Improvement In Performance Metrics Research Articles

A Bio-Inspired Method For Breast Histopathology Image Classification Using Transfer Learning

Breast cancer is one of the deadly cancer among the female population, and still a developing area of research in the field of medical imaging. The fatality rate is more in patients who are not early diagnosed and are given delayed treatment. Hence researchers are keeping their lot of efforts in developing breast cancer detection systems that could provide accurate diagnosis in the initial stages which are relied on medical imaging. Deep learning is offering key solutions to overcome many image classification tasks. Though deep learning techniques have extended their root to many medical fields even it suffers from the problem of lack of sufficient data. Convolutional Neural Networks are more preferred for medical image classification tasks. In this paper, we propose a transfer learning method that overcomes the issue of insufficient data. We use a familiar pre-trained network VGG-16 (Visual Geometric Group) + with Logistic Regression as a binary classifier. Since hyper-parameters of every CNN has a closer impact on the performance of the entire deep learning model, our method focus on optimizing hyper-parameters using particle swarm optimization which is a bio-inspired algorithm, The proposed model performs classification of Breast Histopathology images into benign and malignant images and produce better results. We use Break His Dataset to test our method and achieve an accuracy of around 96.9%. The experimental results show that this study has improved performance metrics when compared to other transfer learning methods.

Coreference Resolution for Improving Performance Measures of Classification Tasks

There are several possibilities to improve classification in natural language processing tasks. In this article, we focused on the issue of coreference resolution that was applied to a manually annotated dataset of true and fake news. This dataset was used for the classification task of fake news detection. The research aimed to determine whether performing coreference resolution on the input data before classification or classifying them without performing coreference resolution is more effective. We also wanted to verify whether it is possible to enhance classifier performance metrics by incorporating coreference resolution into the data preparation process. A methodology was proposed, in which we described the implementation methods in detail, starting from the identification of entity mentions in the text using the neuralcoref algorithm, then through word-embedding models (TF–IDF, Doc2Vec), and finally to several machine learning methods. The result was a comparison of the implemented classifiers based on the performance metrics described in the theoretical part. The best result for accuracy was observed for the dataset with coreference resolution applied, which had a median value of 0.8149, while for the F1 score, the best result had a median value of 0.8101. However, the more important finding is that the processed data with the application of coreference resolution led to an improvement in performance metrics in the classification tasks.

A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning.

Combination therapies have brought significant advancements to the treatment of various diseases in the medical field. However, searching for effective drug combinations remains a major challenge due to the vast number of possible combinations. Biomedical knowledge graph (KG)-based methods have shown potential in predicting effective combinations for wide spectrum of diseases, but the lack of credible negative samples has limited the prediction performance of machine learning models. To address this issue, we propose a novel model-agnostic framework that leverages existing drug-drug interaction (DDI) data as a reliable negative dataset and employs supervised contrastive learning (SCL) to transform drug embedding vectors to be more suitable for drug combination prediction. We conducted extensive experiments using various network embedding algorithms, including random walk and graph neural networks, on a biomedical KG. Our framework significantly improved performance metrics compared to the baseline framework. We also provide embedding space visualizations and case studies that demonstrate the effectiveness of our approach. This work highlights the potential of using DDI data and SCL in finding tighter decision boundaries for predicting effective drug combinations.

MF-RALU: design of an efficient multi-functional reversible arithmetic and logic unit for processor design on the field programmable gate array platform

Most modern computer applications use reversible logic gates to solve power dissipation issues. This manuscript uses an efficient multi-functional reversible arithmetic and logical unit (MF-RALU) to perform 30 operations. The 32-bit MF-RALU includes arithmetic, logical, complement, shifters, multiplexers, different adders, and multipliers. The multi-bit reversible multiplexers are used to construct the MF-RALU structure. The Reduced instruction set computer (RISC) processor is designed to realize the functionality of the MF-RALU. The MF-RALU can perform its operation in a single clock cycle. The 1-bit RALU is developed and compared with existing approaches with improvements in performance metrics. The 32-bit reversible arithmetic units (RAUs) and reversible logical units (RLUs) are constructed using 1-bit RALU. The MF-RALU and RISC processor are synthesized individually in the Vivado environment using Verilog-HDL and implemented on Artix-7 field programmable gate array (FPGA). The MF-RALU utilizes a <11% chip area and consumes 332 mW total power. The RISC processor utilizes a <3% chip area and works at 483 MHZ frequency by consuming 159 mW of total power on Artix-7 FPGA.

Emotional visual stimuli and simulated laparoscopic surgical performance: A pilot cohort study

BackgroundExposure to stress prior to or during surgery can negatively impact performance. Management of stress is an essential non-technical skill required for safe practice. The effects of exposure to emotional visual stressors on surgical performance are poorly understood. This study aims to develop a model to investigate effects of emotive visual stimuli on simulated laparoscopic performance. Methods and materialsA single-centre cohort study. Thirty novice, simulator-naïve medical students were randomly allocated to view either positive, negative, or neutral emotional images (sourced from validated image registry). Participants focused for 5 s on the image before completing a peg-threading laparoscopic task. Time, instrument distance, speed, acceleration, motion smoothness, and ambidexterity were recorded automatically with instrument tracking software. 8 task cycles were completed; 3 control practices followed by 5 with the stimuli, according to group allocation. ResultsThe final performance metrics of students (time, distance, speed, and motion smoothness) were not significantly different when comparing positive and neutral stimuli groups to those shown negative stimuli. However, changes were seen in the rate of performance improvements (positive: p = 0.711, p = 0.837, p = 0.297, and p = 0.393) (neutral: p = 0.285, p = 0.918, p = 0.835, and p = 0.396). Participation improved performance metrics overall (p=<0.001, p=<0.001, p = 0.088, p = 0.025, p=<0.001). ConclusionModel systems may be valuable for investigating the impact of stress on surgeon performance. The effect of emotive visual stimuli on surgical performance is complex. This model may aid the further exploration of these relationships and ultimately can provide an environment in which surgeons can develop strategies to mitigate the adverse effect of stressors.

Pediatric Rheumatology Care and Outcomes Improvement Network's Quality Measure Set to Improve Care of Children With Juvenile Idiopathic Arthritis.

To describe the selection, development, and implementation of quality measures (QMs) for juvenile idiopathic arthritis (JIA) by the Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN), a multihospital learning health network using quality improvement methods and leveraging QMs to drive improved outcomes across a JIA population since 2011. An American College of Rheumatology-endorsed multistakeholder process previously selected initial process QMs. Clinicians in PR-COIN and parents of children with JIA collaboratively selected outcome QMs. A committee of rheumatologists and data analysts developed operational definitions. QMs were programmed and validated using patient data. Measures are populated by registry data, and performance is displayed on automated statistical process control charts. PR-COIN centers use rapid-cycle quality improvement approaches to improve performance metrics. The QMs are revised for usefulness, to reflect best practices, and to support network initiatives. The initial QM set included 13 process measures concerning standardized measurement of disease activity, collection of patient-reported outcome assessments, and clinical performance measures. Initial outcome measures were clinical inactive disease, low pain score, and optimal physical functioning. The revised QM set has 20 measures and includes additional measures of disease activity, data quality, and a balancing measure. PR-COIN has developed and tested JIA QMs to assess clinical performance and patient outcomes. The implementation of robust QMs is important to improve quality of care. PR-COIN's set of JIA QMs is the first comprehensive set of QMs used at the point-of-care for a large cohort of JIA patients in a variety of pediatric rheumatology practice settings.

Improving the prediction of school dropout with the support of the semi-supervised learning approach

School dropout is a phenomenon characterized by being influenced by several variables. This research used Machine Learning techniques, especially in the context of the semi-supervised learning strategy, to predict the risk of dropout in undergraduate courses at a Brazilian higher education institution. Two phases of experiments were conducted, the first using Feature Selection techniques and the second applying a semi-supervised learning strategy to improve performance metrics collected from the increase in the number of instances of students labeled as Graduated. As a main result, we obtained a model capable of classifying dropout with 90% accuracy and 86% Macro-F1.

Investigating the Increased CO2 Capture Performance of Amino Acid Functionalized Nanoporous Materials from First-Principles and Grand Canonical Monte Carlo Simulations

Nanoporous materials such as metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) have been identified as key candidates for environmental remediation through catalytic reduction and sequestration of pollutants. Given the prevalence of CO2 as a target molecule for capture, MOFs and COFs have seen a long history of application in the field. More recently, functionalized nanoporous materials have been demonstrated to improve performance metrics associated with the capture of CO2. We employ a multiscale computational approach including ab initio density functional theory (DFT) calculations and classical grand canonical Monte Carlo (GCMC) simulations, to investigate the impact of amino acid (AA) functionalization in three such nanoporous materials. Our results demonstrate a nearly universal improvement of CO2 uptake metrics such as adsorption capacity, accessible surface area, and CO2/N2 selectivity for six AAs. In this work, we elucidate the key geometric and electronic properties associated with improving the CO2 capture performance of functionalized nanoporous materials.

When performance is not enough-A multidisciplinary view on clinical decision support.

Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make sustainable progress. What does it take to implement a clinical decision support system, what makes it usable for the domain experts, and what brings it eventually into practical usage? So far, there has been little research to answer these questions. This work presents a multidisciplinary view of machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. The target audience is computer scientists, who plan to do research in a clinical context. The paper starts from a relatively straightforward risk prediction system in the subspecialty nephrology that was evaluated on historic patient data both intrinsically and based on a reader study with medical doctors. Although the results were quite promising, the focus of this article is not on the model itself or potential performance improvements. Instead, we want to let other researchers participate in the lessons we have learned and the insights we have gained when implementing and evaluating our system in a clinical setting within a highly interdisciplinary pilot project in the cooperation of computer scientists, medical doctors, ethicists, and legal experts.

IIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers

BackgroundInflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic cytokine that is known to be associated with airway inflammation in asthma and reactive airway diseases, in neoplastic and autoimmune diseases. Interestingly, the recent association of IL-13 with COVID-19 severity has sparked interest in this cytokine. Therefore characterization of new molecules which can regulate IL-13 induction might lead to novel therapeutics.ResultsHere, we present an improved prediction of IL-13-inducing peptides. The positive and negative datasets were obtained from a recent study (IL13Pred) and the Pfeature algorithm was used to compute features for the peptides. As compared to the state-of-the-art which used the regularization based feature selection technique (linear support vector classifier with the L1 penalty), we used a multivariate feature selection technique (minimum redundancy maximum relevance) to obtain non-redundant and highly relevant features. In the proposed study (improved IL-13 prediction (iIL13Pred)), the use of the mRMR feature selection method is instrumental in choosing the most discriminatory features of IL-13-inducing peptides with improved performance. We investigated seven common machine learning classifiers including Decision Tree, Gaussian Naïve Bayes, k-Nearest Neighbour, Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting to efficiently classify IL-13-inducing peptides. We report improved AUC, and MCC scores of 0.83 and 0.33 on validation data as compared to the current method.ConclusionsExtensive benchmarking experiments suggest that the proposed method (iIL13Pred) could provide improved performance metrics in terms of sensitivity, specificity, accuracy, the area under the curve - receiver operating characteristics (AUCROC) and Matthews correlation coefficient (MCC) than the existing state-of-the-art approach (IL13Pred) on the validation dataset and an external dataset comprising of experimentally validated IL-13-inducing peptides. Additionally, the experiments were performed with an increased number of experimentally validated training datasets to obtain a more robust model. A user-friendly web server (www.soodlab.com/iil13pred) is also designed to facilitate rapid screening of IL-13-inducing peptides.

Scalable and Data-Independent Multi-Agent Recommender System Using Social Networks Analysis

Nowadays, many online users find the selection of information and required products challenging due to the growing volume of data on the web. Recommender systems are introduced to deal with information overload. Cold start and data sparsity are the two primary issues in these systems, which lead to a decrease in the efficiency of recommender systems. To solve the problems, this paper proposes a novel method based on social network analysis. Our method leverages a multi-agent system for clustering users and items and predicting relationships between them simultaneously. The information on users and items is extracted from the user-item matrix as distinct graphs. Each of the graphs is then treated as a social network, which is further processed and analyzed by community detection and link prediction procedures. The users are grouped into several clusters by the community detection agent, which results in each cluster as a community. Then link prediction agent identifies the latent relationships between users and items. Simulation results show that the proposed method has significantly improved performance metrics as compared to recent techniques.

Application of A* Algorithm Based on Extended Neighborhood Priority Search in Multi-Scenario Maps

The robustness of the traditional A* algorithm of path planning is poor due to its excessive number of traversal nodes, slow search speed, and large turning angle. Aiming to solve the above problems, a multi-scenario adaptive A* algorithm based on extended neighborhood priority search is proposed. Firstly, this algorithm designs the heuristic function that can adapt to various scene changes by quantifying the scene map information, and the search weight is adjusted adaptively to enhance the robustness and adaptability of the algorithm. Secondly, the search strategy based on extended neighborhood priority is adopted to improve the orientation of the algorithm, and the redundant node removal strategy is used to smooth the path to reduce the number of traversed nodes and the turning angle. Finally, simulation tests are conducted in several representative map environments. The test results show that the proposed algorithm is superior to the traditional A* algorithm due to its stronger robustness and significantly improved performance metrics, with an 84.95% reduction in the number of traversal nodes, an 83.84% reduction in the number of path nodes, a 62.28% reduction in turning points on the path, a 77.38% reduction in the total turning angle, and a 58.47% reduction in the search time.

Deep Survival Analysis With Clinical Variables for COVID-19.

Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients. We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups. Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19. Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner. The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient's chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.

Optimized Resource Allocation and Queue Management for Traffic Control in MANET

A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks (MANETs). Offering better communication services among the users in a centralized organization is the primary objective of the MANET. Due to the features of MANET, this can directly End-to-End Delay (EED) the Quality of Service (QoS). Hence, the implementation of resource management becomes an essential issue in MANETs. This paper focuses on the efficient Resource Allocation (RA) for many types of Traffic Flows (TF) in MANET. In Mobile Ad hoc Networks environments, the main objective of Resource Allocation (RA) is to process consistently available resources among terminals required to address the service requirements of the users. These three categories improve performance metrics by varying transmission rates and simulation time. For solving that problem, the proposed work is divided into Queue Management (QM), Admission Control (AC) and RA. For effective QM, this paper develops a QM model for elastic (EL) and inelastic (IEL) Traffic Flows. This research paper presents an AC mechanism for multiple TF for effective AC. This work presents a Resource Allocation Using Tokens (RAUT) for various priority TF for effective RA. Here, nodes have three cycles which are: Non-Critical Section (NCS), Entry Section (ES) and Critical Section (CS). When a node requires any resources, it sends Resource Request Message (RRM) to the ES. Elastic and inelastic TF priority is determined using Fuzzy Logic (FL). The token holder selects the node from the inelastic queue with high priority for allocating the resources. Using Network Simulator-2 (NS-2), simulations demonstrate that the proposed design increases Packet Delivery Ratio (PDR), decrease Packet Loss Ratio (PLR), minimise the Fairness and reduce the EED.

Recent progress in soft electronics and robotics based on magnetic nanomaterials

Recent advancements in soft electronics and robotics have expanded the possibilities beyond the capabilities of traditional rigid devices, indicating promise for a range of applications in electronic skins, wireless biomedical devices, and others. Magnetic materials exploited in these soft systems can further broaden the modalities in sensing and actuation. These magnetic materials, when constructed in the forms of nanoparticles, nanomembranes, or other types of nanostructures, exhibit some unique characteristics, such as the magnetoresistance effect and size-dependent coercivity. Soft electronics and robotics employing such magnetic nanomaterials offer a variety of functions, including the detection of the intensity and direction of magnetic fields, measurement of various types of mechanical deformations, manipulation and transport at small scales, and multimodal complex locomotion in a controllable fashion. Despite recent advancements in soft electronics and robotics, challenges remain in developing advanced materials and manufacturing schemes to improve performance metrics and facilitate integration with other devices. This review article aims to summarize the progress made in soft electronics and robotics based on magnetic nanomaterials, with an emphasis on introducing material and device performance. The discussions focus on soft electronics and robotics based on magnetic nanomembranes/nanostructures and magnetic composites. As a concluding remark, this article summarizes the current status of the field and discusses opportunities that underpin future progress.

IoT-Driven Healthcare Monitoring with Explainable Machine Learning Models

The need for this work arises from the critical importance of IoT-driven healthcare monitoring. In an era marked by technological advancements, the healthcare sector has not been left untouched. The ability to monitor patients' health remotely through IoT devices has emerged as a promising solution, offering real-time data for healthcare providers. However, amidst this promise, there remain significant challenges. Existing approaches in this domain have limitations. They often lack transparency and interpretability, making it challenging to trust the decisions made by machine learning models. Moreover, their performance metrics often fall short of achieving optimal precision, accuracy, recall, AUC, and speed, which are crucial in healthcare applications where timely and accurate decisions. In response to these challenges, this paper presents a novel approach. The proposed model leverages Convolutional Neural Networks (CNN) and integrates Deep Shap and GridCAM++ techniques to offer a more explainable and interpretable solution for IoT-driven healthcare monitoring. This fusion of methods enhances the model's transparency, allowing healthcare professionals to understand the rationale behind its decisions. The advantages of this approach are multifold. First and foremost, it enhances the precision, accuracy, recall, and AUC by 5.5%, 5.9%, 6.5%, and 8.3%, respectively, when compared to existing methods. These improvements translate to more reliable diagnoses and decisions in healthcare. Additionally, the model achieves a 4.9% boost in speed, ensuring that critical decisions can be made swiftly, reducing the time between data collection and actions. The impact of this work is substantial, it paves the way for more trustworthy and efficient IoT-driven healthcare monitoring systems, addressing the limitations of existing approaches. With improved performance metrics and enhanced explainability, healthcare professionals can make more informed decisions, leading to better patient outcomes. Ultimately, this paper contributes to the advancement of healthcare technology, bringing us closer to a future where IoT-enabled monitoring plays a pivotal role in improving patient care sets.

Modeling and analysis of field-oriented control based permanent magnet synchronous motor drive system using fuzzy logic controller with speed response improvement

&lt;span lang="EN-US"&gt;The permanent magnet synchronous motor (PMSM) acts as an electrical motor mainly used in many diverse applications. The controlling of the PMSM drive is necessary due to frequent usage in various systems. The conventional proportional-integral-derivative (PID) controller’s drawbacks are overcome with fuzzy logic controller (FLC) and adopted in the PMSM drive system. In this manuscript, an efficient field-oriented control (FOC) based PMSM drive system using a fuzzy logic controller (FLC) is modeled to improve the speed and torque response of the PMSM. The PMSM drive system is modeled using abc to &lt;/span&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;αβ&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt; and &lt;/span&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;αβ&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt; to abc transformation, 2-level space vector pulse width modulation (SVPWM), AC to DC rectifier with an inverter, followed by PMSM drive, &lt;a name="_Hlk107566912"&gt;&lt;/a&gt;proportional integral (PI) controller along with FLC. The FLC’s improved fuzzy rule set is adopted to provide faster speed response, less % overshoot time, and minimal steady-state error of the PMSM drive system. The simulation results of speed response, torque response, speed error, and phase currents are analyzed. The FLC-based PMSM drive is compared with the conventional PID-based PMSM drive system with better improvements in performance metrics.&lt;/span&gt;

Design and performance evaluation of successive interference cancellation based Slotted Aloha MAC protocol

As a result of densification, the performance of the wireless networks has become highly interference-limited and energy inefficient. To overcome this problem, interference mitigation techniques such as Successive Interference Cancellation (SIC) can be used to decode multiple packets simultaneously at the receiver. In this context, we analyze a SIC-based Slotted Aloha (SIC-SA) Medium Access Control (MAC) protocol for wireless networks. We derive expressions for packets decoding probability and optimal transmission probability of the nodes of the SIC-SA MAC protocol. Our derivation is based on the order statistics of Independent and Identical/non-Identical exponentially distributed received-signal-powers from the nodes under the Rayleigh channel condition. Throughput, delay, and energy efficiency of the SIC-SA MAC protocol have been derived and validated against simulation. The effect of path loss exponent, SINR threshold, and the number of nodes on the performance of SIC-SA have been studied. The performance of SIC-SA in a network of nodes distributed randomly according to the Poisson Point Process has been analyzed. Extension of our analysis to Power Domain Non-Orthogonal Multiple Access (NOMA) has been demonstrated. We also analyzed the impact of imperfect estimation of channel state information and imperfect SIC at the receiver. Results show an improvement in performance metrics of SIC-SA over the traditional Slotted Aloha.

Efficient Hybrid Iterative Method for Signal Detection in Massive MIMO Uplink System over AWGN Channel

Massive multiple input multiple output (massive MIMO) is a key technology in fifth-generation (5G) and beyond fifth-generation (B5G) networks. It improves performance metrics such as gain, energy efficiency, spectral efficiency, and bit error rate (BER). Because of the large number of users and antennas, sophisticated processing is required to detect the transmitted message signal. One of the challenges in massive MIMO systems is transmitted message signal detection. To respond to these challenges, several detection algorithms have been developed, including minimum mean squared error (MMSE), zero forcing (ZF), matched filter (MF), conjugate-gradient (CG), gauss-seidel (GS), and optimized coordinate descent (OCD). Although the ZF and MMSE algorithms perform well, their computational complexity is high due to direct matrix inversion. When the number of users is much lower than the number of antennas, the MF algorithm performs well. However, as the number of users increases, the performance of the MF algorithm degrades. Although the OCD, CG, and GS algorithms have less computational complexity than the MMSE algorithm, they perform poorly in comparison. To address and resolve the shortcomings of existing methods, an efficient iterative algorithm has been proposed in this manuscript, which is a hybrid method possessing the combination of MMSE with the alternating direction method of multipliers (ADMM) technique and Gauss-Seidel method. The initial vector has a large influence on the performance, complexity, and convergence rate of such iterative algorithms. The proposed detector’s initial solution is determined using the diagonal matrix and MMSE with the ADMM technique. The proposed algorithm’s performance and complexity are compared with existing algorithms based on BER and the real number of multiplications, respectively. The numerical results revealed that the proposed algorithm achieves the desired performance with a small number of iterations and a significant reduction in computational complexity. At 8QAM, SNR = 20 dB, 80 × 120 massive MIMO antenna configuration, and n = 2, the percentage performance improvement of the proposed detector from the GS detector is 99.82%. At 32QAM, SNR = 25 dB, 120 × 180 antenna configuration, and n = 5, performance improvement of the proposed detector is 99.89%. At 64QAM, SNR = 28 dB, 80 × 120 antenna configuration, and n = 3, performance improvement of the proposed detector is 99.93%.

Yttrium-based Double Perovskite Nanorods for Electrocatalysis.

Herein, we investigate the effect of the chemical composition of double perovskite nanorods on their versatile electrocatalytic activity not only as supports for the oxidation of small organic molecules but also as catalysts for the oxygen evolution reaction. Specifically, Y2CoMnO6 and Y2NiMnO6 nanorods with average diameters of 300 nm were prepared by a two-step hydrothermal method, in which the individual effects of synthetic parameters, such as the pH, annealing temperature, and precursor ratios on both the composition and morphology, were systematically investigated. When used as supports for Pt nanoparticles, Y2CoMnO6/Pt catalysts exhibited an electrocatalytic activity for the methanol oxidation reaction, which is 2.1 and 1.3 times higher than that measured for commercial Pt/C and Y2NiMnO6/Pt, respectively. Similarly, the Co-based catalyst support material displayed an ethanol oxidation activity, which is 2.3 times higher than both Pt/C and Y2NiMnO6/Pt. This clear enhancement in the activity for Y2CoMnO6 can largely be attributed to strong metal-support interactions, as evidenced by a downshift in the binding energy of the Pt 4f bands, measured by X-ray photoelectron spectroscopy (XPS), which is often correlated not only with a downshift in the d-band center but also to a decreased adsorption of poisoning adsorbates. Moreover, when used as catalysts for the oxygen evolution reaction, Y2CoMnO6 displayed a much greater activity as compared with Y2NiMnO6. This behavior can largely be attributed not only to a preponderance of comparatively more favorable oxidation states and electronic configurations but also to the formation of an active layer on the surface of the Y2CoMnO6 catalyst, which collectively gives rise to improved performance metrics and greater stability as compared with both IrO2 and Y2NiMnO6. Overall, these results highlight the importance of both the chemical composition and the electronic structure of double perovskites, especially when utilized in multifunctional roles as either supports or catalysts.