Search Results (109)

Background/Objectives: In Ecuador, the prevalence of type 2 diabetes mellitus (T2DM) is the second leading cause of death after ischemic heart disease. Genetic variability in protein-coding genes, single nucleotide variants (SNVs), influences the response to antidiabetic drugs. The frequency of SNVs varies among different populations, so studying the ancestral proportions among SNVs is important for personalized medicine in the treatment of T2DM. This study aimed to evaluate the distribution of Native American, European, and African (NATAM, EUR, and AFR) ancestry in 23 allelic variants of the seven genes that encode the relevant enzymes that metabolize antidiabetic drugs in an Ecuadorian population. Methods: Twenty-three allelic variants of seven genes were analyzed in 297 patients with T2DM from Ecuador, and the molecular ancestry of the samples was analyzed considering three ancestral groups, NATAM, EUR, and AFR using 90 ancestry informative markers (AIMs). Allele and ancestry distributions were analyzed using Spearman’s correlation. Results: The Ecuadorian population presents NATAM (61.33%), EUR (34.48%), and AFR (2.60%) ancestry components. CYP2C8*1 and CYP2C9*1 were positively related to NATAM ancestry, while CYP2C8*4 and CYP2C9*2 were positively related to EUR ancestry. CYP2C19*17 was positively correlated to AFR ancestry. The correlation of SLC22A1 variants such as A in rs594709 was positively correlated with NATAM, while GAT in rs72552763 was positive for EUR. The G variant of rs628031 of the SLC22A1 gene was positively correlated with NATAM and negatively correlated with EUR. The C variant of rs2076828 of the SLC22A3 gene was positively correlated with NATAM ancestry. Conclusions: In the Ecuadorian population, a predominance of Native American ancestry has been observed. Among the allelic variants related to enzymes that metabolize antidiabetic drugs, a relationship has been observed between this ancestral component and variants of the CYP2C8*1, CYP2C9*1, SLC22A1 (rs594709 and rs628031), and SLC22A3 (rs2076828) genes. This information is fundamental for the development of strategies for the implementation of personalized medicine programs for Latin American patients. Full article

Polymer fouling in batch and tubular reactors creates safety hazards from heat buildup and blockages. The continuous Corning Advanced-Flow™ Reactor (AFR) offers enhanced mass and heat transfer, improving safety and efficiency. This study evaluated three reactor systems—a monolithic AFR, an AFR with an external pipe, and a conventional tubular reactor—for the mini-emulsion polymerization of styrene and subsequent styrene–acrylic acid copolymerization. The AFR operability under varying monomer concentrations was assessed and investigated, with the residence time’s effects on conversion. For styrene polymerization at 20–35 wt% monomer, the highest conversions achieved were 88.0% in the AFR, 85.8% in the tubular reactor, and 98.9% in the AFR with pipe. Uniform particles were obtained at ≤30 wt%, whereas at 35 wt%, the monolithic AFR experienced clogging and loss of particle uniformity. Similarly, in styrene–acrylic acid copolymerization (15–17.5 wt% monomer), the maximum conversions reached 80.1% in the AFR and 95.4% in the AFR with pipe, while the monolithic AFR again experienced blockage at 17.5 wt%. In conclusion, integrating an external pipe with the AFR, coupled with higher flow rates, significantly improved initiator diffusion, enhanced monomer conversion, and mitigated blockage. This approach enabled the efficient, continuous production of nanoscale, uniformly sized polystyrene and styrene–acrylic acid copolymers even at high monomer concentrations. Full article

Gas turbine (GT) modeling and optimization have been widely studied at the design level but still lacks focus on real-world operational cases. The concept of a digital twin (DT) allows for the interaction between operation data and the system dynamic performance. Among many DT studies, only a few focus on GT for thermal power plants. This study proposes a digital twin prototype framework including the following modules: process modeling, parameter estimation, and performance optimization. Provided with real-world power plant operational data, key performance parameters such as turbine inlet temperature (TIT) and specific fuel consumption (SFC) were initially unavailable, therefore necessitating further calculation using thermodynamic analysis. These parameters are then used as a target label for developing artificial neural networks (ANNs). Three ANN models with different structures are developed to predict TIT, SFC, and turbine power output (GTPO), achieving high R² scores of 94.03%, 82.27%, and 97.59%, respectively. Physics-informed neural networks (PINNs) are then employed to estimate the values of the air–fuel ratio and combustion efficiency for each time index. The PINN-based estimation resulted in estimated values that align with the literature. Subsequently, an unconventional method of detecting alarms by using conformal prediction were also proposed, resulting in a significantly reduced number of alarms. The developed ANNs are then combined with particle swarm optimization (PSO) to carry out performance optimization in real time. GTPO and SFC are selected as the primary metrics for the optimization, with controllable parameters such as AFR and a fine-tuned inlet guide vane position. The results demonstrated that GTPO could be optimized with the application of conformal prediction when the true GTPO is detected to be higher than the upper range of GTPO obtained from the ANN model with a conformal prediction of a 95% confidence level. Multiple PSO variants were also compared and benchmarked to ensure an enhanced performance. The proposed PSO in this study has a lower mean loss compared to GEP. Furthermore, PSO has a lower computational cost compared to RS for hyperparameter tuning, as shown in this study. Ultimately, the proposed methods aim to enhance GT operations via a data-driven digital twin concept combination of deep learning and optimization algorithms. Full article

With the growing need for lightweight, durable, and high-performance structures, additively manufactured (AM) polymer composite structures have captured significant attention in the engineering community. These structures offer considerable advantages in various dynamic engineering sectors including automotive, aviation, and military. Thus, this investigation emphasizes the numerical analysis of the dynamic properties and vibration control of AM polylactic acid (PLA) composite structures reinforced with continuous glass fibers (CGFR-PLA) and carbon fibers (CCFR-PLA), with 0°–0° and 0°–90° layer orientations. The findings of this numerical study are compared and validated against earlier published experimental results. Initially, the numerical models were created using the Abaqus CAE 2024, replicating the actual experimental models. The numerical bending modal frequency of each numerical model is determined, and the 0°–0° oriented models exhibited considerably higher values compared to the corresponding 0°–90° models. Significant differences were noted between the numerical and experimental values in the higher modes, mainly due to existence of voids and misalignment in the actual models that were not considered in numerical models. Following this, a numerical amplitude frequency response (AFR) analysis was conducted to observe vibration amplitude variations as a function of frequency. The AFR numerical results demonstrated consistent trends with the experimental results despite differences between the absolute values of both scenarios. Afterwards, vibration amplitude control analysis was performed under the influence of a macro fiber composite (MFC) actuator. The findings from both numerical and experimental cases revealed that vibration control was noticeably higher in 0°–0° oriented structures compared to 0°–90° structures. Experimental models demonstrated higher vibration control effectiveness than the corresponding numerical models. Although significant differences between the numerical and experimental vibration response values were observed in each composite structure, the numerical results exhibited consistent trends with the experiments. This discrepancy is attributed to the challenge of capturing all boundary conditions of the experimental scenario and incorporating them into the numerical simulation. Full article

This paper presents an innovative engine optimization model integrated with a friction fitting tool to enhance the accuracy of computed performance for a marine dual-fuel engine. The focus is on determining the terms of the Chen–Flynn correlation—an empirical engine friction model—to improve the precision of friction and performance predictions. The developed model employs WAVE, a 1D engine simulation software, coupled with a nonlinear optimizer to identify the optimal configuration of key parameters, including the turbocharger, injection system, combustion behavior, and friction model. The optimization procedure maximizes the air–fuel ratio (AFR) within the engine while adhering to various predefined constraints. The model is applied to four operational points along the propeller curve, with the optimized results subsequently integrated into a friction fitting tool. This tool predicts the terms of the Chen–Flynn correlation through an updated procedure, achieving highly accurate results with a coefficient of determination (R²) value of 99.88%, eliminating the need for experimental testing. The optimized friction model provides a reliable foundation for future studies and applications, enabling precise friction predictions across various engine types and fuel compositions. Full article

The increasing penetration of distributed photovoltaic (PV) and energy storage (ES) systems in power grids, while advancing the transition to clean energy and enhancing grid flexibility, poses resource dispersion, uncertainty, and scheduling challenges. Consequently, it becomes crucial to manage and optimize these resources. In this paper, we innovatively propose a distribution network day-ahead optimal scheduling model that takes into account distributed resource aggregation and uncertainty. Firstly, distributed PV aggregation (PVA) is performed using the Minkowski summation method, and distributed ES aggregation (ESA) is performed using the polytope inner approximation method. Then, in order to deal with the uncertainty, the supply–demand balance of flexibility is modeled using kernel density estimation (KDE). Finally, the aggregation model and the flexibility supply–demand balance model are incorporated into the distribution network day-ahead optimization. The simulation study of the IEEE 69-node distribution system shows that the aggregate feasible region (AFR) is improved by about 90% and the active flexibility is improved by about 10% compared to box inner approximate aggregation methods, demonstrating their effectiveness in managing operational uncertainties and optimizing the utilization of distributed energy resources in day-ahead scheduling. Full article

Accurate detection of dangerous driving behaviors is crucial for improving the safety of intelligent transportation systems. However, existing methods often struggle with limited feature extraction capabilities and insufficient attention to multiscale and contextual information. To overcome these limitations, we propose YOLO-AFR (YOLO with Adaptive Feature Refinement) for dangerous driving behavior detection. YOLO-AFR builds upon the YOLOv12 architecture and introduces three key innovations: (1) the redesign of the original A2C2f module by introducing a Feature-Refinement Feedback Network (FRFN), resulting in a new A2C2f-FRFN structure that adaptively refines multiscale features, (2) the integration of self-calibrated convolution (SC-Conv) modules in the backbone to enhance multiscale contextual modeling, and (3) the employment of a SEAM-based detection head to improve global contextual awareness and prediction accuracy. These three modules combine to form a Calibration-Refinement Loop, which progressively reduces redundancy and enhances discriminative features layer by layer. We evaluate YOLO-AFR on two public driver behavior datasets, YawDD-E and SfdDD. Experimental results show that YOLO-AFR significantly outperforms the baseline YOLOv12 model, achieving improvements of 1.3% and 1.8% in mAP@0.5, and 2.6% and 12.3% in mAP@0.5:0.95 on the YawDD-E and SfdDD datasets, respectively, demonstrating its superior performance in complex driving scenarios while maintaining high inference speed. Full article

Background. The coevolution of nuclear and mitochondrial genomes has guaranteed mitochondrial function for millions of years. The introduction of European (EUR) and African (AFR) genomes into the Ameridian continent during the Columbus exchange in Latin America created an opportunity to naturally test different combinations of nuclear and mitochondrial genomes. However, the impact of potential “mitonuclear discordance” (MND, differences in ancestries) has not been evaluated in Latin American admixed individuals (AMR) affected with developmental disorders, even though MND alters mitochondrial function and reduces viability in other organisms. Methods. To characterize MND in healthy and affected AMR individuals, we used AMR genotype data from the 1000 Genomes Project (n = 385), two cohorts of 22q.11 deletion syndrome patients 22qDS-ARG (n = 26) and 22qDS-CHL (n = 58), and a cohort of patients with multiple congenital anomalies and/or neurodevelopmental disorders (DECIPHERD, n = 170). Based on their importance to mitochondrial function, genes were divided into all mitonuclear genes (n = 1035), high-mt (n = 167), low-mt (n = 793), or OXPHOS (n = 169). We calculated local ancestry using FLARE and estimated MND as the fraction of nuclear mitochondrial genes ancestry not matching the mtDNA ancestry and ∆MND as (MNDoffspring—MNDmother)/MNDmother. Results. Generally, MND showed distinctive population and haplogroup distributions (ANOVA p < 0.05), with haplogroup D showing the lowest MND of 0.49 ± 0.17 (mean ± s.d.). MND was significantly lower in 22qDS-ARG patients at 0.43 ± 0.24 and DECIPHERD patients at 0.56 ± 0.12 compared to healthy individuals at 0.60 ± 0.09 (ANOVA p < 0.05). OXPHOS and high-mt showed the same trend, but with greater differences between healthy and affected individuals. Conclusions. MND seems to inform population history and constraint among affected individuals, especially for OXPHOS and high-mt genes. Full article

This case report presents the clinical evaluation of an 11-year-old boy with a unilateral polypoid nasal mass causing nasal obstruction, facial asymmetry, and intermittent epistaxis. His clinical picture raised concerns of a juvenile nasopharyngeal angiofibroma; however, further imaging and histopathological evaluation ultimately confirmed the diagnosis of allergic fungal rhinosinusitis (AFRS). Although this patient was younger in age than those traditionally associated with AFRS, classical features present on both computed tomography (CT) and magnetic resonance imaging (MRI) aided in his diagnosis and management. This case underscores the importance of a comprehensive diagnostic approach when evaluating unilateral sinonasal masses in paediatric patients, specifically in atypical presentations where the diagnosis of AFRS may not initially be considered. It highlights the critical role of imaging as a diagnostic tool, specifically CT and MRI, which were pivotal in the work-up and management of this case. Additionally, the need for caution during biopsies of sinonasal masses in children is emphasised, as there is potential for catastrophic bleeding in vascularised masses such as juvenile nasopharyngeal angiofibroma. This case demonstrates that AFRS can occur in younger children, highlighting the need to include this in the differential diagnosis, even in patients outside of the traditionally described age group. Full article

Background/Aims: across protein-coding genes, single nucleotide allelic variants (SNVs) affect antidiabetic drug pharmacokinetics, thus contributing to interindividual variability in drug response. SNV frequencies vary across different populations. Studying ancestry proportions among SNV genotypes is particularly important for personalising diabetes mellitus type 2 (DMT2) treatment. Methods: a sample of 249 Mexican DMT2 patients was gathered. SNVs were determined through real-time PCR (RT-PCR). Molecular ancestries were determined as 3 clusters (Native-American, European, and African) based upon 90 ancestry markers (AIMS). Statistical inference tests were performed to analyse ancestry across 23 SNV genotypes. Allele and ancestry distributions were analysed through Spearman’s correlation. Results: ancestry medians were 65.48% Native-American (NATAM), 28.34% European (EUR), and 4.8% African (AFR). CYP2C8*3 and CYP2C8*4 were negatively correlated to NATAM, whereas positively to EUR. The activity score of CYP2C9 was correlated to NATAM (Rho = 0.131, p = 0.042). CYP2C19*17 and the activity score of CYP2C19 were negatively correlated to NATAM. The correlation throughout SLC22A1 variants, such as GAT in rs72552763, was positive by EUR, while A in rs594709 was negative thereby and positive by NATAM. SLC22A3 variant C in rs2076828 was positively correlated to NATAM. NATAM patients present higher HbA1c levels with respect to Mestizo patients (p = 0.037). Uncontrolled patients (HbA1c ≥ 7%) have a larger NATAM ancestry (p = 0.018) and lower EUR (p = 0.022) as compared to controlled patients (HbA1c < 7%). Conclusions: there is a correlation between ancestry and some pharmacokinetically relevant alleles among Mexican DMT2 patients. Ethnicity is relevant for personalised medicine across different populations. Full article

Active frequency response (AFR) plays a crucial role in addressing the challenge of insufficient frequency regulation caused by the spatiotemporal distribution of power grid frequency. However, power fluctuations in renewable energy sources impact the frequency regulation performance of renewable energy units participating in AFR, and there is a lack of systematic assessment of their frequency regulation capabilities. This paper proposes a process-optimized AFR method for renewable energy based on distributed model predictive control (DMPC) using tube and robust control barrier functions (RCBF). The method integrates tube MPC for renewable energy units in fault regions and constrains control parameters in normal regions using RCBF, forming an enhanced DMPC-based coordination process for interconnected systems. This optimization ensures that both conventional and renewable energy units can effectively perform AFR under fluctuating renewable energy conditions. Furthermore, within the AFR online decision-making process, the optimal deloading rate for renewable energy is determined to maintain sufficient power reserves and frequency regulation capabilities. Finally, simulations of an interconnected system with a high proportion of renewable energy validate the effectiveness of this process-driven approach in enhancing the AFR capabilities of renewable energy sources. Full article

Automated feeding robots (AFR) are increasingly being used on North American dairy farms to reduce dependency on human labor for feeding. These systems mix, deliver, and push up feed to cows at any frequency or interval desired, allowing for more frequent feed delivery than conventional feeding systems (CFS). This observational study investigated differences in ration consistency, milk components, milk fatty acid profile, and cow behavior between herds using AFR and those using CFS. Sixteen commercial dairies with automated milking systems (AMS) in the upper Midwest United States were paired based on herd size and location into eight blocks each consisting of one CFS and one AFR herd. Feed bunk samples were collected at four equally spaced time points for 3 consecutive d and analyzed for coefficient of variation (CV) of nutrient composition and particle size distribution. Bulk tank milk samples were collected 1 ×/d for 3 d and analyzed for fat, protein, milk urea nitrogen (MUN), lactose, and milk fatty acid (FA) profile. Daily AMS visit intervals, milk yield and composition, and rumination time data were collected from AMS software. A linear mixed model tested fixed effects of feeding system, block, and the random effect of day nested within block. The CV of feed bunk DM, ADF, NDF, and lignin was lower in AFR. Bulk tank milk fat, protein, and MUN were not different between AFR or CFS. AFR had a greater proportion of de novo synthesized FA, but no difference in preformed or mixed FA. Herds with AFR had a shorter AMS visit interval with more AMS refusals per day than CFS. Results imply that AFR may be associated with lower daily variation in fiber concentration at the feed bunk, increased mammary de novo fatty acid synthesis, and increased frequency of cow visits to the AMS compared to conventional PMR feeding. Full article

Reducing CO₂ emissions in general aviation is a critical challenge, where battery electric and fuel cell technologies face limitations in energy density, cost, and robustness. As a result, hydrogen (H₂) dual-fuel combustion is a promising alternative, but its practical implementation is constrained by abnormal combustion phenomena such as knocking and pre-ignition, which limit the achievable H₂ energy share. In response to these challenges, this paper focuses on strategies to mitigate these irregular combustion phenomena while effectively increasing the H₂ energy share. Experimental evaluations were conducted on an engine test bench using a one-cylinder dual-fuel H₂ kerosene (Jet A-1) engine, utilizing two strategies, including water injection (WI) and rising the air–fuel ratio (AFR) by increasing the boost pressure. Additionally, crucial combustion characteristics and emissions are examined and discussed in detail, contributing to a comprehensive understanding of the outcomes. The results indicate that these strategies notably increase the maximal possible hydrogen energy share, with potential benefits for emissions reduction and efficiency improvement. Finally, through the use of 0D/1D simulations, this paper offers critical thermodynamic and efficiency loss analyses of the strategies, enhancing the understanding of their overall impact. Full article

Patient demands for aesthetic orthodontic brackets (OBs) has increased since orthodontic treatments are of long duration. Clinicians encounter old composite restorations frequently, against which OBs need to be bonded. This study aims to determine the shear bond strength (SBS) of two aesthetic OBs (ceramic and resin) against aged composite resins (flowable and packable) after standard surface treatment. A total of 96 disk-shaped specimens of two aged (A) composite resins [flowable (F) and packable (P)] were divided into eight groups, using ceramic (C) and plastic (P) brackets, out of which four subgroups served as the control [non-aged (N)FC, NPC, NFR, NPR] and four as experimental [AFC, APC, AFR, APR]. Surface treatment included mechanical [air abrasion] and chemical [Assure Plus and Transbond XT]. After 24 h of storage, the specimens were tested for SBS and observed for failure mode using adhesive remnant index scores. Mean values of SBS in each subgroup were analyzed statistically using a one-way analysis of variance test and Tukey post hoc test. All probability ‘p’ differences were significant at a value of 0.05 and less. All aged composite resin subgroups had decreased bond strength than controls, with all subgroups bonded with plastic brackets having the least bond strengths that were clinically nonacceptable [≤7 to 10 MPa]. Flowable composites when bonded with either ceramic or plastic brackets had higher strength than packable composites. Ceramic brackets had higher SBS than plastic brackets for both flowable and packable composites. Significant differences in bond strength were observed among subgroups of plastic brackets. Ceramic brackets were associated with a higher residue of adhesives on the composite surface. Aged composite resins exhibit significantly lower SBS than fresh composites, with ceramic brackets and flowable composites producing better bond strength values than plastic brackets and packable composites. Full article

The RED II Directive requires European Union member states to increase the share of renewable energy in the transport sector to at least 14% by 2030. In January 2024, Poland replaced E5 gasoline (95 octane) with E10, which contains up to 10% bioethanol derived from second-generation sources such as agricultural residues. The transition to E10 raises concerns about the ability of engine management systems to adapt to its different air–fuel ratio (AFR) requirements. The AFR for E10 (13.82) is 1.98% lower than for E5 (14.25) and 3.88% lower than for pure gasoline (14.7). Research conducted on a spark-ignition engine (with AFR regulation) using an exhaust gas analyzer demonstrated that during the combustion of E5 and E10 fuels with correctly adjusted AFR and operation at λ = 1, the use of E10 potentially increases CO₂ and NO_x emissions despite reductions in CO and HC. However, when calibrated for E5 and operated with E10 fuel, an increase in CO₂ and HC concentrations in the exhaust gases is observed, along with a reduction in CO and NO_x. This phenomenon is attributed to operation with lean mixtures, at λ = 1.02. This study investigates both the theoretical and experimental impact of this fuel transition. Fuel systems typically adjust engine operation based on exhaust gas analysis but cannot recognize fuel type, leading to incorrect λ values when the AFR differs from the ECU’s programming. Effective adaptation would require additional fuel composition sensors and editable ECU mappings. For older vehicles or small non-road engines, manual adjustments to injection or carburetor systems may be necessary. Full article