Computing Applications Research Articles

Improved bounds for the index conjecture in zero-sum theory

The Index Conjecture in zero-sum theory states that when n is coprime to 6 and k equals 4, every minimal zero-sum sequence of length k modulo n has index 1. While other values of (k,n) have been studied thoroughly in the last 30 years, it is only recently that the conjecture has been proven for n>1020. In this paper, we prove that said upper bound can be reduced to 4.6⋅1013, and lower under certain coprimality conditions. Further, we verify the conjecture for n<1.8⋅106 through the application of High Performance Computing (HPC).

All-optical combinational logical units featuring fifth-order cascade

Modern computational technologies are increasingly encountering significant limitations, driving a shift towards alternative paradigms such as optical computing. In this study, we introduce novel all-optical combinational logic units based on diffractive neural networks (D2NNs), designed to perform high-order logical operations both efficiently and swiftly using only two modulation layers. This innovative design offers increased processing speed, improved energy efficiency, robust environmental stability, and high error tolerance, making it exceptionally well-suited for a broad spectrum of applications in optical computing and communications. By leveraging transfer learning, we successfully developed a fifth-order cascaded combinational logic circuit for practical information transmission system. Furthermore, we reveal a pioneering application of our device in Optical Time Division Multiplexing (OTDM), demonstrating its capability to manage high-speed data transfer seamlessly without the need for electronic conversion. Extensive simulations and experimental validations highlight the potential of our model as a foundational technology for future optical computing architectures, paving the way toward more sustainable and efficient optical data processing platforms.

Hidden data recovery using reservoir computing: Adaptive network model and experimental brain signals.

The problem of hidden data recovery is crucial in various scientific and technological fields, particularly in neurophysiology, where experimental data can often be incomplete or corrupted. We investigate the application of reservoir computing (RC) to recover hidden data from both model Kuramoto network system and real neurophysiological signals (EEG). Using an adaptive network of Kuramoto phase oscillators, we generated and analyzed macroscopic signals to understand the efficiency of RC in hidden signal recovery compared to linear regression (LR). Our findings indicate that RC significantly outperforms LR, especially in scenarios with reduced signal information. Furthermore, when applied to real EEG data, RC achieved more accurate signal reconstruction than traditional spline interpolation methods. These results underscore RC's potential for enhancing data recovery in neurophysiological studies, offering a robust solution to improve data integrity and reliability, which is essential for accurate scientific analysis and interpretation.

Quantum Computing and Cloud Technologies: The Next Frontier in Computing Power

Abstract: This article explores the convergence of quantum computing and cloud technologies, representing a pivotal advancement in computational power with far-reaching implications across various industries and scientific domains. It examines the current state of quantum computing, elucidating its fundamental principles and recent hardware advancements, while comparing its capabilities to classical computing systems. The integration of quantum computing with cloud platforms is analyzed, highlighting the emergence of cloud-based quantum services and their advantages in democratizing access to this cutting-edge technology. The article delves into potential applications of quantum cloud computing, including cryptography, material science, artificial intelligence, and financial modeling, showcasing its transformative potential. Critical challenges facing quantum cloud computing are addressed, such as error correction, scalability issues, and the development of quantumsafe encryption methods. Looking ahead, the article discusses prospects and research directions, including anticipated breakthroughs in quantum hardware, emerging quantum cloud architectures, and the potential impact on various industries. By synthesizing current research and industry developments, this comprehensive overview provides insights into the revolutionary potential of quantum cloud computing and its role in shaping the future of computational capabilities.

Survey of encoding techniques for quantum machine learning

Quantum computing is a field of computation that processes information in a fundamentally different way compared to classical computers. Quantum computing is a rapidly expanding research field with ongoing investigations regarding applications of quantum computing in widespread domains such as cryptography, artificial intelligence, communications, etc. The core focus of this paper is quantum encoding which is a crucial branch of quantum computing. Quantum encoding involves mapping classical data into quantum states. This paper provides an in-depth analysis of prominent quantum encoding techniques with their strengths and weaknesses. Furthermore, this paper suggests development of hybrid encoding methods capable of emulating the concept of Euclidean distance by integrating amplitude and angle encoding.

Hydrogen-Induced Topotactic Phase Transformations of Cobaltite Thin Films.

Manipulating physical properties through ion migration in complex oxide thin films is an emerging research direction to achieve tunable materials for advanced applications. While the reduction of complex oxides has been widely reported, few reports exist on the modulation of physical properties through a direct hydrogenation process. Here, we report an unusual mechanism for hydrogen-induced topotactic phase transitions in perovskite La0.7Sr0.3CoO3 thin films. Hydrogenation is performed upon annealing in a pure hydrogen gas environment, offering a direct understanding of the role that hydrogen plays at the atomic scale in these transitions. Topotactic phase transformations from the perovskite (P) to hydrogenated-brownmillerite (H-BM) phase can be induced at temperatures as low as 220 °C, while at higher hydrogenation temperatures (320-400 °C), the progression toward more reduced phases is hindered. Density functional theory calculations suggest that hydroxyl bonds are formed with the introduction of hydrogen ions, which lower the formation energy of oxygen vacancies of the neighboring oxygen, enabling the transition from the P to H-BM phase at low temperatures. Furthermore, the impact on the magnetic and electronic properties of the hydrogenation temperature is investigated. Our research provides a potential pathway for utilizing hydrogen as a basis for low-temperature modulation of complex oxide thin films, with potential applications in neuromorphic computing.

Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru

Cloud Computing Application for the Analysis of Land Use and Land Cover Changes in Dry Forests of Peru

Classical Simulations on Quantum Computers: Interface-Driven Peptide Folding on Simulated Membrane Surfaces

Background:Antimicrobial peptides (AMPs) are crucial in the fight against infections and play significant roles in various health contexts, including cancer, autoimmune diseases, and aging. A key aspect of AMP functionality is their selective interaction with pathogen membranes, which often exhibit altered lipid compositions. These interactions are thought to induce a conformational shift in AMPs from random coil to alpha-helical structures, essential for their lytic activity. Traditional computational approaches have faced challenges in accurately modeling these structural changes, especially in membrane environments, thereby opening and opportunity for more advanced approaches. Method:This study extends an existing quantum computing algorithm, initially designed for peptide folding simulations in homogeneous environments, to address the complexities of AMP interactions at interfaces. Our approach enables the prediction of the optimal conformation of peptides located in the transition region between hydrophilic and hydrophobic phases, resembling lipid membranes. The new method was tested on three 10-amino-acid-long peptides, each characterized by distinct hydrophobic, hydrophilic, or amphipathic properties, across different media and at interfaces between solvents of different polarity. Results:The developed method successfully modeled the structure of the peptides without increasing the number of qubits required compared to simulations in homogeneous media, making it more feasible with current quantum computing resources. Despite the current limitations in computational power and qubit availability, the findings demonstrate the significant potential of quantum computing in accurately characterizing complex biomolecular processes, particularly AMP folding at membrane models. Conclusions:This research highlights the promising applications of quantum computing in biomolecular simulations, paving the way for future advancements in the development of novel therapeutic agents. We aim to offer a new perspective on enhancing the accuracy and applicability of biomolecular simulations in the context of AMP interactions with membrane models.

Inter‐Ion Mutual Repulsion Control for Nonvolatile Artificial Synapse

AbstractOrganic electrochemical transistors (OECTs) are promising candidates for artificial synapses to achieve high‐performance synaptic characteristics. While most research has focused on modifying the properties of organic semiconductors for efficient ion doping, there is a lack of systematic investigation into the relationship between ion‐mediated mechanisms and synaptic performance. In this study, an effective strategy for enhancing electrochemical doping and de‐doping by utilizing different coulombic anions is proposed. The findings reveal that doped ions in the channel layer affect inter‐ion interactions, influencing the non‐volatile effects by improving the doping performance of the synaptic device. Moreover, electrochemical analysis indicates that ions in the channel layer are sequentially de‐doped, enabling high linearity and symmetry. The fabricated devices demonstrate high‐performance synaptic properties including a retention time of ≈102 s with ≈50% retention over peak current and near‐ideal long‐term potentiation/long‐term depression (LTP/LTD) through effective electrochemical doping and de‐doping. These results show that controlling both the properties of organic semiconductors and ion interactions in the electrolyte is crucial for OECTs, opening up various applications for neuromorphic computing.

Exposing the odd-parity superconductivity in CeRh2As2 with hydrostatic pressure

Odd-parity superconductivity is a fundamentally interesting but rare state of matter with a potential for applications in topological quantum computing. Crystals with staggered locally noncentrosymmetric structures have been proposed as platforms where a magnetic field can induce a transition between even- and odd-parity superconducting (SC) states. The strongly correlated superconductor CeRh2As2, with the critical temperature Tc≈0.4K, is likely the first example material showing such a phase transition, which occurs at the magnetic field μ0H*=4T applied along the crystallographic c axis. CeRh2As2 also undergoes a phase transition of an unknown origin at T0=0.5K. By subjecting CeRh2As2 to hydrostatic pressure and mapping the resultant changes to the SC phase diagrams we investigated how the lattice compression and changes to the electronic correlations affect the stability and relative balance of the two SC states. The abnormally high in-plane upper critical field becomes even higher close to a quantum critical point of the T0 order. Remarkably, the SC phase-switching field H* is drastically reduced under pressure, dropping to 0.3 T at 2.7 GPa. This result signals an apparent strengthening of the local noncentrosymmetricity and forecasts a possible stabilization of the putative odd-parity state down to zero field, hitherto not considered by theoretical models. Published by the American Physical Society 2024

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community.

The Community Multiscale Air Quality Model (CMAQ) is a local- to hemispheric-scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Community Modeling and Analysis System (CMAS) center. CMAQ is used for regulatory purposes by the USEPA program offices and state and local air agencies and is also widely used by the broader global research community to simulate and understand complex air quality processes and for computational environmental fate and transport and climate and health impact studies. Leveraging state-of-the-science cloud computing resources for high-performance computing (HPC) applications, CMAQ is now available as a fully tested, publicly available technology stack (HPC cluster and software stack) for two major cloud service providers (CSPs). Specifically, CMAQ configurations and supporting materials have been developed for use on their HPC clusters, including extensive online documentation, tutorials and guidelines to scale and optimize air quality simulations using their services. These resources allow modelers to rapidly bring together CMAQ, cloud-hosted datasets, and visualization and evaluation tools on ephemeral clusters that can be deployed quickly and reliably worldwide. Described here are considerations in CMAQ version 5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of a typical simulation for testing and verifying components of the modeling system. The outcomes of this effort are to provide findings from performing CMAQ simulations on the cloud using popular vendor-provided resources, to enable the user community to adapt this for their own needs, and to identify specific areas of potential optimization with respect to storage and compute architectures.

Exploring rare-earth Kitaev magnets by massive-scale computational analysis

The Kitaev honeycomb model plays a pivotal role in the quest for quantum spin liquids, in which fractional quasiparticles would provide applications in decoherence-free topological quantum computing. The key ingredient is the bond-dependent Ising-type interactions, dubbed the Kitaev interactions, which require strong entanglement between spin and orbital degrees of freedom. Here we investigate the identification and design of rare-earth materials displaying robust Kitaev interactions. We scrutinize all possible 4f electron configurations, which require up to 6+ million intermediate states in the perturbation processes, by developing a parallel computational program designed for massive-scale calculations. Our analysis reveals a predominant interplay between the isotropic Heisenberg J and anisotropic Kitaev K interactions across all realizations of the Kramers doublets. Remarkably, instances featuring 4f3 and 4f11 configurations showcase the prevalence of K over J, presenting unexpected prospects for exploring the Kitaev quantum spin liquids in compounds, including Nd3+ and Er3+, respectively.

Bacteriophage λ exonuclease and a 5'-phosphorylated DNA guide allow PAM-independent targeting of double-stranded nucleic acids.

Sequence-specific recognition of double-stranded nucleic acids is essential for molecular diagnostics and in situ imaging. Clustered regularly interspaced short palindromic repeats and Cas systems rely on protospacer-adjacent motif (PAM)-dependent double-stranded DNA (dsDNA) recognition, limiting the range of targetable sequences and leading to undesired off-target effects. Using single-molecule fluorescence resonance energy transfer analysis, we discover the enzymatic activity of bacteriophage λ exonuclease (λExo). We show binding of 5'-phosphorylated single-stranded DNA (pDNA) to complementary regions on dsDNA and DNA-RNA duplexes, without the need for a PAM-like motif. Upon binding, the λExo-pDNA system catalytically digests the pDNA into nucleotides in the presence of Mg2+. This process is sensitive to mismatches within a wide range of the pDNA-binding region, resulting in exceptional sequence specificity and reduced off-target effects in various applications. The absence of a requirement for a specific motif such as a PAM sequence greatly broadens the range of targets. We demonstrate that the λExo-pDNA system is a versatile tool for molecular diagnostics, DNA computing and gene imaging applications.

Сегнетоэлектрические материалы в современных вычислениях

Background: Ferroelectric materials, known for their spontaneous electric polarization, are reshaping the landscape of modern computing. These materials have unique qualities, such as non-volatility, high-speed operation, and energy efficiency, which are critical for advancing modern computer technology. Objective: This study intends to investigate integrating ferroelectric materials into current computer systems, emphasizing to improve memory devices and processors and solve the accompanying technological obstacles and possibilities. Methods: A thorough analysis of existing literature and experimental data was carried out to determine the capabilities and limitations of ferroelectric materials in computing applications. Key performance indicators examined include polarization retention, energy consumption, scalability, and integration with existing semiconductor technology. Results: The results show that ferroelectric materials considerably increase memory device performance and efficiency by allowing quicker write/read operations and lowering power use. However, concerns such as material deterioration, data retention, and integration complexity with silicon-based technology remain. Conclusion: Ferroelectric materials provide exciting prospects for the next generation of computer technology. While they provide significant gains in memory and processing power, addressing the technological obstacles is critical to their effective adoption into mainstream computer applications. Further research and development are needed to solve these issues and fully realize the promise of ferroelectric materials in improving computer performance.

Path-Independent All-Visible Orthogonal Photoswitching for Applications in Multi-Photochromic Polymers and Molecular Computing.

Synthetic molecular photoswitches have taken center stage as high-precision tools to introduce light-responsiveness at the smallest scales. Today they are found in all areas of applied chemistry, covering materials research, chemical biology, catalysis, or nanotechnology. For a next step of applicability truly orthogonal photoswitching is highly desirable but to date such independent addressability of different photoswitches remains to be highly challenging. In this work we present the first example of all-visible, all-light responsive, and path independent orthogonal photoswitching. By combining two recently developed indigoid photoswitches -peri-anthracenethioindigo and a rhodanine-based chromophore - a four-state system is established and each state can be accessed in high yields completely independently and also with visible light irradiation only. The four states give rise to four different colors, which can be transferred to a solid polymer matrix yielding a versatile multi-state photochromic material. Further, combination with a fluorescent dye as third component is possible, demonstrating applicability of this orthogonal photoswitching system in all-photonic molecular logic behavior and information processing.

Detection of aberration in human behavior using shallow neural network over smartphone inertial sensors data

AbstractThe integration of different Mobile Edge Computing (MEC) applications has significantly enhanced the realm of security and surveillance, with Human Activity Recognition (HAR) standing out as a crucial application. The diverse sensors found in smartphones have made it convenient for monitoring applications to gather and analyze data, rendering them valuable for HAR purposes. Moreover, MEC can be employed to automate surveillance, allowing intelligent monitoring of restricted areas to identify and respond to unwanted or suspicious activities. This research develops a system using motion sensors in smartphones to identify unusual human activities. People's smartphones were employed to monitor both suspicious and regular activities. Information was collected for various actions categorized as either suspicious or regular. When a person performs a certain action, the smartphone records a series of sensory data, analyse important patterns from the basic data, and then determines what the person is doing by combining information from different sensors. To prepare the data, information from different sensors was aligned to a shared timeline. In this study, we used a sliding window approach on synchronized data to feed sequences into LSTM and CNN models. These models, which include initial layers of LSTM and CNN, automatically find important patterns in the order of human activities. We combined SVM with the features extracted by the shallow Neural Network to make a mixed model that predicts suspicious activities. Lastly, we compared LSTM, CNN, and our new shallow mixed neural network using a new real‐time dataset. The mixed model of CNN and SVM achieved an accuracy of 94.43%. Additionally, the sliding window method's effectiveness was confirmed with a 4.28% improvement in accuracy.

Enhancing Long-Term Memory in Carbon-Nanotube-Based Optoelectronic Synaptic Devices for Neuromorphic Computing.

This study investigates the impact of spin-coating speed on the performance of carbon nanotube (CNT)-based optoelectronic synaptic devices, focusing on their long-term memory properties. CNT films fabricated at lower spin speeds exhibited a greater thickness and density compared to those at higher speeds. These denser films showed enhanced persistent photoconductivity, resulting in higher excitatory postsynaptic currents (EPSCs) and the prolonged retention of memory states after UV stimulation. Devices coated at a lower spin-coating speed of 2000 RPM maintained EPSCs above 70% for 3600 s, outperforming their higher-speed counterparts in long-term memory retention. Additionally, the study demonstrated that the learning efficiency improved with repeated UV stimulation, with fewer pulses needed to achieve the maximum EPSC in successive learning cycles. These findings highlight that optimizing spin-coating speeds can significantly enhance the performance of CNT-based synaptic devices, making them suitable for applications in neuromorphic computing and artificial neural networks requiring robust memory retention and efficient learning.

Application of high-performance computing in sustainable energy campus building design

In high-performance computing (HPC) systems, energy and power considerations play an increasingly important role. This work aims to ensure the implementation of China’s green and ecological concepts, respond to China’s strategy of building an environment-friendly and resource-saving society, and actively promote the construction of sustainable development campuses. First, the theoretical basis of sustainable energy campus architecture is described. Next, the teaching feedback model under HPC is constructed. Finally, with the evaluation results of students’ task completion processes and students’ task works as measurement indexes, the corresponding data is collected and comprehensively evaluated and analyzed to verify the effectiveness of the model. The analysis results indicate that most students are able to complete tasks within two hours; however, their proficiency with multimedia technology is inadequate, and they lack initiative in the learning process. There is a correlation between the overall evaluation of task performance and the students’ level of understanding of the tasks. By implementing a teaching feedback model, students’ learning enthusiasm and the quality of their work improved, providing effective educational support for promoting sustainable development on campus. Overall, the awareness of using computers and other multimedia technologies among students is not strong, and their learning process is relatively closed, with insufficient enthusiasm and initiative. This model can achieve the acquisition, integration, and statistical analysis of teacher feedback information. The model can realize the acquisition, integration, and statistical analysis of teachers’ feedback information. This work hopes to use this learning and feedback mode in practical teaching to address specific problems in computer multimedia courses.

A Multi-Class Facial Emotion Recognition System for Still Images 1Omage Micheal, 2Fasola Olusanjo &amp; 3Woods Nancy C. 1,3University of Ibadan, Ibadan, Nigeria 2Federal University of Technology, Minna, Nigeria E-mails: 1micheal.omage@gmail.com, 2Sanjo.fasola@futminna.edu.ng, 3Chyn.woods@gmail.com Phone Nos: 1+2348084121913, 2+2348023138034, 3+2348037273291 ABSTRACT Facial expressions serve as a potent and natural means of human communication, conveying a wide range of emotions. In the realm of

Facial expressions serve as a potent and natural means of human communication, conveying a wide range of emotions. In the realm of artificial intelligence and computer vision, recognizing these emotions from facial cues has gained significant importance. Most research focus on detecting a single dominant emotion on any given facial image, although an image could portray several emotions. This work presents a multi-class facial emotion recognition system capable of predicting and quantifying the presence of multiple emotions in a single facial image. The system employs a Convolutional Neural Network (CNN) architecture optimized for multi-label classification across eight emotion classes. The AffectNet dataset, comprising 27,836 images with diverse ages, ethnicities, and real-world settings, was utilized for training the model, to enhance generalization capabilities. The model's performance was evaluated on an impartial benchmark dataset, FER-Plus, containing 35,710 images. The CNN model performed well, with a micro F1-score of 0.7 and a macro F1-score of 0.7 on the test set from AffectNet, and a micro F1-score of 0.7 and a macro F1-score of 0.4 on the FER-Plus dataset. Qualitative analysis demonstrated the model's capability in recognizing blended and subtle facial expressions, providing significant probability distributions across multiple emotion labels. The developed system advances the state-of-the-art in multi-label facial emotion recognition by successfully modeling emotion mixtures, contributing to a deeper understanding of this challenging task and enabling practical applications in affective computing, human-computer interaction, psychological studies, and improving accessibility and inclusivity in emotional computing technologies. Keywords: Multi-Class, Facial Emotion Recognition, Security, System, Still Images, Models, Detection Omage, M. Fasola, O. &amp; Woods, N.C. (2024): A Multi-Class Facial Emotion Recognition System for Still Images. Journal of Advances in Mathematical &amp; Computational Science. Vol. 12, No. 3. Pp 67-80. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N3P6