Anticandidal applications of selenium nanoparticles biosynthesized with Limosilactobacillus fermentum (OR553490).

Candida albicans is one of the most dangerous pathogenic fungi in the world, according to the classification of the World Health Organization, due to the continued development of its resistance to currently available anticandidal agents. To overcome this problem, the current work provided a simple, one-step, cost-effective, and safe technique for the biosynthesis of new functionalized anticandidal selenium nanoparticles (Se NPs) against C. albicans ATCC10231 using the cell-free supernatant of Limosilactobacillus fermentum (OR553490) strain. The bacterial strain was isolated from yogurt samples available in supermarkets, in Damietta, Egypt. The mixing ratio of 1:9 v/v% between cell-free bacterial metabolites and sodium selenite (5 mM) for 72 h at 37 °C were the optimum conditions for Se NPs biosynthesis. Ultraviolet-visible spectroscopy (UV-Vis), Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), X-ray diffraction (XRD), Zeta analyses, and elemental analysis system (EDS) were used to evaluate the optimized Se NPs. The Se NPs absorption peak appeared at 254 nm. Physicochemical analysis of Se NPs revealed the crystalline-shaped and well-dispersed formation of NPs with an average particle size of 17-30 nm. Se NPs have - 11.8 mV, as seen by the zeta potential graph. FT-IR spectrum displayed bands of symmetric and asymmetric amines at 3279.36 cm-1 and 2928.38 cm-1, aromatic and aliphatic (C-N) at 1393.32 cm-1 and 1237.11.37 cm-1 confirming the presence of proteins as stabilizing and capping agents. Se NPs acted as a superior inhibitor of C. albicans with an inhibition zone of 26 ± 0.03 mm and MIC value of 15 µg/mL compared to one of the traditional anticandidal agent, miconazole, which revealed 18 ± 0.14 mm and 75 µg/mL. The cytotoxicity test shows that Se NPs have a low toxic effect on the normal keratinocyte (IC50 ≈ 41.5 µg/mL). The results indicate that this green synthesis of Se NPs may have a promising potential to provide a new strategy for drug therapy.

Multi-objective optimization of custom implant abutment design for enhanced bone remodeling in single-crown implants using 3D finite element analysis.

The optimal configuration of a customized implant abutment is crucial for bone remodeling and is influenced by various design parameters. This study introduces an optimization process for designing two-piece zirconia dental implant abutments. The aim is to enhance bone remodeling, increase bone density in the peri-implant region, and reduce the risk of late implant failure. A 12-month bone remodeling algorithm subroutine in finite element analysis to optimize three parameters: implant placement depth, abutment taper degree, and gingival height of the titanium base abutment. The response surface analysis shows that implant placement depth and gingival height significantly impact bone density and uniformity. The taper degree has a smaller effect on bone remodeling. The optimization identified optimal values of 1.5 mm for depth, 35° for taper, and 0.5 mm for gingival height. The optimum model significantly increased cortical bone density from 1.2 to 1.937 g/cm3 in 2 months, while the original model reached 1.91 g/cm3 in 11 months. The standard deviation of density showed more uniform bone apposition, with the optimum model showing values 2 to 6 times lower than the original over 12 months. The cancellous bone showed a similar trend. In conclusion, the depth and taper have a significant effect on bone remodeling. This optimized model significantly improves bone density uniformity.

Recombinant human enamelin produced in Escherichia coli promotes mineralization in vitro.

BACKGROUND: Enamelin is an enamel matrix protein that plays an essential role in the formation of enamel, the most mineralized tissue in the human body. Previous studies using animal models and proteins from natural sources point to a key role of enamelin in promoting mineralization events during enamel formation. However, natural sources of enamelin are scarce and with the current study we therefore aimed to establish a simple microbial production method for recombinant human enamelin to support its use as a mineralization agent. RESULTS: In the study the 32 kDa fragment of human enamelin was successfully expressed in Escherichia coli and could be obtained using immobilized metal ion affinity chromatography purification (IMAC), dialysis, and lyophilization. This workflow resulted in a yield of approximately 10 mg enamelin per liter culture. Optimal conditions for IMAC purification were obtained using Ni2+ as the metal ion, and when including 30 mM imidazole during binding and washing steps. Furthermore, in vitro mineralization assays demonstrated that the recombinant enamelin could promote calcium phosphate mineralization at a concentration of 0.5 mg/ml. CONCLUSIONS: These findings address the scarcity of enamelin by facilitating its accessibility for further investigations into the mechanism of enamel formation and open new avenues for developing enamel-inspired mineralized biomaterials.

Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach.

The denitrifying sulfur (S) conversion-associated enhanced biological phosphorus removal (DS-EBPR) process for treating saline wastewater is characterized by its unique microbial ecology that integrates carbon (C), nitrogen (N), phosphorus (P), and S biotransformation. However, operational instability arises due to the numerous parameters and intricates bacterial interactions. This study introduces a two-stage interpretable machine learning approach to predict S conversion-driven P removal efficiency and optimize DS-EBPR process. Stage one utilized the XGBoost regression model, achieving an R2 value of 0.948 for predicting sulfate reduction (SR) intensity from anaerobic parameters with feature engineering. Stage two involved the CatBoost classification and regression model integrating anoxic parameters with the predicted SR values for predicting P removal, reaching an accuracy of 94% and an R2 value of 0.93, respectively. This study identified key environmental factors, including SR intensity (20-45 mg S/L), influent P concentration (<9.0 mg P/L), mixed liquor volatile suspended solids (MLVSS)/mixed liquor suspended solids (MLSS) ratio (0.55-0.72), influent C/S ratio (0.5-1.0), anoxic reaction time (5-6 h), and MLSS concentration (>6.50 g/L). A user-friendly graphic interface was developed to facilitate easier optimization and control. This approach streamlines the determination of optimal conditions for enhancing P removal in the DS-EBPR process.

A systematic literature review on meta-heuristic based feature selection techniques for text classification.

Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications.

An intrusion detection system based on convolution neural network.

With the rapid extensive development of the Internet, users not only enjoy great convenience but also face numerous serious security problems. The increasing frequency of data breaches has made it clear that the network security situation is becoming increasingly urgent. In the realm of cybersecurity, intrusion detection plays a pivotal role in monitoring network attacks. However, the efficacy of existing solutions in detecting such intrusions remains suboptimal, perpetuating the security crisis. To address this challenge, we propose a sparse autoencoder-Bayesian optimization-convolutional neural network (SA-BO-CNN) system based on convolutional neural network (CNN). Firstly, to tackle the issue of data imbalance, we employ the SMOTE resampling function during system construction. Secondly, we enhance the system's feature extraction capabilities by incorporating SA. Finally, we leverage BO in conjunction with CNN to enhance system accuracy. Additionally, a multi-round iteration approach is adopted to further refine detection accuracy. Experimental findings demonstrate an impressive system accuracy of 98.36%. Comparative analyses underscore the superior detection rate of the SA-BO-CNN system.

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects.

Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

Data-driven sales optimization with regression and chaotic pattern search.

Lead generation is the process of gaining potential customers' interest to increase future sales, and it is an essential part of many businesses' (amusement parks, theme parks, clubs, etc.) sales processes as their membership is more expensive. The main objective of these businesses is to increase the count of customers. By generating sales leads, a club/park can find leads who have already expressed interest in its products and services and access their audience potential, allowing them to focus on future marketing and sales efforts on those leads that are more likely to convert. The current work focuses on how to convert a lead to a customer in optimum number of days. We collect two kinds of data: customer data and lead generation data. The customer data consists of all the leads who have taken the membership, and the lead generation data consists of all current leads. The details of those converted from a lead into a customer in the last 60 days are filtered out from the customer data. Using this data, patterns are generated, which are used to predict the following activity (step) for qualified leads, along with the optimal number of days required to complete that activity. This optimal number of days is found using the Hybrid Chaotic Pattern Search Algorithm (HCPSA). This novel approach here helps in boosting sales by prioritizing leads who have expressed interest and identifying the optimal window for converting them into paying customers. This strategy holds significant potential to benefit businesses across various industries.

NALA: a Nesterov accelerated look-ahead optimizer for deep learning.

Adaptive gradient algorithms have been successfully used in deep learning. Previous work reveals that adaptive gradient algorithms mainly borrow the moving average idea of heavy ball acceleration to estimate the first- and second-order moments of the gradient for accelerating convergence. However, Nesterov acceleration which uses the gradient at extrapolation point can achieve a faster convergence speed than heavy ball acceleration in theory. In this article, a new optimization algorithm which combines adaptive gradient algorithm with Nesterov acceleration by using a look-ahead scheme, called NALA, is proposed for deep learning. NALA iteratively updates two sets of weights, i.e., the 'fast weights' in its inner loop and the 'slow weights' in its outer loop. Concretely, NALA first updates the fast weights k times using Adam optimizer in the inner loop, and then updates the slow weights once in the direction of Nesterov's Accelerated Gradient (NAG) in the outer loop. We compare NALA with several popular optimization algorithms on a range of image classification tasks on public datasets. The experimental results show that NALA can achieve faster convergence and higher accuracy than other popular optimization algorithms.

Effects of Sex Differences and Combined Use of Clozapine on Initial Dosage Optimization of Valproic Acid in Patients with Bipolar Disorder.

BACKGROUND: Due to the narrow therapeutic window and large pharmacokinetic variation of valproic acid (VPA), it is difficult to make an optimal dosage regimen. The present study aims to optimize the initial dosage of VPA in patients with bipolar disorder. METHODS: A total of 126 patients with bipolar disorder treated by VPA were included to construct the VPA population pharmacokinetic model retrospectively. Sex differences and combined use of clozapine were found to significantly affect VPA clearance in patients with bipolar disorder. The initial dosage of VPA was further optimized in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively. RESULTS: The CL/F and V/F of VPA in patients with bipolar disorder were 11.3 L/h and 36.4 L, respectively. It was found that sex differences and combined use of clozapine significantly affected VPA clearance in patients with bipolar disorder. At the same weight, the VPA clearance rates were 1.134, 1, 1.276884, and 1.126 in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively. This study further optimized the initial dosage of VPA in male patients without the combined use of clozapine, female patients without the combined use of clozapine, male patients with the combined use of clozapine, and female patients with the combined use of clozapine, respectively. CONCLUSION: This study is the first to investigate the initial dosage optimization of VPA in patients with bipolar disorder based on sex differences and the combined use of clozapine. Male patients had higher clearance, and the recommended initial dose decreased with increasing weight, providing a reference for the precision drug use of VPA in clinical patients with bipolar disorder.

High-throughput combination assay for studying biofilm formation of uropathogenic Escherichia coli.

Uropathogenic Escherichia coli, the most common cause for urinary tract infections, forms biofilm enhancing its antibiotic resistance. To assess the effects of compounds on biofilm formation of uropathogenic Escherichia coli UMN026 strain, a high-throughput combination assay using resazurin followed by crystal violet staining was optimized for 384-well microplate. Optimized assay parameters included, for example, resazurin and crystal violet concentrations, and incubation time for readouts. For the assay validation, quality parameters Z' factor, coefficient of variation, signal-to-noise, and signal-to-background were calculated. Microplate uniformity, signal variability, edge well effects, and fold shift were also assessed. Finally, a screening with known antibacterial compounds was conducted to evaluate the assay performance. The best conditions found were achieved by using 12 µg/mL resazurin for 150 min and 0.023% crystal violet. This assay was able to detect compounds displaying antibiofilm activity against UMN026 strain at sub-inhibitory concentrations, in terms of metabolic activity and/or biomass.

Increasing productivity by using smart gas for optimal management of the gas lift process in a cluster of wells.

In the face of the escalating global energy demand, the challenge lies in enhancing the extraction of oil from low-pressure underground reservoirs. The conventional artificial gas lift method is constrained by the limited availability of high-pressure gas for injection, which is essential for reducing hydrostatic bottom hole pressure and facilitating fluid transfer to the surface. This study proposes a novel 'smart gas' concept, which involves injecting a gas mixture with an optimized fraction of CO2 and N2 into each well. The research introduces a dual optimization strategy that not only determines the optimal gas composition but also allocates the limited available gas among wells to achieve multiple objectives. An extensive optimization process was conducted to identify the optimal gas injection rate for each well, considering the limited gas supply. The study examined the impact of reducing available gas from 20 to 10 MMSCFD and the implications of water production restrictions on oil recovery. The introduction of smart gas resulted in a 3.1% increase in overall oil production compared to using natural gas. The optimization of smart gas allocation proved effective in mitigating the decline in oil production, with a 25% reduction in gas supply leading to only a 10% decrease in oil output, and a 33% reduction resulting in a 26.8% decrease. The study demonstrates that the smart gas approach can significantly enhance oil production efficiency in low-pressure reservoirs, even with a substantial reduction in gas supply. It also shows that imposing water production limits has a minimal impact on oil production, highlighting the potential of smart gas in achieving environmentally sustainable oil extraction. Furthermore, the implementation of the smart gas approach aligns with global environmental goals by potentially reducing greenhouse gas emissions, thereby contributing to the broader objective of environmental sustainability in the energy sector.

An effective video inpainting technique using morphological Haar wavelet transform with krill herd based criminisi algorithm.

In recent times, video inpainting techniques have intended to fill the missing areas or gaps in a video by utilizing known pixels. The variety in brightness or difference of the patches causes the state-of-the-art video inpainting techniques to exhibit high computation complexity and create seams in the target areas. To resolve these issues, this paper introduces a novel video inpainting technique that employs the Morphological Haar Wavelet Transform combined with the Krill Herd based Criminisi algorithm (MHWT-KHCA) to address the challenges of high computational demand and visible seam artifacts in current inpainting practices. The proposed MHWT-KHCA algorithm strategically reduces computation times and enhances the seamlessness of the inpainting process in videos. Through a series of experiments, the technique is validated against standard metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), where it demonstrates superior performance compared to existing methods. Additionally, the paper outlines potential real-world applications ranging from video restoration to real-time surveillance enhancement, highlighting the technique's versatility and effectiveness. Future research directions include optimizing the algorithm for diverse video formats and integrating machine learning models to advance its capabilities further.

Enhancing residential energy access with optimized stand-alone hybrid solar-diesel-battery systems in Buea, Cameroon.

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system's dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator.

Research on bearing fault diagnosis based on improved genetic algorithm and BP neural network.

Health monitoring and fault diagnosis of rolling bearings are crucial for the continuous and effective operation of mechanical equipment. In order to improve the accuracy of BP neural network in fault diagnosis of rolling bearings, a feature model is established from the vibration signals of rolling bearings, and an improved genetic algorithm is used to optimize the initial weights, biases, and hyperparameters of the BP neural network. This overcomes the shortcomings of BP neural network, such as being prone to local minima, slow convergence speed, and sample dependence. The improved genetic algorithm fully considers the degree of concentration and dispersion of population fitness in genetic algorithms, and adaptively adjusts the crossover and mutation probabilities of genetic algorithms in a non-linear manner. At the same time, in order to accelerate the optimization efficiency of the selection operator, the elite retention strategy is combined with the hierarchical proportional selection operation. Using the rolling bearing dataset from Case Western Reserve University in the United States as experimental data, the proposed algorithm was used for simulation and prediction. The experimental results show that compared with the other seven models, the proposed IGA-BPNN exhibit superior performance in both convergence speed and predictive performance.

Dependence of Europe's most threatened mammals on movement to adapt to climate change.

Current rates of climate change and gloomy climate projections confront managers and conservation planners with the need to integrate climate change into already complex decision-making processes. Predicting and prioritizing climatically stable areas and the areas likely to facilitate adaptive species' range adjustments are important stages in maximizing conservation outcomes and rationalizing future land management. I determined, for the most threatened European terrestrial mammal species, the spatial adaptive trajectories (SATs) of highest expected persistence up to 2080. I devised simple spatial network indices for evaluation of species in those SATs: total persistence; proportion of SATs that offer in situ adaptation (i.e., stable refugia); number of SATs converging in a site; and relationship between SAT convergence and persistence and protected areas, the Natura 2000 and Emerald networks, and areas of low human disturbance. I compared the performance of high-persistence SATs with a scenario in which each species remained in the areas with the best climatic conditions in the baseline period. The 1000 most persistence SATs for each of the 39 species covered one fifth of Europe. The areas with the largest adaptive potential (i.e., high persistence, stability, and SAT convergence) did not always overlap for all the species. Predominantly, these regions were located in southwestern Europe, Central Europe, and Scandinavia, with some occurrences in Eastern Europe. For most species, persistence in the most climatically suitable areas during the baseline period was lower than within SATs, underscoring their reliance on adaptive movements. Importantly, conservation areas (particularly protected areas) covered only minor fractions of species persistence among SATs, and hubs of spatial climate adaptation (i.e., areas of high SAT convergence) were seriously underrepresented in most conservation areas. These results highlight the need to perform analyses on spatial species' dynamics under climate change.

Los mamíferos más amenazados de Europa y su dependencia del movimiento para adaptarse al cambio climático Resumen La tasa actual del cambio climático y las proyecciones climáticas pesimistas confrontan a los gestores y a los planeadores de la conservación con la necesidad de integrar este cambio a la ya de por sí compleja toma de decisiones. La predicción y priorización de áreas con estabilidad climática y áreas con probabilidad de facilitarles ajustes adaptativos de distribución a las especies son etapas importantes para maximizar los resultados de conservación y racionalizar la gestión futura de las tierras. Determiné las trayectorias espaciales adaptativas (TEA) para la mayoría de los mamíferos terrestres más amenazados de Europa con la persistencia esperada más alta hasta el 2080. Diseñé los siguientes índices de redes espaciales simples para la evaluación de especies en aquellas TEA: persistencia total, proporción de TEA que brindan adaptación in situ (refugios estables), número de TEA que convergen en un sitio y relación entre la convergencia de TEA y la persistencia con las áreas protegidas, las redes Natura 2000 y Emerald y las áreas de poca perturbación humana. Comparé el desempeño de las TEA de gran persistencia con un escenario en el que las especies permanecían dentro de las áreas con las mejores condiciones climáticas en el periodo de línea base. Las mil TEA más persistentes para cada una de las 39 especies cubrieron la quinta parte de Europa. Las áreas con el mayor potencial adaptativo (es decir, gran persistencia, estabilidad y convergencia de TEA) no siempre se traslaparon para todas las especies. Estas regiones predominaron en el suroeste de Europa, Europa Central y Escandinavia, con algunas ocurrencias en el este de Europa. Para la mayoría de las especies, la persistencia de las áreas con el mejor clima posible durante el periodo de línea base fue menor que dentro de las TEA, lo que resalta su dependencia por los movimientos adaptativos. Destaca que las áreas de conservación (en particular las áreas protegidas) cubrieron sólo pequeñas fracciones de la persistencia de las especies entre las TEA y los núcleos de adaptación climática (es decir, las áreas de gran convergencia de TEA) contaban con muy poca representación dentro de la mayoría de las áreas de conservación. Estos resultados enfatizan la necesidad de realizar análisis de las dinámicas espaciales de las especies bajo el cambio climático.

An Improved Archimedes Optimization-aided Multi-scale Deep Learning Segmentation with dilated ensemble CNN classification for detecting lung cancer using CT images.

Early detection of lung cancer is necessary to prevent deaths caused by lung cancer. But, the identification of cancer in lungs using Computed Tomography (CT) scan based on some deep learning algorithms does not provide accurate results. A novel adaptive deep learning is developed with heuristic improvement. The proposed framework constitutes three sections as (a) Image acquisition, (b) Segmentation of Lung nodule, and (c) Classifying lung cancer. The raw CT images are congregated through standard data sources. It is then followed by nodule segmentation process, which is conducted by Adaptive Multi-Scale Dilated Trans-Unet3+. For increasing the segmentation accuracy, the parameters in this model is optimized by proposing Modified Transfer Operator-based Archimedes Optimization (MTO-AO). At the end, the segmented images are subjected to classification procedure, namely, Advanced Dilated Ensemble Convolutional Neural Networks (ADECNN), in which it is constructed with Inception, ResNet and MobileNet, where the hyper parameters is tuned by MTO-AO. From the three networks, the final result is estimated by high ranking-based classification. Hence, the performance is investigated using multiple measures and compared among different approaches. Thus, the findings of model demonstrate to prove the system's efficiency of detecting cancer and help the patient to get the appropriate treatment.

A novel optimization rainfall coupling model based on stepwise decomposition technique.

Traditional decomposition integration models decompose the original sequence into subsequences, which are then proportionally divided into training and testing periods for modeling. Decomposition may cause data aliasing, then the decomposed training period may contain part of the test period data. A more effective method of sample construction is sought in order to accurately validate the model prediction accuracy. Semi-stepwise decomposition (SSD), full stepwise decomposition (FSD), single model semi-stepwise decomposition (SMSSD), and single model full stepwise decomposition (SMFSD) techniques were used to create the samples. This study integrates Variational Mode Decomposition (VMD), African Vulture Optimization Algorithm (AVOA), and Least Squares Support Vector Machine (LSSVM) to construct a coupled rainfall prediction model. The influence of different VMD parameters α is examined, and the most suitable stepwise decomposition machine learning coupled model algorithm for various stations in the North China Plain is selected. The results reveal that SMFSD is relatively the most suitable tool for monthly precipitation forecasting in the North China Plain. Among the predictions for the five stations, the best overall performance is observed at Huairou Station (RMSE of 18.37 mm, NSE of 0.86, MRE of 107.2%) and Jingxian Station (RMSE of 24.74 mm, NSE of 0.86, MRE of 51.71%), while Hekou Station exhibits the poorest performance (RMSE of 25.11 mm, NSE of 0.75, MRE of 173.75%).

Intuitive and versatile bionic legs: a perspective on volitional control.

Active lower limb prostheses show large potential to offer energetic, balance, and versatility improvements to users when compared to passive and semi-active devices. Still, their control remains a major development challenge, with many different approaches existing. This perspective aims at illustrating a future leg prosthesis control approach to improve the everyday life of prosthesis users, while providing a research road map for getting there. Reviewing research on the needs and challenges faced by prosthesis users, we argue for the development of versatile control architectures for lower limb prosthetic devices that grant the wearer full volitional control at all times. To this end, existing control approaches for active lower limb prostheses are divided based on their consideration of volitional user input. The presented methods are discussed in regard to their suitability for universal everyday control involving user volition. Novel combinations of established methods are proposed. This involves the combination of feed-forward motor control signals with simulated feedback loops in prosthesis control, as well as online optimization techniques to individualize the system parameters. To provide more context, developments related to volitional control design are touched on.