Quantitative analysis of soil potassium by near-infrared (NIR) spectroscopy combined with a three-step progressive hybrid variable selection strategy.

Soil potassium is a crucial nutrient element necessary for crop growth, and its efficient measurement has become essential for developing rational fertilization plans and optimizing crop growth benefits. At present, data mining technology based on near-infrared (NIR) spectroscopy analysis has proven to be a powerful tool for real-time monitoring of soil potassium content. However, as technology and instruments improve, the curse of the dimensionality problem also increases accordingly. Therefore, it is urgent to develop efficient variable selection methods suitable for NIR spectroscopy analysis techniques. In this study, we proposed a three-step progressive hybrid variable selection strategy, which fully leveraged the respective strengths of several high-performance variable selection methods. By sequentially equipping synergy interval partial least squares (SiPLS), the random forest variable importance measurement (RF(VIM)), and the improved mean impact value algorithm (IMIV) into a fusion framework, a soil important potassium variable selection method was proposed, termed as SiPLS-RF(VIM)-IMIV. Finally, the optimized variables were fitted into a partial least squares (PLS) model. Experimental results demonstrated that the PLS model embedded with the hybrid strategy effectively improved the prediction performance while reducing the model complexity. The RMSET and RT on the test set were 0.01181% and 0.88246, respectively, better than the RMSET and RT of the full spectrum PLS, SiPLS, and SiPLS-RF(VIM) methods. This study demonstrated that the hybrid strategy established based on the combination of NIR spectroscopy data and the SiPLS-RF(VIM)-IMIV method could quantitatively analyze soil potassium content levels and potentially solve other issues of data-driven soil dynamic monitoring.

An adaptive testing item selection strategy via a deep reinforcement learning approach.

Computerized adaptive testing (CAT) aims to present items that statistically optimize the assessment process by considering the examinee's responses and estimated trait levels. Recent developments in reinforcement learning and deep neural networks provide CAT with the potential to select items that utilize more information across all the items on the remaining tests, rather than just focusing on the next several items to be selected. In this study, we reformulate CAT under the reinforcement learning framework and propose a new item selection strategy based on the deep Q-network (DQN) method. Through simulated and empirical studies, we demonstrate how to monitor the training process to obtain the optimal Q-networks, and we compare the accuracy of the DQN-based item selection strategy with that of five traditional strategies-maximum Fisher information, Fisher information weighted by likelihood, Kullback‒Leibler information weighted by likelihood, maximum posterior weighted information, and maximum expected information-on both simulated and real item banks and responses. We further investigate how sample size and the distribution of the trait levels of the examinees used in training affect DQN performance. The results show that DQN achieves lower RMSE and MAE values than traditional strategies under simulated and real banks and responses in most conditions. Suggestions for the use of DQN-based strategies are provided, as well as their code.

Evaluation of selection strategies in dual-purpose and specialized breeding of indigenous chicken.

This study aimed to evaluate various selection strategies for adoption in dual-purpose (ICD), meat (ICM) and layer (ICL) breeding goals in indigenous chicken breeding programs. The ICM goal aimed to improve live weight (LW12), daily gain (ADG) and egg weight (EW12) or together with feed efficiency and antibody response. For the ICL goal, age at first egg (AFE) and egg number (EN12) or together with feed efficiency and antibody response were targeted. In the ICD goal, the objective was to improve LW12, ADG, AFE and EN12 or together with feed efficiency and antibody response. Highest total index responses of US$ 49.83, US$ 65.71, and US$ 37.90 were estimated in indices targeting only production traits in the ICD, ICM and ICL goals, respectively. Highest index accuracy estimates of 0.77 and 0.70 were observed in indices that considered production and feed-related traits in the ICD and ICL goals, respectively, while in the ICM goal, the highest estimate of 0.96 was observed in an index targeting only production traits. Inbreeding levels ranged from 0.60 to 1.14% across the various indices considered in the breeding goals. Targeting only production traits in the ICD, ICM and ICL goals required the least number of generations of selection of 7.46, 5.50, and 8.52, respectively, to achieve predefined gains. Generally, a strategy targeting only production traits in a goal was the most optimal but resulted to unfavorable correlated responses in feed efficiency and antibody response. Addition of feed efficiency or/and antibody response in a goal was, however, not attractive due to the decline in total index response and accuracy and increase in inbreeding levels and number of generations of selection. Considering the feed availability and disease challenges in the tropics, choice of including feed efficiency or/and antibody response in the ICD, ICM and ICL goals should depend on targeted production system, resource availability to support breeding activities and magnitude of correlated responses on these traits when not included in the goals.

"Sweet space" strategy for solvent system selection in countercurrent chromatography: A case study on separation of polyunsaturated fatty acids from borage oil.

This study presents a comprehensive strategy for the selection and optimization of solvent systems in countercurrent chromatography (CCC) for the effective separation of compounds. With a focus on traditional organic solvent systems, the research introduces a "sweet space" strategy that merges intuitive understanding with mathematical accuracy, addressing the significant challenges in solvent system selection, a critical bottleneck in the widespread application of CCC. By employing a combination of volume ratios and graphical representations, including both regular and trirectangular tetrahedron models, the proposed approach facilitates a more inclusive and user-friendly strategy for solvent system selection. This study demonstrates the potential of the proposed strategy through the successful separation of gamma-linolenic acid, oleic acid, and linoleic acid from borage oil, highlighting the strategy's effectiveness and practical applicability in CCC separations.

Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R2 increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops.

Real-time implementation of a novel MPPT control based on the improved PSO algorithm using an adaptive factor selection strategy for photovoltaic systems.

Particle Swarm Optimization (PSO) is considered as one of Maximum Power Point Tracking (MPPT) controller algorithm developed for PhotoVoltaic system (PV) to guarantee a maximum power extraction under different climatic conditions of temperature and irradiation. Classical MPPT algorithms like Perturbe and Observe (P&O), PSO, Adaptive Neuro-Fuzzy Inference System (ANFIS) are an effective method for tracking the maximum power point (MPP) for the PV systems, but the problems with these approaches that they are less stable, high oscillation around steady state and slower convergence to the MPP. Based on recent research, the purpose of this paper is to introduces a novel MPPT controller based on a modified version of heterogeneous multi swarm PSO algorithm using an adaptive factor selection strategy (FMSPSO), to overcome the previous shortcomings and compared with conventional PSO, ANFIS and classical P&O controllers. Simulation and experimental results revealed that the new FMSPSO algorithm can overcome the previous shortcomings providing the superior performance to track the MPP efficiently with a shorter convergence time and small oscillations compared to other algorithms. The experimental confirmation of the FMSPSO algorithm has been carried out using NI-myRIO-1900 card and shows that with the proposed MPPT approach efficiency can reach a value greater than 99% even in climatic variation of irradiation and temperature.

Salt Anion Amphiphilicity-Activated Electrolyte Cosolvent Selection Strategy toward Durable Zn Metal Anode.

One effective solution to inhibit side reactions and Zn dendrite growth in aqueous Zn-ion batteries is to add a cosolvent into the Zn(CF3SO3)2 electrolyte, which has the potential to form a robust solid electrolyte interface composed of ZnF2 and ZnS. Nevertheless, there is still a lack of discussion on a convenient selection method for cosolvents, which can directly reflect the interactions between solvent and solute to rationally design the electrolyte solvation structure. Herein, logP, where P is the octanol-water partition coefficient, a general parameter to describe the hydrophilicity and lipophilicity of chemicals, is proposed as a standard for selecting cosolvents for Zn(CF3SO3)2 electrolyte, which is demonstrated by testing seven different types of solvents. The solvent with a logP value similar to that of the salt anion CF3SO3- can interact with CF3SO3-, Zn2+, and H2O, leading to a reconstruction of the electrolyte solvation structure. To prove the concept, methyl acetate (MA) is demonstrated as an example due to its similar logP value to that of CF3SO3-. Both the experimental and theoretical results illustrate that MA molecules not only enter into the solvation shell of CF3SO3- but also coordinate with Zn2+ or H2O, forming an MA and CF3SO3- involved core-shell solvation structure. The special solvation structure reduces H2O activity and contributes to forming an anion-induced ZnCO3-ZnF2-rich solid electrolyte interface. As a result, the Zn||Zn cell and Zn||NaV3O8·1.5H2O cell with MA-involved electrolyte exhibit superior performances to that with the MA-free electrolyte. This work provides an insight into electrolyte design via salt anion chemistry for high-performance Zn batteries.

The effectiveness of artificial microbial community selection: a conceptual framework and a meta-analysis.

The potential for artificial selection at the community level to improve ecosystem functions has received much attention in applied microbiology. However, we do not yet understand what conditions in general allow for successful artificial community selection. Here we propose six hypotheses about factors that determine the effectiveness of artificial microbial community selection, based on previous studies in this field and those on multilevel selection. In particular, we emphasize selection strategies that increase the variance among communities. We then report a meta-analysis of published artificial microbial community selection experiments. The reported responses to community selection were highly variable among experiments; and the overall effect size was not significantly different from zero. The effectiveness of artificial community selection was greater when there was no migration among communities, and when the number of replicated communities subjected to selection was larger. The meta-analysis also suggests that the success of artificial community selection may be contingent on multiple necessary conditions. We argue that artificial community selection can be a promising approach, and suggest some strategies for improving the performance of artificial community selection programs.

Comparisons of different approaches and incisions of thyroid surgery and selection strategy.

To date, the traditional open thyroid surgery via a low collar incision remains the standard approach for patients undergoing thyroidectomy. However, this conventional approach will inevitably leave patients a neck scar and even cause a variety of complications such as paresthesia, hypesthesia, and other uncomfortable sensations. With the progress in surgical techniques, especially in endoscopic surgery, and the increasing desire for cosmetic and functional outcomes, various new approaches for thyroidectomy have been developed to avoid or decrease side effects. Some of these alternative approaches have obvious advantages compared with traditional surgery and have already been widely used in the treatment of thyroid disease, but each has its limitations. This review aims to evaluate and compare the different approaches to thyroidectomy to help surgeons make the proper treatment strategy for different individuals.

Strategy for the Choice of Disinfectants in Practical Medicine and Production.

The problem of the strategy for choosing disinfectants in practical medicine is considered. The pandemic of the new coronavirus infection posed new problems for disinfectology. The expanded spectrum of disinfectants and antiseptics offered by the chemical industry in recent years requires justification for the choice in favor of any product. The goals and types of disinfection considered from current positions and the main groups of disinfectants used in Russia and their properties and spectra of activity are presented.

A novel neighbor selection scheme based on dynamic evaluation towards recommender systems.

Collaborative filtering is a kind of widely used and efficient technique in various online environments, which generates recommendations based on the rating information of his/her similar-preference neighbors. However, existing collaborative filtering methods have some inadequacies in revealing the dynamic user preference change and evaluating the recommendation effectiveness. The sparsity of input data may further exacerbate this issue. Thus, this paper proposes a novel neighbor selection scheme constructed in the context of information attenuation to bridge these gaps. Firstly, the concept of the preference decay period is given to describe the pattern of user preference evolution and recommendation invalidation, and thus two types of dynamic decay factors are correspondingly defined to gradually weaken the impact of old data. Then, three dynamic evaluation modules are built to evaluate the user's trustworthiness and recommendation ability. Finally, A hybrid selection strategy combines these modules to construct two neighbor selection layers and adjust the neighbor key thresholds. Through this strategy, our scheme can more effectively select capable and trustworthy neighbors to provide recommendations. The experiments on three real datasets with different data sizes and data sparsity show that the proposed scheme provides excellent recommendation performance and is more suitable for real applications, compared to the state-of-the-art methods.

Two-level mixed-effects height to crown base model for moso bamboo (Phyllostachys edulis) in Eastern China.

Height to crown base (HCB) is an important predictor variable for forest growth and yield models and is of great significance for bamboo stem utilization. However, existing HCB models built so far on the hierarchically structured data are for arbor forests, and not applied to bamboo forests. Based on the fitting of data acquired from 38 temporary sample plots of Phyllostachys edulis forests in Yixing, Jiangsu Province, we selected the best HCB model (logistic model) from among six basic models and extended it by integrating predictor variables, which involved evaluating the impact of 13 variables on HCB. Block- and sample plot-level random effects were introduced to the extended model to account for nested data structures through mixed-effects modeling. The results showed that bamboo height, diameter at breast height, total basal area of all bamboo individuals with a diameter larger than that of the subject bamboo, and canopy density contributed significantly more to variation in HCB than other variables did. Introducing two-level random effects resulted in a significant improvement in the accuracy of the model. Different sampling strategies were evaluated for response calibration (model localization), and the optimal strategy was identified. The prediction accuracy of the HCB model was substantially improved, with an increase in the number of bamboo samples in the calibration. Based on our findings, we recommend the use of four randomly selected bamboo individuals per sample to provide a compromise between measurement cost, model use efficiency, and prediction accuracy.

Night Vision Anti-Halation Method Based on Infrared and Visible Video Fusion.

In order to address the discontinuity caused by the direct application of the infrared and visible image fusion anti-halation method to a video, an efficient night vision anti-halation method based on video fusion is proposed. The designed frame selection based on inter-frame difference determines the optimal cosine angle threshold by analyzing the relation of cosine angle threshold with nonlinear correlation information entropy and de-frame rate. The proposed time-mark-based adaptive motion compensation constructs the same number of interpolation frames as the redundant frames by taking the retained frame number as a time stamp. At the same time, considering the motion vector of two adjacent retained frames as the benchmark, the adaptive weights are constructed according to the interframe differences between the interpolated frame and the last retained frame, then the motion vector of the interpolated frame is estimated. The experimental results show that the proposed frame selection strategy ensures the maximum safe frame removal under the premise of continuous video content at different vehicle speeds in various halation scenes. The frame numbers and playing duration of the fused video are consistent with that of the original video, and the content of the interpolated frame is highly synchronized with that of the corresponding original frames. The average FPS of video fusion in this work is about six times that in the frame-by-frame fusion, which effectively improves the anti-halation processing efficiency of video fusion.

Design and Implementation of Real-Time Localization System (RTLS) Based on UWB and TDoA Algorithm.

Nowadays, accurate localization plays an essential role in many fields, such as target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and the high cost. So, the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive ultra wide-band (UWB)-based real-time localization system (RTLS) is presented in this paper. We introduce the architecture of a real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, and finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and we analyze the relationship between anchor deployment and positioning accuracy. The extended Kalman filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy on moving targets.

Advances in antibody phage display technology.

Phage display technology can be used for the discovery of antibodies for research, diagnostic, and therapeutic purposes. In this review, we present and discuss key parameters that can be optimized when performing phage display selection campaigns, including the use of different antibody formats and advanced strategies for antigen presentation, such as immobilization, liposomes, nanodiscs, virus-like particles, and whole cells. Furthermore, we provide insights into selection strategies that can be used for the discovery of antibodies with complex binding requirements, such as targeting a specific epitope, cross-reactivity, or pH-dependent binding. Lastly, we provide a description of specialized phage display libraries for the discovery of bispecific antibodies and pH-sensitive antibodies. Together, these methods can be used to improve antibody discovery campaigns against all types of antigens.

A nine-gene signature identification and prognostic risk prediction for patients with lung adenocarcinoma using novel machine learning approach.

BACKGROUND: Lung adenocarcinoma (LUAD) is one the most prevalent cancer with high mortality and its risk stratification is limited due lack of reliable molecular biomarkers. Although several studies have been conducted to identify gene signature involved in LUAD progression, most currently used methods to select gene features did not fully consider the problem of the existence of strong pairwise gene correlations as it resulted inconsistency in gene election. Therefore, it is crucial to develop new strategy to identify reliable gene signatures that improve risk prediction. METHODS AND RESULTS: In this study, novel feature selection strategy (1) univariate Cox regression model to select survival associated genes (2) integrating rigid Cox regression with Adaptive Lasso model to identify informative survival associated genes (3) stepwise Cox regression model to identify optimal gene signature and (4) prognostic risk predictive model for LUAD (PRPML) was constructed. The PRPML was developed-based on four machine learning (ML) methods including logistic regression (LR), K-nearest neighbors (KNN), support vector machine with the radial kernel (SVMR), and average neural network (Avnet). The PRPML model successfully stratified high-risk and low-risk groups of patients with LUAD in three datasets. The PRPML achieved an area under the curve (AUC) of 0.812 and 0.863 in the validation datasets. Finally, a nine-potential gene signature was found and showed great potential for risk prediction. CONCLUSIONS: Our study demonstrates that the developed strategy identified a nine potential gene signature for accurate risk prediction performance and this signature could provide valuable clue into the understanding of the molecular mechanism of LUAD disease.

The reconstructive strategy for pelvic oncological surgery with various types of MS-VRAM flaps.

BACKGROUND: Muscle-sparing vertical rectus abdominis myocutaneous (MS-VRAM) flaps are widely used in pelvic reconstruction. Aiming at optimal reconstructive outcomes, flap design and modification should be individualized to restore various kinds of defects. OBJECTIVE: Summarize an empirical strategy about MS-VRAM selection for different pelvic and perineal reconstructions. METHODS: Thirty patients who underwent total pelvic exenteration and pelvic reconstruction surgery from 2009 to 2017 were enrolled. The patients were divided into four groups according to the type of MS-VRAM-based flap used in the procedure: the modified long vertical flap (n = 10), the wrapping flap (n = 6), the de-epithelialized flap (n = 6), and the cork flap (n = 8). The follow-up period was 1 year after the surgery. Flap size, drainage volume, postoperative satisfaction, and complications were recorded, and postoperative photographs were collected. RESULTS: All of the patients achieved satisfying effect under the targeted reconstruction strategy. Of the four groups, the accurate cork flap finally acquires higher satisfaction, the shortest hospital stay, and the least total drainage volume. Meanwhile, the incidence of complications was not increased compared with the other groups. CONCLUSIONS: A new reconstructive strategy for pelvic reconstruction was established. Functional or non-functional reconstruction was accomplished by using various MS-VRAM flaps. Among them, the cork flap is the most economical flap to reconstruct pelvic floor defects with minimal tissue requirement and donor trauma.

Evaluation of hydrophobic charge-induction ligand efficiency for protein adsorption in one single cycle.

Ligand is an essential part of the cost of adsorbent preparation, which needs to be carefully selected and evaluated. In this paper, we introduced ligand efficiency (Le) with three levels (recovery, preparation and cost) to form a selection strategy for evaluation of the efficiency of hydrophobic charge-induction ligand. These functions were calculated from static/dynamic binding capacity, desorption efficiency, coupling efficiency and ligand cost. Nine kinds of ligand were used to demonstrate this strategy. The coupling efficiency was determined by preparing the adsorbents with different kinds and densities of ligand. These adsorbents were characterized by FT-IR, SEM. Then adsorption equilibrium, adsorption kinetics, and frontal adsorption experiments were used to test the adsorption and desorption performance of these adsorbents. Finally, Les of recovery, preparation and cost were calculated. The results showed there were apparent differences in Les between ligand types and densities under static and dynamic adsorption conditions. 4FF-Tryptophan with 52 µmol/g adsorbent had the best performance with the lowest static/dynamic Le of recovery, preparation and ligand cost. Compared with those methods evaluated by static saturated adsorption capacity or dynamic binding capacity at 10% breakthrough, the selection strategy based on ligand efficiency is more suitable for subsequent research and industrial amplification.

A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks.

The sliding-window-based dynamic functional connectivity networks (SW-D-FCN) derive from resting-state functional Magnetic Resonance Imaging has become an increasingly useful tool in the diagnosis of various neurodegenerative diseases. However, it is still challenging to learn how to extract and select the most discriminative features from SW-D-FCN. Conventionally, existing methods opt to select a single discriminative feature set or concatenate a few more from the SW-D-FCN. However, such reductionist strategies may fail to fully capture the personalized discriminative characteristics contained in each functional connectivity (FC) sequence of the SW-D-FCN. To address this issue, we propose a unit-based personalized fingerprint feature selection (UPFFS) strategy to better capture the most discriminative feature associated with a target disease for each unit. Specifically, we regard the FC sequence between any pair of brain regions of interest (ROIs) is regarded as a unit. For each unit, the most discriminative feature is identified by a specific feature evaluation method and all the most discriminative features are then concatenated together as a feature set for the subsequent classification task. In such a way, the personalized fingerprint feature derived from each FC sequence can be fully mined and utilized in classification decision. To illustrate the effectiveness of the proposed strategy, we conduct experiments to distinguish subjects diagnosed with autism spectrum disorder from normal controls. Experimental results show that the proposed strategy can select relevant discriminative features and achieve superior performance to benchmark methods.

A new buffer selection strategy for land use regression model of PM2.5 in Xi'an, China.

In order to calculate the spatial distribution of high-resolution air-pollutant levels, the land use regression (LUR) model can be an effective method due to the comprehensive consideration of various factors. Traditional LUR models mostly use predefined buffers, which have the disadvantage of not matching high-resolution data well. In order to get a better-fitting model, a few researches have proposed new buffer selection methods. To solve this problem, we propose a new optimal buffer selection method based on the dichotomy to improve the correlation between predicted variables and pollutant concentration. For some socioeconomic data with high spatial resolution that cannot be obtained, for example, building data is used instead of population density data. Compared with the model with the predefined buffers, the model with our buffer selection strategy explained additional 5% variability in measured concentrations, in terms of the R2 of the final model. Our model explained 98% of the samples, and the deviation (1.78%) and root mean square error (5.17 µg/m) were small. It means that the LUR model with our buffer selection strategy can be used as a fit method to better describe spatial variability in atmospheric pollutant levels, which will be conducive to epidemiological research and urban environmental planning.