Search Results (3,550)

Strain-induced deformations and phase evolutions are two hidden factors that may influence cytocompatibility of Gum Metal alloys during processing for relevant implant applications. In the present research, changes in cell viability of a new Gum Metal Ti-Nb-Zr alloy in its cold-rolled state and after heat treatments (at 700, 850, and 900 °C) were investigated by a comprehensive study of microstructural phases and their role in deformation mechanisms as well as mechanical properties. In its cold-rolled state, the alloy showed a lamellar microstructure along with stress-induced α″ martensite and ω phases, as confirmed by optical microscopy (OM) and X-ray diffractometry (XRD) analysis. The instability in the β phase led to a strain-induced martensitic (SIM) transformation from β to α′/α″ phases, causing lower viability of MG-63 cells compared with commercially pure titanium. MG-63 cell viability was significantly higher (p < 0.0001) in the alloy heat-treated at 900 °C compared with those heat-treated at 700 and 850 °C. This can be directly attributed to the increased portion of the stable and dominant β phase. The stabilized β phase greatly improved the alloy’s cellular response by reducing harmful phase interactions and maintaining mechanical compatibility with bone (admissible strain of 1.3%). Importantly, heat treatment at high temperatures (between 850 and 900 °C) effectively converted the stress-induced α″ and ω phases back into a stable β phase matrix as the dominant phase. Full article

The safety, genetic distinctiveness, and functional capabilities of Lactococcus sp. KTH0-1S, a strain isolated from Thai fermented shrimp (Kung-Som), were investigated to assess its potential as a next-generation probiotic and microbial cell factory. Whole-genome sequencing and multilocus sequence typing (MLST) analysis revealed that Lactococcus sp. KTH0-1S is a novel, phylogenetically distinct strain within the Lactococcus genus. Comprehensive in silico safety evaluation confirmed the absence of antimicrobial resistance genes and major virulence factors, supporting its suitability for food-grade applications. The genome encodes multiple probiotic-relevant traits, including stress tolerance (e.g., dnaK, clpP), adhesion and biofilm formation (e.g., gapA, luxS, glf2), and nutrient acquisition genes, enabling adaptation to gastrointestinal and fermentation environments. Notably, Lactococcus sp. KTH0-1S harbors a chromosomally encoded nisin Z biosynthesis gene cluster with auto-induction capability, providing a self-regulated and stable alternative to conventional plasmid-based NICE systems in Lactococcus lactis. The strain also exhibits nisin immunity, allowing tolerance to high nisin concentrations, thus supporting robust protein production. Genomic evidence and phenotypic assays confirmed a functional respiration metabolism activated by heme supplementation, enhancing biomass yield and culture stability. Furthermore, the presence of diverse CAZyme families (GHs, GTs, CEs) enables utilization of various carbohydrate substrates, including lignocellulosic and starchy agro-industrial residues. These properties collectively underscore Lactococcus sp. KTH0-1S as a safe, stable, and metabolically versatile candidate for probiotic applications and as a cost-effective, food-grade expression host for biotechnological production. Full article

To investigate the mechanism by which the rheological properties of drilling fluids affect the stability of the wellbore in brittle mud shale, this study systematically examines the influence of drilling fluids with different rheological properties on the hydration dispersion and rock strength of brittle mud shale through a series of laboratory experiments, including thermal rolling tests and uniaxial compressive strength tests on core samples. The results reveal that for weakly dispersible brittle mud shale, the rheological properties of drilling fluids have a minor effect on hydration dispersion, with rolling recovery rates consistently above 90%. However, the rheological properties of drilling fluids significantly influence the strength of brittle mud shale, and this effect is coupled with multiple factors, including rock fracture intensity index, soaking time, and confining pressure. Specifically, as the viscosity of the drilling fluid increases, the reduction in rock strength decreases; for instance, at 5 MPa confining pressure with an FII of 0.46, the strength reduction after 144 h was 69.8% in distilled water (from an initial 133.2 MPa to 40.2 MPa) compared to 36.3% with 3# drilling fluid (from 133.2 MPa to 88.7 MPa, with 100 mPa·s apparent viscosity). Both increased soaking time and confining pressure exacerbate the reduction in rock strength; a 5 MPa confining pressure, for example, caused an additional 60.9% strength reduction compared to 0 MPa for highly fractured samples (FII = 0.46) in distilled water after 144 h. Rocks with higher fracture intensity indices are more significantly affected by the rheological properties of drilling fluids. Based on the experimental results, this study proposes a strength attenuation model for brittle mud shale that considers the coupled effects of fracture intensity index, soaking time, and drilling fluid rheological properties. Additionally, the mechanism by which drilling fluid rheological properties influence the strength of brittle mud shale is analyzed, providing a theoretical basis for optimizing drilling fluid rheological parameters and enhancing the stability of wellbores in brittle mud shale formations. Full article

Chloride erodes steel bars through concrete pores, seriously affecting the durability of reinforced concrete structures. Improving the binding ability to chloride is an important measure. We explored the effects of W/C, curing age, and premixed Cl⁻ concentration on the compressive strength and Cl⁻ binding capacity in cement pastes. The results indicate that a premixed 5% concentration of Cl⁻ can improve the compressive strength, whereas an excessive Cl⁻ negatively impacts the mechanical properties. The total Cl⁻ content in cement pastes is a crucial factor that influences the binding ability of Cl⁻. When the total Cl⁻ content is within 2% (i.e., the premixed Cl⁻ concentration is 5%), the cement paste has a strong binding ability of Cl⁻. W/C and curing age indirectly affect the binding ability by affecting the total Cl⁻ content. Furthermore, with the increase in content of Cl⁻, the adsorption content of Cl⁻ by C-S-H increased, while the proportion of Cl⁻ bound by Fs to the total bound Cl⁻ initially declines and then tends to stabilize. It is worth noting that a premixed concentration of 5% is a “safety limit” for cement paste, but for reinforced concrete, the presence of free Cl⁻ above normative thresholds should not be underestimated. Full article

The response of the microscopic structure and macroscopic mechanical parameters of SRM under F–T cycles is a key factor affecting the safety and stability of engineering projects in cold regions. In this study, F–T tests, EIS, and uniaxial compression tests were conducted on SRM. The construct equivalent model of different conductive paths based on EIS was constructed. A peak strength prediction model was developed using characteristic parameters derived from the equivalent models, thereby revealing the mechanism by which F–T cycles influenced both microscopic structure and macroscopic strength. The results showed that with increasing cycles, both $R_{CP}$ and $R_{CPP}$ exhibited an exponential decreasing trend, whereas C_DSRP and D_f increased exponentially. Peak strength and peak secant modulus decreased exponentially, but peak strain increased exponentially. The expansion and interconnection of pores with different radii within $CPP$ and $CP$ caused smaller pores to evolve into larger ones while generating new pores, which led to a decline in $R_{CPP}$ and $R_{CP}$. Moreover, this expansion enlarged the soil–rock contact area by connecting adjacent gas-phase pores and promoted the transformation of $CSRPP$ into $DSRPP$, enhancing the parallel-plate capacitance effect and resulting in an increase in $C_{DSRP}$. Moreover, the interconnection increased the roughness of soil–soil and soil–rock contact surfaces, leading to a rising trend in $D_f$. The combined influence of $C_{DSRP}$ and $D_f$ yielded a strength prediction model with higher correlation than a single factor, providing more accurate predictions of UCS. However, the increases in $C_{DSRP}$ and $D_f$ induced by F–T cycles also contributed to microscopic structure damage and strength deterioration, reducing the load-bearing capacity and ultimately causing a decline in UCS. Full article

In this paper, an efficient hybrid photovoltaic (PV) power forecasting model is proposed to enhance the stability and accuracy of PV power prediction under typical weather conditions. First, the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is employed to decompose both meteorological features affecting PV power and the power output itself into intrinsic mode functions. This process enhances the stationarity and noise robustness of input data while reducing the computational complexity of subsequent model processing. To enhance the detail-capturing capability of the Bidirectional Long Short-Term Memory (BiLSTM) model and improve its dynamic response speed and prediction accuracy under abrupt irradiance fluctuations, we integrate a Temporal Convolutional Network (TCN) into the BiLSTM architecture. Finally, a Multi-head Self-Attention (MHA) mechanism is employed to dynamically weight multivariate meteorological features, enhancing the model’s adaptive focus on key meteorological factors while suppressing noise interference. The results show that the ICEEMDAN-TCN-BiLSTM-MHA combined model reduces the Mean Absolute Percentage Error (MAPE) by 78.46% and 78.59% compared to the BiLSTM model in sunny and cloudy scenarios, respectively, and by 58.44% in rainy scenarios. This validates the accuracy and stability of the ICEEMDAN-TCN-BiLSTM-MHA combined model, demonstrating its application potential and promotional value in the field of PV power forecasting. Full article

In this study, a new hybrid hydrogel based on PAM (polyacrylamide)-Ag-g/WS₂/Ti₃C₂T_x was synthesized by radical polymerization using a conductive heterostructural nanocomposite WS₂/Ti₃C₂T_x. The synergy between the polymer matrix and the interface between two-dimensional nanomaterials ensured the production of a hydrogel with high extensibility and conductivity, as well as sensory characteristics. The composite hydrogel exhibited excellent strain-sensing capabilities, with gauge factors of 1.4 at low strain and 2.8 at higher strain levels. In addition, the material showed a fast response time of 2.17 s and a short recovery time of 0.46 s under cyclic stretching, which confirms its high reliability and reproducibility. The integration of Ti₃C₂T_x and WS₂ promoted the formation of a conductive network in the hydrogel structure, which simultaneously increased its mechanical strength and signal stability under variable loads. Measurements confirm some potential of the PAM-Ag-g/WS₂/Ti₃C₂T_x composite hydrogel as a flexible wearable strain sensor. Based on measured numbers, we discussed the impact of the WS₂/Ti₃C₂T_x interface on the Gauge factor and conductivity of the composite. Theoretical modeling demonstrates significant changes in the electronic structure of the WS₂/Ti₃C₂T_x interface, and especially the WS₂ surface, induced by substrate strain. Possible applications of the peculiar properties of PAM-Ag-g/WS₂/Ti₃C₂T_x composite were proposed. Full article

This study distinguishes between observed, uncertain, and stochastic uncertain firm growth. Observed firm growth is measured via historical growth of fixed assets scaled by growth of sales revenue. Uncertain firm growth is the volatility of unobserved (estimated error terms) firm growth. The latter is simulated using nonuniform Monte Carlo to generate stochastic uncertain firm growth. The objective of this study is to examine the relationships among the firm specific, economic, and institutional factors that affect the uncertain and stochastic uncertain growth of a firm. The sample includes the nonfinancial firms listed in the DJIA30 and NASDAQ100, covering quarterly data from 1996Q1 to 2022Q4 for 121 companies. The results reveal that (a) sales growth, profitability, cash flow, and long-term financing help reduce a firm’s uncertain growth, (b) high involvement in exporting exposes firms to higher geopolitical uncertainty, (c) institutional quality (especially political stability and regulatory quality) paradoxically contribute to uncertain firm growth. This study contributes to related studies via offering perspectives to firm managers and policy makers about the factors that help manage the uncertainties of firm growth. Full article

Autonomous robotic manipulators in industrial environments face significant challenges, including time-varying payloads, multi-source disturbances, and real-time computational constraints. Traditional model predictive control frameworks degrade by over 40% under model uncertainties, while conventional adaptive techniques exhibit convergence times incompatible with industrial cycles. This work presents a hybrid adaptive model predictive control framework integrating edge artificial intelligence with dual-stage parameter estimation for 6-DoF industrial manipulators. The approach combines recursive least squares with a resource-optimized neural network (three layers, 32 neurons, <500 KB memory) designed for industrial edge deployment. The system employs innovation-based adaptive forgetting factors, providing exponential convergence with mathematically proven Lyapunov-based stability guarantees. Simulation validation using the Fanuc CR-7iA/L manipulator demonstrates superior performance across demanding scenarios, including precision laser cutting and obstacle avoidance. Results show 52% trajectory tracking RMSE reduction (0.022 m to 0.012 m) under 20% payload variations compared to standard MPC, while achieving sub-5 ms edge inference latency with 99.2% reliability. The hybrid estimator achieves 65% faster parameter convergence than classical RLS, with 18% energy efficiency improvement. Statistical significance is confirmed through ANOVA (F = 24.7, p < 0.001) with large effect sizes (Cohen’s d > 1.2). This performance surpasses recent adaptive control methods while maintaining proven stability guarantees. Hardware validation under realistic industrial conditions remains necessary to confirm practical applicability. Full article

The Sustainable Development Goals (SDGs), established by the United Nations in 2015, aim to address global challenges like poverty, inequality, and climate change, yet only 17% of these goals are on track for 2030. This study investigates the geopolitical, economic, and technological barriers to SDG progress, focusing on the middle-income trap, trade regionalisation, and automation’s impacts. Using quantitative and qualitative methods, we analysed World Bank, IMF, UN, and OECD data (2005–2024) on GDP, FDI, exports, and public debt across various income-level countries. Findings reveal that economic growth is hindered by market saturation, ageing populations, high debt, and declining FDI, while global trade stagnation since 2011 and regionalisation impede cooperation. Automation reduces employment, shrinks the middle class, and threatens stability, with geopolitical tensions disrupting supply chains. The current economic model, reliant on consumption, investment, and exports, is insufficient for sustainable development. The novelty of this study lies in its integrated analysis of three structural global trends—trade stagnation, regionalisation, and automation—over the period 2005–2024. Unlike previous works that typically examine these factors in isolation or over shorter time horizons, our approach highlights their combined impact on SDG achievement. By formulating and testing specific hypotheses, the study contributes to the literature by providing empirical evidence on how these interrelated processes jointly hinder sustainable development under the current global economic model. Full article

With the increasing penetration of renewable energy, power system frequency stability faces multiple challenges. In addition to the decline of system inertia traditionally provided by synchronous machines, uncertainties such as wind power forecast errors, converter control characteristics, and stochastic load fluctuations further exacerbate the system’s sensitivity to power disturbances, increasing the risks of frequency deviation and instability. Among these factors, insufficient inertia is widely recognized as one of the most direct and critical drivers of the initial frequency response. This study focuses on this issue and explores the use of battery energy storage system (BESS) parameter optimization to enhance system stability. To this end, a simulation platform was developed in PSS^®E V34 based on the IEEE New England 39-bus system, incorporating three wind turbines and two BESS units. The WECC generic models were adopted, and three wind disturbance scenarios were designed, including (i) disconnection of a single wind turbine, (ii) derating of two turbines to 50% output, and (iii) derating of three turbines to 50% output. In this study, a one-at-a-time (OAT) sensitivity analysis was first performed to identify the key parameters affecting frequency response, followed by optimization using an improved particle swarm optimization (IPSO) algorithm. The simulation results show that the minimum system frequency was 59.888 Hz without BESS control, increased to 59.969 Hz with non-optimized BESS control, and further improved to 59.976 Hz after IPSO. Compared with the case without BESS, the overall improvement was 0.088 Hz, of which IPSO contributed an additional 0.007 Hz. These results clearly demonstrate that IPSO can significantly strengthen the frequency support capability of BESS and effectively improve system stability under different wind disturbance scenarios. Full article

Helicobacter pylori is the main etiological factor of gastritis, peptic ulcers, and gastric cancer. This bacterium’s antibiotic resistance has led to a lower eradication rate; therefore, new drugs with anti-H. pylori activity are needed. Curcumin exhibits multiple biological activities, but it has low stability and poor bioavailability. To overcome these disadvantages, different metal complexes have been synthesized. The objective of this study was to determine the in vitro anti-H. pylori activity of diacetylcurcumin (DAC), DAC₂-Cu, DAC₂-Zn, DAC₂-Mn, and DAC₂-Mg by obtaining the minimum inhibitory concentration of bacterial growth, and to investigate some mechanisms by which they could affect the bacteria (urease and DNA gyrase activities). Moreover, their gastroprotective potential was assayed in an ethanol-induced gastric ulcer model in mice. The results showed that DAC₂-Cu and DAC₂-Zn have good anti-H. pylori activity, exhibit specific activity against this bacterium, inhibit the urease activity, and provide 70% gastroprotection at a dose of 200 mg/kg of body weight. In a subacute toxicity study in mice, DAC₂-Cu and DAC₂-Zn did not cause death or any deleterious symptoms, nor did they have a significant effect on serum and urine biochemical parameters compared to control mice. These compounds are promising candidates for use in H. pylori eradication schemes. Full article

Background: A range of cancer cells are significantly inhibited by FTY720. It is unknown, nevertheless, how FTY720 influences the onset of non-small cell lung cancer (NSCLC). Using bioinformatics techniques, we analyzed and the possible molecular mechanisms and targets of FTY720 for the treatment of NSCLC. Methods: DEGs (Differentially expressed genes) were acquired by differential analysis of the dataset GSE10072. Obtained FTY720 target genes and NSCLC disease genes from databases such as Swiss-TargetPrediction and GeneCard. Subsequently, target and disease genes, as well as DEGs, were merged for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, gene ontology (GO), and protein interaction analysis. The overlapping genes of DEGs and target genes, and disease genes were also obtained separately and subjected to survival as well as expression analyses. We constructed the regulatory network of miRNAs and transcription factors (TFs) on hub genes. Finally, the immune cell association of hub genes was evaluated using the ssGSEA method, molecular docking of FTY720 to hub genes was carried out utilizing Autodock, and molecular dynamics simulations were conducted. Results: In this study, 444 DEGs, 232 target genes of FTY720, and 466 disease genes were obtained. Moreover, a total of 1062 genes were obtained by removing duplicate values after merging, among which PIK3R1, Akt1, and S1PR1 had the highest DEGREE values in the protein interactions network, and these genes were primarily enriched in MAPK, PI3K-Akt signaling pathways, with the PI3K-Akt signaling pathway being the most prominent. Among the overlapping genes, three potential targets of FTY720 for NSCLC treatment were found: S1PR1, ZEB2, and HBEGF. ZEB2 and S1PR1 were determined to be hub genes and to significantly affect NSCLC prognosis by survival analysis. Furthermore, hsa-miR-132-3p, hsa-miR-192-5p, and hsa-miR-6845-3p were strongly associated with FTY720 for the treatment of NSCLC; CTBP1 (carboxy-terminal binding protein 1), EZH2 (protein lysine N-methyltransferase), and ZNF610 (zinc-finger protein 610) may all influence the expression of ZEB2 and S1PR1. Hub genes had a substantial negative link with memory B cells and a significant positive correlation with memory CD8 T cells and Th17 helper T cells. The molecular docking and kinetic simulation results of FTY720 with the two hub genes indicate that the protein-ligand complex has good stability. Conclusion: Our research indicates that FTY720 may inhibit NSCLC via possible targets ZEB2 and S1PR1, further laying the theoretical foundation for the utilization of FTY720 in NSCLC treatment. Full article

The bark of Dillenia indica L. is a rich source of phenolic and triterpenoid compounds, including betulinic acid (BA), known for their antioxidant and anti-aging properties. This study investigated the antioxidant potential of a BA-enriched extract through a multidisciplinary approach combining computational, experimental, and cell-based evaluations. Molecular docking and molecular dynamics simulations revealed that BA binds stably to Kelch-like ECH-associated protein 1 (KEAP1), suggesting activation of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway. Extraction conditions were optimized using response surface methodology (RSM) and artificial neural network (ANN) modeling, yielding the maximum total phenolic content (TPC; 85.33 ± 2.26 mg gallic acid equivalents/g) and total flavonoid content (TFC; 75.60 ± 1.66 mg catechin equivalents/g), with ANN demonstrating superior predictive performance compared to RSM. Electrospray ionization tandem mass spectrometry (ESI-MS/MS) confirmed the presence of BA in the optimized extract. Simulated gastrointestinal digestion revealed reductions in TPC, TFC, and radical scavenging activity during the gastric phase. In ultraviolet B (UVB)-irradiated human keratinocyte (HaCaT) cells, the optimized extract significantly reduced intracellular reactive oxygen species (ROS) and upregulated the KEAP1-Nrf2-heme oxygenase-1 (HO-1) pathway, confirming its antioxidant mechanism. These findings highlight the extract’s stability, bioactivity, and mechanistic efficacy, supporting its application as a nutraceutical ingredient for combating oxidative stress and skin aging. Full article

This paper focuses on the scoring-wheel cutting process for circular structures of OLED display panels, conducting in-depth research through an experiment–analysis–optimization system. Based on the Taguchi experimental design, a three-factor, five-level experiment is conducted, with the blade wheel angle (A), cutting speed (B), and pressure (C) set as influencing factors, and the scratch depth (h), width (w), median crack depth (l), and transverse crack width (d) set as evaluation indicators. The experiments are carried out using a self-developed dicing-wheel cutting device, and the morphology, roughness, and hardness of the cutting surface and cross-section are characterized by means of ultra-depth-of-field microscopy, laser confocal microscopy, microhardness tester, and other equipment. The research shows that the order of factors affecting the cutting quality is as follows: A > C > B. Through the analysis of morphology and crack characteristics, it is determined that the optimal parameter combination is a dicing wheel angle of 130°, a cutting speed of 20 mm/s, and a pressure of 11 N. The verification results indicate that this combination can reduce surface roughness, stabilize hardness, and realize efficient and precise processing of special-shaped structures in OLED display panels, providing strong theoretical and technical support for industrial process optimization. Full article