Microsystems represent an alternative but proficient approach of analysis outside the laboratory, and their use could help in reducing the impact of pre-analytical errors, in particular in challenging newborn samples. The study purpose is to compare the Horiba Microsemi CRP LC-767G system for rapid 3-part complete blood count (CBC) and C-reactive protein (CRP) determination with the laboratory reference systems (respectively Sysmex XN-9100™ and Roche Cobas® c702) in samples of adult patients and newborns hospitalized in the neonatal intensive care unit (NICU) samples. The comparison between the analyzers was performed through Passing-Bablok regression analysis and Bland-Altman plot. One hundred eighty-three blood samples were analyzed. The regression analysis results, performed in the newborn (n = 70) and in adult (n = 113) populations, showed a good agreement between the instruments. The evaluation of the Bland-Altman plots showed comparable values of bias < 10% for most of the parameters, but not for MPV, lymphocyte, and monocyte count.
CONCLUSIONS: The comparison between the Microsemi CRP LC-767G system and the laboratory instrumentations demonstrated comparable results. The Microsemi CRP LC-767G system provides reliable analytical data and faster turnaround time, particularly useful in NICU.
BACKGROUND: • Microsystems for point-of-care testing (POCT) represent an alternative but proficient approach of analysis outside the laboratory, in order to perform a rapid, safe, and exhaustive evaluation for critical patients\' management, acting as a valid support for treatment in acute care.
BACKGROUND: • The Microsemi CRP LC-767G system can represent an alternative but effective testing approach outside the laboratory, particularly in NICU, to reduce the impact of pre-analytical errors on newborn samples.

In order to address issues such as inaccurate education resource positioning and inefficient resource utilization, this study optimizes the Educational Resource Management System (ERMS) by combining image data visualization techniques with convolutional neural networks (CNNs) technology in deep learning. Firstly, the crucial role of ERMS in education and teaching is analyzed. Secondly, the application of image data visualization techniques and CNNs in the system is explained, along with the associated challenges. Finally, by optimizing the CNNs model and system architecture and validating with experimental data, the rationality of the proposed model is confirmed. Experimental results indicate a significant improvement in various performance metrics compared to traditional models. The recognition accuracy on the Mnist dataset reaches 98.1 %, and notably, on the cifar-10 dataset, the optimized model achieves an accuracy close to 98.3 % with improved runtime reduced to only 640.4 s. Additionally, through systematic simulation experiments, the designed system is shown to fully meet the earlier requirements for system functionality, validating the feasibility and rationality of the model and system in this study. Therefore, this study holds high practical value for optimizing ERMS and provides meaningful insights into image data visualization techniques and CNNs optimization.

As traditional fossil fuel energy development faces significant challenges, two-dimensional layered materials have become increasingly popular in various fields and have generated widespread research interest. MXene is an exceptional catalytic material that is typically integrated into functional composite materials with other substances to enhance its catalytic-reaction performance. Improving the thermal stability, electrical conductivity, and electrochemical activity, as well as enhancing the specific surface structure, can make the material an excellent catalyst for photoelectrocatalysis and energy-regeneration reactions. The article mainly outlines the structural characteristics, preparation methods, and applications of MXene in the field of catalysis. This text highlights the latest progress and performance comparison of MXene-based catalytic functional materials in various fields such as electrochemical conversion, photocatalysis, renewable energy, energy storage, and carbon capture and conversion. It also proposes future prospects and discusses the current bottlenecks and challenges in the development of MXene-based catalytic materials.

Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic excitation. The detection of rotor centres is of crucial importance in guiding ablation strategies for the treatment of arrhythmia. The most popular technique for detecting rotor centres is Phase Mapping (PM), which detects phase singularities derived from the phase of a signal. This method has been proven to be prone to errors, especially in regimes of fibrotic tissue and temporal noise. Recently, a novel technique called Directed Graph Mapping (DGM) was developed to detect rotational activity such as rotors by creating a network of excitation. This research aims to compare the performance of advanced PM techniques versus DGM for the detection of rotors using 64 simulated 2D meandering rotors in the presence of various levels of fibrotic tissue and temporal noise. Four strategies were employed to compare the performances of PM and DGM. These included a visual analysis, a comparison of F2-scores and distance distributions, and calculating p-values using the mid-p McNemar test. Results indicate that in the case of low meandering, fibrosis and noise, PM and DGM yield excellent results and are comparable. However, in the case of high meandering, fibrosis and noise, PM is undeniably prone to errors, mainly in the form of an excess of false positives, resulting in low precision. In contrast, DGM is more robust against these factors as F2-scores remain high, yielding F2≥0.931 as opposed to the best PM F2≥0.635 across all 64 simulations.

In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term \"autonomous\" refers to these variants\' ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential.

To support smart cities in terms of waste management and bioenergy recovery, the co-digestion of sewage sludge (SeS) and food waste (FW) was conducted by the anaerobic membrane bioreactor (AnMBR) under mesophilic and thermophilic conditions in this study. The biogas production rate of the thermophilic AnMBR (ThAnMBR) at the SeS to FW ratio of 0:100, 75:25, 50:50 and 100:0 was 2.84 ± 0.21, 2.51 ± 0.26, 1.54 ± 0.26 and 1.31 ± 0.08 L-biogas/L/d, inconspicuous compared with that of the mesophilic AnMBR (MeAnMBR) at 3.00 ± 0.25, 2.46 ± 0.30, 1.63 ± 0.23 and 1.30 ± 0.17 L-biogas/L/d, respectively. The higher hydrolysis ratio and the poorer rejection efficiencies of the membrane under thermophilic conditions, resulting that the permeate COD, carbohydrate and protein of the ThAnMBR was higher than that of the MeAnMBR. The lost COD that might be converted into biogas was discharged with the permeate in the ThAnMBR, which was partly responsible for the inconspicuous methanogenic performance. Furthermore, the results of energy recovery potential assessment showed that the energy return on investment (EROI) of the MeAnMBR was 4.54, 3.81, 2.69 and 2.22 at the four SeS ratios, which was higher than that of the ThAnMBR at 3.29, 2.97, 2.02 and 1.80, respectively, indicating the advantage of the MeAnMBR over the ThAnMBR in energy recovery potential. The outcomes of this study will help to choose a more favorable temperature to co-digest SeS and FW to support the construction of smart cities.

This study aimed to assess and compare the performance of different machine learning models in predicting selected pig growth traits and genomic estimated breeding values (GEBV) using automated machine learning, with the goal of optimizing whole-genome evaluation methods in pig breeding. The research employed genomic information, pedigree matrices, fixed effects, and phenotype data from 9968 pigs across multiple companies to derive four optimal machine learning models: deep learning (DL), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB). Through 10-fold cross-validation, predictions were made for GEBV and phenotypes of pigs reaching weight milestones (100 kg and 115 kg) with adjustments for backfat and days to weight. The findings indicated that machine learning models exhibited higher accuracy in predicting GEBV compared to phenotypic traits. Notably, GBM demonstrated superior GEBV prediction accuracy, with values of 0.683, 0.710, 0.866, and 0.871 for B100, B115, D100, and D115, respectively, slightly outperforming other methods. In phenotype prediction, GBM emerged as the best-performing model for pigs with B100, B115, D100, and D115 traits, achieving prediction accuracies of 0.547, followed by DL at 0.547, and then XGB with accuracies of 0.672 and 0.670. In terms of model training time, RF required the most time, while GBM and DL fell in between, and XGB demonstrated the shortest training time. In summary, machine learning models obtained through automated techniques exhibited higher GEBV prediction accuracy compared to phenotypic traits. GBM emerged as the overall top performer in terms of prediction accuracy and training time efficiency, while XGB demonstrated the ability to train accurate prediction models within a short timeframe. RF, on the other hand, had longer training times and insufficient accuracy, rendering it unsuitable for predicting pig growth traits and GEBV.
为了比较自动机器学习下不同机器学习模型预测部分猪生长性状与全基因组估计育种值(genomic estimated breeding value，GEBV)的性能，并寻找适合的机器学习模型，以优化生猪育种的全基因组评估方法，本研究利用来自多个公司9968头猪的基因组信息、系谱矩阵、固定效应及表型信息通过自动机器学习方法获取深度学习(deep learning，DL)、随机森林(random forest，RF)、梯度提升机(gradient boosting machine，GBM)和极致梯度提升(extreme gradient boosting，XGB)4种机器学习最佳模型。采用10折交叉验证分别对猪达100 kg校正背膘(correcting backfat to 100 kg，B100)、达115 kg校正背膘(correcting backfat to 115 kg，B115)、达100 kg校正日龄(correcting days to 100 kg，D100)、达115 kg校正日龄(correcting days to 100 kg，D115)的GEBV及其表型进行预测，比较不同机器学习模型应用于猪基因组评估的性能。结果表明：机器学习模型对GEBV的估计准确性高于性状表型；在GEBV预测中，GBM在B100、B115、D100、D115的预测准确性分别为0.683、0.710、0.866、0.871，略高于其他方法；在表型预测中，对猪B100、B115、D100、D115预测性能最好的模型依次为GBM(0.547)、DL(0.547)、XGB(0.672、0.670)；在模型训练所需时间上，RF远高于其他3种模型，GBM与DL居中，XGB所需时间最少。综上所述，通过自动机器学习获取的机器学习模型对GEBV预测的准确性高于表型；GBM模型总体上表现出最高的预测准确性与较短训练时间；XGB能够利用最短的时间训练准确性较高的预测模型；RF模型的训练时间远超其他3种模型，且准确性不足，不适用猪生长性状表型与GEBV预测。.

This article presents a comprehensive analysis of spiking neural networks (SNNs) and their mathematical models for simulating the behavior of neurons through the generation of spikes. The study explores various models, including LIF and NLIF, for constructing SNNs and investigates their potential applications in different domains. However, implementation poses several challenges, including identifying the most appropriate model for classification tasks that demand high accuracy and low-performance loss. To address this issue, this research study compares the performance, behavior, and spike generation of multiple SNN models using consistent inputs and neurons. The findings of the study provide valuable insights into the benefits and challenges of SNNs and their models, emphasizing the significance of comparing multiple models to identify the most effective one. Moreover, the study quantifies the number of spiking operations required by each model to process the same inputs and produce equivalent outputs, enabling a thorough assessment of computational efficiency. The findings provide valuable insights into the benefits and limitations of SNNs and their models. The research underscores the significance of comparing different models to make informed decisions in practical applications. Additionally, the results reveal essential variations in biological plausibility and computational efficiency among the models, further emphasizing the importance of selecting the most suitable model for a given task. Overall, this study contributes to a deeper understanding of SNNs and offers practical guidelines for using their potential in real-world scenarios.

Nanozymes are synthetic compounds with enzyme-like tunable catalytic properties. The success of nanozymes for catalytic applications can be attributed to their small dimensions, cost-effective synthesis, appreciable stability, and scalability to molecular dimensions. The emergence of single atom nanozymes (SANzymes) has opened up new possibilities in bioanalytical applications. In this regard, this review outlines enzyme-mimicking features of SANzymes for food safety applications in relation to the key variables controlling their catalytic performance. The discussion is extended further to cover the applications of SANzymes for the monitoring of various compounds/biomaterials of significance with respect to food safety (e.g., pesticides, veterinary drug residues, foodborne pathogenic bacteria, mycotoxins/bacterial endotoxin, antioxidant residues, hydrogen peroxide residues, and heavy metal ions). Furthermore, the performance of SANzymes is evaluated in terms of various performance metrics such as limit of detection (LOD), linear dynamic range, and figure of merit (FoM). The challenges and future road map for the applications of SANzymes are also addressed along with their upscaling in the area of food safety.

Based on the existing model of an irreversible magnetohydrodynamic cycle, this paper uses finite time thermodynamic theory and multi-objective genetic algorithm (NSGA-II), introduces heat exchanger thermal conductance distribution and isentropic temperature ratio of working fluid as optimization variables, and takes power output, efficiency, ecological function, and power density as objective functions to carry out multi-objective optimization with different objective function combinations, and contrast optimization results with three decision-making approaches of LINMAP, TOPSIS, and Shannon Entropy. The results indicate that in the condition of constant gas velocity, deviation indexes are 0.1764 acquired by LINMAP and TOPSIS approaches when four-objective optimization is performed, which is less than that (0.1940) of the Shannon Entropy approach and those (0.3560, 0.7693, 0.2599, 0.1940) for four single-objective optimizations of maximum power output, efficiency, ecological function, and power density, respectively. In the condition of constant Mach number, deviation indexes are 0.1767 acquired by LINMAP and TOPSIS when four-objective optimization is performed, which is less than that (0.1950) of the Shannon Entropy approach and those (0.3600, 0.7630, 0.2637, 0.1949) for four single-objective optimizations, respectively. This indicates that the multi-objective optimization result is preferable to any single-objective optimization result.