Search Results (127)

A lie point symmetry analysis of flow and heat transfer under the influence of an external magnetic field and viscous dissipation was previously conducted using a couple of lie point symmetries of the model. In this article, we construct a one-dimensional optimal system for the flow model to extend the previous analysis. This optimal system reveals all the solvable classes of the flow model by deducing similarity transformations, reducing flow equations, and solving the obtained equations analytically. A general class of solutions that encompasses all the previously known lie similarity solutions is provided here. Full article

Background: Salmonella infections pose a serious threat to both animal and human health worldwide. Notably, there is an increasing trend in the resistance of Salmonella to fluoroquinolones, the first-line drugs for clinical treatment. Methods: Utilizing Salmonella Typhimurium CICC 10420 as the test strain, ciprofloxacin was used for in vitro induction to develop the drug-resistant strain H1. Changes in the minimum inhibitory concentrations (MICs) of various antimicrobial agents were determined using the broth microdilution method. Transcriptomic and metabolomic analyses were conducted to investigate alterations in gene and metabolite expression. A combined drug susceptibility test was performed to evaluate the potential of exogenous metabolites to restore antibiotic susceptibility. Results: The MICs of strain H1 for ofloxacin and enrofloxacin increased by 128- and 256-fold, respectively, and the strain also exhibited resistance to ceftriaxone, ampicillin, and tetracycline. A single-point mutation of Glu469Asp in the GyrB was detected in strain H1. Integrated multi-omics analysis showed significant differences in gene and metabolite expression across multiple pathways, including two-component systems, ABC transporters, pentose phosphate pathway, purine metabolism, glyoxylate and dicarboxylate metabolism, amino sugar and nucleotide sugar metabolism, pantothenate and coenzyme A biosynthesis, pyrimidine metabolism, arginine and proline biosynthesis, and glutathione metabolism. Notably, the addition of exogenous glutamine, in combination with tetracycline, significantly reduced the resistance of strain H1 to tetracycline. Conclusion: Ciprofloxacin-induced Salmonella resistance involves both target site mutations and extensive reprogramming of the metabolic network. Exogenous metabolite supplementation presents a promising strategy for reversing resistance and enhancing antibiotic efficacy. Full article

Optimizing sensor node coverage remains a central challenge in wireless sensor networks (WSNs), where premature convergence and suboptimal solutions in traditional optimization methods often lead to coverage gaps and uneven node distribution. To address these issues, this paper presents a novel velocity-scaled adaptive search factor particle swarm optimization (VASF-PSO) algorithm that integrates dynamic mechanisms to enhance population diversity, guide the search process more effectively, and reduce uncovered areas. The proposed algorithm is evaluated through extensive simulations across multiple WSN deployment scenarios with varying node densities, sensing ranges, and monitoring area sizes. Comparative results demonstrate that the approach consistently outperforms several widely used metaheuristic algorithms, achieving faster convergence, better global exploration, and significantly improved coverage performance. On average, the proposed method yields up to 14.71% higher coverage rates than baseline techniques. These findings underscore the algorithm’s robustness and suitability for efficient and scalable WSN deployments. Full article

This article discusses the design of a high-performance quasi-optical mode converter for the $T{E}_{33,12}$ mode at 210 GHz. The conversion process is challenging due to a caustic-to-cavity radius ratio of approximately 0.41. The mode converter employs an optimized dimpled wall launcher, analyzed using coupling mode theory with twenty-five coupled modes, compared to the usual nine modes and optimized reflector systems, to effectively address the conversion challenge.Electromagnetic field analysis within the launcher wall was optimized using MATLAB R2021b. The radiation fields from the launcher were analyzed in free space using Gaussian optics and vector diffraction theory. The mirror system consists of a quasi-elliptical mirror, an elliptical mirror, and phase-corrected parabolic mirrors. Following phase correction, the output window achieved a scalar Gaussian mode content of 99.0% and a vector Gaussian mode content of 97.4%. Full article

Structural health monitoring (SHM) has proven to be an effective technique to maintain the safety and reliability of laminated composites. Recently, both deep learning and machine learning methodologies have gained popularity in sensor-based SHM. However, machine learning approaches often require tedious manual feature extraction, while deep learning models require large training datasets, which may not be feasible. To overcome these limitations, this study presents a hybrid deep transfer learning (HTL) framework to identify delamination in composite laminates. The proposed framework enhances SHM performance by utilizing pre-trained EfficientNet and ResNet models to allow for deep feature extraction with limited data. EfficientNet contributes to this by efficiently scaling the model to capture multi-scale spatial features, while ResNet contributes by extracting hierarchical representations through its residual connections. Vibration signals from piezoelectric (PZT) sensors attached to the composite laminates, consisting of three health states, are used to validate the approach. Compared to the existing transfer learning approaches, the suggested method achieved better performance, hence improving both the accuracy and robustness of delamination detection in composite structures. Full article

Physics-Informed Neural Networks (PINNs) integrate physics principles with machine learning, offering innovative solutions for complex modeling challenges. Laminated composites, characterized by their anisotropic behavior, multi-layered structures, and intricate interlayer interactions, pose significant challenges for traditional computational methods. PINNs address these issues by embedding governing physical laws directly into neural network architectures, enabling efficient and accurate modeling. This review provides a comprehensive overview of PINNs applied to laminated composites, highlighting advanced methodologies such as hybrid PINNs, k-space PINNs, Theory-Constrained PINNs, optimal PINNs, and disjointed PINNs. Key applications, including structural health monitoring (SHM), structural analysis, stress-strain and failure analysis, and multi-scale modeling, are explored to illustrate how PINNs optimize material configurations and enhance structural reliability. Additionally, this review examines the challenges associated with deploying PINNs and identifies future directions to further advance their capabilities. By bridging the gap between classical physics-based models and data-driven techniques, this review advances the understanding of PINN methodologies for laminated composites and underscores their transformative role in addressing modeling complexities and solving real-world problems. Full article

Background: Coronary microvascular disease is associated with adverse prognosis in a range of cardiovascular diseases, but its prognostic role in patients with aortic stenosis (AS) is unclear. The aim of this systematic review and meta-analysis is to determine the prognostic role of myocardial perfusion and coronary flow reserve, assessed using non-invasive imaging modalities, in patients with AS. Methods: We conducted a systematic review and meta-analysis of all studies assessing myocardial perfusion reserve (MPR) or coronary flow reserve (CFR) in patients with AS and reporting clinical outcomes, from inception to January 2024. The definition of abnormal MPR/CFR and major adverse cardiovascular events (MACE) was that used in each study. Estimates of effect were calculated from hazard ratios (HRs) and 95% confidence intervals (CIs) using a random-effects model. Results: Four studies comprising 384 participants met the inclusion criteria. Myocardial/coronary blood flow was assessed using Doppler echocardiography (n = 2), PET (n = 1), or cardiac magnetic resonance (n = 1). The median optimal cutoff for MPR/CFR across all studies was 2.01 (range 1.85–2.13), with 109 events. Impaired MPR/CFR was associated with a higher incidence of MACE (HR 3.67, 95% CI: 1.66, 8.09, I² = 63%) in the overall population. Conclusions: Reduced MPR/CFR is associated with increased risk of MACE in patients with AS, although significant heterogeneity exists in published studies. Further studies are required to establish its role in the risk stratification of asymptomatic patients with AS. Full article

The abundance of powered semiconductor devices has increased with the introduction of renewable energy sources into the grid, causing power quality disturbances (PQDs). This represents a huge challenge for grid reliability and smart city infrastructures. Accurate detection and classification are important for grid reliability and consumers’ appliances in a smart city environment. Conventionally, power quality monitoring relies on trivial machine learning classifiers or signal processing methods. However, recent advancements have introduced Deep Convolution Neural Networks (DCNNs) as promising methods for the detection and classification of PQDs. These techniques have the potential to demonstrate high classification accuracy, making them a more appropriate choice for real-time operations in a smart city framework. This paper presents a voting ensemble approach to classify sixteen PQDs, using the DCNN architecture through transfer learning. In this process, continuous wavelet transform (CWT) is employed to convert one-dimensional (1-D) PQD signals into time–frequency images. Four pre-trained DCNN architectures, i.e., Residual Network-50 (ResNet-50), Visual Geometry Group-16 (VGG-16), AlexNet and SqeezeNet are trained and implemented in MATLAB, using images of four datasets, i.e., without noise, 20 dB noise, 30 dB noise and random noise. Additionally, we also tested the performance of ResNet-50 with a squeeze-and-excitation (SE) mechanism. It was observed that ResNet-50 with the SE mechanism has a better classification accuracy; however, it causes computational overheads. The classification performance is enhanced by using the voting ensemble model. The results indicate that the proposed scheme improved the accuracy (99.98%), precision (99.97%), recall (99.80%) and F1-score (99.85%). As an outcome of this work, it is demonstrated that ResNet-50 with the SE mechanism is a viable choice as a single classification model, while an ensemble approach further increases the generalized performance for PQD classification. Full article

This article proposes the development of an optimal and robust control approach for the voltage regulation of a bi-directional DC-DC converter for its integration in battery energy storage and electric vehicle charging station applications. The objective of the proposed controller is to enhance the robustness and disturbance rejection capability of the bidirectional buck-boost converter. The inner current control loop adopts the optimal model predictive control (MPC) scheme while the outer voltage control loop has been developed utilizing the robust sliding mode control (SMC) approach. The results of the proposed robust & optimal control approach show better voltage conversion capabilities with improved transient response and steady-state characteristics in the presence of variations in load and disturbances. Full article

DC–DC converters are extensively utilized in renewable energy systems because of the flexibility in their output voltage and their good conversion efficiency. The design of an adaptive sliding mode controller is proposed in this paper for a buck converter system in the presence of load variations, power disturbances, and model uncertainties. The adaptive control law is designed based on the Lyapunov stability criterion and updated online according to variations in the load and external disturbances. The elimination of the chattering mechanism and robustness of the overall system is confirmed. Simulation results indicate better voltage regulation and disturbance rejection for the proposed adaptive controller as compared to the traditional control algorithms. Full article

Oil refineries play a crucial role in meeting global energy demands, and optimizing the efficiency of critical processes is vital for economic feasibility and environmental sustainability. Simulation is an essential tool for the optimization of valuable products. This work presents the rigorous simulation of a vacuum distillation unit (VDU) based on actual data from the vacuum distillation processes using Aspen HYSYS V10. The Peng–Robinson fluid package is used in this simulation, and an input assay with a standard density of 29 API_60 (879.8 kg/m³) is employed. True boiling point (TBP) assay data are the type that is being used. Methane, ethane, propane, i-Butane, n-Butane, i-Pentane, and n-Pentane are the components listed in the simulation. The research determines that achieving a yield capacity of 685 tons/h requires thirty stages in the atmospheric distillation unit and twelve stages in the vacuum distillation unit while operating at 420 °C temperature and 9 kPa pressure. Adjustments in the flash section temperature (FST) and steam flow rate (SFR) are proposed to enhance operational efficiency. Increasing the FST from 370 °C to 400 °C and adjusting SFR from 10 tons/h to 26 tons/h increases the Light Vacuum Gas Oil (LVGO) yield by 7.2% while elevating the FST from 400 °C to 430 °C and adjusting SFR from 10 tons/h to 26 tons/h enhances the High Vacuum Gas Oil (HVGO) yield by 7.4%. These optimization strategies offer a practical and effective approach for refineries to improve the economic benefits of vacuum distillation units. The implications of this research can act as a computational thinking exercise for higher education students considering the case study where only through changing the operational strategies can the yield be enhanced by 10.81% in the vacuum distillation unit of the oil refinery. Full article

The study investigates the fabrication and analysis of nickel microparticle-reinforced composites fabricated using the digital light processing (DLP) technique. A slurry is prepared by incorporating Ni-micro particles into a resin vat; it is thoroughly mixed to achieve homogeneity. Turbidity fluctuations are observed, initially peaking at 50% within the first two minutes of mixing and then stabilizing at 30% after 15–60 min. FTIR spectroscopy with varying Ni wt.% is performed to study the alterations in the composite material’s molecular structure and bonding environment. Spectrophotometric analysis revealed distinctive transmittance signatures at specific wavelengths, particularly within the visible light spectrum, with a notable peak at 532 nm. The effects of printing orientation in the X, Y, and Z axes were also studied. Mechanical properties were computed using tensile strength, surface roughness, and hardness. The results indicate substantial enhancements in the tensile properties, with notable increases of 75.5% in the ultimate tensile strength and 160% in the maximum strain. Minimal alterations in surface roughness and hardness suggest favorable printability. Microscopic examination revealed characteristic fracture patterns in the particulate composite at different values for the wt.% of nickel. The findings demonstrate the potential of DLP-fabricated Ni-reinforced composites for applications demanding enhanced mechanical performance while maintaining favorable printability, paving the way for further exploration in this domain. Full article

Mycobacterium tuberculosis (Mtb) ranks as the most lethal human pathogen, able to fend off repeated attacks by the immune system or medications. PE_PGRS proteins are hallmarks of the pathogenicity of Mtb and contribute to its antigenic diversity, virulence, and persistence during infection. M. smegmatis is a nonpathogenic mycobacterium that naturally lacks PE_PGRS and is used as a model to express Mtb proteins. PE_PGRS has the capability to evade host immune responses and enhance the intracellular survival of M. smegmatis. Despite the intense investigations into PE_PGRS proteins, their role in tuberculosis remains elusive. We engineered the recombinant M. smegmatis strain Ms-PE_PGRS38. The result shows that PE_PGRS38 is expressed in the cell wall of M. smegmatis. PE_PGRS38 contributes to biofilm formation, confers permeability to the cell wall, and shows variable responses to exogenous stresses. PE_PGRS38 downregulated TLR4/NF-κB signaling in RAW264.7 macrophages and lung tissues of infected mice. In addition, PE_PGRS38 decreased NLRP3-dependent IL-1β release and limited pathogen-mediated inflammasome activity during infection. Moreover, PE_PGRS38 inhibited the apoptosis of RAW264.7 cells by downregulating the expression of apoptotic markers including Bax, cytochrome c, caspase-3, and caspase-9. In a nutshell, our findings demonstrate that PE_PGRS38 is a virulence factor for Mtb that enables recombinant M. smegmatis to survive by resisting and evading the host’s immune responses during infection. Full article

Nuclear power plays a significant role in fulfilling the energy needs of Pakistan and its share in the total energy mix has increased from 4.7% to 8.8% in the past seven years. As per the Pakistan energy outlook report (2021–2030), this share is hypothesized to increase to 10.82% by the year 2030, which will alleviate the energy shortage problem and, at same time, reduce carbon emissions. Like all thermal power plants, it is also necessary for nuclear plants to operate at optimum efficiency. This study is based on the thermodynamic analysis of the conventional side of an advanced HPR-1000 (PWR) nuclear power plant. In this paper, a comparison of indigenously developed model results is made, with vendor-provided sea water temperatures and power curves for year-long sea water temperature variation. Firstly, a computational model is developed using Engineering Equation Solver (EES) software to evaluate the performance of the secondary side of the plant and is validated based on the designer-provided heat balance analysis for full power mode. Then, the condenser heat balance is performed for different cooling medium inlet temperatures and terminal temperature differences to study the relationship of condenser performance, thermal efficiency, and output power. Initial results reveal that sea water temperature varies at the condenser inlet from 5 to 35 °C, the power output of the unit decreases by 54 MW, and the thermodynamic efficiency drops by 1.79%. Thus, this paper highlights the impact of sea water temperature on plant performance and the need to devise more effective techniques to approach the plant’s optimum efficiency. Full article

Various combinations of ship propulsion systems have been developed with low-carbon-emission technologies to meet regulations and policies related to climate change, one of which is the combined gas turbine and steam turbine integrated electric drive system (COGES), which is claimed to be a promising ship propulsion system for the future. The objective of this paper is to perform a techno-economic and environmental assessment of the COGES propulsion system applied to liquefied natural gas (LNG) carriers. A propulsion system design for a 7500 m³ LNG carrier was evaluated through the thermodynamics approach of the energy system. Subsequently, carbon emissions and environmental impact analyses were carried out through a life cycle assessment based on the power and fuel input of the system. Afterwards, a techno-economic analysis was carried out by considering the use of boil-off gas for fuel and additional income from carbon emission incentives. The proposed propulsion system design produces 1832 kilowatts of power for a service speed of 12 knots with the total efficiency of the system in the range of 30.1%. The results of the environmental evaluation resulted an overall environmental impact of 10.01 mPts/s. The results of the economic evaluation resulted in a positive net present value and a logical payback period for investment within 8 years of operation. The impact of this result shows that the COGES has a promising technological commercial application as an environmentally friendly propulsion system. Last, for the economy of the propulsion system, the COGES design has a positive net present value, an internal rate return in the range of 12–18%, and a payback period between 6 and 8 years, depending on the charter rate of the LNG carrier. Full article