as levee piping hazard inspection via remote sensing in emergency scenarios. Based on this, we propose a communication and computation integrated network architecture, referred to as (Com)2INet, that integrates “sensing”, “transmission”, and “computation” phases. In the sensing phase, thermal infrared imagery is utilized to retrieve land surface temperature fields through radiative transfer mechanisms, providing a reliable foundation for visual segmentation of piping hazards. In the transmission phase, we adopt the designed multi-path transmission mechanism to promote the efficient data flow across heterogeneous networks. In the computation phase, the proposed SACM algorithm, which is functionally decomposed and implemented as service chains within the proposed network architecture, dynamically processes the retrieved temperature fields to achieve precise hazard identification. This integrated framework ensures seamless interaction between sensing, communication, and computation, addressing the challenges of real-time hazard detection in emergency scenarios.

Impact of power quality degradation on energy consumption and temperature rise of office devices

Abstract In recent years, power quality has deteriorated due to the widespread use of modern electronic devices. Office buildings employ a variety of devices, such as computers and LCD monitors, contributing to a decrease in power factor and an increase in harmonic distortion. These commonly used devices, typically based on power electronic components, draw distorted current from the grid containing higher-order harmonics. This distorted current impacts the voltage at the point of connection, further degrading power quality. This article examines the impact of total harmonic distortion of voltage on the power consumption parameters and thermal performance of common office equipment. The study focuses on two types of computer power supplies and LCD monitors, evaluating how distorted voltage affects reactive power, power factor, and device temperature. Experimental measurements were conducted under varying levels of voltage distortion to assess changes in electrical parameters and thermal behavior. The results indicate that increased voltage distortion results in higher reactive power consumption, a decrease in power factor, and a significant increase in device temperature. These findings highlight the impact of power quality on the performance and durability of office equipment, underscoring the importance of managing voltage distortion in low-voltage networks.

Seamless optical cloud computing across edge-metro network for generative AI

The rapid advancement of generative artificial intelligence (AI) in recent years has profoundly reshaped modern lifestyles, necessitating a revolutionary architecture to support the growing demands for computational power. Cloud computing has become the driving force behind this transformation. However, it consumes significant power and faces computation security risks due to the reliance on extensive data centers and servers in the cloud. Reducing power consumption while enhancing computational scale remains persistent challenges in cloud computing. Here, we propose and experimentally demonstrate an optical cloud computing system that can be seamlessly deployed across edge-metro network. By modulating inputs and models into light, a wide range of edge nodes can directly access the optical computing center via the edge-metro network. The experimental validations show an energy efficiency of 118.6 mW/TOPs (tera operations per second), reducing energy consumption by two orders of magnitude compared to traditional electronic-based cloud computing solutions. Furthermore, it is experimentally validated that this architecture can perform various complex generative AI models through parallel computing to achieve image generation tasks.

Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning

Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. However, Deep CNNs require substantial computational power and memory, particularly for large datasets and complex architectures. Additionally, optimising the hyperparameters of deep CNNs, although critical for enhancing model performance, is challenging due to the high computational costs involved, making it difficult without access to high-performance computing resources. To address these limitations, this study presents a fast and efficient model that aims to achieve superior classification performance compared to popular Deep CNNs by developing lightweight CNNs combined with the Nonlinear Lévy chaotic moth flame optimiser (NLCMFO) for automatic hyperparameter optimisation. NLCMFO integrates the Lévy flight, chaotic parameters, and nonlinear control mechanisms to enhance the exploration capabilities of the Moth Flame Optimiser during the search phase while also leveraging the Lévy flight theorem to improve the exploitation phase. To assess the efficiency of the proposed model, empirical analyses were performed using a dataset of 2314 brain tumour detection images (1245 images of brain tumours and 1069 normal brain images). The evaluation results indicate that the CNN_NLCMFO outperformed a non-optimised CNN by 5% (92.40% accuracy) and surpassed established models such as DarkNet19 (96.41%), EfficientNetB0 (96.32%), Xception (96.41%), ResNet101 (92.15%), and InceptionResNetV2 (95.63%) by margins ranging from 1 to 5.25%. The findings demonstrate that the lightweight CNN combined with NLCMFO provides a computationally efficient yet highly accurate solution for medical image classification, addressing the challenges associated with traditional deep CNNs.

Use of bovine serum albumin might impair immunofluorescence signal in thick tissue samples

Significant progress in microscopic imaging techniques allowed transition from predominantly qualitative methods to a powerful tool for quantitative research, driven by improved instrumentation and computational power. Furthermore, previously limited to thin, laser-permeable tissue sections, imaging techniques have been revolutionized by the advent of tissue optical clearing. This innovation enables the visualization and quantitative analysis of entire organs and even whole bodies at cellular resolution. However, achieving high-quality imaging depends not only on the transparency of the tissue preparation but also on precise immunofluorescence labeling to ensure accurate signal detection and reliable study outcomes. In this study, we evaluated whether various reagents that are typically applied during the tissue blocking step prior to immunofluorescence staining affect the quality of the obtained image in thick and optically cleared samples. We demonstrate that the commonly employed tissue blocking step does not improve imaging conditions and even can substantially degrade fluorescence signal quality, particularly in large, optically cleared tissues such as whole mouse brain hemispheres.

Attention-LightNet: A Lightweight Deep Learning Real-Time Defect Detection for Laser Sintering

Part defects in additive manufacturing (AM) operations like laser sintering (LS) can negatively affect the quality and integrity of the manufactured parts. Therefore, it is important to understand and mitigate these part defects to improve the performance and safety of the manufactured parts. Integrating machine learning to detect part defects in AM can enable efficient, fast, and automated real-time monitoring, reducing the need for labor-intensive manual inspections. In this work, a novel approach incorporating a lightweight Visual Geometry Group (VGG) structure with soft attention is presented to detect powder bed defects (such as cracks, powder bed ditches, etc.) in laser sintering processes. The model was evaluated on a publicly accessible dataset (called LS Powder bed defects) containing 8514 images of powder bed images pre-split into training, validation, and testing sets. The proposed methodology achieved an accuracy of 98.40%, a precision of 97.45%, a recall of 99.40%, and an f1-score of 98.42% with a computation complexity of 0.797 GMACs. Furthermore, the proposed method achieved better performance than the state-of-the-art in terms of accuracy, precision, recall, and f1-score on LS powder bed images, while requiring lower computational power for real-time application.

Joint Task Coding and Transfer Optimization for Edge Computing Power Networks

Joint Task Coding and Transfer Optimization for Edge Computing Power Networks

FPGA implementation of deep learning architecture for ankylosing spondylitis detection from MRI

Ankylosing Spondylitis (AS), commonly known as Bechterew’s disease, is a complex, potentially disabling disease that develops slowly over time and progresses to radiographic sacroiliitis. The etiology of this disease is poorly understood, making it difficult to diagnose. Therefore, treatment is also delayed. This study aims to diagnose AS with an automated system that classifies axial magnetic resonance imaging (MRI) sequences of AS patients. Recently, the application of deep learning neural networks (DLNNs) for MRI classification has become widespread. The implementation of this process on computer-independent end devices is advantageous due to its high computational power and low latency requirements. In this research, an MRI dataset containing images from 527 individuals was used. A deep learning architecture on a Field Programmable Gate Array (FPGA) card was implemented and analyzed. The results show that the classification performed on FPGA in AS diagnosis yields successful results close to the classification performed on CPU.

Blockchain-based heterogeneous resource configuration scheme in computing power network

To provide users with on-demand computing and network resources while ensuring service quality, the concept of a computational power network has emerged. However, efficiently allocating these resources to achieve an optimal balance between system energy consumption and latency, as well as ensuring the security of the resource allocation process, remain significant challenges. To solve these challenges, this paper proposes a secure edge offloading optimization framework. Within this framework, we incorporate blockchain technology to record critical network information in blocks within the blockchain, thereby ensuring the security of resource allocation. Subsequently, we formulate a joint optimization problem aimed at optimizing network bandwidth, computational resources, and blockchain maintenance resources simultaneously to achieve the best possible balance between system latency and energy consumption. Given the complexity of the original problem, we decompose it into two subproblems and solve each using the Lagrange multiplier iterative algorithm. To validate the effectiveness of our proposed solution, we compare it with several baseline approaches, including those based on Particle Swarm Optimization (PSO) and reinforcement learning methods such as Proximal Policy Optimization (PPO). Simulation results demonstrate that our proposed scheme performs notably better in achieving an optimal balance between system energy consumption and latency.

Quantum-accelerated intelligence in eCommerce: The role of AI, machine learning, and blockchain for scalable, secure digital trade

This paper explores the convergence of quantum-accelerated intelligence, artificial intelligence (AI), machine learning (ML), and blockchain technology to revolutionize digital trade within the eCommerce sector. With the rise of quantum computing, AI and ML techniques are poised for enhanced scalability, enabling more efficient decision-making, personalized customer experiences, and optimized supply chain management. The paper examines how quantum computing can dramatically improve data processing capabilities, allowing for faster and more accurate AI-driven algorithms in real-time eCommerce environments. Furthermore, blockchain technology is discussed in terms of its potential to ensure transparent, secure, and immutable transaction records, fostering trust between consumers and vendors. The integration of quantum computing into AI and blockchain could mitigate existing limitations in computational power and data security, offering transformative benefits for eCommerce platforms. Through an in-depth analysis of current advancements and theoretical models, this study demonstrates how these technologies, when synergized, can create a robust ecosystem for secure, scalable, and efficient digital trade, ultimately reshaping the future of global commerce.

Using MATLAB for Teaching Physics, Direct Current Circuits Problems Case Study

Physics, even though it is guided by simple principles, tends for many topics to be obscured in the mathematics redundancy. MATLAB®, as interactive software for computer algebra, has already had an important impact on the way physics is taught by educators. It has also had a substantial impact on the way research is performed by students to overcome the detention of mathematical difficulties. The literature comparing MATLAB® with other programming languages used in teaching is addressed here in brief, along with the reasons why we choose MATLAB®. This paper aims to introduce, through some examples, the convenience of using MATLAB®, bothfor teaching and learning physics, by taking advantage of its computation power so that one can concentrate on applying the principles of setting equations instead of the technical details of solving equations. Some examples found in physics textbooks are analytically solved. We have developed MATLAB® codes regarding these examples as special cases that can be generalized for analyzing electrical direct current circuits. We conclude that the user-friendly environment of MATLAB®, with its powerful built-in routines for a variety of computations, is an effective tool to teach circuit analysis to physics students.