Key Metrics Research Articles

Enhancement in the safety and reliability of Pressurized Water reactors using Machine Learning approach

The increasing interest in artificial intelligence and automation within the nuclear industry stems from the hope of elevating the safety and reliability of nuclear power plants by minimizing the impact of human errors. This paper explores the capabilities of machine-learning based fault detection and diagnosis (FDD) models in accurately classifying transient events in a nuclear power plant using the Classification Learners Application from MATLAB. The scope of the research is narrowed to the identification of transient events of the same nature occurring at various locations within the plant. To accomplish this, the Generic Pressurized Water Reactor (GPWR) from the Western Services Cooperation (WSC) was used to simulate 14 transients in the primary system and 10 transients in the secondary system, resulting in two distinct datasets of 108,000 and 79,200 observations. Nine types of classifiers, including Decision Trees, Discriminant Analysis, Support Vector Machines, Logistic Regression, Nearest Neighbors, Naive Bayes, Kernel Approximation, Ensembles, and Neural Networks, with a total of 33 predefined models, were trained and validated. The K-Nearest Neighbors model achieved the highest accuracy of 97% in the primary system, while the Efficient Linear Discriminant and Logistic Regression models achieved the highest average accuracy of 99% in the secondary system. The assessment of the top-performing models was conducted through a comprehensive analysis employing key classification model evaluation metrics, such as the confusion matrix, accuracy, precision, recall, and the F1 score. In an effort to optimize the models, the study integrated feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm. Additionally, the investigation explored different validation schemes, including both holdout and k-fold cross-validation schemes, resulting in a substantial reduction in training time without significantly compromising the overall accuracy of the models. The optimized models in this study demonstrated remarkable prediction accuracies, consistently exceeding 94%. Despite the complexity of the models and the intricacies involved in the training process, the training times ranged from 10 to 1800 s, reflecting the efficiency and robustness of the implemented optimization techniques. The results of this study underscore the potential of machine learning models in identifying transient events in nuclear reactors, showcasing their promising capabilities while maintaining low computational and execution costs. This suggests their efficacy in optimizing safe and efficient operational practices within nuclear facilities.

Retrotrigonal Layer Dissection from a Posterior Approach Enables Bladder Neck Preservation in Robot-Assisted Radical Prostatectomy.

We developed a novel surgical technique: dissection of the retrotrigonal layer from a posterior approach in robot-assisted radical prostatectomy (RARP). This approach enables earlier access to the posterior space during bladder neck dissection and helps preserve the bladder neck. We evaluated the safety and efficacy of this technique in terms of bladder neck preservation Methods: We retrospectively reviewed 238 consecutive patients who underwent RARP using this technique from August 2021 to September 2023. Key metrics included the success rate of accessing the posterior space prior to bladder neck opening during the dissection and the rate of bladder neck preservation. In addition, oncological and surgical safety, as well as continence recovery, were assessed. The median age and prostate-specific antigen level were 72 (65-75) years and 7.35 (5.40-11.5) ng/mL, respectively. In 153 (64%) patients, the posterior space was successfully accessed before bladder neck opening, with the success rate increasing from 56% in the series' first half to 72% in the second half (p = 0.015). The bladder neck was preserved in 120 (53%) patients, and this rate increased from 39% in the first half to 63% in the second half (p = 0.0004). Positive surgical margins at the junction between the prostate and bladder were observed in 10 cases (4%). Bladder neck preservation correlated with early continence recovery (hazard ratio 1.37 [95% confidence interval 1.03-1.83], p = 0.030). The grade 3 complication associated with this technique occurred in one case (0.5%). Retrotrigonal layer dissection from a posterior approach in RARP enhances the safety and ease of bladder neck dissection and aids in its preservation, potentially leading to improved continence recovery.

Nature-inspired optimal tuning of input membership functions of fuzzy inference system for groundwater level prediction

We present a novel regression algorithm that combines a Fuzzy Inference System (FIS) with a nature-inspired algorithm to predict variations in GroundWater Levels (GWLs). Initially, we considered several input features, including precipitation, temperature, evaporation, relative humidity, soil type, and GWL Lag. A feature importance analysis using regression tree ensemble learning reveals GWL lag as the most relevant feature and soil type as the least relevant. We eliminate features with low importance scores (soil type, temperature, and evaporation) to improve the computational efficiency. Our approach leverages Fuzzy C-Means (FCM) clustering to develop a Sugeno-type FIS with definite clusters. The dataset is clustered based on feature similarity, and Gaussian membership functions are assigned to each cluster. The mapping of each point is controlled by the range and standard deviation of the Gaussian membership functions. We train the model keeping 70% of the dataset, iteratively tuning the parameters of the Gaussian membership function for all clusters through the Invasive Weed Optimization (IWO) algorithm. The performance of the model is then evaluated using the remaining 30% of the datasets. The F-IWO-GWL model accurately predicts GWL fluctuations, achieving a high correlation coefficient (R = 0.89), low normalized root mean square error (nRMSE = 0.18), and minimal bias (bias = 0.08). A comparative analysis involving seventeen benchmark algorithms reveals the superior performance of our algorithm. To ensure a fair comparison, we calculated key metrics including Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), and Corrected AIC (AICc). The F-IWO-GWL algorithm exhibits the lowest values for AIC, BIC, and AICc among all tested algorithms, suggesting the best goodness-of-fit. This study provides a robust approach for predicting GWL fluctuation, applicable to various groundwater management scenarios. It offers valuable insights for policymakers and stakeholders involved in groundwater management, aiding informed decision-making.

Impact of survey item wording on reported tobacco use among youth: effect of adding ‘even one or two puffs’ to use questions

ABSTRACT Having ever used tobacco is a key surveillance metric. Existing tobacco use survey items differ in their inclusion of minimal use language, such as ‘even one or two puffs.’ This study aimed to quantify how minimal use language affects tobacco ever use prevalence estimated from adolescent surveys. Participants (N = 5127) in the 2022 Teens, Nicotine, and Tobacco Project online panel survey of California adolescents (ages 12–17) were randomized to one of two differently worded ever use survey items (i.e. with or without minimal use language) for eight different tobacco products independently. For seven of the eight products (except hookah), minimal use language resulted in numerically higher ever use prevalence estimates. Averaged across all products, ever use prevalence was 0.7-percentage points higher when items included minimal use language (95% CI: 0.1, 1.4). Findings suggest that minimal use language yields modestly higher tobacco use prevalence, with implications for comparing and interpretating surveillance data.

Can Credit Ratings be Modelled on the Basis of Financial Metrics? An Evidence from Ind-Ra Ratings of Indian Firms Using Conjoint Analysis Approach

The credit ratings play an important role in the allocation of capital among the enterprises, and thereby, in the growth of any economy. The different credit rating methodologies and criteria are used by different credit rating agencies, and the criteria may also differ from industry to industry. This research study tries to explore if it is possible to model credit ratings as the outcome of financial metrics. The study deploys the conjoint analysis approach to forecast the Ind-Ra ratings given to 50 firms, as a dependent variable and the key financial metrics as independent variables. First, the independent variables are computed based on financial statements. Then, the model is executed. It is found that the debt-equity ratio is negatively rated to credit rating, while the other three variables (profitability, asset turnover ratio and current ratio) are positively related to credit ratings. The study shows that financial metrics are not the only influencer of credit ratings but many subjective criteria such as future expected consumer trends, leadership overview, management aptitude and so on. Therefore, the model proposed in this study using financial statements has a 60% accuracy only because the subjective factors as mentioned above are difficult to be quantified and captured in the model as predictor variables.

Measuring and Managing Healthcare Supply and Demand in Real-Time.

Measuring the supply and demand for access to and wait-times for healthcare is key to managing healthcare services and allocating resources appropriately. Yet, few jurisdictions in distributed, socialized medicine settings have any way to do so. In this paper, we propose the requirements for a jurisdictional patient scheduling system that can measure key metrics, such as supply of and demand for regulated health care professional care, access to and wait times for care, real-time health system utilization and provide the data to compute patient journeys. The system is also capable of tracking new supply of providers and who does not have access to a primary care provider. Benefits, limitations and risks of the model are discussed.

A U-Net Architecture for Inpainting Lightstage Normal Maps

In this paper, we investigate the inpainting of normal maps that were captured from a lightstage. Occlusion of parts of the face during performance capture can be caused by the movement of, e.g., arms, hair, or props. Inpainting is the process of interpolating missing areas of an image with plausible data. We build on previous works about general image inpainting that use generative adversarial networks (GANs). We extend our previous work on normal map inpainting to use a U-Net structured generator network. Our method takes into account the nature of the normal map data and so requires modification of the loss function. We use a cosine loss rather than the more common mean squared error loss when training the generator. Due to the small amount of training data available, even when using synthetic datasets, we require significant augmentation, which also needs to take account of the particular nature of the input data. Image flipping and inplane rotations need to properly flip and rotate the normal vectors. During training, we monitor key performance metrics including the average loss, structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR) of the generator, alongside the average loss and accuracy of the discriminator. Our analysis reveals that the proposed model generates high-quality, realistic inpainted normal maps, demonstrating the potential for application to performance capture. The results of this investigation provide a baseline on which future researchers can build with more advanced networks and comparison with inpainting of the source images used to generate the normal maps.

Indoor Temperature Forecasting in Livestock Buildings: A Data-Driven Approach

The escalating global population and climate change necessitate sustainable livestock production methods to meet rising food demand. Precision Livestock Farming (PLF) integrates information and communication technologies (ICT) to improve farming efficiency and animal health. Unlike traditional methods, PLF uses machine learning (ML) algorithms to analyze data in real time, providing valuable insights to decision makers. Dairy farming in diverse climates is challenging and requires well-designed structures to regulate internal environmental parameters. This study explores the application of the Facebook-developed Prophet algorithm to predict indoor temperatures in a dairy farm over a 72 h horizon. Exogenous variables sourced from the Open-Meteo platform improve the accuracy of the model. The paper details case study construction, data acquisition, preprocessing, and model training, highlighting the importance of seasonality in environmental variables. Model validation using key metrics shows consistent accuracy across different dates, as the mean absolute percentage error on daily base ranges from 1.71% to 2.62%. The results indicate excellent model performance, especially considering the operational context. The study concludes that black box models, such as the Prophet algorithm, are effective for predicting indoor temperatures in livestock buildings and provide valuable insights for environmental control and optimization in livestock production. Future research should explore gray box models that integrate physical building characteristics to improve predictive performance and HVAC system control.

Alarm rationalization and dynamic risk analyses for rare abnormal events

In this paper, improved alarm rationalization strategies are introduced to evaluate the quality of the multivariate alarm systems developed in previous work – with the alarm thresholds and response actions modified appropriately, based on key statistical metrics. For an exothermic CSTR, our strategies resulted in a significant reduction in the number of nuisance alarms, focusing on only quality alarms, which, if ignored, are more likely to result in an abnormal shift in operation to the undesirable regions. Next, their real-time performance is evaluated using dynamic risk analysis (DRAn), in which, the associated risk is analyzed by estimating the failure probabilities of the multivariate alarm systems using Bayesian statistics, based on multiple dynamic simulations for the process, inclusive of control, alarms and response actions. As expected, the failure probability distribution developed for the exothermic CSTR has a much lower variance as compared to one developed using a flat prior distribution.

Seasonal prediction of end-of-dry-season watershed behavior in a highly interconnected alluvial watershed in northern California

Abstract. In undammed watersheds in Mediterranean climates, the timing and abruptness of the transition from the dry season to the wet season have major implications for aquatic ecosystems. Of particular concern in many coastal areas is whether this transition can provide sufficient flows at the right time to allow passage for spawning anadromous fish, which is determined by dry season baseflow rates and the timing of the onset of the rainy season. In (semi-) ephemeral watershed systems, these functional flows also dictate the timing of full reconnection of the stream system. In this study, we propose methods to predict, approximately 5 months in advance, two key hydrologic metrics in the undammed rural Scott River watershed in northern California. The two metrics are intended to characterize (1) the severity of a dry year and (2) the relative timing of the transition from the dry to the wet season. The ability to predict these metrics in advance could support seasonal adaptive management. The first metric is the minimum 30 d dry season baseflow volume, Vmin, which occurs at the end of the dry season (September–October) in this Mediterranean climate. The second metric is the cumulative precipitation, starting 1 September, necessary to bring the watershed to a “full” or “spilling” condition (i.e., initiate the onset of wet season storm- or baseflows) after the end of the dry season, referred to here as Pspill. As potential predictors of these two metrics, we assess maximum snowpack, cumulative precipitation, the timing of the snowpack and precipitation, spring groundwater levels, spring river flows, reference evapotranspiration, and a subset of these metrics from the previous water year. Though many of these predictors are correlated with the two metrics of interest, we find that the best prediction for both metrics is a linear combination of the maximum snowpack water content and total October–April precipitation. These two linear models could reproduce historical values of Vmin and Pspill with an average model error (RMSE) of 1.4 Mm3 per 30 d (19.4 cfs) and 25.4 mm (1 in.), corresponding to 49 % and 37 % of mean observed values, respectively. Although these predictive indices could be used by governance entities to support local water management, careful consideration of baseline conditions used as a basis for prediction is necessary.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Hybrid PSO with tree-based models for predicting uniaxial compressive strength and elastic modulus of rock samples

The mechanical characteristics of rocks, specifically uniaxial compressive strength (UCS) and elastic modulus (E), serve as crucial factors in ensuring the integrity and stability of relevant projects in mining and civil engineering. This study proposes a novel hybrid PSO (particle swarm optimization) with tree-based models, such as gradient boosting regressor (GBR), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) for predicting UCS and E of rock samples from Block IX of the Thar Coalfield in Pakistan. A total of 122 datasets were divided into training and testing sets, with an 80:20 ratio, respectively, to develop the predictive models. Key performance metrics, including the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were employed to assess the model’s predictive performance. The results indicate that the PSO-XGBoost model demonstrated the highest accuracy in predicting UCS and E, outperforming the other models, which exhibited inferior predictive performance. Furthermore, this study utilized the SHAP (Shapley Additive exPlanations) machine learning method to enhance our understanding of how each input feature variable influences the output values of UCS and E. In conclusion, the proposed framework offers significant advantages in evaluating the strength and deformation of rocks at Thar Coalfield, with promising applications in the field of mining and rock engineering.

Advancing Global Health Surveillance of Mycotoxin Exposures using Minimally Invasive Sampling Techniques: A State-of-the-Science Review.

Mycotoxins are a heterogeneous group of toxins produced by fungi that can grow in staple crops (e.g., maize, cereals), resulting in health risks due to widespread exposure from human consumption and inhalation. Dried blood spot (DBS), dried serum spot (DSS), and volumetric tip microsampling (VTS) assays were developed and validated for several important mycotoxins. This review summarizes studies that have developed these assays to monitor mycotoxin exposures in human biological samples and highlights future directions to facilitate minimally invasive sampling techniques as global public health tools. A systematic search of PubMed (MEDLINE), Embase (Elsevier), and CINAHL (EBSCO) was conducted. Key assay performance metrics were extracted to provide a critical review of the available methods. This search identified 11 published reports related to measuring mycotoxins (ochratoxins, aflatoxins, and fumonisins) using DBS/DSS and VTS assays. Multimycotoxin assays adapted for DBS/DSS and VTS have undergone sufficient laboratory validation for applications in large-scale population health and human biomonitoring studies. Future work should expand the number of mycotoxins that can be measured in multimycotoxin assays, continue to improve multimycotoxin assay sensitivities of several biomarkers with low detection rates, and validate multimycotoxin assays across diverse populations with varying exposure levels. Validated low-cost and ultrasensitive minimally invasive sampling methods should be deployed in human biomonitoring and public health surveillance studies to guide policy interventions to reduce inequities in global mycotoxin exposures.

A Comparative Study of Vehicle Velocity Prediction for Hybrid Electric Vehicles Based on a Neural Network

Vehicle velocity prediction (VVP) plays a pivotal role in determining the power demand of hybrid electric vehicles, which is crucial for establishing effective energy management strategies and, subsequently, improving the fuel economy. Neural networks (NNs) have emerged as a powerful tool for VVP, due to their robustness and non-linear mapping capabilities. This paper describes a comprehensive exploration of NN-based VVP methods employing both qualitative theory analysis and quantitative numerical simulations. The used methodology involved the extraction of key feature parameters for model inputs through the utilization of Pearson correlation coefficients and the random forest (RF) method. Subsequently, three distinct NN-based VVP models were constructed comprising the following: a backpropagation neural network (BPNN) model, a long short-term memory (LSTM) model, and a generative pre-training (GPT) model. Simulation experiments were conducted to investigate various factors, such as the feature parameters, sliding window length, and prediction horizon, and the prediction accuracy and computation time were identified as key performance metrics for VVP. Finally, the relationship between the model inputs and velocity prediction performance was revealed through various comparative analyses. This study not only facilitated the identification of an optimal NN model configuration to balance prediction accuracy and computation time, but also serves as a foundational step toward enhancing the energy efficiency of hybrid electric vehicles.

Review of heterozygosity visualization approaches in the context of conservation research

The assessment of heterozygosity level is one of the key metrics in conservation biology, as it contributes to the accurate design of conservation programs for endangered species. With the development of whole-genome sequencing technologies, it is now possible to more accurately estimate heterozygosity not only at the organismal level, but also at the population and species level. Contemporary conservation studies involve the processing of large volumes of whole-genome data, leading to problems of interpretation and necessitates the study of modern visualization methods for clear and correct presentation of results. In this review, we comprehensively examine the main types of visualization of heterozygosity assessments obtained using various approaches. We delve into the theory underlying each visualization method and discuss their characteristics using examples from studies of non-model species with different conservation statuses. The review provides insight into current tools for heterozygosity assessment and subsequent visualization, as well as current trends in this field.

Nonreciprocity in cavity magnonics at millikelvin temperature

Incorporating cavity magnonics has opened up a new avenue in controlling non-reciprocity. This work examines a yttrium iron garnet sphere coupled to a planar microwave cavity at millikelvin temperature. Non-reciprocal device behavior results from the cooperation of coherent and dissipative coupling between the Kittel mode and a microwave cavity mode. The device’s bi-directional transmission was measured and compared to the theory derived previously in the room temperature experiment. Investigations are also conducted into key performance metrics such as isolation, bandwidth, and insertion loss. The findings point to the coexistence of coherent and dissipative interactions at cryogenic conditions, and one can leverage their cooperation to achieve directional isolation. This work foreshadows the application of a cavity magnonic isolator for on-chip readout and signal processing in superconducting circuitry.

Comparative analysis of machine learning algorithms for predicting Dubai property prices

IntroductionPredicting property prices is a crucial task in the real estate market, and machine learning algorithms offer valuable tools for accurate predictions. In this study, we introduce a comprehensive comparison of eight well-known machine learning algorithms, namely, ensemble empirical mode decomposition (EEMD)–stochastic (S) + deterministic (D)–support vector machine (EEMD-SD-SVM), support vector machine (SVM), gradient boosting, random forest, K-nearest neighbors (KNN), linear regression, artificial neural networks (ANN), and decision trees. The focus is on predicting property prices in Dubai, with the primary objective of assessing the predictive performance of these algorithms within this specific market context.MethodsThe evaluation is based on four key performance metrics: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into prediction errors, accuracy in percentage terms, and the proportion of variance in property prices explained by independent variables. The study compares the strengths and limitations of each algorithm for predicting property prices in Dubai, highlighting scenarios where certain algorithms excel based on the nature of decision boundaries, handling complex data, capturing localized patterns, and offering interpretability.ResultsFindings from the comparative analysis shed light on the performance of each algorithm in predicting property prices in Dubai. EEMD-SD-SVM and SVM excel in scenarios requiring precise decision boundaries, while gradient boosting and random forests demonstrate robust performance with complex and noisy property price data. KNN captures localized patterns effectively, linear regression is suitable for straightforward regression tasks, ANN excels with extensive datasets, and decision trees offer interpretability in understanding factors influencing property prices.DiscussionThe study emphasizes the significance of model tuning, feature selection, and data pre-processing to enhance predictive power. Additionally, practical aspects such as computational efficiency, model interpretability, and scalability in real-world applications are discussed. The comparative analysis provides valuable guidance for stakeholders, including real estate professionals, data scientists, and stakeholders interested in selecting the most suitable machine learning algorithm for predicting property prices in Dubai, with a focus on the essential evaluation metrics of MSE, RMSE, MAPE, and R2. This study offers insights into the applicability and performance of different machine learning algorithms for predicting property prices in Dubai. Stakeholders such as real estate agents, buyers, sellers, or investors can leverage these insights to make informed decisions in the Dubai real estate market.

Meta-lasso: new insight on infection prediction after minimally invasive surgery.

Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients. In the realm of predictive healthcare, machine learning algorithms have been instrumental in anticipating various surgical outcomes, including SSI. However, accurately predicting infection after minimally invasive surgery remains a clinical challenge due to the multitude of physiological and surgical factors associated with it. Furthermore, clinical patient data, in addition to being high-dimensional, often exists the long-tail problem, posing difficulties for traditional machine learning algorithms in effectively processing such data. Based on this insight, we propose a novel approach called meta-lasso for infection prediction following minimally invasive surgery. Our approach leverages the sparse learning algorithm lasso regression to select informative features and introduces a meta-learning framework to mitigate bias towards the dominant class. We conducted a retrospective cohort study on patients who had undergone minimally invasive surgery for lung cancer at Shanghai Chest Hospital between 2018 and 2020. The evaluation encompassed key performance metrics, including sensitivity, specificity, precision (PPV), negative predictive value (NPV), and accuracy. Our approach has surpassed the performance of logistic regression, random forest, Naive Bayes classifier, gradient boosting decision tree, ANN, and lasso regression, with sensitivity at 0.798, specificity at 0.779, precision at 0.789, NPV at 0.798, and accuracy at 0.788 and has greatly improved the classification performance of the inferior class.

Enhancing efficiency in photo chemical machining: a multivariate decision-making approach

Non-Traditional Machining (NTM) outperforms traditional processes by offering superior geometric and dimensional accuracy, along with a better surface finish. Photo Chemical Machining (PCM) represents one such NTM process, using chemical etching for material removal. PCM finds substantial application in the creation of microchannels in pharmaceutical, chemical and energy industries. Several input parameters—such as etchant concentration, etching time and etchant temperature—profoundly influence the machining’s quality and efficiency. Therefore, the optimization of these parameters is crucial. This study presents a comparative analysis of five Multiple Criteria Decision Making (MCDM) techniques—Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multi-Objective Optimization on the basis of Ratio Analysis (MOORA), Additive Ratio Assessment (ARAS), Weighted aggregated sum product assessment method (WASPAS) and Multi-Attributive Border Approximation Area Comparison Method (MABAC)—for the optimization of the PCM process. Key performance metrics considered are Material Removal Rate (MRR), Surface Roughness (SR), Undercut (Uc) and etch factor (EF). The weights of these criteria were calculated using the Criterion-Induced Aggregation Technique (CRITIC) and was compared with other popular methods like MEREC, Entropy and equal weights. MRR and EF are seen as beneficial criteria, while SR and Uc are perceived as cost criteria. Optimum process parameters were identified as 850 g/L etchant concentration, 40 min etching time and 70°C etchant temperature. Two of the three employed MCDM techniques agreed on these optimal parameters, reinforcing the findings. Furthermore, a strong correlation was observed amongst the employed MCDM techniques, further validating the results.

LiFi-enabled UAV swarm networks.

Unmanned aerial vehicle (UAV) swarm communication is a powerful component of aerial relays; however, conventional radio frequency (RF)-based UAV swarm networks struggle to ensure timely and reliable communication. To this end, a light spectrum-based wireless system, LiFi, is presented to supplement in this work thanks to its distinctive benefits. We present the analytical derivation of the average block error probability (ABEP) as Chebyshev approximation, lower and upper bounds. Then, the key performance metrics of reliability, throughput, and latency expressions are provided as a function of the ABEP. The results show that the severe requirements of ultra-reliable (99.99%) and low-latency (sub-millisecond) communication (URLLC) are satisfied at even low signal-to-noise ratio (SNR) values. Additionally, in the numerical results, the impact of the blocklength, packet size, different distances among UAVs, SNR value, and light-emitting diode (LED) semi-angle are explored.