Throughput Rate Research Articles

Healthcare Data Security in Cloud Storage Using Light Weight Symmetric Key Algorithm

Now-a-days, healthcare monitoring system is very much important system in the medical field to know the patient‘s health status immediately. In the proposed system, the sensors are fixed over the patient’s body or placed at some distances around the body to collect patient’s significant parameters like blood pressure, temperature, heart beat rate, etc. These parameters are collected by the healthcare professionals through some connectivity mechanisms like Bluetooth, ZigBee, etc. These significant data will be outsourced to the cloud storage in a secure way to avoid attack from the attackers. So, we need some protection mechanism to safeguard this information. This article proposes a light weight cryptographic algorithm (symmetric key) via random number key generation using Diffie-Hellman key exchange based on Elliptic Curve (ECDH) cryptography. As a result of substituting bytes (S-box) and folding (horizontal and vertical) operations, the proposed symmetric key algorithm achieves the foremost properties of cryptography such as confusion and diffusion quite well. Experimental results showed that the overall execution time of the proposed algorithm is superior to the standard Advanced Encryption Standard (AES) algorithm. The throughput rate of the proposed algorithm is 20.525095 KB/seconds whereas for the standard AES algorithm throughput rate is 18.727215 KB/seconds. So, the proposed algorithm is faster than the existing AES algorithm. Moreover, the construction of S-box, IS-box and the key generation procedures are entirely different in the proposed algorithm so, it increases the complexity for the attacker and it will create confusion to the attacker

Neural Networks Based Smart E-Health Application for the Prediction of Tuberculosis Using Serverless Computing.

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serless-based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments.

Evaluation of FBMC Channel Estimation using multiple Auxiliary symbols for high throughput and low BER 5G and beyond communications

Filter Bank Multi-Carrier (FBMC) modulation has been recognized as a consistent contender and a possible successor for Orthogonal Frequency Division Multiplexing (OFDM) in 5G and beyond because of its outstanding spectral properties. The channel is assessed in FBMC using pilot-symbol aided channel estimation that provides robust estimates even for severe channel conditions. In the present work, neutraliz ing the imaginary interference at the pilot positions is focused while estimating the channel. To neutralize the imaginary in terference, multiple auxiliary symbols have been proposed to enhance the throughput and channel capacity. The Iterative Minimum Mean Squared Error (IMMSE) cancellation scheme has been proposed to reduce the interference at the pilot and data positions. Transmission power, Bit Error Rate (BER) and throughput are computed for Filter Bank Multi Carrier (FBMC), OFDM and proven that better system performance is obtained for FBMC. The performance of channel estimation is evaluated through 5G standards and indicates that the usage of multiple auxiliary symbols per pilot leads to better throughput and low Bit Error Rate at low power transmission.

Machine Learning Driven Threat Identification to Enhance FANET Security using Genetic Algorithm

Emerging as a highly promising technology, Flying Ad-hoc Networks represent self-organizing networks of Unmanned Aerial Vehicles (UAVs), garnering attention for their diverse applications spanning environmental monitoring, disaster management, precision agriculture, surveillance, and military operations. However, these networks face challenges to various security threats, including malicious node detection due to their deployment in dynamic environments. To address this issue, we present an improved novel security solution, Machine Learning-based Threat Identification for FANET using a Genetic Algorithm (ML-TIFGA) in this paper. The research includes the detection of abnormal behavior nodes using a basic genetic algorithm and dynamically adapting the changing network conditions by utilizing a reputation system. To enhance our security solution ML-TIFGA, we evaluated two key factors: cooperation and trustworthiness, which act as genetic elements within the chromosome of the flying node in our genetic population. Further, a mechanism is incorporated to reconfigure the trust, addressing the challenge of dynamically extracting threats through the updated weighted reputation system while considering past behavior monitoring. Significant improvements were found in the experimental results using actual sample values from the NSL-KDD dataset, which produced a remarkable 99.829% classification accuracy. Additionally, threat identification rates reached 98.36% for training and 98.86% for testing samples, with a remarkable improvement of 99.3% in network reliability through ML-TIFGA. When benchmarked against state-of-the-art approaches, performance metrics such as delay, throughput, and data delivery rate exhibited notable enhancements of 24.65%, 29.16%, and 31.73%, respectively.

Performance of Opportunistic Data Networks Based on Public Transportation Systems

Smart Cities are those which use technology to promote the well-being of the population, economic growth and, at the same time, improve sustainability. The application of these technologies in road transportation services has been conducted within programs known as Intelligent Transportation Systems (ITS). Vehicular Ad Hoc Networks (VANETs) are a key part of the intelligent transportation systems framework. The buses belonging to a Public Transportation System (PTS) can be considered as nodes of a kind of opportunistic data network. However, the transportation literature does not provide acceptable models for bus movements in an urban environment since they are affected by vehicular and passenger traffic conditions, lanes organization, traffic signal management, company policies, and others. This article uses the Engine for Map Matching to SUMO (EMMS), a computational tool that automatically imports the public transportation system characteristics into a simulation environment in order to study the system performance. Specifically, geolocation data from public transportation buses, in the city of Curitiba (Brazil), are used. The results obtained show that the formation of small temporary networks is possible, allowing the exchange of data between buses. However, those networks provide low throughput and packet delivery rates, as well as large routing overhead and end-to-end delay.

Construction of a Database for Vocal Music Teaching in Colleges and Universities Based on the Inheritance and Protection of the Central Plains Culture

Abstract Multi-cultural integration is an inevitable path to enhance the inheritance and protection of the Central Plains culture and to innovate the teaching mode of vocal music in colleges and universities. In this paper, we constructed a university vocal music teaching database system based on distributed database, and used semi-connection algorithm to query and optimize the data related to the integration of Central Plains culture into vocal music teaching resources. In order to make up for the defects of the semi-connection algorithm when facing a large number of tuples, the semi-connection algorithm is improved by introducing the idea of symmetric homology to realize the massive data scheduling of the vocal music teaching resources database. For the college vocal teaching database constructed in this paper, the query optimization test and performance test are carried out, and the teaching practice application is carried out with the vocal teaching database. The results show that the query throughput rate of vocal music teaching resources reaches 3.18*103MB/s under Read-only load when the number of running rounds reaches 16 rounds, and when the number of concurrent users of the vocal music teaching database is 1000, the CPU occupancy rate and memory utilization rate are 1.37% and 1.06%, respectively. By integrating Zhongyuan culture into the vocal music teaching database, more than 70% of the students indicated that it could enhance their interest in learning about Zhongyuan culture. The use of technology to realize the integration of vocal music teaching and the culture of the Central Plains provides a new reference for the inheritance and protection of the culture of the Central Plains.

Production control problem for multi-product multi-resource make-to-stock systems

Most of today's production systems are working with parallel production resources to increase throughput rate due to the increase in high variability in demand and product mix. Effective control and performance evaluation of such systems is of paramount importance to minimize production and inventory-related costs. We examine a production-inventory system featuring parallel production resources capable of producing various products. In many industries such as automotive, white goods, electronics, and paint, multiple/parallel production resources are widely used to produce the ideal amount and satisfy incoming demands for distinct products. In this study, shortage cost is not restricted to only one type and both lost sales and backordering cases are analyzed. In order to analyze the optimal production policies' behavior, we initially formulate dynamic programming models for both lost sales and backordering systems, treating them as Markov Decision Processes. Subsequently, we solve these models using the value iteration algorithm. Given the challenges posed by the curse of dimensionality in the value iteration algorithm, we suggest alternative heuristic production policies. These policies extend the existing ones described for multi-item single-resource make-to-stock (MTS) systems to accommodate multiple resources. We construct simulation models to assess the efficacy of the heuristic policies, conducting comparisons of their performance against both the optimal policy and among one another. To the best of our knowledge, there has been no exploration of scenarios involving multiple production resources concurrently manufacturing distinct products in a MTS environment. Hence, this study serves as an extension to the examination of multi-item, multi-production resource MTS systems.

Block Cipher Algorithms for Data Security in Cloud: A Review

Cloud computing simplifies access to data in a virtualized setting through the internet ubiquitously and users only pay for what they use. If the data is not sufficiently safeguarded from threats like data breaches, side-channel attacks, data loss transit, etc., unauthorized users can access the data stored in the cloud. Encryption methods have been used to protect the information. Currently, the Advanced Encryption Standard algorithm is preferred over the Data Encryption Standard algorithm. This paper conducts a thorough examination of research related to topics like data security threats in the cloud, including occurrence, threats, and remediation. It also assesses encryption algorithms based on parameters such as design, security, computational efficiency, flexibility, and limitations to prevent data breaches. It has been found that maximum throughput rate and minimum memory usage are two measure performance metrics that are competent to prevent data from security breaches by applying experimental and simulation techniques, concluding that the Advanced Encryption Standard is superior to both Data Encryption Standard and 3-DES

A Perspective on the Supersonic Solution Blowing: Nanofibers at the Forefront of Energy Storage and Conversion, and Environmental Remediation

Nanofibers play a crucial role in energy storage and conversion, catalysis, and environmental remediation applications. These nanomaterials, characterized by their high surface-to-volume ratio and porous structure, exhibit exceptional properties that render them ideal for various energy applications such as in fuel cells, lithium-ion batteries, supercapacitors, catalysis, and hydrogen production. This discussion outlines the historical context of electrospinning (ES), acknowledging its limitations in terms of commercial viability. Introducing the supersonic solution blowing (SSB) technique as a promising alternative, its energy-efficient process holds potential for commercial feasibility. The ES has remarkable advantages in nanofiber fabrication but faces limitations due to high operating costs. Solution blow spinning (SBS) offers speed and versatility; however, fibers have a broad distribution of diameters. SSB therefore overcomes some of the limitations of both ES and SBS techniques to produce defect-free nanofibers with smaller diameters at higher throughput rates. In this concise perspective paper, we emphasize the primary contributions of nanofibers to the development and optimization of materials used in energy applications. Drawing parallels with the prevalent ES and SBS techniques in the literature, we conclude with a discussion on the potential benefits that may arise as researchers increasingly explore this emerging and more efficient technique.

Aging Aware Battery Operation and State of Health Evaluation in Energy Storage Systems

The sources of performance loss in lithium-ion batteries are many and can vary widely dependent on battery usage [1]. Understanding these mechanisms and coupling usage patterns to dominating degradation phenomena can allow smarter use and longer lifetime of the batteries. This can especially bring value in stationary applications, as the usage patterns have different characteristics and second life applications for batteries are considered as well [2].Driven by the development of battery electric vehicles, batteries with higher power and energy densities have revolutionized the market. The lifetime of the batteries is another crucial criterion. A significant amount of work has been performed studying the degradation phenomena and evaluating stress factors. High/low potentials, high C-rates, and temperature deviations from room temperature should be avoided to keep the degradation rate low [3-5]. The power demand for batteries in stationary energy storage systems is lower than vehicle applications and some of these extremes could therefore be avoided [6]. Finding optimal sizing of the batteries, with high energy throughput and low degradation rate, is therefore of interest. The energy delivered by the storage system is further part of the power trading market, hence motivating the need to accurately correlate delivered energy and battery performance loss from different services.In this work the degradation of commercial 18650-type Nickel Cobalt Manganese (NCM)/Graphite cells, in stationary applications is studied. Different scenarios including type of service, sizing, and second-life applications are studied through accelerated testing. The work expands upon a previous study focusing on the degradation effects of combining services [7]. The degradation analysis involves different techniques such as the capacity evolution (Figure 1), electrochemical impedance spectroscopy (EIS), as well as changes in the electrodes revealed by differential voltage analysis (DVA). The electrode balancing, capacity and cell polarization is evaluated in terms of battery state-of-health and important characteristics highlighted. Preliminary results show no additional degradation introduced by scaling the current amplitude 50%. A clear relationship between state-of-charge window and degradation rate is shown, where the graphite two-phase regions are of importance. These regions are also affected by the electrode slippage observed in the DVA. Strategies for battery characterization and operation strategies for second-life applications will be further investigated.Figure 1. Preliminary results of the normalized capacity development of the cells in the study, as an averaged value of duplicate cells. Frequency regulation (FR) is a mild cycle around 50% SOC, current maximum 1C. FR_high has a current maximum of 1.5C. Peak shaving (PS) is a 1C constant current cycling between 22-78% SOC, PS_low has a 0.5C constant current cycling. FRPS is the two cycles combined.References Birkl, C. R.; Roberts, M. R.; McTurk, E.; Bruce, P. G.; Howey, D. A., Degradation diagnostics for lithium ion cells. Journal of Power Sources 2017, 341, 373-386. Martinez-Laserna, E.; Gandiaga, I.; Sarasketa-Zabala, E.; Badeda, J.; Stroe, D. I.; Swierczynski, M.; Goikoetxea, A., Battery second life: Hype, hope or reality? A critical review of the state of the art. Renewable and Sustainable Energy Reviews 2018, 93, 701-718. Baure, G.; Dubarry, M., Battery durability and reliability under electric utility grid operations: 20-year forecast under different grid applications. Journal of Energy Storage 2020, 29. Keil, P.; Schuster, S. F.; Wilhelm, J.; Travi, J.; Hauser, A.; Karl, R. C.; Jossen, A., Calendar Aging of Lithium-Ion Batteries. Journal of The Electrochemical Society 2016, 163 (9), A1872-A1880. Ecker, M.; Nieto, N.; Käbitz, S.; Schmalstieg, J.; Blanke, H.; Warnecke, A.; Sauer, D. U., Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries. Journal of Power Sources 2014, 248, 839-851. Dubarry, M.; Devie, A.; Stein, K.; Tun, M.; Matsuura, M.; Rocheleau, R., Battery Energy Storage System battery durability and reliability under electric utility grid operations: Analysis of 3 years of real usage. Journal of Power Sources 2017, 338, 65-73. Ohrelius, M.; Berg, M.; Wreland Lindström, R.; Lindbergh, G., Lifetime Limitations in Multi-Service Battery Energy Storage Systems. Energies 2023, 16 (7). Figure 1

A Vehicle Density Estimation Traffic Light Control System Using a Two-Dimensional Convolution Neural Network

One of the world’s challenges is the amount of traffic on the roads. Waiting for the green light is a major cause of traffic congestion. Low throughput rates and eventual congestion come from many traffic signals that are hard coded, irrespective of the volume of the amount of traffic. Instead of depending on predefined time intervals, it is essential to build a traffic signal control system that can react to changing vehicle densities. Emergency vehicles, like ambulances, must be given priority at the intersection so as not to spend more time at the traffic light. Computer vision techniques can be used to improve road traffic signal control and reduce real-time traffic delays at intersections without the requirement for substantial infrastructure analysis. Long wait times and significant energy consumption are just two of the problems of the current traffic signal control system. To optimal efficiency, the traffic signal’s duration must be dynamically changed to account for current traffic volume. To lessen congestion, the approach taken in this research focuses on modifying traffic signal time determined by the density of vehicles at the crossroads. The main purpose of this article is to demonstrate heavy traffic and emergency vehicle prioritization from all directions at the traffic intersection for a speedy passage. Using the Pygame tool, the proposed method in this study, which includes a mechanism for estimating traffic density and prioritization by counting vehicles at a traffic junction, is demonstrated. The vehicle throughput for the adaptive traffic light built using Pygame is compared with the vehicle pass rate for the adaptive traffic light built using Simulation of Urban Mobility (SUMO). The simulation results show that the adaptive traffic light built using Pygame achieves 90% throughput compared to the adaptive traffic light built using SUMO. A Two-Dimensional Convolutional Neural Network (2D-CNN) is implemented using Tensorflow for vehicle classification. The 2D-CNN model demonstrated 96% accuracy in classifying vehicles using the test dataset. Additionally, emergency vehicles, such as ambulances, are given priority for quick passing.

A Descriptive Learning Analytics: An Online Learning Programmes and Load-shedding Conundrum

Electricity load-shedding in South Africa has become a new challenge for students pursuing online learning following the Covid-19 outbreak. This article thus investigated the way load-shedding affects students in pursuing the Advanced Certificate in Teaching (ACT) programmes. Pre-Covid-19 and during load-shedding data between 2018 and 2022 was gathered and analysed using a descriptive learning analytics tool. The research took place at a South African university where the researcher is currently employed. Learning analytics data has been available for years, but it is now being effectively harnessed to improve learning and teaching through relatively recent developments in learning analytics tools. However, the load-shedding conundrum works against this objective of harnessing learning and teaching. Research has also shown that in the least developed countries, effective e-learning practices face significant barriers, primarily stemming from insufficient infrastructure, economic challenges, low levels of computer literacy, and the added challenge of power outages. Using a constructivist paradigm, the study employed a mixed qualitative approach, which included desktop historical data and semi-structured interviews. Three participants were purposely selected because of their role in overseeing ACT students situated in both rural and urban areas. The study revealed that numerous programmes were discontinued, enrollment declined, ACT Blended shifted to e-learning, persisting load-shedding conundrum affected e-learning, a low throughput rate, and digital illiteracy contributed to an increased rate of student dropouts. This research recommends that universities should embrace the evolution of e-learning through the digitalisation of learning processes to enhance efficiency. Keywords: Load-shedding, E-learning, Learning Analytics Tools, Declined Enrolment, Low Throughput Rate vs High Distinction Module Pass Rate

Exploiting Propagation Delay in Underwater Acoustic Communication Networks via Deep Reinforcement Learning.

This article proposes a novel deep-reinforcement learning-based medium access control (DL-MAC) protocol for underwater acoustic networks (UANs) where one agent node employing the proposed DL-MAC protocol coexists with other nodes employing traditional protocols, such as time division multiple access (TDMA) or q -Aloha. The DL-MAC agent learns to exploit the large propagation delays inherent in underwater acoustic communications to improve system throughput by either a synchronous or an asynchronous transmission mode. In the sync-DL-MAC protocol, the agent action space is transmission or no transmission, while in the async-DL-MAC, the agent can also vary the start time in each transmission time slot to further exploit the spatiotemporal uncertainty of the UANs. The deep Q -learning algorithm is applied to both sync-DL-MAC and async-DL-MAC agents to learn the optimal policies. A theoretical analysis and computer simulations demonstrate the performance gain obtained by both DL-MAC protocols. The async-DL-MAC protocol outperforms the sync-DL-MAC protocol significantly in sum throughput and packet success rate by adjusting the transmission start time and reducing the length of time slot.

Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels Based on Deep Reinforcement Learning

In heterogeneous wireless networks, when multiple nodes need to share the same wireless channel, they face the issue of multiple access, which necessitates a Medium Access Control (MAC) protocol to coordinate the data transmission of multiple nodes on the shared communication channel. This paper presents Proximal Policy Optimization-based Multiple Access (PPOMA), a novel multiple access protocol for heterogeneous wireless networks based on the Proximal Policy Optimization (PPO) algorithm from deep reinforcement learning (DRL). Specifically, we explore a network scenario where multiple nodes employ different MAC protocols to access an Access Point (AP). The novel PPOMA approach, leveraging deep reinforcement learning, adapts dynamically to coexist with other nodes. Without prior knowledge, it learns an optimal channel access strategy, aiming to maximize overall network throughput. We conduct simulation analyses using PPOMA in two scenarios: perfect channel and imperfect channel. Experimental results demonstrate that our proposed PPOMA continuously learns and refines its channel access strategy, achieving an optimal performance level in both perfect and imperfect channel scenarios. Even when faced with suboptimal channel conditions, PPOMA outperforms alternative methods by achieving higher overall network throughput and faster convergence rates. In a perfect channel scenario, PPOMA’s advantage over other algorithms is primarily evident in its convergence speed, reaching convergence on average 500 iterations faster. In an imperfect channel scenario, PPOMA’s advantage is mainly reflected in its higher overall network throughput, with an approximate increase of 0.04.

Finding the right one: Decision support for selecting cost-efficient order picking solutions

Enabled via recent technological advances coupled with the advent of new systems providers and decreased price points, automated and robotized order-picking solutions (e.g., pick-assisting autonomous mobile robots) have evolved as a surging market. Such innovative picking technologies aim to reduce labor costs, use available space more efficiently, and increase throughput rates. As implementation projects and the variety of solutions rise, managers decide which ones to select for their specific warehouse and products. However, comprehensive decision models for this strategic problem are lacking in the pertinent literature. We propose a mathematical optimization model for the novel problem that selects and sizes order-picking solutions and assigns them products and warehouse spaces. Expert interviews are used to identify the comprehensive decision-relevant costs and constraints. Specifically, we minimize setup, module, labor, and error costs while adhering to characteristics related to the area (e.g., available space), technology (e.g., throughput, handling capabilities of certain products), and product (e.g., physical dimensions). We conduct a case study and complement our findings with numerical experiments. We find significant cost reduction potential of up to 57% by selecting a mix of different order-picking solutions. Further analyses highlight the need to retain human workers and to account for maximum labor capacity.

Effects of Furnace Length on the Thermal Performance of a Walking Beam Reheating Furnace

The influence of furnace length on several key performance indicators of a reheating furnace such as the fuel consumption, thermal efficiency and through-thickness temperature uniformity of a discharged slab was investigated. Energy balance equations of radiation set up by the zone method were solved by coupling them with the heat conduction equations of the slab. The zone model was validated by actual data collected during an instrumented slab trial. The simulation results showed that the model predictions were in good agreement with actual measurements. Under the premise that the throughput rate and the total fuel consumption were fixed, every additional meter of the preheating zone length increased the slab discharge temperature by about 10.75 °C and decreased the exhaust temperature by about 13.29 °C, but made no change in the section temperature difference in the slab at discharge; every additional meter of the soaking zone length decreased the section temperature difference in the slab at discharge by about 2.88 °C, but made a somewhat gentle change in the slab discharge temperature and exhaust temperature. For the existing furnace, the actual length was not suitable to be changed, so the relative length was defined as representing the actual length, which could be indirectly changed by adjusting the current fuel consumption. After optimization, the relative lengths of the preheating zone and soaking zone increased by five meters and one meter, respectively, while the relative lengths of the first and second heating zones decreased by five meters and one meter, respectively, and the section temperature difference in the slab at discharge, exhaust temperature and fuel consumption simultaneously decreased.

A Proficient Obtrusion Recognition Clustered Mechanism for Malicious Sensor Nodes in a Mobile Wireless Sensor Network

A collection of wireless nodes that may be installed at any location and at any time without requiring an established network structure is called a mobile wireless sensor network. The problem of network performance arises from the mobility of nodes and their misbehaviour. Network performance is negatively impacted by data loss and sensor node misbehaviour. In certain cases, there are malicious sensor nodes that are designed to destroy the network’s capacity. This work aims to identify hostile nodes using an irregular set technique. The route entry table’s broadcasting metadata helps identify rogue nodes. Every sensor node in the network broadcasts information about adjacent nodes and maintains a sentry table. Premeditated data delivery proportion, throughput, delay, packet drop, and fault rate are used to estimate broadcasting record parameters. In the NS2 environment, mobile nodes with varying velocities are simulated. To generate an information table, mobile nodes with varying speeds are examined based on their broadcasting records. On the basis of guidelines taken from the irregular set tactic table, good and bad nodes are distinguished. Packets are disseminated along the shortest path that doesn’t contain any malicious nodes. The results of the proposed technique show that an irregular set tactic increases throughput, network capability, data delivery percentage, and end-to-end delay reduction in mobile sensors.

Business Sensitivity Options of Carrier Aggregation Strategies: A Simulation Model for 4G Deployment in Emerging Countries

Indonesia’s growing population increasingly relies on 4G networks, presenting challenges for the Ministry managing mobile broadband adoption. There are severe data network congestion problems in metropolitan areas, particularly Jakarta, Indonesia’s capital city. Urgent action is required to address infrastructure and capacity limitations and maintain reliable connectivity. Carrier aggregation (CA) may alleviate bottlenecks by giving users higher bandwidth, throughput, and data rates. This research examined the techno-economic aspects of using 2.1 GHz CA versus non-CA in an urban region of North Jakarta. CA required fewer eNodeB and performed better on key performance indicators like received signal power, signal-to-noise ratio, and throughput. Cellular operators also saw faster investment returns with CA. CA’s performance and economic benefits make it a promising solution for Indonesia’s 4G network congestion.

Towards artificial intelligence-aided MIMO detection for 6G communication systems: A review of current trends, challenges and future directions

In recent times, artificial intelligence (AI) has gained considerable attention as a highly promising technology for enhancing the performance of multiple-input multiple-output(MIMO) detection in wireless communication networks. AI approaches such as deep learning and reinforcement learning have enabled MIMO receivers to learn and adapt to complex channel conditions and interference scenarios, significantly improving throughput and error rate performance. This study provides a detailed review of the existing status of AI-aided MIMO detection and discusses the potential of this technology for future wireless communication systems, such as 6G. The study also discusses the challenges and opportunities associated with integrating AI techniques into MIMO detection and emphasizes the necessity for additional research in this field.

Process Modularity Impact on Manufacturing Lead Time and Throughput Rate in Terms of Mass Customization

As mass customization becomes more pervasive in many sectors, researchers need to update traditional approaches to the optimization of critical performance and design parameters in order to help companies in their effort to implement this strategy. In general, implementation of mass customization from a manufacturing perspective is frequently focused on shortening cycle times, reducing production cost, and increasing throughput rate of parts. In this paper, process structure modularity impact on manufacturing lead times and throughput rates is explored. An important precondition to explore these relationships is the awareness that process modularity is conceptualized and quantified in an appropriate way. For this purpose, three independent modularity measures were employed to provide more reliable assessment of this system property. The relationships were investigated on the basis of simulation experiments using deterministic models of alternative process structures. For the purpose of the relationships exploration, two case studies were conducted, theoretical and practical ones. The results from the experiments showed that there are moderate correlations between process modularity and manufacturing lead time (ρ = −0.45), as well as between process modularity and throughput rate (ρ = 0.45).