SUMOylation of yeast Pso2 enhances its translocation and accumulation in the mitochondria and suppresses methyl methanesulfonate-induced mitochondrial DNA damage.

Saccharomyces cerevisiae Pso2/SNM1 is essential for DNA interstrand crosslink (ICL) repair; however, its mechanism of action remains incompletely understood. While recent work has revealed that Pso2/Snm1 is dual-localized in the nucleus and mitochondria, it remains unclear whether cell-intrinsic and -extrinsic factors regulate its subcellular localization and function. Herein, we show that Pso2 undergoes ubiquitination and phosphorylation, but not SUMOylation, in unstressed cells. Unexpectedly, we found that methyl methanesulfonate (MMS), rather than ICL-forming agents, induced robust SUMOylation of Pso2 on two conserved residues, K97 and K575, and that SUMOylation markedly increased its abundance in the mitochondria. Reciprocally, SUMOylation had no discernible impact on Pso2 translocation to the nucleus, despite the presence of steady-state levels of SUMOylated Pso2 across the cell cycle. Furthermore, substitution of the invariant residues K97 and K575 by arginine in the Pso2 SUMO consensus motifs severely impaired SUMOylation and abolished its translocation to the mitochondria of MMS-treated wild type cells, but not in unstressed cells. We demonstrate that whilst Siz1 and Siz2 SUMO E3 ligases catalyze Pso2 SUMOylation, the former plays a dominant role. Notably, we found that the phenotypic characteristics of the SUMOylation-defective mutant Pso2K97R/K575R closely mirrored those observed in the Pso2Δ petite mutant. Additionally, leveraging next-generation sequencing analysis, we demonstrate that Pso2 mitigates MMS-induced damage to mitochondrial DNA (mtDNA). Viewed together, our work offers previously unknown insights into the link between genotoxic stress-induced SUMOylation of Pso2 and its preferential targeting to the mitochondria, as well as its role in attenuating MMS-induced mtDNA damage.

Optimizing inset-fed rectangular micro strip patch antenna by improved particle swarm optimization and simulated annealing.

The recent wireless communication systems require high gain, lightweight, low profile, and simple antenna structures to ensure high efficiency and reliability. The existing microstrip patch antenna (MPA) design approaches attain low gain and high return loss. To solve this issue, the geometric dimensions of the antenna should be optimized. The improved Particle Swarm Optimization (PSO) algorithm which is the combination of PSO and simulated annealing (SA) approach (PSO-SA) is employed in this paper to optimize the width and length of the inset-fed rectangular microstrip patch antennas for Ku-band and C-band applications. The inputs to the proposed algorithm such as substrate height, dielectric constant, and resonant frequency and outputs are optimized for width and height. The return loss and gain of the antenna are considered for the fitness function. To calculate the fitness value, the Feedforward Neural Network (FNN) is employed in the PSO-SA approach. The design and optimization of the proposed MPA are implemented in MATLAB software. The performance of the optimally designed antenna with the proposed approach is evaluated in terms of the radiation pattern, return loss, Voltage Standing Wave Ratio (VSWR), gain, computation time, directivity, and convergence speed.

Evaluating the influence of Nano-GO concrete pavement mechanical properties on road performance and traffic safety using ANN-GA and PSO techniques.

The burgeoning demand for durable and eco-friendly road infrastructure necessitates the exploration of innovative materials and methodologies. This study investigates the potential of Graphene Oxide (GO), a nano-material known for its exceptional dispersibility and mechanical reinforcement capabilities, to enhance the sustainability and durability of concrete pavements. Leveraging the synergy between advanced artificial intelligence techniques-Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO)-it is aimed to delve into the intricate effects of Nano-GO on concrete's mechanical properties. The empirical analysis, underpinned by a comparative evaluation of ANN-GA and ANN-PSO models, reveals that the ANN-GA model excels with a minimal forecast error of 2.73%, underscoring its efficacy in capturing the nuanced interactions between GO and cementitious materials. An optimal concentration is identified through meticulous experimentation across varied Nano-GO dosages that amplify concrete's compressive, flexural, and tensile strengths without compromising workability. This optimal dosage enhances the initial strength significantly, and positions GO as a cornerstone for next-generation premium-grade pavement concretes. The findings advocate for the further exploration and eventual integration of GO in road construction projects, aiming to bolster ecological sustainability and propel the adoption of a circular economy in infrastructure development.

Signal Denoising of Traffic Speed Deflectometer Measurement Based on Partial Swarm Optimization-Variational Mode Decomposition Method.

To accurately identify the deflection data collected by a traffic speed deflectometer (TSD) and eliminate the noise in the measured signals, a TSD signal denoising method based on the partial swarm optimization-variational mode decomposition (PSO-VMD) method is proposed. Initially, the VMD algorithm is used for modal decomposition, calculating the correlation coefficients between each decomposed mode and the original signal for modal selection and signal reconstruction; Then, the particle swarm optimization algorithm is utilized to optimize the number of modes K and the value α for the VMD algorithm, adopting fuzzy entropy as the affinity function to circumvent effects from sequence decomposition and forecasting accuracy, thus identifying the optimal combination of hyperparameters. Finally, the analysis on simulated signals indicates that the PSO-VMD method secures the best parameters, showing a clear advantage in denoising. Denoising real TSD data validates that the approach proposed herein achieves commendable outcomes in TSD deflection noise reduction, offering a feasible strategy for TSD signal denoising.

Efficiency Improvement for Chipless RFID Tag Design Using Frequency Placement and Taguchi-Based Initialized PSO.

Frequency encoding chipless Radio Frequency Identification (RFID) tags have been frequently using the radar cross section (RCS) parameter to determine the resonant frequencies corresponding to the encoded information. Recent advancements in chipless RFID design have focused on the generation of multiple frequencies without considering the frequency position and signal amplitude. This article proposes a novel method for chipless RFID tag design, in which the RCS response can be located at an exact position, corresponding to the desired encoding signal spectrum. To achieve this, the empirical Taguchi method (TM), in combination with particle swarm optimization (PSO), is used to automatically search for optimal design parameters for chipless RFID tags with a fast response time, to comply with the frequency encoding requirements in the presence of the mutual coupling effect. The proposed design method is validated using I-slotted chipless tag structures that are fabricated and measured with different sets of resonant frequencies.

Study on the Determination of Flavor Value of Rice Based on Grid Iterative Search Swarm Optimization Support Vector Machine Model and Hyperspectral Imaging.

In the field of rice processing and cultivation, it is crucial to adopt efficient, rapid and user-friendly techniques to detect the flavor values of various rice varieties. The conventional methods for flavor value assessment mainly rely on chemical analysis and technical evaluation, which not only deplete the rice resources but also incur significant time and labor costs. In this study, hyperspectral imaging technology was utilized in combination with an improved Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm, i.e., the Grid Iterative Search Particle Swarm Optimization Support Vector Machine (GISPSO-SVM) algorithm, introducing a new non-destructive technique to determine the flavor value of rice. The method captures the hyperspectral feature data of different rice varieties through image acquisition, preprocessing and feature extraction, and then uses these features to train a model using an optimized machine learning algorithm. The results show that the introduction of GIS algorithms in a PSO-optimized SVM is very effective and can improve the parameter finding ability. In terms of flavor value prediction accuracy, the Principal Component Analysis (PCA) combined with the GISPSO-SVM algorithm achieved 96% accuracy, which was higher than the 93% of the Competitive Adaptive Weighted Sampling (CARS) algorithm. And the introduction of the GIS algorithm in different feature selection can improve the accuracy to different degrees. This novel approach helps to evaluate the flavor values of new rice varieties non-destructively and provides a new perspective for future rice flavor value detection methods.

Research on a Fault Diagnosis Method for Crankshafts Based on Improved Multi-Scale Permutation Entropy.

As the crucial part of a transmission assembly, the monitoring of the status of the crankshaft is essential for the normal working of a reciprocating machinery system. In consideration of the interaction between crankshaft system components, the fault vibration feature is typically non-stationary and nonlinear, and the single-scale feature extraction method cannot adequately assess the fault features, therefore a novel impact feature extraction method based on genetic algorithms to optimize multi-scale permutation entropy is proposed. Compared with other traditional feature extraction methods, the proposed method illustrates good robustness and high adaptability in the signal processing of crankshaft vibrations. Firstly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is developed on the signal to obtain several intrinsic mode function (IMF) components, and the IMF components with a large kurtosis are selected for array reorganization. Then, the parameters of multi-scale permutation entropy (MPE) are optimized based on genetic algorithm (GA), the multi-scale permutation entropy is calculated and the feature vector set is constructed. The feature vector set is input into the support vector machine (SVM) and optimized by a particle swarm optimization (PSO) model for training and final pattern recognition, where the Variational Mode Decomposition(VMD)-GA-MPE with a PSO-SVM recognition model and the ICEEMDAN-MPE with PSO-SVM recognition model without GA optimization are constructed for a comparison with the proposed method. The research result illustrates that the proposed method, which inputs the genetic algorithm optimized multi-scale permutation entropy extracted from the ICEEMDAN decomposition into the PSO-SVM, performs well in impact feature extraction and the pattern recognition of crankshaft vibrations.

Optimizing Internet of Things Fog Computing: Through Lyapunov-Based Long Short-Term Memory Particle Swarm Optimization Algorithm for Energy Consumption Optimization.

In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution-a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption.

Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography.

During robot-assisted rehabilitation, failure to recognize lower limb movement may efficiently limit the development of exoskeleton robots, especially for individuals with knee pathology. A major challenge encountered with surface electromyography (sEMG) signals generated by lower limb movements is variability between subjects, such as motion patterns and muscle structure. To this end, this paper proposes an sEMG-based lower limb motion recognition using an improved support vector machine (SVM). Firstly, non-negative matrix factorization (NMF) is leveraged to analyze muscle synergy for multi-channel sEMG signals. Secondly, the multi-nonlinear sEMG features are extracted, which reflect the complexity of muscle status change during various lower limb movements. The Fisher discriminant function method is utilized to perform feature selection and reduce feature dimension. Then, a hybrid genetic algorithm-particle swarm optimization (GA-PSO) method is leveraged to determine the best parameters for SVM. Finally, the experiments are carried out to distinguish 11 healthy and 11 knee pathological subjects by performing three different lower limb movements. Results demonstrate the effectiveness and feasibility of the proposed approach in three different lower limb movements with an average accuracy of 96.03% in healthy subjects and 93.65% in knee pathological subjects, respectively.

Exploration of MPSO-Two-Stage Classification Optimization Model for Scene Images with Low Quality and Complex Semantics.

Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%.

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment.

Autonomous sleep tracking at home has become inevitable in today's fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO-XGBoost, combining particle swarm optimisation (PSO) with extreme gradient boosting (XGBoost) to enhance the XGBoost model's performance. Our model achieves improved overall accuracy and faster convergence by leveraging PSO to fine-tune hyperparameters. Our proposed model utilises features extracted from EEG signals, spanning time, frequency, and time-frequency domains. We employed the Pz-oz signal dataset from the sleep-EDF expanded repository for experimentation. Our model achieves impressive metrics through stratified-K-fold validation on ten selected subjects: 95.4% accuracy, 95.4% F1-score, 95.4% precision, and 94.3% recall. The experiment results demonstrate the effectiveness of our technique, showcasing an average accuracy of 95%, outperforming traditional machine learning classifications. The findings revealed that the feature-shifting approach supplements the classification outcome by 3 to 4 per cent. Moreover, our findings suggest that prefrontal EEG derivations are ideal options and could open up exciting possibilities for using wearable EEG devices in sleep monitoring. The ease of obtaining EEG signals with dry electrodes on the forehead enhances the feasibility of this application. Furthermore, the proposed method demonstrates computational efficiency and holds significant value for real-time sleep classification applications.

I s pelvic support osteotomy (PSO) suitable for ordinary high-riding hip dysplasia?

PURPOSE: Pelvic support osteotomy (PSO) is regarded to provide pelvic stability and improve abductor function to delay or even avoid total hip arthroplasty (THA) in young patients with high-riding hip dysplasia. However, some of these patients eventually have to undergo THA. Because of the double-angulation deformity of the femur after PSO, subsequent THA is challenging. This study aimed to analyze whether PSO surgery is suitable for high-riding hip dysplasia and summarize orthopaedic strategy during THA for patients with previous PSO. METHODS: This case-control study included eight cases of THA for high-riding hip dysplasia patients with previous PSO (study group) and 24 cases of high-riding hip dysplasia patients without any hip surgical therapy (control group) by a 1:3 match (from May 2018 to January 2022). We compared demographics and joint function before and after THA between two groups and recorded all patients' preoperative imaging data, surgical procedures, postoperative imaging data, and complications. The surgical techniques for patients with previous PSO were highlighted. RESULTS: There was no statistical difference between the two groups in demographic (p > 0.05). The study group had worse hip Harris score (HHS), range of motion (ROM), visual analogue scale (VAS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) (p < 0.05) compared with the control group before THA. All patients had concurrent THA and osteotomy at the proximal femur, but the study group experienced longer operation time (p = 0.047) with more blood loss (p = 0.027) and higher complication rate compared with the control group (p = 0.009). At the last follow-up, the study group's HHS, ROM, VAS, and WOMAC were still worse than those in the control group. CONCLUSIONS: PSO did not improve the joint function of high-riding hip dysplasia patients but brought challenges to subsequent THA and affected the surgical outcomes. In short, we suggested that PSO is unsuitable for routine high-riding hip dysplasia patients.

Intelligent algorithms-aided modeling and optimization of the deturbidization of abattoir wastewater by electrocoagulation using aluminium electrodes.

The removal of turbidity from abattoir wastewater (AWW) by electrocoagulation (EC) was modeled and optimized using Artificial Intelligence (AI) algorithms. Artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), particle swarm optimization (PSO), and genetic algorithms (GA) were the AI tools employed. Five input variables were considered: pH, current intensity, electrolysis time, settling time, and temperature. The ANN model was evaluated using the Levenberg-Marquardt (trainlm) algorithm, while the ANFIS modeling was accomplished using the Sugeno-type FIS. The ANN and ANFIS models demonstrated linear adequacy with the experimental data, with an R2 value of 0.9993 in both cases. The corresponding statistical error indices were RMSE (ANN = 5.65685E-05; ANFIS = 2.82843E-05), SSE (ANN = 1.60E-07; ANFIS = 3.4E-08), and MSE (ANN = 3.2E-09; ANFIS = 8E-10). The error indices revealed that the ANFIS model had the least performance error and is considered the most reliable of the two. The process optimization performed with GA and PSO considered turbidity removal efficiency, energy requirement, and electrode material loss. An optimal turbidity removal efficiency of 99.39 % was predicted at pH (3.1), current intensity (2 A), electrolysis time (20 min), settling time (50 min), and operating temperature (50 °C). This represents a potential for the delivery of cleaner water without the use of chemicals. The estimated power consumption and the theoretical mass of the aluminium electrode dissolved at the optimum condition were 293.33 kW h/m3 and 0.2237 g, respectively. The work successfully affirmed the effectiveness of the EC process in the removal of finely divided suspended particles from AWW and demonstrated the suitability of the AI algorithms in the modeling and optimization of the process.

RBFNN-PSO Intelligent Synchronisation Method for Sprott B Chaotic Systems with External Noise and Its Application in an Image Encryption System.

In the past two decades, research in the field of chaotic synchronization has attracted extensive attention from scholars, and at the same time, more synchronization methods, such as chaotic master-slave synchronization, projection synchronization, sliding film synchronization, fractional-order synchronization and so on, have been proposed and applied to chaotic secure communication. In this paper, based on radial basis function neural network theory and the particle swarm optimisation algorithm, the RBFNN-PSO synchronisation method is proposed for the Sprott B chaotic system with external noise. The RBFNN controller is constructed, and its parameters are used as the particle swarm particle optimisation parameters, and the optimal values of the controller parameters are obtained by the PSO training method, which overcomes the influence of external noise and achieves the synchronisation of the master-slave system. Then, it is shown by numerical simulation and analysis that the scheme has a good performance against external noise. Because the Sprott B system has multiple chaotic attractors with richer dynamics, the synchronization system based on Sprott B chaos is applied to the image encryption system. In particular, the Zigzag disambiguation method for top corner rotation and RGB channel selection is proposed, and the master-slave chaotic system synchronisation sequences are diffused to the disambiguated data streams, respectively. Therefore, the encryption and decryption of image transmission are implemented and the numerical simulation results are given, the random distribution characteristics of encrypted images are analysed using histogram and Shannon entropy methods, and the final results achieve the expected results.

Optimization of laccase enzyme extraction from spent mushroom waste of Pleurotus florida through ANN-PSO modeling: An ecofriendly and economical approach.

The cardinal focus of this study is to optimize the best reaction conditions for maximizing laccase activity from spent mushroom waste (SMW) of Pleurotus florida. Optimization process parameters were studied by the modeling techniques, artificial neural networking (ANN) embedded in particle swarm optimization (PSO), and response surface model (RSM). The best topology of ANN-PSO architecture was obtained on 4-10-1. The R2, IOA, MSE, and MAE values of the ANN model were obtained as 0.98785, 0.9939, 0.0023, and 0.0251 while, that of the RSM model were obtained as 0.74290, 0.9210, 0.0244, and 0.1110 respectively. The higher values of R2, IOA, and lower values of MSE and MAE of the ANN-PSO model depict that ANN-PSO outperformed compared to RSM and also verified the effectiveness of the ANN-PSO model. The ANN-PSO model performance demonstrates the robustness of the technique in optimizing laccase activity in SMW of P. florida. The optimization results revealed that pH 4.5, time 3 h, solid: solution ratio 1:5, and ABTS concentration of 1 mM was optimal for achieving maximum laccase activity at temperature 30 °C. The enzymatic activity of crude laccase enzyme was obtained as 1.185 U ml-1 without loss of enzyme activity. Additionally, crude laccase enzyme was 1.74 fold partially purified, and 83.54% of the enzyme was yielded. Out of all the independent process variables, ABTS and pH had an influence on laccase activity. Therefore, we anticipate that the findings of this investigation will reduce the ambiguity in maximizing laccase activity and ease the screening process. This study also highlights the comparative cost evaluation of crude laccase enzyme extracted from P. florida and commercial enzymes. There is a great potential for the utilization of the laccase enzyme extracted from SMW and using it for the degradation of recalcitrant micropollutants. Thus, SMW promises a cost-effective and sustainable approach leading towards circular economy.

KPCA-WRF-prediction of heart rate using deep feature fusion and machine learning classification with tuned weighted hyper-parameter.

The rapid advancement of technology such as stream processing technologies, deep-learning approaches, and artificial intelligence plays a prominent and vital role, to detect heart rate using a prediction model. However, the existing methods could not handle high -dimensional datasets, and deep feature learning to improvise the performance. Therefore, this work proposed a real-time heart rate prediction model, using K-nearest neighbour (KNN) adhered to the principle component analysis algorithm (PCA) and weighted random forest algorithm for feature fusion (KPCA-WRF) approach and deep CNN feature learning framework. The feature selection, from the fused features, was optimized by ant colony optimization (ACO) and particle swarm optimization (PSO) algorithm to enhance the selected fused features from deep CNN. The optimized features were reduced to low dimensions using the PCA algorithm. The significant straight heart rate features are plotted by capturing out nearest similar data point values using the algorithm. The fused features were then classified for aiding the training process. The weighted values are assigned to those tuned hyper parameters (feature matrix forms). The optimal path and continuity of the weighted feature representations are moved using the random forest algorithm, in K-fold validation iterations.

Investigating small molecule compounds targeting psoriasis based on cMAP database and molecular dynamics simulation.

OBJECTIVE: Psoriasis (PSO) is a chronic inflammatory skin disease that severely affects the physical and mental health of patients. Drug resistance has been developed upon current drug treatments, and there is no specific therapy. The aim of this study was to screen promising novel drug candidates for PSO using molecular dynamics (MD) simulations. METHODS: The data of PSO were downloaded from gene expression omnibus (GEO) database and subjected to variance analysis. Target proteins and small molecule compounds targeting PSO were predicted in the connective map (cMAP) database. Molecular docking, MD simulation, and trajectory analysis were conducted to predict the binding of target proteins to compounds. RESULTS: 1999 differentially expressed genes in PSO were obtained by differential analysis. Through cMAP database prediction, a low Score value of -45.69 for lymphocyte cell-specific protein-tyrosine kinase (LCK) was revealed, and aminogenistein was identified as the compound targeting LCK, and LCK was notably highly expressed in the PSO samples. The drugScore of the binding pocket P_0 was 0.814656, which was docked with aminogenistein. The results showed that there were more than one binding site between LCK and aminogenistein with binding energy less than -7.0 kJ/mol, and the docking was relatively stable. The results of root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), Gyrate, number of hydrogen bonds and total free binding energy in MD simulations showed that the binding of aminogenistein to LCK was relatively solid. CONCLUSION: Aminogenistein has good protein-ligand interaction and stability with LCK, a target of PSO, and is a novel drug candidate for PSO.

Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China.

BACKGROUND: This study aimed to construct a more accurate model to forecast the incidence of hand, foot, and mouth disease (HFMD) in mainland China from January 2008 to December 2019 and to provide a reference for the surveillance and early warning of HFMD. METHODS: We collected data on the incidence of HFMD in mainland China between January 2008 and December 2019. The SARIMA, SARIMA-BPNN, and SARIMA-PSO-BPNN hybrid models were used to predict the incidence of HFMD. The prediction performance was compared using the mean absolute error(MAE), mean squared error(MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation analysis. RESULTS: The incidence of HFMD in mainland China from January 2008 to December 2019 showed fluctuating downward trends with clear seasonality and periodicity. The optimal SARIMA model was SARIMA(1,0,1)(2,1,2)[12], with Akaike information criterion (AIC) and Bayesian Schwarz information criterion (BIC) values of this model were 638.72, 661.02, respectively. The optimal SARIMA-BPNN hybrid model was a 3-layer BPNN neural network with nodes of 1, 10, and 1 in the input, hidden, and output layers, and the R-squared, MAE, and RMSE values were 0.78, 3.30, and 4.15, respectively. For the optimal SARIMA-PSO-BPNN hybrid model, the number of particles is 10, the acceleration coefficients c1 and c2 are both 1, the inertia weight is 1, the probability of change is 0.95, and the values of R-squared, MAE, and RMSE are 0.86, 2.89, and 3.57, respectively. CONCLUSIONS: Compared with the SARIMA and SARIMA-BPNN hybrid models, the SARIMA-PSO-BPNN model can effectively forecast the change in observed HFMD incidence, which can serve as a reference for the prevention and control of HFMD.

Health-related quality of life 2 years after pedicle subtraction osteotomy for sagittal imbalance: a single-center experience of 65 patients.

PURPOSE: Pedicle subtraction osteotomy (PSO) as an invasive procedure with high reoperation and complication rates in an often elderly population has often been questioned. The purpose of our study was to evaluate the impact of PSO for sagittal imbalance (SI) on patient-reported outcomes including self-reported satisfaction and health-related quality of life 2 years postoperatively. METHODS: Consecutive patients who underwent correction of their spinal deformity by thoracolumbar PSO were assessed using self-reporting questionnaires 2 years postoperatively. Outcome was measured by visual analogue scale (VAS) for back and leg pain, Oswestry Disability Index (ODI), and EQ-5D scores. Additionally, a Patient Satisfaction Index (PSI) rated in four grades (A: very satisfied to D: not satisfied), walking range, and the Timed Up and Go (TUG) Test were evaluated. RESULTS: Sixty-five patients were included, and each parameter was assessed preoperatively and 24 months postoperatively. The intervention led to significant improvements in back pain (8.1 ± 1.2 vs. 2.9 ± 1.9; p < 0.001), as well as ODI scores (57.7 ± 13.9 vs. 32.6 ± 18.9; p < 0.001), walking range (589 ± 1676 m vs. 3265 ± 3405 m; p < 0.001), and TUG (19.2 s vs. 9.7 s; p < 0.05). 90.7% of patients (n = 59/65) reported a PSI grade "A" or "B" 24 months postoperatively. CONCLUSION: Patient satisfaction 24 months after PSO for SI is high. Quality of life improved significantly by restoring sagittal balance.

Research on Fire-Detection Algorithm for Airplane Cargo Compartment Based on Typical Characteristic Parameters.

To clarify the reasons for inaccurate fire detection in aircraft cargo holds, this article depicts research from the perspective of a single type of sensor detection. In terms of fire smoke, we select dual-wavelength photoelectric smoke sensors for fire-data collection and a genetic algorithm to optimize the classification and detection of random forest fires. From the perspective of fire CO concentration, we use PSO-LSTM to train a CO concentration compensation model to reduce sensor measurement errors. Research is then conducted from the perspective of various types of sensor detection, using the improved BP-AdaBoost algorithm to train a fire-detection model and achieve the high-precision identification of complex environments and fire situations.