Processing System Research Articles

COVID-19 burden in Iran: disability-adjusted life years analysis from hospital data, 2020–2021

BackgroundThe burden of disease based on disability-adjusted life years (DALYs) is one of the internationally accepted metrics for assessing the impact of a disease or injury on population health. This study aimed to provide evidence of the burden of COVID-19 on health in Iran based on hospital-level data from the Iran Health Insurance Organization (IHIO), which covers almost half of the country’s population.MethodsThe data of all IHIO enrollees who were referred to hospitals across the country from the beginning of the COVID-19 pandemic (February 2020) to December 30, 2021, with assigned diagnosis codes of COVID-19, were extracted from the hospital information processing system. The DALYs due to COVID-19 were estimated using the standard approach of the World Health Organization and the European Burden of Disease Network guideline.ResultsIn the years 2020 and 2021, among a population of about 42 million people, 1,040,367 individuals were admitted to the hospital due to COVID-19 infection, of whom 73% were hospitalized (760,963 patients). The total estimated DALYs for these two years were 665,823 and 928,393, respectively (1,603 and 2,234 per 100,000 population). 99.7% of DALYs were attributed to years of life lost due to premature death (YLLs). The share of the disease burden in the age groups of under 20 years, 20–49 years, 50–80 years, and over 80 years was 6.6%, 26.4%, 58.4%, and 8.6%, respectively.ConclusionsBased on the hospital-level data estimates, COVID-19 has had a significant burden on health in Iran. COVID-19 was identified as the fifth leading cause of disease burden in Iran during the study period, ranking after cardiovascular diseases, psychological disorders, neoplasms, and musculoskeletal disorders. Additionally, COVID-19 was the third major cause of death, following cardiovascular diseases and neoplasms. Policymaking and the implementation of comprehensive programs to enhance the response of the health system and society to outbreaks of emerging and re-emerging infectious diseases are of utmost importance.

Meta-Analysis Reveals That Explore-Exploit Decisions are Dissociable by Activation in the Dorsal Lateral Prefrontal Cortex, Anterior Insula, and the Anterior Cingulate Cortex.

Explore-exploit research faces challenges in generalizability due to a limited theoretical basis for exploration and exploitation. Neuroimaging can help identify whether explore-exploit decisions involve an opponent processing system to address this issue. Thus, we conducted a coordinate-based meta-analysis (N=23 studies) finding activation in the dorsal lateral prefrontal cortex, anterior insula, and anterior cingulate cortex during exploration versus exploitation, which provides some evidence for opponent processing. However, the conjunction of explore-exploit decisions was associated with activation in the dorsal anterior cingulate cortex and dorsal medial prefrontal cortex, suggesting that these brain regions do not engage in opponent processing. Furthermore, exploratory analyses revealed heterogeneity in brain responses between task types during exploration and exploitation respectively. Coupled with results suggesting that activation during exploration and exploitation decisions is generally more similar than it is different suggests, there remain significant challenges in characterizing explore-exploit decision making. Nonetheless, dlPFC, AI, and ACC activation differentiate explore and exploit decisions and identifying these responses can aid in targeted interventions aimed at manipulating these decisions.

Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system

Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO2 memristor including endurance (>1012), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (<30 ns), and flexibility (bendable to a curvature radius of 1 mm). A flexible hardware processing system is implemented based on the CSSN, which can directly perceive and encode pressure and temperature bimodal information into spikes, and then enables the real-time haptic-feedback for human-machine interaction. We successfully construct a crossmodal in-sensor spiking reservoir computing system via the CSSNs, which can achieve dynamic objects identification with a high accuracy of 98.1% and real-time signal feedback. This work provides a feasible approach for constructing flexible bio-inspired crossmodal in-sensor computing systems for wearable human-machine interfaces.

Effect of Cattaneo–Christov heat flux model and elastic deformation on Walters'B viscoelastic fluid flow with porosity

In the current work, flow of 2-D MHD Walters'B viscoelastic fluid is discussed in the existence of elastic deformation, Cattaneo–Christov Heat Flux Model (CCHFM), heat source, Newtonian heating, viscous dissipation and porous medium. Dimensionless equations that are in charge of the problem's analysis are produced by using the suitable similarity transformation, and Optimal Auxiliary Functions Method (OAFM) is employed to solve them. In the ongoing investigation, the main results are decreasing behavior of temperature profile for the thermal relaxation parameter and elastic deformation parameter, while the reverse effect is noticed while increasing the Weissenberg number and porosity parameter. Our findings reveal significant insights into the fluid dynamics and heat transfer characteristics. Integrating the Cattaneo–Christov heat flux model and elastic deformation analysis in Walters'B viscoelastic fluid flow having its importance in polymer processing, aerospace engineering and waste treatment systems.

Smart structural health monitoring using computer vision and edge computing

Structural health monitoring (SHM) provides real-time data on the condition and performance of infrastructure, enabling timely and cost-effective maintenance interventions, and hence enhanced safety and extended service life. The computer vision-based non-contact sensor has emerged as a promising alternative to conventional contact-type sensors for structural displacement measurement and SHM. Many of the currently reported vision-based structural displacement measurement systems typically temporarily set up a video camera from a distance to the structure. The collected images or videos are usually stored locally and post-processed offline to obtain structural displacement responses, which is cumbersome and limited to short-term SHM applications. The recent development of technologies empowered by the Internet of Things (IoT) and edge computing has enabled real-time video processing and analysis at the source, minimizing latency, reducing bandwidth requirements, and enabling prompt decision-making, thereby enhancing efficiency and responsiveness compared to traditional offline video recording and processing systems. In this paper, an edge computing vision-based displacement measurement system (EdgeCVDMS) is developed. Video recording, processing, and displacement response identification are entirely performed on an edge device integrated with the vision-based displacement tracking algorithm, thereby greatly reducing the amount of data transmitted to the cloud server. The feasibility and applicability of the developed sensing system are experimentally validated on a laboratory-scaled transmission tower structure. The proposed EdgeCVDMS is cost-effective, easily deployable, and of great potential to be applied for the condition assessment of a larger population of aging civil infrastructure.

Long-term Outcomes of Peripheral Artery Disease In Veterans: Analysis of the PEripheral ARtery Disease Long-term Survival Study (PEARLS).

Contemporary research in peripheral artery disease (PAD) remains limited due to lack of a national registry and low accuracy of diagnosis codes to identify PAD patients in electronic health records. Leveraging a novel natural language processing (NLP) system that identifies PAD with high accuracy using ankle brachial index (ABI) and toe-brachial index (TBI) values, we created a registry of 103,748 patients with new onset PAD patients in the Veterans Health Administration (VHA). Study endpoints include mortality, cardiovascular (hospitalization for acute myocardial infarction or stroke) and limb events (hospitalization for critical limb ischemia or major amputation) and were identified using VA and non-VA encounters. The mean age was 70.6 years; 97.3% were males, and 18.5% self-identified as Black race. The mean ABI value was 0.78 (SD: 0.26) and the mean TBI value was 0.51 (SD: 0.19). Nearly one-third (32.4%) patients were currently smoking and 35.4% formerly smoked. Prevalence of hypertension (86.6%), heart failure (22.7%), diabetes (54.8%), renal failure (23.6%), and chronic obstructive pulmonary disease (35.4%) was high. At 1-year, 9.4% of patients had died. The 1-year incidence of cardiovascular events was 5.6 per 100 patient-years and limb events was 4.5 per 100 patient-years. We have successfully launched a registry of >100,000 patients with a new diagnosis of PAD in the VHA, the largest integrated health system in the U.S. The ncidence of death and clinical events in our cohort is high. Ongoing studies will yield important insights regarding improving care and outcomes in this high-risk group.

Development of Methodologies and Software for Design, Simulation and Optimization of Oil Hydraulic Cylinders of Large Dimensions and Power

As part of the research carried out in the field of processing systems and the production process of oil-hydraulic cylinders of large dimensions and power, the specifics of fluid power transmission, in the functioning of hydropower facilities, were analyzed. The research also includes the optimization of the physical–mathematical model of non-stationary processes, which take place inside the chamber of a large hydrocylinder. In parallel with the definition of the optimization model, the work parameters that affect the process of fluid flow and piston movement were determined. The operating and technological construction parameters of the hydraulic cylinder, which most significantly affect the operation of the hydraulic cylinder, were defined, and the observed parameters were optimized, based on which a prototype with improved characteristics compared to existing solutions was realized.

Inactivation of Highly Pathogenic Avian Influenza Virus with High-temperature Short Time Continuous Flow Pasteurization and Virus Detection in Bulk Milk Tanks

Infections of dairy cattle with clade 2.3.4.4b H5N1 highly pathogenic avian influenza virus (HPAIV) were reported in March 2024 in the U.S. and viable virus was detected at high levels in raw milk from infected cows. This study aimed to determine the potential quantities of infectious HPAIV in raw milk in affected states where herds were confirmed positive by USDA for HPAIV (and therefore were not representative of the entire population), and to confirm that the commonly used continuous flow pasteurization using the FDA approved 72 °C (161°F) for 15 s conditions for high−temperature short time (HTST) processing, will inactivate the virus. Double-blinded raw milk samples from bulk storage tanks from farms (n = 275) were collected in four affected states. Samples were screened for influenza A using quantitative real-time RT-PCR (qrRT-PCR) of which 158 (57.5%) were positive and were subsequently quantified in embryonating chicken eggs. Thirty-nine qrRT-PCR positive samples (24.8%) were positive for infectious virus with a median titer of 3.5 log10 50% egg infectious doses (EID50) per mL. To closely simulate commercial milk pasteurization processing systems, a pilot-scale continuous flow pasteurizer was used to evaluate HPAIV inactivation in artificially contaminated raw milk using the most common legal conditions in the US: 72 °C (161°F) for 15 s. Among all replicates at two flow rates (n = 5 at 0.5 L/min; n = 4 at 1 L/min), no viable virus was detected. A mean reduction of ≥5.8 ± 0.2 log10 EID50/mL occurred during the heating phase where the milk is brought to 72.5 °C before the holding tube. Estimates from heat-transfer analysis support that standard U.S. continuous flow HTST pasteurization parameters will inactivate >12 log10 EID50/mL of HPAIV, which is ∼9 log10 EID50/mL greater than the median quantity of infectious virus detected in raw milk from bulk storage tank samples. These findings demonstrate that the US milk supply is safe when pasteurized.

An optical measurement method of DC total electric field with space charges based on probe self-rotation modulation

Researching the measurement of DC total electric field with space charges (Poisson field) can help establish a monitoring system for the electromagnetic environment of UHVDC transmission lines and protect the health of the ecological environment. This paper proposes a DC total electric field optical measurement method based on the Pockels effect and probe self-rotation modulation. It can address the issues of internal charge drift and external charge accumulation in traditional optical probes when measuring DC electric fields with space charges. Theoretical analysis was conducted on measurement under probe rotation, and the optical modulation system was integrated into a probe with dimensions of 60 mm × 30 mm × 20 mm. A self-rotating platform for the probe was designed, and the influence of rotation mode on the results was analyzed. It was found that the sensitivity was higher when using the horizontal-horizontal mode. The signal processing system was tested, showing that the probe retained around 50 % of its original output when rotating at low speeds (frequency 1–3 Hz), fully meeting the measurement requirements. The test platform has been constructed, and the probe’s sensitivity was assessed under an AC electric field and validated under a DC electric field. Finally, the optical method proposed in this paper was used to measure the DC total electric field with space charges under a small model of a unipolar HVDC transmission line. Five locations under the line were selected as measurement points, and the results were compared with those obtained from a field mill probe and numerical simulation. This comparison indicates that the proposed method agrees with experimental and simulation results. The average sensitivity of the self-rotating optical probe was 3.71 mV/(kV/m). The error did not exceed 5 % in high-field strength areas (above 20 kV/m) and was also controlled within 15 % in low-field strength areas (below 20 kV/m).

A conceptual framework for integrating volunteers in emergency response planning and optimization assisted by decision support systems

In disaster response, the overwhelming amount of time-sensitive information and response options, combined with the dynamic nature of disasters, makes decision-making challenging for emergency service providers. Furthermore, it is often not economically feasible for countries to maintain a large number of full-time emergency responders. As such, many countries rely heavily on volunteer emergency responders during major disasters. This means that the success of disaster response often hinges on the efficient use of this volunteer workforce. We propose a framework for a Decision Support System (DSS) designed to optimize the use of volunteers by emergency services. This framework includes the data management layer, integrating necessary inputs and information; the analytical layer, which serves as the system's processing core; the user interface layer; and the decision-making layer. We argue that, while significant academic focus has been on the analytical layer, practical implementation requires the integration of all four components. Additionally, we emphasize the need for coordination with a broad spectrum of stakeholders involved in data provision, decision-making, and resource deployment for operationalizing this DSS. We also explore and analyze existing methodologies for developing the analytical layers, the requirements of these models, and the current methodological gaps. The proposed framework establishes a clear roadmap for adopting emergency response approaches that are human-centric, but at the same time, effectively utilize advancements in modeling, optimization, machine learning, and data integration.

Advanced image analysis for medical diagnostics: a system for segmentation and classification using level set methods and AI algorithms

This work aims to implement and utilize an advanced computer system for image analysis and processing through artificial intelligence. The system will evaluate images from multiple sources. As a result, a comprehensive e-Medicus system will be developed to capture and analyze X-ray data and classify cancer cells. This innovative tool, with its unique features tailored for medical facilities, will assist them in capturing and analyzing X-ray images and CT scan results. The e-Medicus system offers several benefits for medical facilities, including efficient automation of photo analysis, tracking changes in a patient's condition over time, and facilitating the identification of medical changes and data classification. A multimedia presentation of the change process will use the contour set function, allowing for topological changes in solution properties. The system integrates novel procedures and algorithms from theoretical computer science and numerical mathematics, leveraging neural networks, genetic algorithms, semantic networks, image ontology, rough set theory, contour set methods, and hybrid algorithms. These developed algorithms enhance methods and concepts in image segmentation, gathering, transmitting, storing, extracting, and effectively presenting information.

Research on the expansion, shrinkage properties and fracture evolution of red clay stabilised with phosphogypsum under dry-wet cycles.

In view of the special engineering properties of red clay and the waste of phosphogypsum resources, the expansion and contraction deformation and fissure evolution of phosphogypsum stabilized red clay under different conditions were investigated by laboratory tests and image processing system. The research results show that: (1) the absolute expansion and absolute shrinkage of phosphogypsum stabilized red clay are positively correlated with the compaction degree, the number of dry and wet cycles and the cement dosage, and negatively correlated with the initial water content and the phosphogypsum dosage; (2) the fissure rate increases with the increase of the number of dry and wet cycles, and decreases with the increase of the initial water content, the compaction degree, the cement, and the phosphogypsum dosage; (3) The relationship among absolute expansion rate (absolute shrinkage), degree of compaction and fracture rate can be fitted by the equation f(x,y) = ax+by+cx2+dy2+e; (4) Phosphogypsum has an obvious inhibiting effect on the expansion, shrinkage and cracking of the mix. It is recommended that the cement mixing amount of 6% and phosphogypsum: red clay = 1:1~1:2 as roadbed filler.

PMGraph: Accelerating Concurrent Graph Queries over Streaming Graphs

There are usually a large number of concurrent graph queries (CGQs) requirements in streaming graphs. However, existing graph processing systems mainly optimize a single graph query in streaming graphs or CGQs in static graphs. They have a large number of redundant computations and expensive memory access overhead, and cannot process CGQs in streaming graphs efficiently. To address these issues, we propose PMGraph , a software-hardware collaborative accelerator for efficient processing of CGQs in streaming graphs. First, PMGraph centers on fine-grained data, selects graph queries that meet the requirements through vertex data, and utilizes the similarity between different graph queries to merge the same vertices they need to process to address the problem of a large amount of repeated access to the same data by different graph queries in CGQs, thereby reducing memory access overhead. Furthermore, it adopts the update strategy that regularizes the processing order of vertices in each graph query according to the order of the vertex dependence chain, consequently effectively reducing redundant computations. Second, we propose a CGQs-oriented scheduling strategy to increase the data overlap when different graph queries are processed, thereby further improving the performance. Finally, PMGraph prefetches the vertex information according to the global active vertex set Frontier of all graph queries, hiding the memory access latency. It also provides prefetching for the same vertices that need to be processed by different graph queries, reducing the memory access overhead. Compared with the state-of-the-art concurrent graph query software systems Kickstarter-C and Tripoline, PMGraph achieves average speedups of 5.57 × and 4.58 ×, respectively. Compared with the state-of-the-art hardware accelerators Minnow, HATS, LCCG and JetStream, PMGraph achieves the speedup of 3.65 ×, 3.41 ×, 1.73 × and 1.38 × on average, respectively. Experimental results show that our proposed PMGraph outperforms the state-of-the-art concurrent graph processing systems and hardware accelerators.

Performance Modeling of Distributed Data Processing in Microservice Applications

Microservice applications are increasingly adopted in distributed data processing systems, such as in mobile edge computing and data mesh architectures. However, existing performance models of such systems fall short in providing comprehensive insights into the intricate interplay between data placement and data processing. To address these issues, this paper proposes a novel class of performance models that enables joint analysis of data storage access workflows, caching, and queueing contention. Our proposed models introduce a notion of access path for data items to model hierarchical data locality constraints. We develop analytical solutions to efficiently approximate the performance metrics of these models under different data caching policies, finding in particular conditions under which the underlying Markov chain admits a product-form solution. Extensive trace-driven simulations based on real-world datasets indicate that service and data placement policies based on our proposed models can respectively improve by up to 35% and 37% the average response time in edge and data mesh case studies compared to baseline resource allocation heuristics.

Design and development of FPGA based trigger system for automation of metallic tokamak (MT-I)

A field-programmable gate array (FPGA)-based trigger system (FTS) is designed and developed to replace the existing microcontroller-based system for the automation of metallic tokamak (MT-I). MT-I consists of three main systems: MT-I vacuum vessel and gas filling system, a pulsed power supply system, an electronic system based on diagnostics and data acquisition (DAQ) systems. The power supply system provides pulsed power input to the central solenoid (CS), poloidal field coils (PF), toroidal field coils (TF), and microwave systems using thyristors and insulated-gate bipolar transistors (IGBTs). The (DAQ) system acquires experimental data from MT-I diagnostics using national instruments (NI) DAQ cards. A concurrent processing system is required to incorporate time delay in the triggering process of central solenoid (CS), poloidal field coils (PF), toroidal field coils (TF), microwave source and DAQ systems. Therefore, an FTS is designed and developed to complement the processing capability, unlike a microcontroller. The FTS has twenty concurrent triggering channels, adjustable from time reference zero, and control from a simple graphical user interface (GUI) designed on LabVIEW. For current buffering and optical isolation against high voltages in the MT-I power supply system, a peripheral electronic system (PES) and field trigger modules (FTM) have been developed as part of FTS. The designed and developed FTS was tested successfully on MT-I.

How do affective and cognitive involvement affect tourism intentions: a perspective on blogger impact

ABSTRACT Social Network Services (SNS) platforms are crucial sources of travel information. Xiaohongshu, a highly esteemed platform in China and globally, provides travel guides and lifestyle information. Empirical studies on how influencers, as well as the affective and rational factors of travel user-generated content (UGC) affect travel intentions are limited. Based on involvement theory and dual-system theory, this study uses topic modelling, sentiment analysis, and semantic network visualization to analyze travel posts and comments on Xiaohongshu. It reveals the affective and functional factors of posts by bloggers of varying impact and their effects on audience affective and cognitive involvement, processing systems, and travel intentions. The results are as follows: (1) Posts by medium-impact bloggers are more successful in generating and enhancing travel intentions. (2) Affective factors trigger affective involvement and processing, driving heuristic travel intentions; functional factors drive cognitive processing through cognitive involvement, leading to cognitive travel intentions. (3) The proper emotional intensity of affective and functional factors effectively transmits information, builds audience confidence, and maximises the enhancement of travel intentions. This study enriches the research viewpoint and theoretical foundation of enhancing travel intentions, providing practical recommendations for tourism practitioners in creating appropriate and effective emotional promotional content.

Landscape, chronology and function of Bronze Age terrace-shaped settlements: A case study from the Youzhashan site in Yangxin County, Hubei Province, China

The Middle and lower reaches of the Yangtze River, constituting a region rich in metal resources, played an important role in the formation and early development of the Bronze Age civilization in China, mainly including mainly the southeastern part of Hubei Province, the northern part of Jiangxi Province and the southern part of Anhui Province. Geological surveys have revealed that this region, which stretches for 500 km along the Yangtze River, is rich in mines, and archaeological institutions have discovered a large number of Bronze Age mining and metallurgical sites in the region over the past three decades. This region is an important area for studying the rise and prosperity of the Chinese Bronze Age civilization. Therefore, exploring the production pattern of the bronze industry in the copper mining area in the middle and lower reaches of the Yangtze River, as well as the resource circulation network between this region and the northern part of China, has become a hot issue in Chinese archaeological research in recent years.From March to July 2023, Wuhan University and the Cultural Relics Bureau of Yangxin County formed a joint archaeological team to carry out an archaeological survey of mining and metallurgical sites in an independent geographic unit in the northern part of Yangxin County, Hubei Province, covering an area of 120 km2, and a total of 60 mining and metallurgical sites were discovered during the survey, and approximately two-thirds of the 60 sites exhibited the landscape with a more typical terrace-shaped site. In this study, we have carried out systematic archaeological exploration and drone mapping of the Youzhashan site, a typical terrace-shaped site in this region, and reconstructed the process of terrace-shaped site accumulation formation using materials from archaeological excavation, exploration and survey. In addition, scientific and technological tests were conducted on the metallurgical relics collected from the Youzhashan site, which revealed the occurrence e of copper smelting activities at the Youzhashan site, and we analyzed the processing system. In addition, the carbon samples excavated from the Youzhashan site were carbon dated to determine the exact date of the formation of the Youzhashan site.

Spacing mode multi-wavelength erbium doped fiber laser based on a symmetrical long-period fiber grating

We present a multiwavelength erbium-doped fiber laser based on a symmetrical long-period fiber grating (LPFG) with tunable spacing mode between single lasing mode and multiwavelength spectra. The LPFG was manufactured using a thermal expansion technique using an LZM-100 glass processing system equipped with a CO2 laser, and it was used as a wavelength-selective filter (WSF) in the laser ring cavity. The laser can emit single, double, triple, quadruple, quintuple, or sextuple lines in the range from 1546 to 1563 nm, which can be tunable by controlling the radius of curvature of the LPFG in the range 0–0.2662 m −1. A minimal spacing mode of 1.92 nm was observed in the multiwavelength region; meanwhile, the fiber laser offers an average spacing mode of 6.945 nm between the multiwavelength region and the single emission. This laser has a 3 dB linewidth of 0.11 nm and a side mode suppression ratio (SMSR) of 55.56 dB. Finally, according to experimental results, the laser has high wavelength stability at room temperature for 88 min.

Design of an image processing system for heterogeneous computer architectures

Embedded real-time image processing systems are widely applied in various fields, enhancing visual experience and making life more convenient. With the development of embedded processors and operating systems, as well as in-depth research into image processing algorithms, the effects and speed of image processing have been significantly improved. This paper proposes an embedded image processing system design based on the current technological developments, centered around the Zybo processor. This includes a collaborative hardware and software design, specific hardware circuit design, and the design of a system software platform. The paper also discusses the establishment of a development environment for the Zybo system, the transplantation of the Linux operating system, the design of the file system, and the application of OpenCV library functions in image processing.

Performance and energy evaluation of dynamic adaptive deterministic routing algorithm for multicore architectures

Chip Multiprocessors (CMPs) leverage multiple processing units to improve computational speed and efficiency. Routing algorithms in NoC (Network-on-Chip) architectures ensure efficient data communication between these units, addressing challenges such as congestion, latency, and power consumption. This paper delves into an extensive analysis of the performance evaluation concerning the Dynamic Adaptive Deterministic (DyAD) routing for Network-on-Chip (NoC) and Wireless Network-on-Chip (WiNoC) configurations within the framework of a 64-core and 100-core multicore architecture. The evaluation involved a congestion threshold analysis of data transmission latency, the efficiency of network data throughput, and the amount of energy consumed. To substantiate our conclusions, we conducted simulations of the 64-core and 100-core mesh-based NoC and WiNoC architectures under the Video Image Processing System (VIPS) benchmark workload. These simulations were executed utilizing the Noxim simulator, a well-recognized tool acclaimed for its capacity to provide cycle-accurate simulations. Analyzing the simulation outcomes, it becomes evident that the DyAD routing approach for the 64-core and 100-core WiNoC architecture performs better in terms of network performance. It is characterized by enhanced capabilities at higher Packet Injection Rate (PIR) saturation loads, improved network throughput, and minimized latencies. This dominance is evident from its ability to handle heavier workloads and achieve lower delays in the investigated VIPS workload situations, compared to the 64-core and 100-core NoC multicore architecture.