Error Rate Reduction Research Articles

Reducing potential dual-use risks in synthetic biology laboratory research: a dynamic model of analysis

Synthetic biology (SynBio) is an interdisciplinary field that includes biology, genomics, engineering, and informatics. The introduction of machine learning technology reduces the requirement for human participation in SynBio research, which may lead to increased biosafety and biosecurity concerns within the complex human–technology-management system in SynBio laboratories compared with traditional biology. The rapid development of SynBio and machine learning technology has added to the systemic complexity of management and technology subsystems, the risks of which remain unclear. To fully understand and address these risks, there is an urgent need to develop a quantitative model. In this study, three-round Delphi interviews were conducted with SynBio experts who have published articles in top journals (e.g., Nature, Science, Cell, etc.) and applied the system dynamics method to build a quantitative analysis model. The model explores the potential risk factors and their complex relationships in the SynBio research. We analyzed three subsystems (biotechnology, information technology, and management), identifying seven key risk factors. Among these, the Average Experience of Newcomers and Tolerable Error Frequency play an essential role in laboratory management. Notably, the biosafety atmosphere has the greatest impact on reducing error rate and increasing safety awareness, as well as on the infrastructure safety of SynBio laboratory. In addition, we also consider the impact of the progressiveness of machine learning in the SynBio research and find that using more advanced machine learning technology leads to lower instrument safety. This study provides a framework for quantifying potential risk factors in SynBio research and lays a theoretical foundation for future laboratory safety management and the development of a global code of conduct for interdisciplinary scientists.

The Role of Nursing in Reducing Medical Errors: Best Practices and Systemic Solutions

Medical errors remain a significant challenge in healthcare, contributing to patient harm and increased costs. As frontline caregivers, nurses play a pivotal role in minimizing these errors by implementing best practices and advocating for systemic improvements. This article explores the various types of medical errors and emphasizes the critical role of nursing interventions in error prevention. Key best practices discussed include effective communication strategies such as SBAR, medication management protocols, and the use of technological tools like Electronic Health Records (EHR) and Barcode Medication Administration (BCMA). Additionally, the article addresses the importance of continuous training, fatigue management, and interdisciplinary collaboration in reducing error rates. Systemic solutions are also reviewed, including fostering a culture of safety, ensuring adequate staffing, and implementing evidence-based protocols. The findings suggest that while nurses are integral to preventing errors, healthcare institutions must provide the necessary support and resources to optimize their efforts in promoting patient safety. Ultimately, the article highlights the need for both individual and organizational commitment to reducing medical errors.

The effects of caffeine mouth rinsing on selective attention as a function of different caffeine concentrations and perceived taste intensity in recreationally active males at rest: a randomized placebo-controlled cross-over trial.

The effect of caffeine mouth rinsing (CAF-MR) on cognitive performance has not been thoroughly investigated. To evaluate the effects of different concentrations of CAF-MR on selective attention in relation to perceived taste intensity. A total of 30 healthy and recreationally active male subjects were included in this randomized, double-blind, placebo-controlled crossover trial. Interventions included MR for 20s at rest with three different caffeine solutions (0.24% [60 mg/25 mL], 0.6% [150 mg/25 mL], and 1.2% [300mg/25 mL]), MR with 25 mL water (placebo), and no MR (control). Data on Victoria Stroop Test (VST) and the perceived taste intensity were recorded at five sessions. CAF-MR-300mg intervention significantly decreased completion time (from 62.93 ± 19.07 to 57.01 ± 16.74s, p = 0.002 in Part D), while CAF-MR-150mg intervention significantly decreased number of errors in Part D (7.00 ± 6.21 vs. 5.63 ± 5.76, p = 0.04) and Part C (8.77 ± 8.80 vs. 7.10 ± 7.11, p = 0.02). Perceived difficulty was significantly decreased both after CAF-MR with 150mg (5.57 ± 1.65 vs. 4.77 ± 1.98, p = 0.006) and 300mg (5.95 ± 1.77vs. 4.67 ± 1.96, p < 0.001). Perceived taste intensity for 300mg of caffeine was negatively correlated with completion time (r: ranged, 0.37 to 0.46, p ranged, 0.045 to 0.009) after 300mg, 150mg (p ranged, 0.04 to 0.005) and placebo (p ranged 0.044 to 0.03) interventions. This study is the first to demonstrate that CAF-MR shows dose-dependent effects on selective attention in healthy recreational males, such as improved speed (for 300mg caffeine), reduced error rate (for 150mg caffeine) and decrease in perceived difficulty (for 150 and 300mg caffeine).

Flexible Hair‐Like Piezoelectric Acoustic Particle Velocity Sensor with Enhanced Sensitivity for Speaker Recognition

AbstractFlexible resonant acoustic sensors exhibit enhanced sensitivity near their resonant frequencies and have attracted increasing interest as essential components for next‐generation human‐machine speech interactions. However, membrane‐based resonant acoustic sensors are bulky owing to the inherently high bending stiffness of the sensing membrane. Inspired by the tiny hair‐based sensitive acoustic receptors of spiders, this study reports a hair‐like piezoelectric resonant acoustic particle velocity sensor (HP‐APVS) that enables highly sensitive sound sensing with a significantly reduced size. The HP‐APVS device is essentially a polyimide cantilever array fabricated using Nb0.02‐Pb(Zr0.6Ti0.4)O3 (PZT) on polyimide and self‐bending processes. The experimental results demonstrate that the HP‐APVS shows a linear response to acoustic particle velocity instead of acoustic pressure, with enhanced sensitivity in the resonance mode. Additionally, the proposed HP‐APVS exhibits an outstanding speaker recognition rate of 95.3% with an error rate reduction of 75% compared with that of a reference commercial microphone, indicating many potential applications in the realms of biometric authentication, voice user interfaces, and other intelligent acoustic electronics.

A Novel Non-Contact Multi-User Online Indoor Positioning Strategy Based on Channel State Information

The IEEE 802.11bf-based wireless fidelity (WiFi) indoor positioning system has gained significant attention recently. It is important to recognize that multi-user online positioning occurs in real wireless environments. This paper proposes an indoor positioning sensing strategy that includes an optimized preprocessing process and a new machine learning (ML) method called NKCK. The NKCK method can be broken down into three components: neighborhood component analysis (NCA) for dimensionality reduction, K-means clustering, and K-nearest neighbor (KNN) classification with cross-validation (CV). The KNN algorithm is particularly suitable for our dataset since it effectively classifies data based on proximity, relying on the spatial relationships between points. Experimental results indicate that the NKCK method outperforms traditional methods, achieving reductions in error rates of 82.4% compared to naive Bayes (NB), 85.0% compared to random forest (RF), 72.1% compared to support vector machine (SVM), 64.7% compared to multilayer perceptron (MLP), 50.0% compared to density-based spatial clustering of applications with noise (DBSCAN)-based methods, 42.0% compared to linear discriminant analysis (LDA)-based channel state information (CSI) amplitude fingerprinting, and 33.0% compared to principal component analysis (PCA)-based approaches. Due to the sensitivity of CSI, our multi-user online positioning system faces challenges in detecting dynamic human activities, such as human tracking, which requires further investigation in the future.

Enhancing Accuracy of Flame Equivalence Ratio Measurements: An Attention-Based Convolutional Neural Network Approach for Overcoming Limitations in Traditional Color Modeling

This paper addresses the inherent limitations in traditional color modeling techniques for measuring the flame equivalence ratio (Φ), particularly focusing on the subjectivity involved in threshold settings and the challenges posed by uneven 2D color distribution. To overcome these issues, this study introduces an attention-based convolutional neural network (ACN) model, a novel approach that transcends the conventional reliance on B/G color features (Tf). The ACN model leverages adaptive feature extraction, augmented by a spatial attention mechanism, to more effectively analyze flame images. By amplifying key features, autonomously minimizing background noise, and standardizing variations in color distribution, the ACN model in this experiment achieved a prediction accuracy of 99%, with a 76% reduction in error rate compared to the original model, significantly improving the accuracy and objectivity of flame Φ measurement. This method marks a substantial development in the precision and reliability of flame analysis.

Throughput improvement in ACO-OFDM-based VLC systems using noise cancellation and precoding techniques

One of the primary challenges faced by visible light communication (VLC) systems employing optical orthogonal frequency division multiplexing is the peak-to-average power ratio (PAPR). This study is dedicated to designing, simulating, and evaluating bit error rate (BER) and PAPR reduction methods tailored for the VLC broadcasting system. The asymmetric clipped optical orthogonal frequency division multiplexing (ACO-OFDM) scheme is highlighted in this work for its impressive performance. Therefore, the proposed PAPR mitigation methodologies applied to ACO-OFDM. The proposed PAPR reduction strategy involves 5 distinct precoding methodologies. The PAPR was mitigated by 3.485 dB after applying the DST precoding methodology. Still, the WHT precoding methodology can achieve PAPR reduction by 1.131 dB, without BER performance degradation, with respect to the conventional ACO-OFDM system. Furthermore, the work addresses another challenge in VLC systems: the bit error rate (BER). This is accomplished by introducing approaches to Time Domain Noise Cancelation and Frequency Domain Noise Cancelation (FDNC). The BER performance of these 2 receiver models is nearly the same. The simulation results indicate the system performance enhancement after applying noise cancellation approaches by 1.65 dB at the 4-QAM modulation scheme and 2.97 dB at the 1024-QAM modulation scheme. The 16-QAM modulation scheme, after applying DST and WHT methodologies alongside noise cancellation approaches, can enhance both PAPR by 20.83% and 6.76%, but the Eb/N0 performance enhancement by 10.10% and 14.64%, respectively. Additionally, the effectiveness and validity of the proposed schemes are verified by comparing them with relevant literature reviews on PAPR reduction techniques and selecting an optimal choice among them.

Adaptive Hybrid Prediction Model for Adapting to Data Distribution Shifts in Machining Quality Prediction

Abstract The accuracy of data-driven intelligent prediction for machining quality relies on the training samples. However, in actual applications, the continuous operation of machining equipment leads to gradual distribution shifts between the process data and the training samples for modeling. The shifts result in a degradation in the performance of predictive model, previous studies have often overlooked this issue. To tackle with the intricate problem, this research proposes a real-time model optimization approach. Firstly, a method for detecting machining data distribution shifts based on the two-sample Kolmogorov-Smirnov (KS) test is proposed. Then, an adaptive hybrid prediction model (AHPM) capable of real-time optimization is developed. This model consists of a deep neural network (DNN) and a broad learning system (BLS). DNN plays a primary role in prediction within the hybrid model with excellent generalization capability. BLS quickly completes optimization prior to DNN with its unique parameter update mechanism to compensate for prediction loss. Experimental results indicate that AHPM achieves the shortest optimization time while maintaining high accuracy, with post-optimization error reduction rates for MSE, MAE, and MAPE all exceeding 10%. In the test of application to actual machining cases, accuracy improved by 8.88% compared to traditional methods without optimization.&amp;#xD;

Review on Automated Storage and Retrieval System for Warehouse

The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context of warehouse operations. It offers a comprehensive introduction to the operational systems of warehouses, detailing each type of ASRS along with the technologies that can improve the efficiency and accuracy of these systems. Moreover, the paper thoroughly investigates and classifies the ASRS design decision problem and compares multiple types of ASRS. The analysis aims to delineate the distinctions among various ASRS configurations, assessing their scalability, adaptability, and their impact on operational efficacy in warehouse environments Through this comparative review, the paper emphasizes the potential enhancements in sorting processes that modern ASRS can provide, ensuring that warehouse operations can rapidly adapt to market changes and demands. The goal is to highlight best practices and technological innovations that can lead to more responsive and efficient warehouse management systems. This exploration contributes to a better understanding of how cutting-edge automation and adaptable system designs can significantly influence the efficiency of warehouse sorting processes.

A Machine Learning and Deep Learning-Based Account Code Classification Model for Sustainable Accounting Practices

Accounting account codes are created within a specific logic framework to systematically and accurately record a company’s financial transactions. Currently, accounting reports are processed manually, which increases the likelihood of errors and slows down the process. This study aims to use image processing techniques to predict cash codes in accounting reports, automate accounting processes, improve accuracy, and save time. Deep learning embeddings from Inception V3, SqueezeNet, VGG-19, VGG-16, Painters, and DeepLoc networks were utilized in the feature extraction phase. A total of six learning algorithms, namely Logistic Regression, Gradient Boosting, Neural Network, kNN, Naive Bayes, and Stochastic Gradient Descent were employed to classify the images. The highest accuracy rate of 99.2% was achieved with the combination of the Inception V3 feature extractor and the Neural Network classifier. The results demonstrate that image processing methods significantly reduce error rates in accounting records, accelerate processes, and support sustainable accounting practices. This indicates that image processing techniques have substantial potential to contribute to digital transformation in accounting, helping businesses achieve their sustainability goals.

Risk Assessment of Employing Digital Robots in Process Automation

Using digital technologies is essential to gain a competitive advantage in the global market by adapting to new business models. While digital technologies make business processes efficient, they enable companies to make faster and more accurate decisions by automating daily and routine process tasks. Robotic process automation (RPA) automates routine and repetitive business processes, allowing many jobs performed by humans to be performed faster. This way, advantages such as reduced error rates, reduced costs, increased production speed, and labor productivity are provided. For the successful implementation of RPA, potential risks need to be considered. In this study, failure mode and effect analysis (FMEA) based on decomposed fuzzy sets (DFSs), a new extension of intuitionistic fuzzy sets, has been used to evaluate subjectiveness in expert judgments. Differing from the other extensions of fuzzy set theory, the advantage of DFSs is to simultaneously consider decision-makers’ optimistic and pessimistic answers. Thus, the answer given by the decision-maker to the positive and negative questions on the same subject defines the indeterminacy of the decision-maker, and the method takes this indeterminacy into account in the evaluation. This study assesses and evaluates the potential risks of six digital robots in process automation. Thirteen risks were individually assessed for each automated process. This study found “Sustainability challenge” critical in three processes, “Absence of governance management” in two, and “Security“ in one. Variability in risk importance arose from process vulnerabilities.

Deep learning-driven channel coding: enhancing performance in LDS-CDMA and NOMA systems

This paper presents an innovative approach to channel coding for LDS-CDMA and NOMA systems through the integration of deep learning techniques. By leveraging neural networks within the channel coding process, the proposed model achieves substantial improvements in error correction, leading to a significant reduction in Bit Error Rate (BER) and computational complexity when compared to traditional methods such as Unpunctured Turbo Trellis Coded Modulation (UTTCM). The results demonstrate that the deep learning-based system is particularly effective in challenging communication environments, such as those with low Signal-to-Noise Ratios (SNR) and high levels of multi-user interference, where it outperforms conventional coding techniques. This research underscores the potential of deep learning to enhance the efficiency and adaptability of wireless communication systems, particularly as networks evolve towards 6G and beyond. The flexibility offered by this approach allows for improved handling of dynamic and complex environments, ensuring higher data throughput and reduced latency. Future work may focus on implementing the proposed model in real-time applications, as well as investigating its synergy with emerging technologies like massive MIMO and NOMA, which could lead to even greater performance enhancements in next-generation communication systems.

Impact of a Bundle of Interventions on the Spectrum of Parenteral Drug Preparation Errors in a Neonatal and Pediatric Intensive Care Unit.

Background/Objectives: This study aimed to evaluate the impact of a bundle of interventions on the error rates in preparing parenteral medications in a neonatal and pediatric intensive care unit (NICU/PICU). Methods: We conducted a prospective interventional study in a NICU/PICU in a tertiary university hospital as a follow-up to a prior study in the same setting. A clinical pharmacist and a pharmacy technician (PT) analyzed the workflow of drug preparation on the ward, identified high-alert medications, and defined a bundle of five interventions, which include the following: Drug Labeling: 1. EN ISO-DIVI labeling; Training: 2. Standardized preparation process on the ward; 3. eLearning Program; 4. Expert Consultations; and Location of Preparation: 5. Transfer of the preparation of high-alert medications and standardized preparations to the central pharmacy. After implementing the bundle of interventions, we observed the preparation process on the ward to evaluate if the implementation of the interventions had an impact on the quality of the drug preparation. Results: We observed 262 preparations in the NICU/PICU. Each single step of the preparation process was defined as an error opportunity. We defined seven error categories with an overall error opportunity of 1413. In total, we observed 11 errors (0.78%). The reduction in the overall error rate from 1.32% in the former study to 0.78% per preparation opportunity demonstrated that the implemented interventions were effective in enhancing medication safety. Conclusions: This study provides evidence that a bundle of interventions, including standardizing drug labeling, enhancing training, and centralizing the preparation of high-alert medications, can reduce medication errors in NICU/PICU settings.

Hybrid Intelligent Anomaly Detection System Using Attention based Deep Learning Approach for Cyber Attacks Prevention

Objectives: Network Intrusion Detection System (NIDS) plays an important role in finding and preventing cyber-attacks, which helps to improve the entire security posture of an organization’s network infrastructure. The development of Deep Learning (DL) techniques possess the ability of IDS to detect attacks without delay and protects from intrusions even in real-time environment. Methods: The present study proposes an improved IDS framework called Enhanced Gated Recurrent Unit Hyper-Model combined Attention Bidirectional Long-Short Term Memory (EGHAB) approach, to effectively address the detection of attacks with maximum accuracy and minimal error rate. The proposed model is enhanced by using methods like numericalization and normalisation for pre-processing the input data, Genetic Algorithm (GA) for extracting intricate features from the input data, and the EGHAB classifier, which works on the principles of both Gated Recurrent Unit (GRU) and Bidirectional Long-Short Term Memory (BiLSTM) models along with attention mechanism. Findings: The EGHAB model efficiently learns the abnormal behaviour of network and classified the attack and non-attack data using the publicly available NSL-KDD dataset and a generated real time data set, scapy. And it achieved 99.94% accuracy over NSL KDD dataset during multiclass classification and 93.3% on real time data set with reduced Error rate. Additionally, to examine the efficacy, the proposed approach is compared with other existing methods and proved its improved performance. Novelty: A combined ensemble classifier with GRU, BiLSTM and attention mechanism is designed to predict the attacks in earlier stage rather than due over. The model achieved better accuracy and reduced false assumption using anomaly prediction mechanism. Keywords: Cyber Attack, Anomaly Detection, Deep learning, Gated Recurrent Unit, Bi­ Directional Long­-Short Term Memory, Attention mechanism

A hybrid detection model for acute lymphocytic leukemia using support vector machine and particle swarm optimization (SVM-PSO).

Leukemia, a hematological disease affecting the bone marrow and white blood cells (WBCs), ranks among the top ten causes of mortality worldwide. Delays in decision-making often hinder the timely application of suitable medical treatments. Acute lymphoblastic leukemia (ALL) is one of the primary forms, constituting approximately 25% of childhood cancer cases. However, automated ALL diagnosis is challenging. Recently, machine learning (ML) has emerged as an important tool for building detection models. In this study, we present a hybrid detection model that improves the accuracy of the detection process by combining support vector machine (SVM) and particle swarm optimization (PSO) approaches to automatically identify ALL. We use SVM to represent a two-dimensional image and complete the classification process. PSO is employed to enhance the performance of the SVM model, reducing error rates and enhancing result accuracy. The input images are obtained from two public datasets (ALL-IDB1 and ALL-IDB2), and public online datasets are utilized for training and testing the proposed model. The results indicate that our hybrid SVM-PSO model has high accuracy, outperforming stand-alone algorithms and demonstrating superior performance, an enhanced confusion matrix, and a higher detection rate. This advancement holds promise for enhancing the quality of technical software in the medical field using machine learning.

Predicting e-commerce product prices through the integration of variational mode decomposition and deep neural networks

Product prices frequently manifest nonlinear and nonstationary time-series attributes, indicating potential variations in their behavioral patterns over time. Conventional linear models may fall short in adequately capturing these intricate properties. In addressing this, the present study leverages the adaptive and non-recursive attributes of the Variational Mode Decomposition (VMD) methodology. It employs VMD to dissect the intricate time series into multiple Intrinsic Mode Functions (IMF). Subsequently, a method rooted in the minimum fuzzy entropy criterion is introduced for determining the optimal modal number (K) in the VMD decomposition process. This method effectively mitigates issues related to modal confusion and endpoint effects, thereby enhancing the decomposition efficacy of VMD. In the subsequent phase, deep neural networks (DNN) are harnessed to forecast the identified modes, with the cumulative modal predictions yielding the ultimate e-commerce product price prognostications. The predictive efficacy of the proposed Variational Mode Decomposition-deep neural network (VMD-DNN) decomposition model is assessed on three public datasets, wherein the mean absolute percentage error (MAPE) on the E-commerce Price Prediction Dataset and Online Retail Dataset is notably low at 0.6578 and 0.5414, respectively. This corresponds to a remarkable error reduction rate of 66.5% and 70.4%. Moreover, the VMD-DNN decomposition model excels in predicting e-commerce product prices through DNN, thereby amplifying the VMD decomposition capability by 4%. The VMD-DNN model attains superior results in terms of directional symmetry, boasting the highest Directional Symmetry (DS) score of 86.25. Notably, the forecasted trends across diverse price ranges closely mirror the actual trends.

A-181 The Effectiveness Module to Reduce Laboratory Error Reporting Rates

Abstract Background In a hospital in central Taiwan, the number of tests conducted in 2022 was 1.88 million, there were 26 cases of laboratory error reports, with an error rate of 14 cases per million. Error reports may lead to diagnostic errors, posing a significant risk to patient safety. Therefore, our team aims to establish a simple and universal module that can be applicable to various laboratory operations to minimize errors. Methods Initially, we conducted an analysis of error causes, identifying 25 cases of human error and 1 case of computer system error. Human errors were categorized according to the laboratory process, including 4 specimen identification errors, 3 procedural errors, 9 typing errors, 7 validation of analytical data errors, and 2 instances of failure to communicate issues during handover. The proximate cause of human error was non-compliance with SOPs, and the root cause was inadequate SOPs leading to inconsistent practices. We reviewed the workflow, implemented corrective actions, revised SOPs, and provided training for personnel. Additionally, three important strategies were introduced: 1.High-Frequency Audits: Corrective actions were included in the internal audit checklist, initially increasing the frequency to at least once a month for the first 3 months, followed by at least once every 6 months within the next 6 months. If no further occurrences were noted, audits could be conducted annually or not. Each audit should be completed within 20 minutes to avoid excessive burden, with direct observation of operations and random questioning being the preferred audit methods. 2. Audit deficiencies should be documented: Personnel were requested to conduct cause analysis and improvements. This not only expressed the supervisor's commitment to improvement but also provided an opportunity to discover reasons why countermeasures cannot not be implemented, allowing for strategic adjustments until successful implementation. 3.Case-Based Training: Benchmark cases were developed into teaching materials, and compiled errors regularly for looking for patterns or trends to identity key steps. Make these key steps the focus of your educational training. Results In 2023, 1.94 million tests were conducted, there were 4 error reports, with an error rate of 3 cases per million, all were attributed to human error. In another hospital in southern Taiwan, laboratory error reports were primarily due to human error. From June 2022 to July 2023, a total of 3.46 million tests were conducted, there were 14 error reports and an error rate of 5 cases per million. After implementing the same improvement module from August to December 2023, a total of 1.56 million tests were conducted, there were 2 error reports and an error rate of 2 cases per million, all were attributed to human error. Conclusions We applied this improvement module to two hospitals in central and southern Taiwan, resulting in a significant reduction in error rate. The key factors of effectiveness were utilizing high-frequency audits to ensure sustained results, and demonstrating the positive attitude of supervisors during the process. This motivated personnel to demand higher standards from themselves, ultimately showcasing remarkable improvements.

Enhancing Wireless Communication Efficiency Using Dynamic Nonlinear Distortion Adaptive Optimization Analysis

Abstract Modern communication networks require improved signal quality and spectrum efficiency. Modern wireless communication relies heavily on the multiple input multiple output (MIMO) technology to improve spectrum efficiency and achieve faster data rates. However, the performance of MIMO systems is significantly hampered by non-linear distortions in power amplifiers (PA), especially under dynamic operating conditions. Traditional methods to reduce these distortions often lack the adaptability required for effective optimization. The proposed dynamic nonlinear distortion adaptive optimization analysis (DNDAOA) therefore introduces an adaptive approach that dynamically adjusts the pre-distortion signals using real-time feedback mechanisms. DNDAOA effectively reduces non-linear distortions and thus improves the performance of MIMO systems. Simulation results show that DNDAOA significantly increases spectrum efficiency, reduces error rates, and improves signal quality. This method is widely used in areas such as wireless telecommunications, internet of things (IoT), autonomous vehicles, and smart infrastructure, driving advances in connectivity, reliability, and data integrity.

Multicarrier dual‐index permuted chaos shift keying system for efficiency improvement in chaotic communications

AbstractWe propose a multicarrier chaotic system that incorporates dual‐index and M‐ary modulation techniques. The proposed system improves the energy and spectrum efficiencies with a tradeoff in computational complexity. In addition, it applies index modulation to select the chaotic signal and permuted chaotic signals along with ‐ary modulated symbols. As a result, the spectral and energy efficiencies are increased in chaotic systems. Theoretical expressions are derived to calculate the bit error rate of the proposed system under additive white Gaussian noise and Rayleigh fading channels. The equations are validated through Monte Carlo simulations, whose results demonstrate a reduction in bit error rate for the proposed system. The data rate, spectral efficiency, energy efficiency, and computational complexity of the proposed system are analyzed and compared with those of existing multicarrier chaotic systems.

A Machine Learning Approach to Optimal Group Formation Based on Previous Academic Performance

In today’s educational institutions, student performance can vary widely due to differences in cognition, motivation, and environmental factors. These variations create challenges in achieving optimal learning outcomes. To address these challenges, Optimal Group Formation (OGF) has emerged as a promising research area. Optimal Group Formation (OGF) aims to form student groups that maximize learning efficiency based on past academic performance. Group formation problems are inherently complex and time-consuming, but their applications are extensive, spanning from manufacturing systems to educational contexts. This paper introduces a machine learning-based model designed to create optimal student groups using academic records as the primary input. The goal is to enhance overall group performance and reduce error rates by organizing students into cohesive, efficient teams. What sets this research apart is its focus on educational group formation, leveraging machine learning to improve collaborative learning outcomes. The paper also reviews prior research, emphasizing the importance of Optimal Group Formation (OGF) in various fields and its relevance in education. The model’s effectiveness is demonstrated through comparative analysis, showcasing its potential to improve group dynamics in both theoretical and lab-based courses. Ultimately, the aim is to improve educational outcomes by ensuring that student groups are optimally balanced and structured.