This study analyzes the impact of local thermal non-equilibrium on the bioconvection flow of hybrid nanofluid across a slender extending sheet containing gyrotactic bacteria using artificial neural networks trained using a Bayesian regularization backpropagation approach (ANN-BRS). The effects of magnetic fields, thermal radiation, and Hall current are all things related to fluid flow. The suggested model has particular applicability in microscale drug delivery systems, where gyrotactic microorganisms and hybrid nanofluid can be employed to control nutrition and medication dispersion under non-equilibrium temperature circumstances. It can be used in lab-on-chip and organ-on-chip technologies to improve bio-mixing and accurate heat control. The model also applies to micro-solar collectors and porous micro-heat exchangers, which use hybrid nanoparticles to boost thermal efficiency. It can also be used in bioreactors and biomedical cooling systems, where local thermal non-equilibrium effects and ANN-based prediction allow for precise control of heat, mass, and microbe transfer, resulting in optimal performance. Similarity transformations are used to convert the original nonlinear PDEs into non-dimensional ODEs and the bvp4c program is applied to numerically resolve the resulting boundary-value problem. The training, testing, and validation processes yield the expected outcomes for every scenario based on the chosen data points. Regression analysis, histograms of error, and mean square error (MSE) metrics are employed to assess the ANN-BRS model's outcome. The liquid phase heat thermal profile increases as the interphase heat transfer parameter values rise, while the solid phase thermal profile decreases.

Incorporating recycled polyethylene terephthalate (PET) into asphalt mixtures offers a sustainable approach to enhance pavement performance while reducing plastic waste. However, the mesoscale mechanisms governing the influence of PET on stiffness, deformation resistance, and fracture behavior remain unclear. In this study, a three-dimensional Discrete Element Method (DEM) framework was developed to investigate the constitutive response of PET-modified asphalt concrete through the explicit representation of aggregates, asphalt mortar, PET inclusions, and air voids. Two bonding schemes, the Contact Bond Model (CBM) and Parallel Bond Model (PBM), were implemented and compared in terms of stiffness, tensile strength, damage evolution, and crack propagation. The experimental dynamic modulus (|E*|), indirect tensile strength (ITS), resilient modulus (Mr), rutting, and moisture susceptibility tests were conducted for mixtures containing 0–10% PET by volume. The DEM microparameters were calibrated using |E*| and ITS data, whereas Mr, rut depth, and tensile strength ratio (TSR) were used for independent validation. The results show that PET incorporation increases the mixture stiffness, with the dynamic modulus rising from 3500 to 5159 MPa and improves the resilient response under repeated loading. ITS increased from 0.44 MPa for the control mixture to a peak value of 1.15 MPa at 6% PET before decreasing to 0.89 MPa at 10% PET due to interfacial weakening. The rut depth decreased consistently with increasing PET content, indicating enhanced resistance to permanent deformation, whereas the TSR values confirmed acceptable moisture durability. Mesoscale analyses revealed that PET modified the force-chain distribution and promoted interface-controlled damage at the PET–mortar contacts. Compared with CBM, PBM more accurately reproduces progressive stiffness degradation and distributed cracking. An optimum PET content of approximately 6% was identified, providing the best balance between stiffness enhancement, tensile resistance and durability. These findings provide mechanistic insights into PET-modified asphalt mixtures and support the development of performance-based sustainable pavement materials.

In recent times, the world has been dealing with multiple environmental crises, including rising global temperatures, excessive energy consumption, elevated urban temperatures, and increasing carbon emissions from the building industry. As buildings have a significant impact on energy use, it is essential to address these challenges. In particular, window glass plays a significant role in controlling heat transfer, regulating indoor temperatures and reducing the dependence on artificial cooling. Therefore, selecting the most suitable energy-efficient glass has become crucial for sustainable development. Construction technology has introduced specialized glass materials for windows, including smart window technologies that automatically regulate heat and light. This study aims to identify the most suitable energy-efficient glass using a fuzzy multi-criteria decision-making method. Here, eight different alternatives are evaluated against eleven conflicting criteria classified as performance enhancing criteria (PEC) and performance reducing criteria (PRC). The Simultaneous Evaluation of Criteria and Alternative (SECA) method is employed for determining criteria weights, and the COmbinative Distance-based ASsessment (CODAS) approach is utilized to evaluate ranks of the alternatives. To ensure reliability and robustness, the study performed comparative and sensitivity analyses. The results indicate that electrochromic (EC) windows (A4) are the most suitable smart glazing technology among the considered alternatives. The results help urban planners and architects select energy-efficient glass for smart windows.

The complete mitochondrial genome of the invasive terrestrial nemertean Geonemertes pelaensis Semper, 1863 (Nemertea: Prosorhochmidae) was sequenced from two specimens collected in geographically distant French overseas territories—Martinique in the Caribbean and New Caledonia in the South-West Pacific. In both specimens, the mitogenome contained 13 protein-coding genes, two rRNA genes, and 21 tRNA genes, and was unusually large, approaching 32 kb. The two genomes differed by only four single nucleotide polymorphisms and one indel. A comparison with 22 cox1 sequences available in GenBank confirmed this high level of genetic conservation, suggesting a recent introduction from related source populations. The extraordinary length of the mitogenome was largely attributable to two extended regions comprising only tRNA genes and long intergenic sequences. These results were contrasted with data from an unpublished SRA sequencing project (SRS20559370) of an unlocalized specimen identified as G. pelaensis; its reconstructed mitogenome was only 18 kb in length (14 kb shorter) and showed extensive sequence divergence. Phylogenetic analyses placed this specimen as the sister lineage to G. pelaensis, highlighting the need for further investigation of this taxon. In the Martinique specimen, several NUMTs (nuclear mitochondrial pseudogenes) were also detected, which could complicate future studies relying solely on Sanger sequencing. Sequencing additionally revealed prey DNA from the gut contents of both worms: the New Caledonian specimen had consumed an unidentified noctuid moth, while the Martinique specimen had likely fed on the invasive cockroach Periplaneta australasiae (Fabricius, 1775), itself an introduced species.

Background:Mycoplasma pneumoniae pneumonia (MPP) is a pulmonary inflammatory disease caused by Mycoplasma pneumoniae (Mp) infection that primarily involves the bronchi, alveoli, and pulmonary interstitium. It is the most common cause of community-acquired pneumonia in children. Andrographolide (AG) is a natural diterpenoid lactone with anti-inflammatory and immunomodulatory activities; however, its specific role and mechanism in MPP remain unclear. Objectives: This study aimed to investigate the mechanism by which AG inhibits pyroptosis in alveolar epithelial cells via the HMGB1/TLR4/NF-κB signaling pathway in the treatment of MPP in children. Methods: Mp-stimulated MLE-12 cells were treated with AG at 10, 20, or 50 μM. Cell viability and injury were assessed using CCK-8 and LDH assays, respectively. Protein expression levels of NLRP3, HMGB1, TLR4, and p-NF-κB p65 were determined by Western blotting, and cytokine levels (IL-6 and TNF-α) were measured by ELISA. In an Mp-infected mouse model, mice received AG at 25 or 50 mg/kg. Body weight, lung index, lung histopathology, pathway-related protein expression, and cytokine levels (IL-1β and TNF-α) in bronchoalveolar lavage fluid (BALF) were evaluated. Results: Mp significantly induced cytotoxicity, pyroptosis, and inflammation in vitro and in vivo (all P < 0.001). Andrographolide treatment dose-dependently reduced LDH release, suppressed HMGB1/TLR4/NF-κB/NLRP3 activation, and decreased proinflammatory cytokine levels (P < 0.05). In mice, AG improved survival-related metrics, ameliorated lung pathology, and inhibited pathway activity and cytokine secretion. Conclusions: Andrographolide mitigates MPP via the HMGB1/TLR4/NF-κB signaling pathway by inhibiting pulmonary epithelial cell pyroptosis and inflammation. These findings indicate its potential as a therapeutic agent.

This study scrutinizes the effect of thermal radiation and Stefan blowing on the chemical reactive flow of Boger nanofluid across a stretched sheet with Darcy Forchheimer medium and heat generation using an intelligent computational framework based on Artifice neural network—Bayesian regularization. Furthermore, Brownian motion and thermophoresis properties have been examined. The suggested model of how Stefan blowing affects the chemical reactive flow of a Boger nanofluid with thermophoresis effects and Brownian motion has useful applications in a number of industrial and engineering operations. In chemical reactors, nano-coating technologies, and polymer processing, this model is essential for improving heat and mass transport processes. While the Boger nanofluid model accurately depicts non-Newtonian behaviour pertinent to biofluids and complex lubricants, Stefan blowing consideration offers insights on evaporation or suction effects. For the purpose of maximizing nanoparticle dispersion in cooling systems, fuel cells, and medicinal devices like targeted drug delivery systems where exact control over particle motion and chemical reactivity is crucial, Brownian motion and thermophoresis are also critical. The velocity profile improves as the Stefan blowing parameter values rise, but the thermal and concentration profiles decrease.

Background: Age-related cardiomyocyte vulnerability is driven by a convergent triad of persistent oxidative stress, chronic low-grade inflammation, and senescence-like signaling, which together accelerate functional decline and maladaptive remodeling. Angiotensin II (Ang II), a clinically relevant stress effector, amplifies reactive oxygen species (ROS) production and inflammatory activation, thereby impairing cardiomyocyte survival and repair capacity. Although metformin has emerging cardiovascular benefits beyond glycemic control, its coordinated capacity to counteract Ang II-driven inflammaging-like injury through the coupled regulation of antioxidant and anti-inflammatory pathways in human cardiomyocytes remains insufficiently defined. Objectives: This study aimed to determine whether metformin attenuates Ang II-induced injury and impaired repair in human AC16 cardiomyocytes and to evaluate whether this protection is associated with coordinated activation of Nuclear Factor Erythroid 2-Related Factor 2 (Nrf2) and suppression of Nuclear Factor-κB (NF-κB). Methods: Human AC16 cardiomyocytes were preconditioned with metformin (0.5 or 1.0 mM) for 2 h and then challenged with Ang II (0.1 - 2.0 μM) under continuous metformin exposure. The primary efficacy endpoints were cell survival, quantified using the MTT assay, and repair competence, assessed by scratch wound closure. Secondary mechanistic endpoints included inflammatory mediators (TNFA, IL6, and IL1B); senescence-associated markers (CDKN2A/p16 and CDKN1A/p21); antioxidant genes linked to Nrf2 signaling (NFE2L2/Nrf2, HMOX1/HO-1, and NQO1); NF-κB pathway activation; antioxidant and apoptosis-related proteins; Nrf2 compartmentalization; and intracellular ROS. Results: Angiotensin II induced a dose-dependent injury phenotype in AC16 cardiomyocytes, reducing viability from 100.0 ± 1.1% in control cells to 64.9 ± 1.6% at 1.0 μM and 53.0 ± 1.6% at 2.0 μM, while markedly impairing wound closure (80.0 ± 2.0% in control vs 32.3 ± 2.5% with Ang II). Metformin attenuated this injury in a concentration-dependent manner, restoring viability to 81.7 ± 1.0% with 0.5 mM and 89.7 ± 1.0% with 1.0 mM and improving wound closure to 52.3 ± 2.5% and 70.0 ± 2.0%, respectively. Angiotensin II also robustly increased inflammatory cytokine expression, with TNFA, IL6, and IL1B increasing to 27.86 ± 2.79-fold, 29.86 ± 2.99-fold, and 29.86 ± 2.99-fold, respectively, accompanied by NF-κB activation, ROS accumulation (187.7 ± 2.5% of control), apoptosis-associated signaling, and upregulation of the senescence-like markers p16 and p21. Metformin markedly suppressed these responses, reducing cytokine expression toward near-baseline levels, lowering ROS to 120.0 ± 2.0%, decreasing the BAX/BCL-2 ratio from 2.98 to 1.19, and reducing cleaved caspase-3 and cleaved PARP to 120% and 112% of control, respectively. In parallel, metformin enhanced Nrf2-associated antioxidant signaling, increasing NFE2L2 to 2.14 ± 0.17-fold, HMOX1 to 4.29 ± 0.30-fold, and NQO1 to 3.81 ± 0.27-fold, consistent with enhanced antioxidant defense under Ang II stress. Conclusions: Metformin attenuated Ang II-driven cardiac inflammaging-like injury and impaired repair, in association with reduced inflammatory signaling and enhanced activation of antioxidant pathways. These findings support a mechanistically coherent model in which NF-κB suppression and Nrf2 activation may contribute to metformin-mediated protection; however, direct pathway dependence requires confirmation through perturbation-based studies.

Conventional sludge disposal, including incineration and landfilling, is unsustainable and can cause secondary pollution; thus, sludge solidification is emerging as a sustainable alternative. This study aims to combine machine learning (ML) and metaheuristic optimization to maximize the unconfined compressive strength (UCS) of municipal sludge modified with slag, desulfurized gypsum, and fly ash. A total of 190 specimens were tested, and predictive models based on Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), LightGBM, XGBoost, CatBoost, K-Nearest Neighbors (KNN), and Histogram Gradient Boosting (HistGBoost) were coupled with the Whale Optimization Algorithm (WOA). In addition, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gazelle optimization algorithm (GOA), Octopus Optimization Algorithm (OOA), Hiking Optimization Algorithm (HOA), and Young’s double-slit experiment optimizer (YDSE) were applied for comparison. Sensitivity analysis identified optimal WOA–ML parameter settings. The results demonstrated that the WOA–RF model outperformed all metaheuristic and other WOA–ML approaches by achieving the highest predicted UCS (8.29851 MPa). The WOA-ML models yielded an average optimal mix comprising sludge (44.2%), gypsum (19%), slag (18.7%), fly ash (16%), and NaOH (2.1%). Among the metaheuristic algorithms, PSO, GOA, OOA, TJO, DOA, GA, and YDSE demonstrated competitive performance. GWO achieved the highest UCS (8.226109 MPa), while HOA yielded the lowest (5.15366 MPa). The optimal mix averaged 38.9% sludge, 23.7% gypsum, 21.6% fly ash, 13.4% slag, and 2.5% NaOH. Partial dependence analysis confirmed the nonlinear effects of these parameters, while SHAP sensitivity analysis validated the optimization results. RSM validation further confirmed that both WOA–ML and metaheuristic approaches reliably predict the optimal UCS of modified sludge.

The growing pace of environmental crisis around the world has aggravated the necessity of more vigorous environmental diplomacy and stringency in policy to develop renewable energy and promote sustainable growth in leading economies. This research study examines the relationship between financial globalization (FG), environmental diplomacy (ED), economic growth (GDP), environmental policy stringency (EPS), urbanization (URB), and renewable energy (RE) and ecological sustainability in G20 countries between 1995 and 2023. Based on the CS-ARDL, FMOLS, and DOLS tests, we use the Load Capacity Factor (LCF) as a holistic sustainability measure and analyze the short- as well as longer-term dynamics. Prolonged outcomes reveal that FG, ED, GDP, and URB adversely affect LCF, which suggests an increase in ecological stress. Nonetheless, RE enhances LCF and EPS moderates the negative consequences of globalization. The positive effect of ED is small in the short-run, whereas EPS will have a high contribution to ecological benefits. The ED-GDP relation indicates a long-term worsening of the environment, which underscores the inefficiencies of diplomatic enforcement. These results confirm the modulating effect of stringent environmental policies and the necessity to develop policy frameworks that would harmonize economic integration and sustainability. Urbanization is a threat to the environment unless controlled with sustainable planning, and renewable energy continues to be a major contributor to ecological health in the long run. The study provides practical recommendations to policymakers to incorporate strict regulation, green investment and environmental diplomacy in the strategies of sustainable development.