Computer Processing Research Articles

Artificial Intelligence Based on Resilient Leadership in the Health Sector – A Secondary Publication

At present, it is impossible to deny the existence of artificial intelligence in various areas of social life, understood as the simulation of expert human intelligence from computer processes that involve learning, reasoning, and self-correction, its benefits to the medical field, in particular, are innumerable, but their incorporation into health systems has been gradual for many reasons. According to the above, this research analyzed artificial intelligence based on resilient leadership in the health sector, for which qualitative research was carried out with a documentary-bibliographic design with printed and electronic documentary sources with theoretical contributions from Ávila, Mayer, and Quesada, Morgan, Villa, and Finol, among others. It is highlighted that resilient leadership has become a strategic factor in all organizations, since times of uncertainty and changes lead institutions to properly manage the incorporation of technologies specifically AI, achieving in this way that the centers and professionals in the field of health assume the needs of the contexts and the innovations of the same. It is concluded that resilient leadership will allow artificial intelligence in the health sector to generate higher levels of learning and adaptability to the transformations that are necessary, whose resistance would make its application difficult and in the long run it will leave behind professionals who refuse to assume the contributions of these innovative techniques in medical practice.

Computational Fluid Dynamics Process for Front Windshield Mist Deposition and Its Subsequent Demisting

<div>A vehicle’s heating, ventilation, and air-conditioning system plays a dual role in passenger thermal comfort and safety. The functional aspects of safety include the front windshield demist and deicing feature of the system. The thin-film mist is a result of condensation of water vapor on the inner side of the windshield, which occurs at low ambient temperatures or high humidity. This mist deposition depends on the air saturation pressure at the front windshield. Indian regulation AIS-084 defines the experimental setup for testing, which encompasses both the mist deposition and its subsequent demist process. This regulation mandates testing, which occurs at a later stage of product development. This performance validation can be performed using a three-dimensional computational fluid dynamics approach.</div> <div>Current work summarizes the simulation process for both the mist deposition and the subsequent demisting phenomenon. The complexity of the flow physics is captured via the transient multiphase fluid flow phenomenon subjected to buoyancy effects. This phenomenon is simulated using the Eulerian wall film approach. The wall film deposition of the mist is modeled via species transport. Further, the near-wall thermal effects of surface conduction and heat transfer are simulated by modeling shell conduction layers. Vapor diffusion and relative humidity inside the cabin are modeled using a user-defined function. The process correlation is achieved for two categories of vehicles to establish the process efficacy and robustness. Moving ahead, a design of experiments (DOE) is planned to mitigate the need to simulate mist deposition. The DOE is planning to incorporate deviations in both the input airflow conditions and the imposed ambient conditions. The results from DOE point toward factors that might cause deviations in simulation results with respect to the test. Importantly, the study concludes that the uniform initial thickness assumption of the mist layer can be used for subsequent demist analysis.</div>

Using Feature Selection Enhancement to Evaluate Attack Detection in the Internet of Things Environment

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects of supervised classification, including feature selection, model training, and evaluation methodologies, to comprehensively evaluate their impact on attack detection effectiveness. The key features selected to improve IDS efficiency and reduce dataset size, thereby decreasing the time required for attack detection, are drawn from the extensive network dataset. This paper introduces an enhanced feature selection method designed to reduce the computational overhead on IoT resources while simultaneously strengthening intrusion detection capabilities within the IoT environment. The experimental results based on the InSDN dataset demonstrate that our proposed methodology achieves the highest accuracy with the fewest number of features and has a low computational cost. Specifically, we attain a 99.99% accuracy with 11 features and a computational time of 0.8599 s.

ПОБУДОВА КОДІВ ХЕММІНГА В СКІНЧЕННИХ ПОЛЯХ ГАЛУА

Hamming codes and their modifications are key in numerous technological processes and systems, where minimizing errors is crucial for enhancing reliability and efficiency in data transmission or storage. They allow for the detection and automatic correction of single errors in each data block. Their relative simplicity to implement at the hardware and software levels, renders them useful in systems demanding high reliability. Moreover, they optimize transmission channel use due to their minimal redundancy. At work is the task of applying the properties of modular arithmetic to develop codes that can effectively correct errors in systems with higher bit rates than binary has been considered. Conducted research confirms that such codes can be tailored to meet specific data transmission system requirements, showcasing adaptability. The use of Hamming codes within finite Galois fields, leveraging modular arithmetic, further enhances correction capabilities and efficiency, streamlining implementation and computation processes. Error correction codes in finite Galois fields use the same principles applied in Hamming codes but take into account the mathematical properties of these fields for effective encoding, detection, and correction of errors.The proposed error correction method, through integration with modular arithmetic, opens possibilities for optimizing encoding and decoding processes, allowing for a higher level of data transmission reliability with minimal redundancy, which was previously limited by the properties of traditional codes. The proposed solution demonstrates the potential for using modular arithmetic for error correction but can provide a basis for further research in this area, opening new possibilities for improving information processing and transmission technologies.

Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications

Abstract. The wflow_sbm hydrological model, recently released by Deltares, as part of the Wflow.jl (v0.7.3) modelling framework, is being used to better understand and potentially address multiple operational and water resource planning challenges from a catchment scale to national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrological modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrological models. Most wflow_sbm parameters are based on physical characteristics or processes, and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally result in a satisfactory (0.4 ≥ Kling–Gupta efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance for discharge a priori (without further tuning). Wflow_sbm includes relevant hydrological processes such as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater, and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resource modelling, the addition of a deep groundwater concept, and computational efficiency improvements through for example distributed computing and graphics processing unit (GPU) acceleration.

Manufacturing Industry Resource Endowment Index and Its Big Data Model

This study aims to demonstrate the application of big data models in the evaluation of manufacturing competitiveness across different countries and economic entities. It involves analyzing algorithms and data structures, designing algorithms for evaluating manufacturing competitiveness suitable for different countries and economic entities, and providing algorithmic and theoretical support for building computer assessment models for manufacturing competitiveness in economic entities. The research adopts qualitative and quantitative methods, comparing and analyzing empirical data on manufacturing resource endowment and competitiveness in various countries, combining theoretical analysis and empirical data. By conducting a literature review and empirical research, relevant information and data are collected to establish the relationship between manufacturing resource endowment index and competitiveness. Through case studies and comparative research, this study explores the main factors affecting manufacturing competitiveness in different countries and their interrelationships. It confirms that the competitiveness of manufacturing industries in different countries is influenced by factors such as resource endowment. Further analysis quantifies the relationship between national manufacturing resource endowment and competitiveness, leading to the design of computer algorithms, data structures, and processing logic. The results demonstrate the significant role of resource endowment in manufacturing competitiveness, serving as a crucial basis for formulating comprehensive national strategies. The innovations of this study include: 1. Introducing the manufacturing resource endowment index as a key variable for evaluating competitiveness, enriching the content of competitiveness big data models. 2. Designing comprehensive data structures, algorithms, and processing logic for assessing manufacturing competitiveness from multiple perspectives. 3. In-depth analysis of the relationship and analytical methods between manufacturing strategies, resource endowment, and competitiveness, providing theoretical and model references for the design of computer big data operation platforms.

MULTISOURCE AND MULTITEMPORAL LAND COVER MAPPING OF GREATER LUZON ISLAND USING GOOGLE EARTH ENGINE

Abstract. A variety of research endeavors and practical applications necessitate the use of land cover maps. These maps are valuable for tasks such as change detection, forest monitoring, urban expansion monitoring, natural resource mapping, catering to diverse user requirements. While satellite sensors offer essential data for comprehending spatial and temporal variations in land cover, relying on a single satellite system can be limiting, especially considering the potential hindrance of cloud cover in the case of optical sensors. To enhance temporal frequency, it becomes essential to utilize multiple satellite systems, albeit requiring harmonization to ensure consistent outcomes. This study presents a large-scale annual land cover mapping which utilizes harmonized Landsat-8 and Sentinel-2 satellite imagery, in conjunction with supplementary data, and a machine learning algorithm. In addition, the use of powerful computational processing platforms such as Google Earth Engine and Google Colaboratory is now a requirement to manage big geospatial data as well as to run different algorithms for processing and analysis.

Survey of research on confidential computing

AbstractAs the global data strategy deepens and data elements accelerate integrating and flowing more rapidly, the demand for data security and privacy protection has become increasingly prominent. Confidential computing emerges as a crucial security technology to solve security and privacy problem, and it is also a hot subject of in contemporary security technologies. Leveraging collaborative security in both hardware and software, it builds a trusted execution environment to ensure confidentiality and integrity protection for data in use. This paper provides a comprehensive overview of the development process of confidential computing, summarizing its current research status and issues, which focuses on the security requirements for data security and privacy protection. Furthermore, it deeply analyses the common technical features of confidential computing, and proposes a trusted confidential computing architecture based on collaborative hardware and software trust. Then, it elaborates on the research status and issues of confidential computing from four aspects: hardware security, architecture and key technologies, applications, and standards and evaluation. Finally, this paper provides a synthesis and outlook for the future development of confidential computing. In summary, confidential computing is currently in a rapidly developing stage and will play an important role in cyber security in the future.

Analisis Kemampuan Berpikir Komputasi Matematis Siswa pada Pembelajaran Matematika

The aim of this research is to provide a study of the computational thinking process of mathematics learning. This research uses library research methods. The data obtained is the publication of research articles in scientific journals. Data analysis includes three stages: organize, synthesize, and identify The result of previous research is students' computational thinking abilities need to be improved in abstraction and algorithms.

Score distillation for anomaly detection

Recently, significant performance improvements have been achieved in deep learning-based anomaly detection methods by introducing large neural network architectures and complex anomaly scoring functions. However, the computational cost and memory usage required in the inference phase have also increased significantly, thereby limiting their use in real-time applications. In this paper, we propose a score distillation method that adopts the concept of knowledge distillation. An existing high-performance anomaly detection method is used as the teacher. A small neural network is then trained as the student to mimic the scoring function of the teacher. In the inference phase, the anomaly score for a query instance is obtained by a single forward pass through the student network without requiring any complicated computation processes. We demonstrate that the proposed method makes anomaly detection faster and more efficient while maintaining high performance.

Noise correction in differential phase contrast for improving phase sensitivity

Differential phase contrast (DPC) imaging relies on computational analysis to extract quantitative phase information from phase gradient images. However, even modest noise level can introduce errors that propagate through the computational process, degrading the quality of the final phase result and further reducing phase sensitivity. Here, we introduce the noise-corrected DPC (ncDPC) to enhance phase sensitivity. This approach is based on a theoretical DPC model that effectively considers most relevant noise sources in the camera and non-uniform illumination in DPC. In particular, the dominating shot noise and readout noise variance can be jointly estimated using frequency analysis and further corrected by block-matching 3D (BM3D) method. Finally, the denoised images are used for phase retrieval based on the common Tikhonov inversion. Our results, based on both simulated and experimental data, demonstrate that ncDPC outperforms the traditional DPC (tDPC), enabling significant improvements in both phase reconstruction quality and phase sensitivity. Besides, we have demonstrated the broad applicability of ncDPC by showing its performance in various experimental datasets.

Optimal Molecular Dynamics System Size for Increased Precision and Efficiency for Epoxy Materials.

Molecular dynamics (MD) simulation is an important tool for predicting thermo-mechanical properties of polymer resins at the nanometer length scale, which is particularly important for efficient computationally driven design of advanced composite materials and structures. Because of the statistical nature of modeling amorphous materials on the nanometer length scale, multiple MD models (replicates) are typically built and simulated for statistical sampling of predicted properties. Larger replicates generally provide higher precision in the predictions but result in higher simulation times. Unfortunately, there is insufficient information in the literature to establish guidelines between MD model size and the resulting precision in predicted thermo-mechanical properties. The objective of this study was to determine the optimal MD model size of epoxy resin to balance efficiency and precision. The results show that an MD model size of 15,000 atoms provides for the fastest simulations without sacrificing precision in the prediction of mass density, elastic properties, strength, and thermal properties of epoxy. The results of this study are important for efficient computational process modeling and integrated computational materials engineering (ICME) for the design of next-generation composite materials for demanding applications.

MALARIA DETECTION USING CNN

The primary objective of this project is to develop a robust and efficient malaria detection system based on Convolutional Neural Networks (CNNs). CNNs have demonstrated remarkable performance in image recognition tasks, making them ideal for analyzing medical images such as blood smears for the presence of malaria parasites. By harnessing the power of CNN architecture, our goal is to create a highly accurate and reliable system capable of detecting malaria parasites with precision and consistency. Furthermore, we aim to ensure the efficiency of the developed system, allowing for the rapid processing of large volumes of blood smear images. This efficiency is crucial for facilitating timely diagnosis and treatment, particularly in regions where malaria is prevalent. By optimizing the computational processes involved in malaria detection, we seek to streamline the diagnostic workflow and enhance the overall effectiveness of malaria control efforts.In addition to accuracy and efficiency, adaptability is another key objective of this project. We aim to design a malaria detection system that can effectively handle variations in image quality and parasite morphology. This adaptability ensures that the system remains robust and reliable across different datasets and real-world scenarios, enabling healthcare professionals to confidently rely on its diagnostic capabilities.

MEC-enabled resource allocation in Internet of Vehicles

Mobile edge computing (MEC), a promising technology, is widely used in the context of the Internet of Vehicles (IoV) owing to its robust computing capabilities and proximity to mobile devices. Leveraging its advantageous features, MEC facilitates lower latency and more efficient computing services for intelligent vehicles compared to preceding technologies. The tasks inherent to IoV exhibit varying sensitivities to latency, necessitating a nuanced approach to processing. Therefore, this study aims to develop a sophisticated solution in the form of a joint task sequencing and resource allocation (JTSARA) algorithm based on deep reinforcement learning. The primary objective is to adeptly manage both computing and communication resources within the MEC environment, thereby ensuring the fulfillment of performance requirements of tasks. Initially, tasks are categorized into distinct levels according to their latency tolerance. Subsequent to this classification, a method is formulated to optimize task sequencing, improving the efficiency of computational processing. Furthermore, we tackle the intricate challenge of resource allocation by transforming it into a Markov decision process, employing deep reinforcement learning for resolution. The feasibility and effectiveness of the proposed scheme are demonstrated through comprehensive simulation experiments, affirming its potential for enhancing the performance of IoV systems.

Bond analysis in meta- and para-substituted thiophenols: overlap descriptors, local mode analysis, and QTAIM.

This study delves into the chemical nuances of thiophenols and their derivatives through a comprehensive computational analysis, moving beyond traditional energetic perspectives such as bond dissociation enthalpy and S-H dissociation dynamics. By employing the overlap model along with its topological descriptors (OP/TOP), quantum theory of atoms in molecules (QTAIM), and local vibrational mode (LVM) theories, the research provides a deeper understanding of the S-H and C-S bonding scenarios in substituted thiophenols. The investigation follows the electron-donating capacity of S-H substituent variation with the nature and positioning of other ring substituents. Energy profile analyses indicate distinct stability differences in the cis and trans conformations of meta- and para-PhSH systems, influenced by the electron-donating strength of these substituents. The study also uncovers significant variations in S-H bond distances and descriptor values, particularly in para-substituted PhSH, reflecting the influence of electron-donating or withdrawing substituents. In contrast, alterations at the meta-position show minimal effects on C-S bond descriptors, while para-substitutions markedly influence C-S bond characteristics, demonstrating a clear correlation with the electron-donating or withdrawing capabilities of the substituents. This research sheds light on the intricate bond dynamics in aromatic systems with diverse substituents, highlighting the complex interaction between electronic effects and molecular conformation. The study employs the B97X-D/Def2TZVP level of theory for molecular geometries, ensuring accurate characterization of structures as true minima via analytical harmonic frequency determination. The electronic properties of S-H and C-S bonds in variously substituted thiophenols were analyzed using OP/TOP, QTAIM, and LVM methodologies. Computational processes, including conformational scans, geometry optimizations, and vibrational frequency calculations, were conducted using Gaussian 09, with ultra-fine integration grids and tight convergence criteria for the SCF procedure. Bond descriptors were computed utilizing ChemBOS, Multiwfn, and LModeA software, providing a robust and detailed examination of bond properties.

Deep secure PalmNet: A novel cancelable palmprint template protection scheme with deep attention net and randomized hashing security mechanism

Overcoming challenges in the current cancelable palmprint template is essential for enhancing privacy and security in biometric systems, as these templates exhibit vulnerabilities in both template security and recognition performance. To address these challenges, we present a novel cancelable palmprint template protection scheme with a deep attention net and randomized hashing security mechanism. Firstly, to enhance recognition performance, we designed a deep attention net that is integrated into the model, aiming to improve its feature extraction ability. Additionally, in recognition of the paramount importance of security and privacy, we introduce a randomized hashing security mechanism. This mechanism, incorporating chaotic sequences as weights in a neuron activation layer, appends to the model and enables dynamic control of neuron activation while generating diverse palmprint feature templates. Subsequently, it enhances security and privacy by combining a Logistic-Tent-Sine (LTTS) random key with palmprint feature values through matrix multiplication. Furthermore, to optimize efficiency, particularly with a large dataset, a binarization layer is implemented using the Straight-Through Estimation (STE) algorithm. This layer contributes to computational efficiency and expedient data processing, further improving the performance of our palmprint template protection. The experimental results validate the outstanding accuracy of this scheme on TJU and PolyU palmprint datasets, establishing it as a state-of-the-art solution with remarkable recognition performance. Moreover, security analysis confirms its compliance with cancelable biometric template protection criteria, ensuring superior irreversibility, unlinkability, revocability, and privacy against various attacks.

Large Span Sizes and Irregular Shapes Target Detection Methods Using Variable Convolution-Improved YOLOv8.

In this work, an object detection method using variable convolution-improved YOLOv8 is proposed to solve the problem of low accuracy and low efficiency in detecting spanning and irregularly shaped samples. Aiming at the problems of the irregular shape of a target, the low resolution of labeling frames, dense distribution, and the ease of overlap, a deformable convolution module is added to the original backbone network. This allows the model to deal flexibly with the problem of the insufficient perceptual field of the target corresponding to the detection point, and the situations of leakage and misdetection can be effectively improved. In order to solve the issue that small target detection is susceptible to image background and noise interference, the Sim-AM (simple parameter-free attention mechanism) module is added to the backbone network of YOLOv8, which enhances the attention to the underlying features and, thus, improves the detection accuracy of the model. More importantly, the Sim-AM module does not need to add parameters to the original network, which reduces the computation of the model. To address the problem of complex model structures that can lead to slower detection, the spatial pyramid pooling of the backbone network is replaced with focal modulation networks, which greatly simplifies the computation process. The experimental validation was carried out on the scrap steel dataset containing a large number of targets of multiple shapes and sizes. The results showed that the improved YOLOv8 network model improves the AP (average precision) by 2.1%, the mAP (mean average precision value) by 0.8%, and reduces the FPS (frames per second) by 5.4, which meets the performance requirements of real-time industrial inspection.

A general framework and decentralised algorithms for collective computational processes

Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is the aggregate process abstraction: in an asynchronous round-based computational model, it allows to specify how aggregate-level computations are spawned, take form or spread on a domain of devices, and ultimately quit. Previous presentations of aggregate processes, however, are given in the formal framework of the field calculus, requiring knowledge of its syntax and articulated semantics. To provide a more accessible and language-agnostic presentation of such an abstraction, in this paper we introduce a general formal framework of collective computational processes (CCP). Specifically, as key contribution, we model and describe the programming interface (spawn construct) and dynamics of CCPs on event structures. Furthermore, we also propose novel algorithms for efficient propagation and termination of CCPs, based on statistics on the information speed and a notion of progressive wave-like closure. Crucially, thanks to our theoretical framework, we can provide optimality guarantees for the proposed algorithms, whose performance, superior to the state of the art, is assessed by simulation. Finally, to show applicability of CCPs, we provide a case study of situated service discovery in peer-to-peer networks.

A Secure Web-based Result Computation and Transcript Processing System for Federal Polytechnic Ukana

In this era of digital transformation, educational establishments must manage student records effectively while maintaining data security. This work describes how Federal Polytechnic Ukanadeveloped a safe, web-based transcript and results computation system by utilizing Agile methodology, which improved responsiveness and flexibility throughout the project. Iterative development, which allowed for constant feedback and quick adjustments to requirements that changed, was made possible by the Agile methodology. Ensuring data integrity, confidentiality, and accessibility, the system is designed to make managing academic records easier. It has an intuitive interface for both administrators and students withrole-based access control, secure authentication procedures, and data encryption to stop illegal access and data breaches are important parts of the system. The system's design, implementation, and potential advantages in terms of improved data security and operational efficiency are all covered in this paper. The system's successful implementation at Federal Polytechnic Ukana indicates that it can be scaled up to meet the needs of educational institutions looking to enhance their result management procedures.

COMPUTER NAVIGATION AND ROBOTIC SURGERY DURING TOTAL KNEE ARTHROPLASTY

Total knee arthroplasty (TKA) is a successful treatment for knee osteoarthritis. The emphasis on optimal sizing and alignment of the components has led to an increase in the use of tools that allow for preoperative planning and verification of intraoperative steps. Computer navigation and robotic surgery have emerged as valuable tools for planning and performing surgery with greater precision and consistency. Objective. The aim of this paper is to organise information on the use of robotic systems in total knee arthroplasty based on own personal experience and analysis of contemporary literature sources. Methods. This study analysed professional articles that discussed the advantages and disadvantages of using robotic systems during knee arthroplasty. The information was obtained from electronic databases including PubMed, Scopus, Web of Science and Google Scholar, with a search span of over 20 years. Computerised or navigation devices allow the surgeon to enter anatomical data via an interface and receive feedback on the alignment of the implant and the knee as a whole, but cannot be programmed to perform additional tasks. Currently, several patented systems are available, and rapid technological advances in computer processing power have allowed for the rapid development of robotic surgical systems. Robotic systems usually provide feedback similar to navigation systems, but they can also be programmed to assist in specific surgical tasks. It is expected that these systems will become more reliable and accurate in the future, potentially leading to a reduced role for physicians in certain aspects of the surgical process, limiting their involvement to supervision, and thus improving the workflow of the operating room. The integration of new technologies, such as mixed reality, which overlays simulated images on real-life images, is expected to further expand the range of capabilities of these devices. But for now, it is crucial to establish the long-term outcomes of robotic-assisted total knee arthroplasty as a process to determine the viability of widespread adoption of these devices.