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The construction industry is exploring alternatives to traditional steel reinforcement in concrete due to steel’s corrosion vulnerability. Glass Fiber Reinforced Polymer (GFRP) and Basalt Fiber Reinforced Polymer (BFRP), known for their high tensile strength and corrosion resistance, are viable options. This study evaluates the flexural performance of concrete beams reinforced with GFRP, BFRP, and hybrid systems combining these materials with steel, following ACI 440.1R-15 guidelines. Twelve beams were assessed under three-point bending to compare their flexural strength, ductility, and failure modes against steel reinforcement. The results indicate that GFRP and BFRP beams achieve 8% and 12% higher ultimate load capacities but 38% and 58% lower deflections at failure than steel, respectively. Hybrid reinforcements enhance both load capacity and deflection performance (7% to 17% higher load with 11% to 58% lower deflection). However, GFRP and BFRP beams show reduced energy absorption, suggesting that hybrid systems could better support critical applications like seismic and impact-prone structures by improving ductility and load handling. In addition, BFRP beams predominantly failed due to debonding and concrete crushing, while GFRP beams failed due to bar rupture, reflecting key differences in their flexural failure mechanisms. Full article

There is a global demand for reducing the adoption of traditional chemical insecticides in agriculture. Among the most promising alternatives, botanical insecticides have been increasingly gaining attention due to their efficacy combined with a more environmentally safe impact. Among the different botanical insecticides commercially available, oxymatrine is an alkaloid found in the roots of Sophora flavescens which exhibits wide insecticide activity. However, their side-effects on non-target organisms have not been extensively evaluated. Therefore, this study aimed to investigate in laboratory conditions the insecticidal potential of a commercial botanical insecticide (Matrine^®) based on ethanolic extract of S. flavescens roots at 0.2; 0.6; 1.0; 1.4; 1.8; and 2.2 L of commercial product per hectare to control third-instar larvae of Rachiplusia nu and its selectivity in the egg parasitoid Trichogramma pretiosum. Overall, our results showed that the ethanolic extract of S. flavescens is an efficient tool to control R. nu from 0.6 to 2.2 L/ha, with similar R. nu mortality at 48 and 72 h after spraying (close to 100% mortality) associated with no impact to pupae and minimum impact to adults (slightly harmful) of the egg parasitoid. The botanical insecticide was classified as harmless to the pupae and slightly harmful to the adults of T. pretiosum according to the International Organization for Biological Control (IOBC) protocols. Thus, the use of the ethanolic extract of S. flavescens emerges as a relevant alternative to control R. nu, which needs to be confirmed in future field trials. Full article

Soil-transmitted helminths (STHs) and Herpes Simplex Virus type 2 (HSV-2) are highly prevalent infections with overlapping distribution, particularly in resource-poor regions. STH/HSV-2 co-infections may impact female reproductive health. However, many aspects of STH/HSV-2 co-infections, including the role of microRNAs (miRNAs) in regulating female genital tract (FGT) immunity and their potential contribution to pathologies such as chronic inflammation, impaired mucosal defense, and reproductive tract cancers remain unclear. In this study we investigated the miRNA expression profiles in murine FGT tissues following single or co-infection with Nippostrongylus brasiliensis (Nb) and HSV-2 and explored predicted miRNA-mRNA targets and pathways. An analysis of miRNA sequencing data was conducted to determine differentially expressed (DE) miRNAs between infected FGT tissues and uninfected controls. Ingenuity Pathway Analysis was conducted to predict the immune-related target genes of the DE miRNAs and reveal enriched canonical pathways, top diseases, and biological functions. Selected representative DE miRNAs were validated using RT-qPCR. Our results showed a total of eight DE miRNAs (mmu-miR-218-5p, mmu-miR-449a-5p, mmu-miR-497a-3p, mmu-miR-144-3p, mmu-miR-33-5p, mmu-miR-451a, mmu-miR-194-5p, and mmu-miR-192-5p) in the comparison of Nb-infected versus uninfected controls; nine DE miRNAs (mmu-miR-451a, mmu-miR-449a-5p, mmu-miR-144-3p, mmu-miR-376a-3p, mmu-miR-192-5p, mmu-miR-218-5p, mmu-miR-205-3p, mmu-miR-103-3p, and mmu-miR-200b-3p) in the comparison of HSV-2-infected versus uninfected controls; and one DE miRNA (mmu-miR-199a-5p) in the comparison of Nb/HSV-2 co-infected versus uninfected controls (p-value < 0.05, |logFC| ≥ 1). Core expression analysis showed that, among other canonical pathways, the DE miRNAs and their predicted mRNA targets were involved in neutrophil degranulation, interleukin-4 and interleukin-13 signaling, natural killer cell signaling, interferon alpha/beta signaling, and ISGylation. Additionally, cancer was predicted as one of the significantly enriched diseases, particularly in the co-infected group. This is the first study to provide insights into the FGT miRNA profiles following Nb and HSV-2 single and co-infection, as well as the predicted genes and pathways they regulate, which may influence host immunity and pathology. This study highlights the role of miRNAs in regulating FGT immunity and pathology in the context of STH/HSV-2 co-infection. Full article

The salinization of agricultural soils is a serious threat to farming and ecological balance in arid and semi-arid regions. Accurate estimation of soil water-soluble ions (calcium, carbonate, magnesium, and sulfate) is necessary for correct monitoring of soil salinization and sustainable land management. Hyperspectral ground-based data are valuable in soil salinization monitoring, but the acquisition cost is high, and the coverage is small. Therefore, this study proposes a two-stage deep learning framework with multispectral remote-sensing images. First, the wavelet transform is used to enhance the Transformer and extract fine-grained spectral features to reconstruct the ground-based hyperspectral data. A comparison of ground-based hyperspectral data shows that the reconstructed spectra match the measured data in the 450–998 nm range, with R² up to 0.98 and MSE = 0.31. This high similarity compensates for the low spectral resolution and weak feature expression of multispectral remote-sensing data. Subsequently, this enhanced spectral information was integrated and fed into a novel multiscale self-attentive Transformer model (MSATransformer) to invert four water-soluble ions. Compared with BPANN, MLP, and the standard Transformer model, our model remains robust across different spectra, achieving an R² of up to 0.95 and reducing the average relative error by more than 30%. Among them, for the strongly responsive ions magnesium and sulfate, R² reaches 0.92 and 0.95 (with RMSE of 0.13 and 0.29 g/kg, respectively). For the weakly responsive ions calcium and carbonate, R² stays above 0.80 (RMSE is below 0.40 g/kg). The MSATransformer framework provides a low-cost and high-accuracy solution to monitor soil salinization at large scales and supports precision farmland management. Full article

Breast cancer remains one of the most prevalent malignancies worldwide, and radiation therapy is a central component of its management. However, intrinsic or acquired resistance to radiation significantly compromises therapeutic efficacy. This systematic review aimed to identify and evaluate molecular mechanisms and interventions that influence radiation sensitivity in breast cancer models. A comprehensive PubMed search was conducted using the terms “breast cancer” and “radiation resistance” for studies published between 2002 and 2024. Seventy-nine eligible studies were included. The most frequently investigated mechanisms included the dysregulation of the PI3K/AKT/mTOR and MAPK signaling pathways, enhanced DNA damage repair via non-homologous end joining (NHEJ), and the overexpression of cancer stem cell markers such as CD44⁺/CD24⁻/low and ALDH1. Several studies highlighted the role of non-coding RNAs, particularly the lncRNA DUXAP8 and microRNAs such as miR-21, miR-144, miR-33a, and miR-634, in modulating radiation response. Components of the tumor microenvironment, including cancer-associated fibroblasts and immune regulators, also contributed to radiation resistance. By synthesizing current evidence, this review provides a consolidated resource to guide future mechanistic studies and therapeutic development. This review highlights promising molecular targets and emerging strategies to enhance radiosensitivity and offers a foundation for translational research aimed at improving outcomes in radiation-refractory breast cancer. Full article

Existing Simultaneous Localization and Mapping (SLAM) algorithms provide precise local pose estimation and real-time scene reconstruction, widely applied in autonomous navigation for land vehicles. However, the odometry of SLAM algorithms exhibits localization drift and error divergence over long-distance operations due to the lack of inherent global constraints. In this paper, we propose a real-time geo-localization method for land vehicles, which only relies on a LiDAR-inertial-visual SLAM (LIV-SLAM) and a referenced image. The proposed method enables long-distance navigation without requiring GPS or loop closure, while eliminating accumulated localization errors. To achieve this, the local map constructed by SLAM is real-timely projected onto a downward-view image, and a highly efficient cross modal matching algorithm is proposed to estimate the global position by aligning the projected local image to a geo-referenced satellite image. The cross-modal algorithm leverages dense texture orientation features, ensuring robustness against cross-modal distortion and local scene changes, and supports efficient correlation in the frequency domain for real-time performance. We also propose a novel adaptive Kalman filter (AKF) to integrate the global position provided by the cross-modal matching and the pose estimated by LIV-SLAM. The proposed AKF is designed to effectively handle observation delays and asynchronous updates while simultaneously rejecting the impact of erroneous matches through an Observation-Aware Gain Scaling (OAGS) mechanism. We verify the proposed algorithm through R³LIVE and NCLT datasets, demonstrating superior computational efficiency, reliability, and accuracy compared to existing methods. Full article

Nokoué Lake is located in the south of Benin and is fed by the Ouémé and Sô Rivers. Its hydrosedimentary dynamics were modelled using Telemac2D, incorporating the main environmental factors of this complex ecosystem. The simulations accounted for flow rates and suspended solids concentrations during periods of high and low water. The main factors controlling sediment transport were identified. The model was validated using field measurements of water levels and suspended solids. The results show that the north–south current velocity ranges from 0.5 to 1 m/s during periods of high water and 0.1 to 0.5 m/s during low-water periods. Residual currents are influenced by rainfall, river discharge, and tides. Complex circulation patterns are caused by increased river flow during high water, while tides dominate during low water and transitional periods. The northern, western, and south-eastern parts of the lake have weak residual currents and are, therefore, deposition zones for fine sediments. The estimated average annual suspended solids load for 2022–2023 is 17 Mt. The model performance shows a strong agreement between the observed and simulated values: R² = 0.91 and NSE = 0.93 for water levels and R² = 0.86 and NSE = 0.78 for sediment transport. Full article

Let us consider the finite commutative ring R, whose unity is $1\ne 0.$ Its weakly zero-divisor graph, represented as $W\Gamma \left(R\right)$, is a basic undirected graph with two distinct vertices, $c_1$ and $c_2$, that are adjacent if and only if there exist $r\in$ ann$\left(c_1\right)$ and $s\in$ ann$\left(c_2\right)$ that satisfy the condition $rs=0$. Let $D\left(G\right)$ be the distance matrix and $Tr\left(G\right)$ be the diagonal matrix of the vertex transmissions in basic undirected connected graph G. The $D_{\alpha }$ matrix of graph G is defined as $D_{\alpha }\left(G\right)=\alpha Tr\left(G\right)+(1-\alpha )D\left(G\right)$ for $\alpha \in [0,1]$. This article finds the $D_{\alpha }$ spectrum for the graph $W\Gamma \left({\mathbb{Z}}_n\right)$ for various values of n and also shows that $W\Gamma \left({\mathbb{Z}}_n\right)$ for $n={\vartheta }_1{\vartheta }_2{\vartheta }_3\cdots {\vartheta }_t{\eta }_1^{d_1}{\eta }_2^{d_2}\cdots {\eta }_s^{d_s}(d_i\ge 2,t\ge 1,s\ge 0)$, where ${\vartheta }_i$’s and ${\eta }_i$’s are the distinct primes, is $D_{\alpha }$ integral. Full article

Air pollution, particularly fine (PM_2.5) and coarse (PM₁₀) particulate matter, poses significant risks to public health and environmental sustainability. This study aims to develop robust predictive and forecasting models for hourly PM concentrations in Craiova, Romania, using advanced hybrid Artificial Intelligence (AI) approaches. A five-year dataset (2020–2024), comprising 20 meteorological and pollution-related variables recorded by four air quality monitoring stations, was analyzed. The methodology consists of three main phases: (i) data preprocessing, including anomaly detection and missing value handling; (ii) exploratory analysis to identify trends and correlations between PM concentrations (PMs) and predictor variables; and (iii) model development using 23 machine learning and deep learning algorithms, enhanced by 50 feature selection techniques. A deep Nonlinear AutoRegressive Moving Average with eXogenous inputs (Deep-NARMAX) model was employed for multi-step-ahead forecasting. The best-performing models achieved R² values of 0.85 for PM_2.5 and 0.89 for PM₁₀, with low RMSE and MAPE scores, demonstrating high accuracy and generalizability. The GEO-based feature selection method effectively identified the most relevant predictors, while the Deep-NARMAX model captured temporal dynamics for accurate forecasting. These results highlight the potential of hybrid AI models for air quality management and provide a scalable framework for urban pollution monitoring, predicting, and forecasting. Full article

Water migration occurs in unsaturated loess subgrade due to extreme rainfall, making it prone to subgrade subsidence and other water damage disasters, which seriously impact road safety and sustainable development of the Loess Plateau. The study performed a rainfall test using a compacted loess subgrade model based on a self-developed water migration test device. The effects of extreme rainfall on the water distribution, wetting front, and infiltration rate in the subgrade were systematically explored by setting three rainfall intensities (4.6478 mm/h, 9.2951 mm/h, and 13.9427 mm/h, namely J1 stage, J2stage, and J3 stage), and a lateral water migration model was proposed. The results indicated that the range of water content change areas constantly expands as rainfall intensity and time increase. The soil infiltration rate gradually decreased, and the ratio of surface runoff to infiltration rainfall increased. The hysteresis of lateral water migration refers to the physical phenomenon in which the internal water response of the subgrade is delayed in time and space compared to changes in boundary conditions. The sensor closest to the side of the slope changed first, with the most significant fluctuations. The farther away from the slope, the slower the response and the smaller the fluctuation. The bigger the rainfall intensity, the faster the wetting front moved horizontally. The migration rate at the slope toe is the highest. The migration rate of sensor W3 increased by 66.47% and 333.70%, respectively, in the J3 stage compared to the J2 and J1 stages. The results of the model and the measured data were in good agreement, with the R² exceeding 0.90, which verifies the reliability of the model. The study findings are important for guiding the prevention and control of disasters caused by water damage to roadbeds in loess areas. Full article

Calendula officinalis L. is a widely used medicinal plant whose secondary metabolism and morphology are influenced by light. This study evaluated the effects of 2 and 4 h end-of-day (EOD) red/far-red (R:FR) and green (G) light on the growth, physiology, and phytochemical profile of hydroponically grown C. officinalis under a constant red/blue light background, compared with a red/blue control without EOD treatment. Morphological, physiological (gas exchange, chlorophyll fluorescence), biochemical (chlorophyll, anthocyanin), and chemical composition (attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and Gas Chromatography-Mass Spectrometry (GC-MS)) were evaluated. EOD G 2h enhanced photosynthetic pigments, anthocyanins, and biomass, while control plants showed higher phenolic content. EOD R:FR induced stem elongation but reduced pigment and metabolite accumulation. GC-MS revealed organ-specific metabolic specialization, with flowers displaying greater chemical diversity than leaves. EOD G favored sesquiterpene diversity in flowers, while EOD R:FR increased nitrogen-containing compounds and unsaturated fatty acids. Vibrational data supported these shifts, with spectral signatures of esters, phenolics, and lipid-related structures. Bioactive compounds, including α-cadinol and carboxylic acids, were identified across treatments. These findings demonstrate that EOD light modulates physiological and metabolic traits in C. officinalis, highlighting EOD G as an enhancer of biomass and phytochemical richness for pharmaceutical applications under controlled conditions. Full article

Wheat powdery mildew is a major fungal disease threatening global wheat production. To develop an effective and environmentally friendly control strategy, this study systematically evaluated the disease-suppressive efficacy of strong oxidative free radicals across a series of treatment parameters, including radical concentrations (3.0–8.0 mg/L), spraying durations (20–60 s), solution pH levels (5–8), spraying heights (0–20 cm), and treatment timings corresponding to different infection stages (0–120 h post-inoculation). Response surface methodology (RSM) was used to optimize these variables with the objective of maximizing disease control efficacy. The results showed that control efficacy increased with radical concentration up to 5.0 mg/L, beyond which a saturation effect was observed. The most effective conditions included a spraying duration of 50 s and a height of 6.5 cm. Maximum suppression was achieved when the treatment was applied within 0–12 h post-infection. Moreover, adjusting the solution pH to a range of 5–7 further enhanced the efficacy. The RSM-based predictive model demonstrated high accuracy (R² = 0.9942), and the optimized parameters—6.65 mg/L radical concentration, 50.84 s spraying duration, and treatment at 15.67 h post-infection—yielded a predicted control efficacy of 97.64%, with a validation error below 0.5%. This study provides a quantitative basis for the precise and sustainable deployment of free radical-based treatments in wheat disease management. Full article

E36 steel, widely used in shipbuilding and offshore structures, offers moderate strength and excellent low-temperature toughness. However, its welded joints are highly susceptible to fatigue failure. Cracks typically initiate at weld toes or within the heat-affected zone (HAZ), severely limiting the fatigue life of fabricated components. Traditional life prediction methods are complex, inefficient, and lack accuracy. This study proposes a machine learning (ML) framework for efficient fatigue life prediction of E36 welded joints. Welded specimens using SQJ501 filler wire on prepared E36 steel established a dataset from 23 original fatigue test data points. The dataset was expanded via Z-parameter model fitting, with data scarcity addressed using SMOTE. Pearson correlation analysis validated data relationships. After grid-optimized training on the augmented data, models were evaluated on the original dataset. Results demonstrate that the machine learning models significantly outperformed the Z-parameter formula (R² = 0.643, MAPE = 16.15%). The artificial neural network (R² = 0.972, MAPE = 4.45%) delivered the best overall performance, while the random forest model exhibited high consistency between validation (R² = 0.888, MAPE = 6.34%) and testing sets (R² = 0.897), with its error being significantly lower than that of support vector regression. Full article

The integration of computational intelligence techniques into pharmaceutical wastewater treatment offers promising opportunities to improve process efficiency and minimize operational costs. This study compares the predictive capabilities of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models in forecasting the rejection efficiencies of caffeine and paracetamol using AFC 40 and AFC 80 nanofiltration (NF) membranes. Experiments were conducted under varying operating conditions, including transmembrane pressure, feed concentration, and flow rate. The predictive performance of both models was evaluated using statistical metrics such as the Coefficient of Determination (R²), Root Mean Square Error (RMSE), Marquardt’s Percentage Squared Error Deviation (MPSED), Hybrid fractional error function (HYBRID), and Average Absolute Deviation (AAD). Both models demonstrated strong predictive accuracy, with R² values of 0.9867 and 0.9832 for RSM and ANN, respectively, in AFC 40 membranes, and 0.9769 and 0.9922 in AFC 80 membranes. While both approaches closely matched the experimental results, the ANN model consistently yielded lower error values and higher R² values, indicating superior predictive performance. These findings support the application of ANNs as a robust modelling tool in optimizing NF membrane processes for pharmaceutical removal. Full article

Choline has been recognized as an essential nutrient involved in various physiological functions critical to human health. Adequate daily intake of choline has been established by the US National Academy of Medicine in 1998, considering choline requirements for different ages, sex differences and physiological states (e.g., pregnancy). By serving as a precursor for acetylcholine and phospholipids, choline is important for cholinergic transmission and the structural integrity of cell membranes. In addition, choline is involved in lipid and cholesterol transport and serves as a methyl donor after oxidation to betaine. Extracellular choline is transported across the cell membrane via various transport systems (high-affinity and low-affinity choline transporters) with distinct features and roles. An adequate dietary intake of choline during pregnancy supports proper fetal development, and throughout life supports brain, liver, and muscle functions, while choline deficiency is linked to disease states like fatty liver. Choline has important roles in neurodevelopment, cognition, liver function, lipid metabolism, and cardiovascular health. While its signaling role has been considered mostly indirect via acetylcholine and phosphatidylcholine which are synthesized from choline, emerging evidence supports a role for choline as an intracellular messenger acting on Sigma-1R, a non-opioid intracellular receptor. These new findings expand the cell signaling repertoire and increase the current understanding of the role of choline while warranting more research to uncover the molecular mechanisms and significance in the context of GPCR signaling, the relevance for physiology and disease states. Full article