Mean Maximum Research Articles

Thermally anisotropic building envelope for thermal management: finite element model calibration using field evaluation data

The thermally anisotropic building envelope (TABE) is an active building envelope that redistributes thermal loads in response to weather conditions and building energy demand. Conductive layers throughout the TABE distribute low-grade heat among hydronic loops, altering heat flow direction and intensity. Finite element models of TABE roof and wall panels were developed and calibrated using field evaluation data. The calibration results showed that heat flux differences between the experimental data and finite element models averaged −0.42% and 3.57%, with a maximum mean square error of 1.78 and 3.96 for roof and wall panels, respectively. A reduction in heat flux from the environment to the building living space over the entire testing period (weeks in July/August) was found to be 85% for roof panels and 335% (load reversed) for wall panels. These results indicate TABE can effectively harness low-grade thermal energy sources to achieve high energy efficiency and promote demand-side management.

Forecasting solar irradiance for the strategic integration of hybrid hydro and solar photovoltaic systems in rural Indian regions

ABSTRACT Conversion of the conventional electrical grid into a smart and sustainable grid involves several considerations. The primary factors, however, are renewable energy penetration, associated storage systems, and energy generation costs. This research endeavors to conduct a thorough survey and analysis of the solar irradiance on various hydropower locations in India, including Run-of-River (RoR), Run-of-River with Pondage (RoRP), Reservoir Storage (S), Multi-Purpose Storage (MP) and Pumped Storage Systems (PSS). The hydroelectric projects in rural Indian regions have been the subject of the proposed case study. As a preliminary study, the probabilistic variables like minimum, maximum, and mean solar irradiance are calculated for 252 High-Scale Hydropower Plant locations (HSHPs) using the past 40 years day ahead solar radiation data to identify the high-irradiance hydropower plant location in each state of India. This study concludes that the maximum mean solar irradiance location in each state as these sites are well suited for hybrid PV-hydro systems. The identified high-irradiance locations 40 years day ahead data sets are analyzed employing 8 machine learning models and 2 deep learning models. This analysis aims to forecast solar irradiance, serving as a crucial foundation for the initial phase of the implementation of hybrid PV-hydro.

Robust knowledge transfer for bearing diagnosis in neural network models using multilayer maximum mean discrepancy loss function

Abstract This paper introduces a robust transfer learning method to enhance bearing diagnosis, particularly in cross-machine scenarios. The method trains a shallow neural network using labeled data from a different machine and unlabeled data from the target monitoring machine. To facilitate effective knowledge transfer, a multilayer maximum mean discrepancy loss function is employed, enabling the model to adapt learned features from the source machine to the target machine’s unlabeled data. This approach addresses the challenges of low accuracy and robustness often seen in transfer learning, especially when dealing with different machines. Experiments conducted on the Hanoi University of Science and Technology bearing dataset validate the effectiveness of the proposed method. The results show significant improvements in prediction accuracy and robustness, making this method superior to existing transfer learning models for cross-machine bearing diagnosis.

An electronic nose drift compensation algorithm based on semi-supervised adversarial domain adaptive convolutional neural network

Sensor drift is a significant challenge leading to performance degradation in electronic nose(E-nose) systems. Effectively addressing sensor drift represents the most daunting problem in E-nose technology. This work proposes a domain adaptive approach called Semi-Supervised Adversarial Domain Adaptive Convolutional Neural Network (SAD-CNN) to tackle the long-term drift in E-nose and device displacement. SAD-CNN leverages adversarial learning to minimize distribution disparities between the source and target domains. Unlike traditional methods employing projection matrices, SAD-CNN utilizes one-dimensional convolutional neural networks as feature extractors, circumventing the complexities associated with parameter adjustments and matrix calculations in the projection process. During the training process, utilizing pseudo-labels generated by the semi-supervised self-training method to train the model, thereby reducing the labeling costs. Additionally, confidence threshold screening is introduced during the self-training phase to minimize erroneous pseudo-labels. Furthermore, the regenerative Hilbert space’s Maximum Mean Difference combined with Minimum Variance is introduced as a domain constraint function to mitigate distribution discrepancies between domains and enhance feature discriminability across domains. The experimental results demonstrate that the SAD-CNN method outperforms others. Within the long-term drift dataset, the classification accuracies for different scenarios are 78.01 % and 82.53 %, respectively. Meanwhile, the instrument change dataset yields a classification accuracy of 96.45 %.

Relationship between coking properties measured by automatic Sapozhnikov plastometer with other measurements

ABSTRACT A Sapozhnikov plastometer is equipment that is commonly used, particularly in Asia, for measuring the contraction of the coal bed as well as the plastic layer thickness during the coking process. The relationship between the Sapozhnikov contraction X and the maximum plastic layer thickness Y values and other thermal rheology testing techniques is not fully understood. It leads to difficulties in the interpretation of data using different thermal rheology properties measurement techniques. In this work, a series of coal samples were analyzed in parallel using the automatic Sapozhnikov plastometer, the Gieseler plastometer, and the Ruhr dilatometer. The measurement results were compared to assist in the interpretation of data generated using different coal thermal rheology property measurement techniques. The Sapozhnikov X results are found not comparable due to the difference in sample particle size and testing conditions for component coals with the wide range of maximum mean reflectance (Ro), but a significant linear correlation on the contraction/expansion behavior between Sapozhnikov and Ruhr dilatation test is observed for experimental coal blends Ro between 1.00 and 1.20. Sapozhnikov Y results are linearly related to the melting range from both the Gieseler plastometer and the Ruhr dilatometer for all the experimental single coals and coal blends.

Accelerated CEST imaging through deep learning quantification from reduced frequency offsets.

To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra. Fisher information gain analysis identified optimal frequency offsets (termed "Fisher offsets") for the multi-pool fitting model, maximizing information gain for the amplitude and the FWHM parameters. These offsets guided initial subsampling levels. A U-NET, trained on undersampled brain CEST images from 18 volunteers, produced CEST maps at 3 T with varied undersampling levels. Feasibility was first tested using retrospective undersampling at three levels, followed by prospective in vivo undersampling (15 of 53 offsets), reducing scan time significantly. Additionally, glioblastoma grade IV pathology was simulated to evaluate network performance in patient-like cases. Traditional multi-pool models failed to quantify CEST maps from undersampled images (structural similarity index [SSIM] <0.2, peak SNR <20, Pearson r <0.1). Conversely, U-NET fitting successfully addressed undersampled data challenges. The study suggests CEST scan time reduction is feasible by undersampling 15, 25, or 35 of 53 Z-spectrum offsets. Prospective undersampling cut scan time by 3.5 times, with a maximum mean squared error of 4.4e-4, r = 0.82, and SSIM = 0.84, compared to the ground truth. The network also reliably predicted CEST values for simulated glioblastoma pathology. The U-NET architecture effectively quantifies CEST maps from undersampled Z-spectra at various undersampling levels.

Federated transfer learning-based distributed fault diagnosis method for rolling bearings

Abstract Current methods for bearing fault diagnosis often fall short in addressing data privacy concerns and typically rely on one-to-one transfer strategies, which are inadequate for achieving knowledge transfer in distributed environments. To address this issue, a distributed fault diagnosis method for rolling bearings based on federated transfer learning is proposed. This method ensures data privacy while integrating fault knowledge from multiple domains, thereby enabling more efficient knowledge transfer. Specifically, a Domain Adversarial Neural Network (DANN) is introduced as the base model within the federated learning framework. Additionally, Maximum Mean Discrepancy (MMD) is incorporated into the DANN to enhance the transfer of fault knowledge. Finally, a dynamic weighting parameter update method based on MMD is designed to evaluate the feature discrepancies between source and target domains, thereby updating the parameters of the federated framework and achieving global model aggregation. Experimental results on two bearing datasets demonstrate that the proposed method excels in both distribution alignment and fault diagnosis.

Experimental investigation of solid propellants combustion response to pressure oscillations using advanced high-resolution diagnostics

The combustion response of solid propellants plays a crucial role in determining the combustion stability of solid rocket motors (SRMs). This study employs multiple advanced combustion diagnostic techniques, including high-speed photomicrography, two-color pyrometry, and laser shadowgraphy, to simultaneously capture mesoscale flame dynamics and burning surface regression characteristics of AP/HTPB composite propellants under pressure oscillations. This experimental configuration enables the simultaneous achievement of high temporal resolution (0.25 ms) and spatial resolution (4.9μm/pixel). Pressure oscillations are generated by loudspeakers, cover a range of frequencies (168.9 Hz to 506.7 Hz) and amplitudes up to approximately 0.44% of the equilibrium pressure. The investigated propellants exhibit varying oxidizer characteristics, including coarse (330μm) and fine (150μm) AP particle sizes, as well as different AP mass fractions (81% and 86%). Results reveal that finer oxidizer particles and higher oxidizer content both result in an increased mean steady burning rate, with the former exhibiting a more pronounced influence on the burning rate increment. Under pressure oscillations, the mean burning rate increases with increasing pressure oscillation amplitude, accompanied by a reduction in flame height, an expansion in mean flame swing angle, and an elevation in mean flame temperature. The observed frequency dependency indicates a stronger combustion response when the oscillation frequency aligns with the characteristic frequency of propellant, which is closely associated with the mean burning rate, irrespective of the amplitude of pressure oscillation. This frequency-dependent phenomenon results in the highest combustion response, characterized by a maximum mean burning rate increment exceeding 165%. The innovative experimental platform employed in this study demonstrates the capability to simultaneously capture various flame fluctuation characteristics, such as flame swinging, flame temperature, and burning rate fluctuation in a single test. This facilitates a more thorough and comprehensive analysis of combustion responses in solid propellants under pressure oscillations.

The maximum geometric mean criterion: revisiting the Markowitz–Samuelson debate: survey and analysis

The maximum geometric mean criterion: revisiting the Markowitz–Samuelson debate: survey and analysis

A transfer learning based patent transfer prediction model: Discussion about the condition of China and U.S.

In the same patent classification, there are significant differences in the patent transfer quantity for different countries. For the countries with small patent transfer quantities, the performance of the patent transfer prediction is worse than that of high-transfer countries. To improve the performance of the patent prediction model learned for the low-transfer countries, we propose a transfer learning based patent transfer prediction model, for which the high-transfer countries are the source domain and the low-transfer countries are the target domain. In the model, the textual feature of each patent text is embedded using BERT-CNN with the parameters fine-tuned on the target domain from the source domain. The textual features are optimized to minimize the domain loss of Maximum Mean Discrepancy between source and target domain. What is more, we extract graph feature vectors by embedding the patent node in a three-layer heterogeneous network which consists of patent nodes, International Patent Classification nodes, consumer nodes, consumer location nodes and their relations. Finally, the patent text vectors, structural feature vectors, and graph feature vectors are spliced together to predict target domain patents transfer or not. The performance of the model is verified by some comparative experiments. The experimental results show that the proposed model outperforms significantly the baseline models in the values of Precision, Recall, and F1 of patent data improve by 6.47%, 6.54%, and 6.58% on average, respectively.

Thermal comfort driven space heating control via hierarchical state machine strategy interacting with multiphysics simulation

Improving energy efficiency of space heating systems is as important as adopting emerging clean energy technologies for decarbonising heating. While exploring strategies for reducing energy consumption, the space occupants’ expectations to thermal comfort should not be sacrificed. Conventional space heating control systems are often developed under the hypothesis of uniform temperature distribution in a bounded space and the heating system controller is primarily operated based on the information from the temperature sensor at a fixed position. That is, the measured temperature does not represent the temperature surrounding the occupant’s location. This paper explores a new control method; the heating system control is led by the occupant’s thermal experience, expressed by the temperature surrounding the occupant. A new hierarchical state machine control strategy interacting with a Multiphysics simulation model to dynamically estimate the temperature at the point of the occupant is developed. The accuracy of the Multiphysics simulation model is verified based on the corresponding experimental test room data. A co-simulation platform is built to allow COMSOL and SIMULINK to communicate in real-time for control strategy realisation. The experimental results show that the maximum and average mean square error between the measured and simulated temperature under different operating modes is less than 0.21℃ and 0.06℃, respectively. The simulation results indicate that energy consumption could be potentially reduced around 20% with an improved thermal comfort of the occupant by 30%, compared with the conventional fixed sensor control method.

Optical design of a 5× zoom afocal telescope with deformable mirrors

All-reflective zoom afocal telescopes can be integrated with existing imaging systems as foreoptics, enabling flexible imaging with adjustable resolution. This paper proposes an approach to designing a 5× zoom afocal telescope using deformable mirrors. Based on matrix optics, the first-order parameters for a coaxial reflective 5× zoom afocal telescope with a fixed primary mirror and three deformable mirrors are determined. To prevent obscuration, the mirrors are tilted at their vertices, which introduces asymmetric aberrations. To correct these aberrations, XY polynomial surfaces are employed on the primary mirror and an additional flat fold mirror. The final design and image simulations indicate that the system achieves good image quality across all zoom positions, enabling 5× zoom imaging with a maximum root mean square wavefront error of less than 0.125λ (λ = 632.8 nm), thereby validating the efficacy of the proposed design methodology.

Spatiotemporal Variation in Absorption Aerosol Optical Depth over China

Absorbing aerosols can absorb solar radiation, affect the atmospheric radiation balance, and further have a profound influence on the global and regional climates. The absorption aerosol optical depth (AAOD) as well as the absorption Angstrom exponent (AAE) across China over 2005–2018 were systematically studied through the Ozone Monitoring Instrument (OMI) dataset. The monthly AAOD samples from the OMI generally showed a good correlation (~0.55) compared to the monthly data from AERONET at four typical sites (North: Xianghe, East: Taihu, South: Hongkong Polytechnic Univ; Northwest: Sacol) across China. The ensemble annual average of the OMI AAOD at 388 and 500 nm is 0.046 and 0.022, with minor changes during 2005–2015, and a relatively fast increase after that. The winter and spring seasons depict the maximum mean AAODs, followed by autumn, whereas summer shows minimum levels. On the contrary, the high AAE values appear in summer and low values in winter. The order of the annual average AAOD500 from 2005 to 2018 is the Tarim Basin (TB, 0.041) &gt; the Yellow River Basin (YRB, 0.023) &gt; Beijing and Tianjin (BT, 0.026) &gt; the Sichuan Basin (SB, 0.023) &gt; Nanjing and Shanghai (NS, 0.021) &gt; the Pearl River Delta (PRD, 0.017), whereas the AAE388–500 exhibits the opposite trend except for the TB (3.058). From 2005 to 2018, the AAOD rises by nearly 1.5–2.0 fold in the six typical regions, implying a severe situation of dust and/or BC aerosol pollution in the last several years. The monthly mean AAOD388 over the TB, the SB, the YRB, BT, the PRD, and NS is estimated to be smallest at 0.072, 0.024, 0.026, and 0.027 in July, 0.024 in June, and 0.025 in September, respectively, whilst largest in January for NS, the YRB and BT, April for the TB, February for the SB, and March for the PRD with 0.055, 0.077 and 0.067, 0.123, and 0.073 and 0.075, respectively. The monthly averaged AAOD500 in each region is consistently about half of the AAOD388. The highest AAE appears in June while the lowest values are in December and January, and the daily AAE values in episode days slightly decrease as compared to non-episode days. Our study indicates that northwestern China plays an important role in the overall AAOD as a result of dust aerosols stemming from desert areas. Moreover, the meteorological conditions in winter and early spring are associated with more energy consumption conducive to the accumulation of high black carbon (BC) aerosol pollution, causing high alert levels of AAOD from November to the following March.

Fault diagnosis method for bearings under variable working conditions based on transfer relation network

Abstract In order to achieve robust fault diagnosis under varying conditions with limited labeled data, this study combines metric-based meta-learning with feature-based domain adaptation. It introduces a new approach for variable-condition bearing fault diagnosis using transfer relation networks. To enhance the network’s ability to generalize across different domains, the paper integrates local maximum mean discrepancy (LMMD) into the relation network architecture. LMMD aligns the data distributions of various classes between the source and target domains, effectively addressing distributional differences and improving model generalization. To accurately and swiftly extract meaningful fault features, the study proposes a lightweight feature extraction module based on Shuffle Attention (SA). This module employs depth-wise separable convolutions for efficiency and integrates SA after each convolutional layer to bolster feature representation. Finally, experiments on two bearing datasets under varying conditions validate the efficacy and superiority of the proposed model over alternative methods.

On the role of wake-capture and resonance in spanwise-flexible flapping wings in tandem

Numerical simulations of the flow around spanwise-flexible flapping wings in tandem are reported, focusing on a thrust-generating configuration. Wings of aspect ratio 2 and 4 in forward flight undergo heaving and pitching motion following optimal 2D kinematics. The Reynolds number of the simulations is Re=1000. The effect of flexibility is explored by varying the effective stiffness of the wings, while the effective inertia is kept constant. The aerodynamic performance of the tandem system results from a combination of unsteady aerodynamics mechanisms, fluid–structure resonance, vortex–wing interactions (denoted wake capture in this study) and aerodynamic tailoring. It is found that the aerodynamic performance and structural behavior of forewings are dominated by a fluid–structural resonance. The maximum mean thrust for the forewings is obtained when the driving frequency approaches the first natural frequency of the structure, ωn,f/ω≈1, similarly to what is observed in isolated wings undergoing the same kinematics. On the other hand, hindwings show optimal performance in a broad region near ωn,f/ω≈2, and their aerodynamic performance seems to be dominated by wake–capture and aerodynamic–tailoring effects. The aerodynamic performance of the hindwings is dependent on the flexibility of the forewing, which impacts the intensity of the vortices shed into the wake and the resulting effective angle of attack (i.e., wake capture). The timing between the effective angle of attack and the pitching motion of the hindwing controls the generation of thrust (or drag) of each spanwise section of the hindwing (i.e., aerodynamic tayloring). A proof of concept study on the aerodynamic performance of systems made of wings with different flexibility suggests that they could outperform tandem systems with equally flexible wings. Thus, the optimal mixed–flexibility tandem system is composed by a resonant forewing, which maximizes the thrust generation of the forewing and the intensity of the vortices shed into the wake, and a hindwing whose flexibility must be tuned to maximize wake capture effects.

Towards a configurable and non-hierarchical search space for NAS

Neural Architecture Search (NAS) outperforms handcrafted Neural Network (NN) design. However, current NAS methods generally use hard-coded search spaces, and predefined hierarchical architectures. As a consequence, adapting them to a new problem can be cumbersome, and it is hard to know which of the NAS algorithm or the predefined hierarchical structure impacts performance the most. To improve flexibility, and be less reliant on expert knowledge, this paper proposes a NAS methodology in which the search space is easily customizable, and allows for full network search. NAS is performed with Gaussian Process (GP)-based Bayesian Optimization (BO) in a continuous architecture embedding space. This embedding is built upon a Wasserstein Autoencoder, regularized by both a Maximum Mean Discrepancy (MMD) penalization and a Fully Input Convex Neural Network (FICNN) latent predictor, trained to infer the parameter count of architectures. This paper first assesses the embedding’s suitability for optimization by solving 2 computationally inexpensive problems: minimizing the number of parameters, and maximizing a zero-shot accuracy proxy. Then, two variants of complexity-aware NAS are performed on CIFAR-10 and STL-10, based on two different search spaces, providing competitive NN architectures with limited model sizes.

The Helicopter Turboshaft Engine’s Reconfigured Dynamic Model for Functional Safety Estimation

This research substantiates the necessity for developing and implementing structural reconfiguration methods for automatic control systems in the event of a parametric sensor failure to enhance the helicopter turboshaft engine’s overall reliability and safety. The research aim is the substantiation of the helicopter turboshaft engine’s mathematically reconfigured automatic control system in the event of the failure of a standard sensor, which will ensure the helicopter turboshaft engine’s stable operation under failure conditions, minimizing the impact on engine control and performance. A theorem was developed and proven concerning the reconfiguration of the helicopter turboshaft engine’s automatic control system structure, defining the system’s new mathematical form using nonlinear thermogas-dynamic parameters. A method was proposed to determine the values of these parameters that keep the reconfigured control system stable. This method uses numerical optimization to find the best thermogas-dynamic parameters to ensure system stability. Experimental results showed that for slow changes, using parameters from the previous step works best, while for fast changes, restarting is more effective due to significant differences in the system states. The accuracy of the proposed mathematical model for the reconfigured control system was confirmed through mean square error analysis (within 0.4% and 0.77% under white noise), regression analysis (with a determination coefficient of 0.986), and cross-validation (with a metric deviation from the maximum mean square error of 3.88%).

Effects of Cross-sectional Shape on the Low Observability of Double Serpentine Nozzles

When unmanned combat aerial vehicles (UCAVs) egress from the battlefield after attacking the target, the defensive radar systems detect signals reflected by the cavity structure of the nozzle and engine of the UCAVs, threatening their survivability. Infrared (IR) guided missiles can also threaten them by detecting the gaseous plume and hot engine parts. We present a comprehensive investigation of the effects of the cross-sectional shape of double serpentine nozzles on thrust, IR signature, and radar cross section (RCS), which are associated with propulsive performance and low observability. The fillet radius and cross-sectional shape of the nozzles were considered as the main design variables. The thermal flow field was analyzed using the compressible Navier-Stokes equations, while the IR signature was analyzed with the radiative heat transfer equation with a narrow band model. The RCS was calculated using a multi-level fast multipole method of full Maxwell's equations instead of the approximate methods often employed. The double serpentine (DS) nozzle with an elliptical cross-section showed high pressure recovery and a 9.66% lower IR signature than the DS nozzle with a rectangular cross section. The maximum RCS and mean RCS were lowest for the DS nozzle with an elliptical cross section, with a maximum difference in maximum RCS of 383% and mean RCS of 90% compared with the DS nozzle with a rectangular cross section. The DS nozzle with an elliptical cross section distributes the current more evenly across the central part of the engine than the DS nozzle with a rectangular cross section. The DS nozzle with an elliptical cross-section showed excellent nozzle performance with a negligible difference in thrust. In addition, the results show that the cross-sectional shape of the nozzles has different levels of impact on thrust and pressure recovery (negligible), IR signature (limited), and RCS (significant).

Label adversarial domain adaptation network for predicting remaining useful life based on cross-domain condition

Most data-driven methods for predicting remaining useful life assume that the data under different operating conditions follow the same distribution. However, this assumption rarely holds in real-world situation. Additionally, traditional methods do not fully utilize the hidden label information from the target domain or account for the transfer quality of source domain data. To address these issues, Label Adversarial Domain Adaptation (LADA) network is introduced in this paper. Specifically, LADA aims to filter the source domain data and maximize the use of hidden label information from the target domain. Firstly, a similarity measurement indicator based on the pearson correlation coefficient (PCC) and dynamic time warping (DTW) is employed to filter source domain data similar to the target domain data distribution. Then, in order to fully utilize the hidden label information from the target domain, the cloud model and golden section are utilized to create pseudo class labels. Furthermore, a feature difference module is established that minimizes the disparity between domain features. This is realized by using the maximum mean difference (MMD) and Kolmogorov–Smirnov (K–S) statistical test. The experimental results indicate that LADA has advantages in cross-domain RUL prediction.

Microfluidics assisted green synthesis of Copper Oxide nano particles from the aqueous leaf extract of Ipomoea quamoclit L.with its antimicrobial and antioxidant activity

IntroductionGreen nanosynthesis of medicinal plants are useful for the development of antibiotic agents. In this work, we have explored the antimicrobial and antioxidant activity of green synthesized CuO NPs from tropical medicinal plant Ipomoea quamoclit. L. MethodsWith the aid of microfluidic technology, a low-cost, PDMS lab-on-chip micromixer for green nanosynthesis of Copper Oxide (CuO) nanoparticle from Ipomoea is presented. The UV- Vis characteristics, FTIR spectrum, antimicrobial and antioxidant response using DPPH and H2O2 were reported. The antimicrobial efficacy of the synthesized CuO-NPs is tested against two-gram positive bacteria Staphylococcus aureus, Micrococcus luteus, two gram negative bacteria, Enterobacter cloacae, Klebsiella pneumoniae and two fungi species Candida albicans, Aspergillus niger. ResultsUV-Vis spectrum for the synthesized CuO-NPs extract showed the absorbance at 670 nm, SEM image analysis and DLS analysis confirmed the presence of spherical sized nanoparticle between 65 and 94.5 nm. The presence of bioactive functional groups of N-H, C-H and C=0 stretching was identified using Fourier Transform Infrared spectroscopy (FTIR).The maximum mean zone of inhibition obtained was 10.45±0.25mm for fungi Aspergillus niger. The antioxidant activity using DPPH scavenging assay and Hydrogen peroxide scavenging assay showed the IC50 value 33.94 μg/ml and42.54μg/ml respectively. ConclusionsThe obtained results reveal the potential of CuO NPs synthesised from Ipomoea quamoclit. L. as a remarkable medicinal entity for wound healing, respiration, fungal infection and urinary tract infection along with the improved antimicrobial and antioxidant activity.