Analysis of lymph node metastasis and survival prognosis in early gastric cancer patients: A retrospective study.

BACKGROUND: Early gastric cancer (EGC) is a common malignant tumor of the digestive system, and its lymph node metastasis and survival prognosis have been concerning. By retrospectively analyzing the clinical data of EGC patients, we can better understand the status of lymph node metastasis and its impact on survival and prognosis. AIM: To evaluate the prognosis of EGC patients and the factors that affect lymph node metastasis. METHODS: The clinicopathological data of 1011 patients with EGC admitted to our hospital between January 2015 and December 2023 were collected in a retrospective cohort study. There were 561 males and 450 females. The mean age was 58 ± 11 years. The patient underwent radical gastrectomy. The status of lymph node metastasis in each group was determined according to the pathological examination results of surgical specimens. The outcomes were as follows: (1) Lymph node metastasis in EGC patients; (2) Analysis of influencing factors of lymph node metastasis in EGC; and (3) Analysis of prognostic factors in patients with EGC. Normally distributed measurement data are expressed as mean ± SD, and a t test was used for comparisons between groups. The data are expressed as absolute numbers or percentages, and the chi-square test was used for comparisons between groups. Rank data were compared using a nonparametric rank sum test. A log-rank test and a logistic regression model were used for univariate analysis. A logistic stepwise regression model and a Cox stepwise regression model were used for multivariate analysis. The Kaplan-Meier method was used to calculate the survival rate and construct survival curves. A log-rank test was used for survival analysis. RESULTS: Analysis of influencing factors of lymph node metastasis in EGC. The results of the multifactor analysis showed that tumor length and diameter, tumor site, tumor invasion depth, vascular thrombus, and tumor differentiation degree were independent influencing factors for lymph node metastasis in patients with EGC (odds ratios = 1.80, 1.49, 2.65, 5.76, and 0.60; 95%CI: 1.29-2.50, 1.11-2.00, 1.81-3.88, 3.87-8.59, and 0.48-0.76, respectively; P < 0.05). Analysis of prognostic factors in patients with EGC. All 1011 patients with EGC were followed up for 43 (0-13) months. The 3-year overall survival rate was 97.32%. Multivariate analysis revealed that age > 60 years and lymph node metastasis were independent risk factors for prognosis in patients with EGC (hazard ratio = 9.50, 2.20; 95%CI: 3.31-27.29, 1.00-4.87; P < 0.05). Further analysis revealed that the 3-year overall survival rates of gastric cancer patients aged > 60 years and ≤ 60 years were 99.37% and 94.66%, respectively, and the difference was statistically significant (P < 0.05). The 3-year overall survival rates of patients with and without lymph node metastasis were 95.42% and 97.92%, respectively, and the difference was statistically significant (P < 0.05). CONCLUSION: The lymph node metastasis rate of EGC patients was 23.64%. Tumor length, tumor site, tumor infiltration depth, vascular cancer thrombin, and tumor differentiation degree were found to be independent factors affecting lymph node metastasis in EGC patients. Age > 60 years and lymph node metastasis are independent risk factors for EGC prognosis.

Structure, Microheterogeneity, and Transport Properties of Ethaline Decoded by X-ray/Neutron Scattering and MD Simulation.

Ethaline, a deep eutectic solvent (DES) composed of choline chloride (ChCl)-ethylene glycol (EG) in a 1:2 molar ratio, is garnering significant interest for its wide potential applications. The nature of liquid formation and the structure of H-bonds within ethaline were investigated by X-ray scattering (XRS), neutron scattering (NS), and MD simulations. The sum of the dissociation energy barriers of Ch-EG (3.31 kJ·mol-1) and EG-Cl (4.28 kJ·mol-1) exceeds that of Ch-Cl (5.97 kJ·mol-1). This results in a more pronounced solvation of ChCl by EG compared to ChCl association, facilitating the solubilization of ChCl crystals by EG to form a DES. A partial radial distribution function (PDF) reveals that Cl- solvation is dominated by the hydroxyl group of EG, while the methylene group dominates Ch+ solvation. The spatial distribution function (SDF) shows that the distribution of EG and Cl- around Ch+ partially overlaps with that of the quaternary ammonium group. However, the center of mass distance of Ch-Cl (4.95 Å) is significantly lower than that of Ch-EG (5.65 Å), suggesting a favorable advantage for Cl- in this competition. Chain and ring structure distributions provide direct evidence of the microheterogeneity of ethaline. Hydroxyl groups on the EG promote the formation of a chain structure in ethaline, while methylene groups favor a ring structure. H-bond, carbon H-bond, and Cl- bridge bond restrict Cl- diffusion. This new understanding is crucial for a deeper comprehension of the microstructure of ethaline and for elucidating its mechanisms in applications.

Bimetallic Photo-Activated and Steerable Janus Micromotors as Active Microcleaners for Wastewater.

Photoactive colloidal motors whose motion can be controlled and even programed via external magnetic fields have significant potential in practical applications extending from biomedical fields to environmental remediation. Herein, we report a "three in one" strategy in a Co/Zn-TPM (3-trimethoxysilyl propyl methacrylate) bimetallic Janus colloidal micromotor (BMT-micromotor) which can be controlled by an optical field, chemical fuel, and magnetic field. The speed of the micromotors can be tuned by light intensity and with the concentration of the chemical fuel of H2O2, while it could be steered and programed through magnetic field due to the presence of Co in the bimetallic part. Finally, the BMT-micromotors were employed to effectively remove rubidium metal ions and organic dyes (methylene blue and rhodamine b). Benefited of excellent mobility, multiple active sites, and hierarchical morphology, the micromotors exhibit excellent adsorption capacity of 103 mg·g-1 to Rb metal ions and high photodegradation efficiency toward organic dyes in the presence of a lower concentration of H2O2. The experimental characterizations and DFT calculations confirmed the strong interaction of Rb metal ions on the surface of BMT-micromotors and the excellent decomposition of H2O2 which enhanced the photodegradation process. We expect the combination of light and fuel sensitivity with magnetic controllability to unlock an excess of opportunities for the application of BMT-micromotors in water treatments.

Deciphering the structure and potassium-ion transport mechanism of potassium borate glass.

Potassium borate glass has great potential as an ion transport material. The ion transport rate is closely related to the microstructure of the glass. However, the disorder and variations in boron and oxygen atom types in the glass structure pose challenges in the analysis of this complex glass structure. In this work, the structure of potassium borate glass was unveiled through the neutron diffraction method and ab initio molecular dynamics (AIMD) simulations. The B-O, K-O, and O-O atomic interactions, bond lengths, coordination numbers, cavity distribution, ring structure distributions and other detailed information in the microstructure of potassium borate glass were obtained. By comparing the structure and properties of potassium borate glass with those of crystals of similar components, it is found that the bond lengths of 3B-BO (BO, bridging oxygen), 4B-BO and 3B-NBO (NBO, non-bridging oxygen) are longer than those of corresponding crystals, so the structure of the boron-oxygen network is looser and the density is smaller than that of similar crystals. Moreover, we found a rule that in both borate glass and crystal, the increase of NBO shortened the length of the B-O bond, and the increase of 4B increased the length of the B-O bond. The key structures affecting the transport rate of K+ were NBO, chain structure units and cavities. This work will provide reference data for designing and developing electrically conductive amorphous materials with faster potassium-ion transport rates.

BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection.

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.

Optimal stochastic power flow using enhanced multi-objective mayfly algorithm.

For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case. They are mainly a combination of 2-4 objectives from fuel cost, emissions, power loss and voltage deviation (VD). In addition, reasonable prediction of renewable power is a major difficulty due to the discontinuous, disordered and unstable nature of renewable energy. In this paper, the Weibull probability distribution function (PDF) and lognormal PDF are applied to evaluate the available wind and available solar power, respectively. In this paper, an enhanced multi-objective mayfly algorithm (NSMA-SF) based on non-dominated sorting and the superiority of feasible solutions is implemented to tackle the MOOPF problem with wind and solar energy. The algorithm NSMA-SF is applied to the modified IEEE-30 and standard IEEE-57 bus test systems. The simulation results are analyzed and compared with the recently reported MOOPF results.

Bald eagle search algorithm for solving a three-dimensional path planning problem.

Three-dimensional path planning refers to determining an optimal path in a three-dimensional space with obstacles, so that the path is as close to the target location as possible, while meeting some other constraints, including distance, altitude, threat area, flight time, energy consumption, and so on. Although the bald eagle search algorithm has the characteristics of simplicity, few control parameters, and strong global search capabilities, it has not yet been applied to complex three-dimensional path planning problems. In order to broaden the application scenarios and scope of the algorithm and solve the path planning problem in three-dimensional space, we present a study where five three-dimensional geographical environments are simulated to represent real-life unmanned aerial vehicles flying scenarios. These maps effectively test the algorithm's ability to handle various terrains, including extreme environments. The experimental results have verified the excellent performance of the BES algorithm, which can quickly, stably, and effectively solve complex three-dimensional path planning problems, making it highly competitive in this field.

Hydrolysis mechanism of the cyclohexaborate anion: Unraveling the intricacies.

B 6 O 7 OH 6 2 - is a highly polymerized borate anion of three six-membered rings. Limited research on the B 6 O 7 OH 6 2 - hydrolysis mechanism under neutral solution conditions exists. Calculations based on density functional theory show that B 6 O 7 OH 6 2 - undergoes five steps of hydrolysis to form H3BO3 and B OH 4 - . At the same time, there are a small number of borate ions with different degrees of polymerization during the hydrolysis process, such as triborate, tetraborate, and pentaborate anions. The structure of the borate anion and the coordination environment of the bridging oxygen atoms control the hydrolysis process. Finally, this work explains that in existing experimental studies, the reason for the low B 6 O 7 OH 6 2 - content in solution environments with low total boron concentrations is that it depolymerizes into other types of borate ions and clarifies the borate species. The conversion relationship provides a basis for identifying the possibility of various borate ions existing in the solution. This work also provides a certain degree of theoretical support for the cause of the "dilution to salt" phenomenon.

NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm.

Addressing the challenge of efficiently solving multi-objective optimization problems (MOP) and attaining satisfactory optimal solutions has always posed a formidable task. In this paper, based on the chicken swarm optimization algorithm, proposes the non-dominated sorting chicken swarm optimization (NSCSO) algorithm. The proposed approach involves assigning ranks to individuals in the chicken swarm through fast non-dominance sorting and utilizing the crowding distance strategy to sort particles within the same rank. The MOP is tackled based on these two strategies, with the integration of an elite opposition-based learning strategy to facilitate the exploration of optimal solution directions by individual roosters. NSCSO and 6 other excellent algorithms were tested in 15 different benchmark functions for experiments. By comprehensive comparison of the test function results and Friedman test results, the results obtained by using the NSCSO algorithm to solve the MOP problem have better performance. Compares the NSCSO algorithm with other multi-objective optimization algorithms in six different engineering design problems. The results show that NSCSO not only performs well in multi-objective function tests, but also obtains realistic solutions in multi-objective engineering example problems.

MIT: Mutual Information Topic Model for Diverse Topic Extraction.

To automatically mine structured semantic topics from text, neural topic modeling has arisen and made some progress. However, most existing work focuses on designing a mechanism to enhance topic coherence but sacrificing the diversity of the extracted topics. To address this limitation, we propose the first neural-based topic modeling approach purely based on mutual information maximization, called the mutual information topic (MIT) model, in this article. The proposed MIT significantly improves topic diversity by maximizing the mutual information between word distribution and topic distribution. Meanwhile, MIT also utilizes Dirichlet prior in latent topic space to ensure the quality of mined topics. The experimental results on three publicly benchmark text corpora show that MIT could extract topics with higher coherence values (considering four topic coherence metrics) than competitive approaches and has a significant improvement on topic diversity metric. Besides, our experiments prove that the proposed MIT converges faster and more stable than adversarial-neural topic models.

Advances in Slime Mould Algorithm: A Comprehensive Survey.

The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning.

With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA's performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor.

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem.

In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the traversal ability of the search space. Secondly, the dynamic disturbance factor is introduced to balance the exploratory and exploitative search ability of the algorithm. Finally, the unique nesting strategy of the cuckoo and Levy flight is introduced to enhance the search ability. AMRFOCS is tested on CEC2017 and CEC2020 benchmark functions, which is also compared and tested by using different dimensions and other state-of-the-art metaheuristic algorithms. Experimental results reveal that the AMRFOCS algorithm has a superior convergence rate and optimization precision. At the same time, the nonparametric Wilcoxon signed-rank test and Friedman test show that the AMRFOCS has good stability and superiority. In addition, the proposed AMRFOCS is applied to the three-dimensional WSN coverage problem. Compared with the other four 3D deployment methods optimized by metaheuristic algorithms, the AMRFOCS effectively reduces the redundancy of sensor nodes, possesses a faster convergence speed and higher coverage and then provides a more effective and practical deployment scheme.

Bio-Inspired Polyanionic Electrolytes for Highly Stable Zinc-Ion Batteries.

For zinc-ion batteries (ZIBs), the non-uniform Zn plating/stripping results in a high polarization and low Coulombic efficiency (CE), hindering the large-scale application of ZIBs. Here, inspired by biomass seaweed plants, an anionic polyelectrolyte alginate acid (SA) was used to initiate the in situ formation of the high-performance solid electrolyte interphase (SEI) layer on the Zn anode. Attribute to the anionic groups of -COO- , the affinity of Zn2+ ions to alginate acid induces a well-aligned accelerating channel for uniform plating. This SEI regulates the desolvation structure of Zn2+ and facilitates the formation of compact Zn (002) crystal planes. Even under high depth of discharge conditions (DOD), the SA-coated Zn anode still maintains a stable Zn stripping/plating behavior with a low potential difference (0.114 V). According to the classical nucleation theory, the nucleation energy for SA-coated Zn is 97 % less than that of bare Zn, resulting in a faster nucleation rate. The Zn||Cu cell assembled with the SA-coated electrode exhibits an outstanding average CE of 99.8 % over 1,400 cycles. The design is successfully demonstrated in pouch cells, where the SA-coated Zn exhibits capacity retention of 96.9 % compared to 59.1 % for bare Zn anode, even under the high cathode mass loading (>10 mg/cm2 ).

Hydration of Alkali Metal and Halide Ions from Static and Dynamic Viewpoints.

Ion hydration in aqueous solutions plays a paramount role in many fields. Despite many studies on ion hydration, the nature of ion hydration is not consistently understood at the molecular level. Combining neutron scattering (NS), wide-angle X-ray scattering (WAXS), and molecular dynamics (MD), we quantify the ionic hydration degree (hydration ability) systematically for a series of alkali metal and halide ions based on static and dynamic hydration numbers. The former is based on the orientational correlation of water molecules bound to an ion derived from the positional information from NS and WAXS. The latter is defined as the mean number of water molecules remaining in the first coordination shell of an ion over a residence time of bound water molecules around the ion from MD. The static and dynamic hydration numbers distinguish hydration from coordination and quantify the ionic hydration degree, which provides a valuable reference for understanding various phenomena in nature.

Functional extreme learning machine.

Introduction: Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. However, the ELM also has some shortcomings, such as structure selection, overfitting and low generalization performance. Methods: This article a new functional neuron (FN) model is proposed, we takes functional neurons as the basic unit, and uses functional equation solving theory to guide the modeling process of FELM, a new functional extreme learning machine (FELM) model theory is proposed. Results: The FELM implements learning by adjusting the coefficients of the basis function in neurons. At the same time, a simple, iterative-free and high-precision fast parameter learning algorithm is proposed. Discussion: The standard data sets UCI and StatLib are selected for regression problems, and compared with the ELM, support vector machine (SVM) and other algorithms, the experimental results show that the FELM achieves better performance.

Multi-objective pathfinder algorithm for multi-objective optimal power flow problem with random renewable energy sources: wind, photovoltaic and tidal.

In this paper, the multi-objective optimal power flow (MOOPF) problem optimization objectives focus on four optimization objectives: generation cost, emission, real power loss and voltage deviation (VD). Three renewable energy sources with successful industrial applications, including wind energy, solar energy, and tidal energy are introduced. Renewable energy supply is uncertain, so Weibull distribution probability, lognormal probability and Gumbel probability are used to calculate the instability and intermittency of wind energy, solar energy and tidal energy, respectively. The inclusion of four energy supplies on the IEEE-30 test system and the consideration of renewable energy reserves and penalty cost calculation improve the realism of the model. In order to obtain the control parameters that minimize the four optimization objectives, a named multi-objective pathfinder algorithm (MOPFA) based on elite dominance and crowding distance was proposed to solve this multi-objective optimization problem. Simulation results show the feasibility of the model, and MOPFA can get more evenly distributed Pareto front and provide more diverse solutions. A compromise solution was selected by the fuzzy decision system. Comparison with the recently published literature also shows that the proposed model can effectively reduce emissions and other indicators. In addition, the statistical test results show that MOPFA's multi-objective optimization performance ranks first. In solving this complex optimization problem, results show the MOPFA is superior to other multi-objective algorithms in optimization accuracy and speed.

Combined Gaussian Mixture Model and Pathfinder Algorithm for Data Clustering.

Data clustering is one of the most influential branches of machine learning and data analysis, and Gaussian Mixture Models (GMMs) are frequently adopted in data clustering due to their ease of implementation. However, there are certain limitations to this approach that need to be acknowledged. GMMs need to determine the cluster numbers manually, and they may fail to extract the information within the dataset during initialization. To address these issues, a new clustering algorithm called PFA-GMM has been proposed. PFA-GMM is based on GMMs and the Pathfinder algorithm (PFA), and it aims to overcome the shortcomings of GMMs. The algorithm automatically determines the optimal number of clusters based on the dataset. Subsequently, PFA-GMM considers the clustering problem as a global optimization problem for getting trapped in local convergence during initialization. Finally, we conducted a comparative study of our proposed clustering algorithm against other well-known clustering algorithms using both synthetic and real-world datasets. The results of our experiments indicate that PFA-GMM outperformed the competing approaches.

Alkali Metal Ion Recognition by 18-Crown-6 in Aqueous Solutions: Evidence from Local Structures.

The underlying recognition mechanisms of alkali metal ions by crown ethers in aqueous solutions are yet to be fully understood at the molecular level. We report direct experimental and theoretical evidence for the structure and recognition sequence of alkali metal ions (Li+, Na+, K+, Rb+, and Cs+) by 18-crown-6 in aqueous solutions by wide-angle X-ray scattering combined with an empirical potential structure refinement modeling and ab initio molecular dynamics simulation. Li+, Na+, and K+ are located in the negative potential cavity of 18-crown-6, with Li+ and Na+ deviating from the centroid of 18-crown-6 by 0.95 and 0.35 Å, respectively. Rb+ and Cs+ lie outside the 18-crown-6 ring and deviate from the centroid of 18-crown-6 by 0.05 and 1.35 Å, respectively. The formation of the 18-crown-6/alkali metal ion complexes is dominated by electrostatic attraction between the cations and the oxygen atoms (Oc) of 18-crown-6. Li+, Na+, K+, and Rb+ form the H2O···18-crown-6/cation···H2O "sandwich" hydrates, while water molecules only hydrate with Cs+ of the 18-crown-6/Cs+ complex on the same side of Cs+. Based on the local structure, the recognition sequence of 18-crown-6 for alkali metal ions in an aqueous solution follows K+ > Rb+ >Na+ >Li+, which is completely different from that (Li+ > Na+ > K+ > Rb+ > Cs+) in the gas phase, confirming that the solvation medium seriously affects the cation recognition of crown ethers. This work provides atomic insights into understanding the host-guest recognition and solvation behavior of crown ether/cation complexes.

Adaptive Image Enhancement Algorithm Based on Variable Step Fruit Fly Optimization Algorithm and Nonlinear Beta Transform.

Due to the traditional use of manual methods for the parameter adjustment of a nonlinear beta transform, which is inefficient and unstable, an adaptive image enhancement algorithm based on a variable step size fruit fly optimization algorithm and a nonlinear beta transform is proposed. Utilizing the intelligent optimization characteristics of the fruit fly algorithm, we automatically optimize the adjustment parameters of a nonlinear beta transform to achieve better image enhancement effects. Firstly, the dynamic step size mechanism is introduced into the fruit fly optimization algorithm (FOA) to obtain a variable step size fruit fly optimization algorithm (VFOA). Then, with the adjustment parameters of the nonlinear beta transform as the optimization object, and the gray variance of the image as the fitness function, an adaptive image enhancement algorithm (VFOA-Beta) is obtained by combining the improved fruit fly optimization algorithm with the nonlinear beta function. Finally, nine sets of photos were used to test the VFOA-Beta algorithm, while seven other algorithms were used for comparative experiments. The test results show that the VFOA-Beta algorithm can significantly enhance images and achieve better visual effects, which has a certain practical application value.