Search Results (216)

The automotive suspension must perform competently to support comfort and safety when driving. Traditionally, car suspension control tuning is performed through trial and error or with classical techniques that cannot guarantee optimal performance under varying road conditions. The study aims at designing a Linear Quadratic Regulator-based Bacterial Memetic Algorithm (LQR-BMA) for suspension systems of automobiles. BMA combines the bacterial foraging optimization algorithm (BFOA) and the memetic algorithm (MA) to enhance the effectiveness of its search process. An LQR control system adjusts the suspension’s behavior by determining the optimal feedback gains using BMA. The control objective is to significantly reduce the random vibration and oscillation of both the vehicle and the suspension system while driving, thereby making the ride smoother and enhancing road handling. The BMA adopts control parameters that support biological attraction, reproduction, and elimination-dispersal processes to accelerate the search and enhance the program’s stability. By using an algorithm, it explores several parts of space and improves its value to determine the optimal setting for the control gains. MATLAB 2024b software is used to run simulations with a randomly generated road profile that has a power spectral density (PSD) value obtained using the Fast Fourier Transform (FFT) method. The results of the LQR-BMA are compared with those of the optimized LQR based on the genetic algorithm (LQR-GA) and the Virus Evolutionary Genetic Algorithm (LQR-VEGA) to substantiate the potency of the proposed model. The outcomes reveal that the LQR-BMA effectuates efficient and highly stable control system performance compared to the LQR-GA and LQR-VEGA methods. From the results, the BMA-optimized model achieves reductions of 77.78%, 60.96%, 70.37%, and 73.81% in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the GA-optimized model. Moreover, the BMA-optimized model achieved a −59.57%, 38.76%, 94.67%, and 95.49% reduction in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the VEGA-optimized model. Full article

In this paper, RF sub-modules with millimeter-wave functionality are considered and verified for designing an ultra-wideband receiver (18–40 GHz) required in the electronic support measure (ESM) field. The pre-design of an ultra-wideband super heterodyne receiver (SHR) requires a front-end module (FEM) with four units in the system. Each FEM has four channels with the same path, while the quadrature millimeter down-converter (QMDC) needs to have a converting function that uses a broadband mixer. The FEM includes the ability to provide built-in test (BIT) path functionality to the antenna ports prior to system field installation. Each path of the QMDC requires the consideration of several factors, such as down-converting, broadband gain flatness, and high isolation. As this is an RF module requiring high frequency and wideband characteristics, it is necessary to identify risk factors in advance within a predictable range. Accordingly, the blind-mate A (BMA) connector connection method, the phase-alignment test method in the down-conversion structure, and the LO signal, IF path inflow-blocking method were analyzed and designed. Full article

Background/Objectives: Prenatal adverse events may influence the development of complications of prematurity, including patent ductus arteriosus (PDA). We conducted a systematic review and Bayesian model-averaged (BMA) meta-analysis of observational studies exploring the association between hypertensive disorders of pregnancy (HDP) and the risk of PDA in preterm infants. Methods: PubMed/Medline and Embase databases were searched. We used BMA analysis to calculate Bayes factors (BFs). The BF₁₀ is the ratio of the probability of the data under the alternative hypothesis (H₁, presence of association) over the probability of the data under the null hypothesis (H₀, absence of association). Results: We included 41 studies (58,004 infants). BMA analysis showed moderate evidence in favour of H₀ for the association between HDP and any PDA (BF₁₀ = 0.20) as well as for the association between HDP and hemodynamically significant PDA (BF₁₀ = 0.27). Subgroup analyses based on the subtype of HDP showed that the moderate evidence in favour of H₀ was only conclusive (i.e., BF₁₀ < 0.33) for the associations of any PDA with preeclampsia (BF₁₀ = 0.30) and hemodynamically significant PDA with preeclampsia (BF₁₀ = 0.17). Conclusions: The currently available evidence suggests a lack of association between HDP and the risk of developing PDA. Full article

Water temperature is a fundamental parameter influencing a range of biotic and abiotic processes occurring within various components of the hydrosphere. This study presents a multi-step, data-driven predictive modeling framework to estimate water temperatures for the period 2021–2100 in three aquatic environments in Central Europe: the Odra River, the Szczecin Lagoon, and the Baltic Sea. The framework integrates Bayesian Model Averaging (BMA), Random Sample Consensus (RANSAC) regression, Gradient Boosting Regressor (GBR), and Random Forest (RF) machine learning models. To assess the performance of the models, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) were used. The results showed that the application of statistical downscaling methods improved the prediction of air temperatures with respect to the BMA. Moreover, the RF method was used to predict water temperature. The best model performance was obtained for the Baltic Sea and the lowest for the Odra River. Under the SSP2-4.5 and SSP5-8.5 scenario-based simulations, projected air temperature increases in the period 2021–2100 could range from 1.5 °C to 1.7 °C and 4.7 to 5.1 °C. In contrast, the increase in water temperatures by 2100 will be between 1.2 °C and 1.6 °C (SSP2-4.5 scenario) and between 3.5 °C and 4.9 °C (SSP5-8.5). Full article

In this study, we explore the design of novel random poly(lauryl methacrylate-co-tert-butyl methacrylate-co-methacrylic acid), P(LMA-co-tBMA-co-MAA) copolymers via the RAFT copolymerization of LMA and tBMA followed by the selective hydrolysis of tBMA segments. For the molecular characterization of the novel copolymer, a series of physicochemical techniques were implemented, including size exclusion chromatography (SEC), proton nuclear magnetic resonance (¹H-NMR) and attenuated total reflectance–Fourier transform infrared (ATR–FTIR) spectroscopy. Our experimental results confirmed the successful synthesis of the targeted copolymers. The compositions were in accordance with the targeted differing fraction of hydrophobic tBMA/LMA elements, and hydrolysis resulted in at least 64% conversion to hydrophilic MAA units. The copolymers, bearing both an amphiphilic character and polyelectrolyte properties while being composed of randomly distributed monomeric segments of biocompatible materials, were subsequently investigated in terms of their self-assembly behavior in aqueous solutions. Dynamic light scattering and fluorescence spectroscopy experiments demonstrated the formation of self-assembled nanoaggregates (average hydrodynamic radii, R_h < 100 nm) that formed spontaneously, having low critical aggregation concentration (CAC) values (below 3.5 × 10⁻⁶ g/mL), and highlighted the feasibility of using these copolymer systems as nanocarriers for biomedical applications. Full article

Accurate runoff simulation and prediction are crucial for water resources management, especially under the impact of climate change. In this study, a multi-physics ensemble Bayesian model averaging (MPE-BMA) model is developed to improve runoff prediction accuracy by integrating a soil and water assessment tool (SWAT), hydrologiska byråns vattenbalansavdelning (HBV) model, and Bayesian model averaging (BMA) into a general framework. The MPE-BMA model integrates the strengths of the SWAT and HBV models. This approach enhances the robustness of simulation outputs and reduces uncertainties from single-model methods. MPE-BMA is subsequently employed to simulate and predict runoff for the upper reaches of Xiangxi River Basin (XXRB) in China, where four general circulation models (GCMs) and three shared socioeconomic pathways (SSP126, SSP245, and SSP585) are considered. Multiple statistical metrics (R², NSE, and RMSE) prove that the MPE-BMA model outperforms the single models of SWAT and HBV. Results reveal that higher-emission scenarios generally lead to significant decreases in runoff, particularly by the 2080s. Specifically, under SSP585, runoff is projected to decrease by approximately 4.61–12.68% by the 2040s and 5.96–11.28% by the 2080s compared to the historical period. From the perspective of monthly and seasonal runoff changes, the peak runoff is projected to shift from June to May by the 2080s. Additionally, under SSP585, spring and summer runoffs tend to significantly increase, while winter runoff decreases sharply, leading to wetter summers and drier winters. These findings underscore the importance of enhancing water use efficiency, upgrading hydropower stations, and implementing watershed management practices to ensure sustainable water resources management in the XXRB amidst climate change. Full article

Polyphenols exert anti-inflammatory and antioxidant effects by modulating cell signalling pathways and transcription factors involved in inflammatory bowel disease (IBD). However, their stability during digestion can be compromised. Polymer coatings like chitosan (-C) help preserve their stability. Maqui (Aristotelia chilensis) and murta (Ugni molinae) are rich in antioxidant and anti-inflammatory compounds. This work aims to obtain extracts (E) and blends (B) enriched in delphinidin and quercetin glucosides from maqui (Ma) and murta (Mu) crude extracts using preparative chromatography methodology (Prep-HPLC) and to evaluate their effectiveness through in vitro and cellular assays. HPLC-DAD analysis revealed a marked increase in phenolic compound concentration in the BEMaMu and BCMaMu extract blends. Total quercetin glycosides (TQG) increased by 11-fold, and total anthocyanins increased by approximately 8-fold compared to the fruit blend (BMaMu). BCMaMu exhibited a significantly higher ORAC value compared to the estimated additive mixture, suggesting a synergistic effect. No cytotoxicity was observed for BEMaMu, BCMaMu, and their chitosan-coated versions (BEMaMu-C and BCMaMu-C) in Caco-2 and HT29-MTX-E12 cells at concentrations of 0.1–50 mg/mL. Notably, only chitosan-coated BCMaMu inhibited NF-κB expression and activated Nrf2 in TNF-α-challenged Caco-2 cells at 0.1 and 0.5 mg/mL. Full article

The rise in global temperatures and increased extreme weather events, such as heatwaves, underscore the need for accurate regional projections of daily maximum temperature (T_max) to inform effective adaptation strategies. This study develops the CNN-BMA-QDM model, which integrates convolutional neural networks (CNNs), Bayesian model averaging (BMA), and quantile delta mapping (QDM) to downscale and project T_max under future climate scenarios. The CNN-BMA-QDM model stands out for its ability to capture nonlinear relationships between T_max and atmospheric circulation factors, reduce model uncertainty, and correct bias, thus improving simulation accuracy. The CNN-BMA-QDM model is applied to Fujian Province, China, using three CMIP6 GCMs and four shared socioeconomic pathways (SSPs) to project T_max from 2015 to 2100. The results show that CNN-BMA-QDM outperforms CNN-BMA, CNNs, and other downscaling methods (e.g., RF, BPNN, SVM, LS-SVM, and SDSM), particularly in simulating extreme value at the 99% and 95% percentiles. Projections of T_max indicate consistent warming trends across all SSP scenarios, with spatially averaged warming rates of 0.0077 °C/year for SSP126, 0.0269 °C/year for SSP245, 0.0412 °C/year for SSP370, and 0.0526 °C/year for SSP585. Coastal areas experience the most significant warming, with an increase of 4.62–5.73 °C under SSP585 by 2071–2100, while inland regions show a smaller rise of 3.64–3.67 °C. Monthly projections indicate that December sees the largest increase (5.30 °C under SSP585 by 2071–2100), while July experiences the smallest (2.40 °C). On a seasonal scale, winter experiences the highest warming, reaching 4.88 °C under SSP585, whereas summer shows a more modest rise of 3.10 °C. Notably, the greatest discrepancy in T_max rise between the south and north occurs during the summer. These findings emphasize the importance of developing tailored adaptation strategies based on spatial and seasonal variations. The results provide valuable insights for policymakers and contribute to the advancement of regional climate projection research. Full article

Background: The diagnosis of hematologic neoplasms is usually based on a synopsis of the peripheral blood (PB) and bone marrow findings. Morphology continues to be the cornerstone, but genetic analysis plays an increasingly important role. In routine workup, molecular genetic analysis is performed from a bone marrow aspirate (BMA). In the event of inadequate aspiration, PB is used. Not infrequently, however, PB only partially represents the disease. In this situation, molecular genetic analysis of formalin-fixed and paraffin-embedded (FFPE) bone marrow core biopsy (BMCB) could be a better alternative than PB. However, no systematic correlation of genetic findings from BMCB with results from BMA and PB has been reported. Methods: Therefore, BMCB obtained during routine diagnostics were subjected to post hoc molecular genetic analysis using next generation sequencing (NGS). The identified molecular genetic alterations were then compared with data within routine diagnostics of the corresponding BMA and/or PB. Results: In total, 29 BMCB and corresponding BMA samples were analyzed, and in 12/29 cases PB was additionally available. The analysis of BMCB and BMA showed identical results in 17 cases, but BMCB revealed a gain of information in 11, while in only 1 case, BMCB failed to identify the mutations in comparison to BMA. Conclusions: Despite the small numbers, molecular genetic analysis of bone marrow core biopsy using next generation sequencing could detect relevant additional gene mutations compared to bone marrow aspirate and/or peripheral blood. Full article

Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood and drought risk prediction. This study proposes a Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The framework enhances estimation accuracy for hydrological simulations. The BMA framework synthesizes four precipitation products—Climate Hazards Group Infrared Precipitation with Station (CHIRPS), the fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 to 2020. We evaluated the merged dataset’s performance against its constituent datasets and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) at daily, monthly, and seasonal scales. Evaluation metrics included the correlation coefficient (CC), root mean square error (RMSE), and Kling–Gupta efficiency (KGE). The Variable Infiltration Capacity (VIC) hydrological model was further applied to assess how these datasets affect runoff simulations. The results indicate that the BMA-merged dataset substantially improves precipitation estimation accuracy when compared with individual inputs. The merged product achieved optimal daily performance (CC = 0.72, KGE = 0.70) and showed superior seasonal skill, notably reducing biases in autumn and winter. In hydrological applications, the BMA-driven VIC model effectively replicated observed runoff patterns, demonstrating its efficacy for regional long-term predictions. This study highlights BMA’s potential for optimizing hydrological model inputs, providing critical insights for sustainable water management and risk reduction in complex basins. Full article

Exploring new polymerization strategies for currently available monomers is a challenge in polymer science. Herein, a bifunctional initiator (BFI) is introduced for the conventional radical polymerization of a vinyl monomer, resulting in linear radical additions-coupling polymerization (LRAsCP). In LRAsCP, the coupling reaction alongside the addition reaction of the radicals contributes to the construction of polymer chains, which leads to stepwise growth of the multiblock structure. Theoretical analysis of LRAsCP predicted variation of some structural parameters of the resulting multiblock polymer (MBP) with the extent of initiation of the BFI and the termination factor of the radicals. Simultaneous and cascade initiations of the BFI were compared. LRAsCP of styrene was conducted, and a kinetics study was carried out. The increment in M_n with polymerization time demonstrated the stepwise mechanism of the formation of the MBP. The variation of the structural parameters of MBP fitted well with the theoretical prediction. Two-step LRAsCP was conducted and multiblock copolymers (MBcP) were obtained either by in situ copolymerization of styrene and MMA or by a second copolymerization of styrene and BMA. The current results demonstrate that the introduction of a BFI to conventional radical polymerization generates a new polymerization strategy, leading to a new chain architecture, which can be extended to other radical polymerizable monomers. Full article

This paper presents placement optimization for battery energy storage system installation using a fuzzy expert system. Nowadays, the Bangkok Metropolitan Administration (BMA) has installed CCTV cameras for surveillance, deterrence, and to record events as evidence for legal proceedings. However, in some areas, there is no BESS, so when the power goes out, recording cannot continue. This article uses a Fuzzy Logic Expert System to assess critical areas for the consideration of future BESS installation in Bangkok. The key factors include (1) the number of CCTV image requests from the Bangkok Metropolitan Administration, (2) the duration of power outages from the BMA, and (3) the total power consumption of the CCTV in each subdistrict. The study results show that the fuzzy expert system can effectively handle ambiguous data and improve decision-making. The Latkrabang and Lamphlatiew subdistricts have the most critical points where investment in BESS installation is most appropriate. The size of the BESS was determined based on the maximum recorded power outage duration of 57 min, with the backup power design for the BESS set at 1 h. The DIgSILENT program was used to determine the size of the BESS at each critical point, which was calculated to be 160.2 Wh. Full article

This study highlights the superior performance of Bayesian Model Averaging (BMA) in credit risk modeling under IFRS 9, particularly during economic uncertainty, such as the COVID-19 pandemic. Using granular bank-level data from Malta, spanning 2017–2023, the analysis integrates macroeconomic scenarios and sector-specific transition matrices to assess credit risk dynamics. Key findings demonstrate BMA’s ability to outperform Single-Equation Models (SEM) in predictive accuracy, robustness, and adaptability. The results emphasize BMA’s resilience to structural economic changes, making it a critical tool for regulatory stress testing and provisioning in small open economies highly exposed to external shocks. This work underscores the importance of forward-looking, flexible frameworks for credit risk management and policy decisions. Full article

Background and Objectives: Bone metastases in patients can cause significant quality-of-life declines due to skeletal-related events (SREs). SRE is defined as the occurrence of radiotherapy for bone pain, pathologic fracture, bone surgery, spinal cord compression, or hypercalcemia. Bone-modifying agents (BMAs), such as denosumab and zoledronic acid, are crucial in reducing the frequency and severity of SREs. The inhibition of cyclin-dependent kinase 4/6 (CDK4/6) inhibitors has emerged as the standard treatment for hormone receptor-positive metastatic breast cancer, demonstrating significant improvements in survival outcomes. This study aims to compare the effectiveness of denosumab and zoledronic acid in preventing SRE in patients receiving CDK4/6 inhibitors with endocrine therapy. Materials and Methods: This retrospective study included 328 patients diagnosed with bone metastatic breast cancer receiving first-line CDK4/6 inhibitor therapy (palbociclib or ribociclib). Patients were assigned to receive either subcutaneous denosumab or intravenous zoledronic acid every 4 weeks. Time to the first skeletal-related event post bone-modifying agent initiation, SRE incidence, and the safety data were evaluated. The data were analyzed using independent samples t-tests, chi-square tests, and Kaplan–Meier methods for time-to-event data. Results: In the denosumab group, the median time to the first skeletal-related event was significantly longer than in the zoledronic acid group (44.55 months and 29.16 months, respectively). Denosumab treatment was associated with a statistically significant reduction in the risk of developing the first SRE after bone-modifying agent initiation compared to zoledronic acid (HR: 0.56, p = 0.001). Additionally, ECOG PS and the number of metastatic bone sites were identified as independent prognostic factors for time to the first SRE. The safety profile was consistent with previous studies reported in the literature. Conclusions: Our study demonstrated that when used with CDK4/6 inhibitors, denosumab is associated with a delay in SREs and a lower SRE incidence than zoledronic acid in patients with bone metastases. These findings support the efficacy of denosumab in preventing SREs and suggest that CDK4/6 inhibitors may have distinct effects on the bone microenvironment, particularly when combined with denosumab. Full article

Biomass offers a renewable pathway for sustainable infrastructure, particularly in bio-oil production from biomass through processes such as fast pyrolysis to be used as an alternative to asphalt binders. This review explores biomass sources, production techniques, and the role of bio-oil in addressing the demand for eco-friendly materials in the pavement construction industry. The review also examines the upgrading processes of bio-oil, its physical and chemical properties, and its application in producing bio-modified asphalt binder (BMA). The use of bio-oils in asphalt binders not only reduces the carbon footprint but also promotes the utilization of renewable resources, contributing to a more sustainable pavement industry. Additionally, bio-oil incorporation enhances asphalt binder performance by improving rutting resistance at high temperatures and stiffness at low temperatures, while reducing susceptibility to low-temperature cracking. Challenges include variability in high-temperature performance and moisture sensitivity. Based on the findings of this comprehensive review, future research directions should focus on optimizing production processes, broadening biomass feedstocks, and mitigating moisture issues to align bio-oil properties with asphalt binder specifications. Full article