To quickly assess slope stability based on field displacement monitoring data, this paper constructs a hybrid optimization model that predicts surface displacement during tunnel excavation in base-overburden slopes. The model combines Wavelet Decomposition (WD) with a Gated Recurrent Unit (GRU), and the GRU\'s hyperparameters are optimized using an Improved Particle Swarm Optimization algorithm (IPSO). The specific steps are as follows: First, the Wavelet Decomposition (WD) technique is applied to decompose the raw displacement data, extracting features at different time-frequency scales. Next, the Dropout technique is incorporated into the GRU model to prevent overfitting. Additionally, nonlinear inertia weight ω improved cognitive factor c1, and social factor c2 are introduced. The PSO algorithm is improved by integrating crossover and mutation concepts from genetic algorithms. Finally, the IPSO is used to optimize the number of neural units hN, HN, LN and dropout rates D1 and D2 in the GRU network architecture. After constructing the WD-IPSO-GRU model, a comprehensive comparison is made with various swarm intelligence algorithms and state-of-the-art models. The experimental results demonstrate that the WD-IPSO-GRU model significantly improves the prediction accuracy of surface displacement in slopes during tunnel excavation. Compared to directly using raw data for prediction, the introduction of the WD preprocessing technique improved the prediction accuracy at measurement points 01 and 02 by 28% and 45.9%, respectively. Additionally, with the model optimized by IPSO, the prediction accuracy at measurement points 01 and 02 increased by 76% and 56.7%, respectively. The WD-IPSO-GRU model effectively addresses the challenges of extracting features from univariate displacement time-series data and determining the parameters of the GRU network. It improves the prediction accuracy of surface displacement in base-overburden type slopes and demonstrates excellent generalization ability and reliability. The research results validate the potential application of the model in geotechnical engineering and provide strong support for assessing slope stability during tunnel excavation.

In this work, intelligent numerical models for the prediction of debris flow susceptibility using slope stability failure factor of safety (FOS) machine learning predictions have been developed. These machine learning techniques were trained using novel metaheuristic methods. The application of these training mechanisms was necessitated by the need to enhance the robustness and performance of the three main machine learning methods. It was necessary to develop intelligent models for the prediction of the FOS of debris flow down a slope with measured geometry due to the sophisticated equipment required for regular field studies on slopes prone to debris flow and the associated high project budgets and contingencies. With the development of smart models, the design and monitoring of the behavior of the slopes can be achieved at a reduced cost and time. Furthermore, multiple performance evaluation indices were utilized to ensure the model\'s accuracy was maintained. The adaptive neuro-fuzzy inference system, combined with the particle swarm optimization algorithm, outperformed other techniques. It achieved an FOS of debris flow down a slope performance of over 85%, consistently surpassing other methods.

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work.

BACKGROUND: The wound healing process, restoring the functionality of the damaged tissue, can be accelerated by various compounds. The recent experimental analysis highlights the beneficial effects of phytochemicals in improving skin regeneration and wound healing. In traditional medicine, one of the widespread plants used for treating different injuries or skin afflictions is Galium aparine L. (GA). Besides, previously reported chemical compounds of GA suggested its therapeutic effects for the wound healing process, yet its regulatory effects on the cellular and molecular stages of the wound healing process have not been investigated.
METHODS: In the present study, the phytochemical profile of the GA extract was analyzed using HPTLC fingerprinting, and further scientific evaluation of its phytochemicals was done. The wound-healing effects of GA extract were explored at the cellular and molecular levels while accounting for cell toxicity. The wound closure enhancing effect, antibacterial activity, and antioxidant activity were assessed.
RESULTS: The HPTLC fingerprinting of the GA extract proved its previously reported phytochemical profile including phenols, flavonoids, tannins, plant acids, ergot alkaloids, flavonoids, anthraquinones, terpenoids, sterols, salicin, lipophilic compounds, saponins, iridoids, and heterocyclic nitrogen compounds. Antimicrobial assessment, of the extract, indicated the more susceptibility of S. aureus to the inhibitory effects of GA rather than E. coli and S. epidermidis. DPPH test results revealed the antioxidant property of GA extract, which was comparable to ascorbic acid. The results of the viability assay showed no cytotoxicity effects on human umbilical endothelial cell (HUVEC) and normal human dermal fibroblast (NHDF) cell lines treated with different concentrations of whole plant extract and cell viability increased in a dose-dependent manner. The results of the scratch assay showed improved cell migration and wound closure.
CONCLUSIONS: This study shows the anti-oxidant, anti-microbial, and in vitro wound healing wound-healing effects of GA hydroalcoholic extract, which aligns with its use in traditional medicine. No cytotoxicity effects were shown. The results from this study can be the basis for further investigations such as animal models and phytochemical studies. Further evaluation of its effects on mechanisms and signaling pathways involved in the wound healing processes such as angiogenesis and cell proliferation can provide novel insights into the potential therapeutic effects of the GA extract.

During nutrient scarcity, plants can adapt their developmental strategy to maximize their chance of survival. Such plasticity in development is underpinned by hormonal regulation, which mediates the relationship between environmental cues and developmental outputs. In legumes, endosymbiosis with nitrogen fixing bacteria (rhizobia) is a key adaptation for supplying the plant with nitrogen in the form of ammonium. Rhizobia are housed in lateral root-derived organs termed nodules that maintain an environment conducive to Nitrogenase in these bacteria. Several phytohormones are important for regulating the formation of nodules, with both positive and negative roles proposed for gibberellin (GA). In this study, we determine the cellular location and function of bioactive GA during nodule organogenesis using a genetically-encoded second generation GA biosensor, GIBBERELLIN PERCEPTION SENSOR 2 in Medicago truncatula. We find endogenous bioactive GA accumulates locally at the site of nodule primordia, increasing dramatically in the cortical cell layers, persisting through cell divisions and maintaining accumulation in the mature nodule meristem. We show, through mis-expression of GA catabolic enzymes that suppress GA accumulation, that GA acts as a positive regulator of nodule growth and development. Furthermore, increasing or decreasing GA through perturbation of biosynthesis gene expression can increase or decrease the size of nodules, respectively. This is unique from lateral root formation, a developmental program that shares common organogenesis regulators. We link GA to a wider gene regulatory program by showing that nodule-identity genes induce and sustain GA accumulation necessary for proper nodule formation.

OBJECTIVE: The main purpose of the current study was to investigate the effect of season change and the influence of the COVID-19 pandemic on the ratio of glycoalbumin to hemoglobin A1c (GA/HbA1c) in patients with type 2 diabetes.
METHODS: A total of 267 patients in whom both HbA1c and GA were measured at baseline were included in this retrospective study. GA/HbA1c was investigated for three years, 2018, 2019, and 2020 (COVID-19 pandemic period).
RESULTS: The mean values for GA/HbA1c per year in 2018, 2019, and 2020 were 2.64±0.35, 2.61±0.35, 2.64±0.39, respectively. There were no significant differences in GA/HbA1c during these years. There was a tendency toward seasonal variation in GA/HbA1c (i.e., higher in summer or autumn and lower in spring or winter).
CONCLUSIONS: In patients with type 2 diabetes, GA/HbA1c tended to show seasonal variation, which was not influenced by the COVID-19 pandemic.

In this research, an upgraded and environmentally friendly process involving WO3/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation. The primary goal of applying different models was to achieve the best conditions with high accuracy in Cefixime degradation. Based on R analysis, the quadratic factorial model in RSM was selected as the best model, and the regression coefficients obtained from it were used to evaluate the performance of artificial intelligence models. According to the quadratic factorial model, interactions between pH and time, pH and catalyst amount, as well as reaction time and catalyst amount were identified as the most significant factors in predicting results. In a comparison between the different models based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2 Score) indices, the SVR model was selected as the best model for the prediction of the results, with a higher R2 Score (0.98), and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter in the prediction of the results. According to the Genetic Algorithm, interactions between the initial concentration of Cefixime with reaction time, as well as between the initial concentration of Cefixime and catalyst amount, had the greatest impact on selecting the optimal values. Using the Genetic Algorithm and SOLVER models, the optimum values for the initial concentration of Cefixime, pH, time, and catalyst amount were determined to be (6.14 mg L-1, 3.13, 117.65 min, and 0.19 g L-1) and (5 mg L-1, 3, 120 min, and 0.19 g L-1), respectively. Given the presented results, this research can contribute significantly to advancements in intelligent decision-making and optimization of the pollutant removal processes from the environment.

Soybean, a major legume crop, has seen a decline in its production owing to challenges in seed germination and the development of seedlings. Thus, in this study, we systematically investigated the influence of various chitosan-S-nitrosoglutathione (chitosan-GSNO) nanoparticle (0, 25, 50, and 100 µM) and Si (0, 0.5, and 1 mM) priming concentrations on soybean seed germination and seedling growth over five different priming durations (range: 1-5 h at each concentration). Significant differences were observed in all parameters, except seedling diameter, with both treatments. Seed germination was significantly enhanced after 3 h of priming in both treatments. The final germination percentage (FGP), peak germination percentage (PGP), vigor index (VI), seedling biomass (SB), hypocotyl length (HL), and radical length (RL) of 100 μM chitosan-GSNO-nanoparticle-primed seeds increased by 20.3%, 41.3%, 78.9%, 25.2%, 15.7%, and 65.9%, respectively, compared with those of the control; however, the mean germination time (MGT) decreased by 18.43%. Si priming at 0.5 mM increased the FGP, PGP, VI, SB, HL, and RL by 13.9%, 55.17%, 39.2%, 6.5%, 22.5%, and 25.1%, respectively, but reduced the MGT by 12.29% compared with the control treatment. Chitosan-GSNO and Si treatment up-regulated the relative expression of gibberellic acid (GA)-related genes (GmGA3ox3 and GmGA2ox1) and down-regulated that of abscisic acid (ABA)-related genes (GmABA2, GmAAO3, and GmNCED5). Chitosan-GSNO and Si application increased bioactive GA4 levels and simultaneously reduced ABA content. Hence, the use of exogenous chitosan-GSNO nanoparticles and Si as priming agents had a beneficial effect on seed germination and seedling growth because of the up-regulation in the expression of GA and down-regulation in the expression of ABA. Additional research is needed to understand the combined impact of Si and chitosan-GSNO nanoparticles, including their effects on the expression levels of other hormones and genes even in the later growth stage of the crop.

Rice (Oryza sativa) is one of the most important crops for humans. The homologs of ent-kaurene synthase (KS) in rice, which are responsible for the biosynthesis of gibberellins and various phytoalexins, are identified by their distinct biochemical functions. However, the KS-Like (KSL) family\'s potential functions related to hormone and abiotic stress in rice remain uncertain. Here, we identified the KSL family of 19 species by domain analysis and grouped 97 KSL family proteins into three categories. Collinearity analysis of KSLs among Poaceae indicated that the KSL gene may independently evolve and OsKSL1 and OsKSL4 likely play a significant role in the evolutionary process. Tissue expression analysis showed that two-thirds of OsKSLs were expressed in various tissues, whereas OsKSL3 and OsKSL5 were specifically expressed in the root and OsKSL4 in the leaf. Based on the fact that OsKSL2 participates in the biosynthesis of gibberellins and promoter analysis, we detected the gene expression profiles of OsKSLs under hormone treatments (GA, PAC, and ABA) and abiotic stresses (darkness and submergence). The qRT-PCR results demonstrated that OsKSL1, OsKSL3, and OsKSL4 responded to all of the treatments, meaning that these three genes can be candidate genes for abiotic stress. Our results provide new insights into the function of the KSL family in rice growth and resistance to abiotic stress.

Voltage and reactive power regulation in a deregulated microgrid can be achieved by strategically placing the Static Synchronous Compensator (STATCOM) in coordination with other renewable energy sources, thus ensuring high-end stability and independent control. STATCOM plays a crucial role in effectively addressing power quality issues such as voltage fluctuation and reactive power imbalances caused by the intermittent nature of wind energy conversion systems. To successfully integrate STATCOM into the existing system, it is essential that the control system employed for STATCOM coordination aligns with the Doubly-Fed Induction Generator (DFIG) controller within the microgrid. Therefore, an efficient control algorithm is required in the microgrid, capable of coordinating with the DFIG controller while maintaining system stability. The utilization of a Genetic Algorithm (GA) in calibrating the Restricted Boltzmannn Machine (RBM) can streamline the process of determining optimal hyperparameters for specific tasks, eliminating the need for computationally intensive and time-consuming grid searches or manual tuning. This approach is particularly advantageous when dealing with large datasets within short time durations. In this research, a Simulink model comprising a DFIG-based microgrid and STATCOM has been developed to demonstrate the effectiveness of the proposed control system using RBM in managing STATCOM and facilitating microgrid operations.