Method For Measurement Of Density Research Articles

Study of Temporal Dynamics of Urban Heat Island Surface in Padang West Sumatra, Indonesia

Padang as the capital of the province, is a strategic area and also the center of the economy. Annual population growth affects changes in land use from vegetated land to built-up areas. An increase in barren land will trigger an increase in temperature. SUHI is a temperature phenomenon that occurs on the surface resulting from the increase in temperature. SUHI can be observed through surface temperature data or Land Surface Temperature. This study aims to identify changes in land surface temperature that are affected by changes in land use in the form of building density conditions. In analyzing this using Landsat 7 ETM+ imagery in 2001, 2006, 2011, 2016, and 2020. The building density measurement method LST transformations to measure surface temperature and helps the Surface Urban Heat Island phenomenon. The results of the analysis showed that there was an increase in the building density of the city of Padang over a period of 20 years. This phenomenon affects the surface temperature, indicating that the surface temperature has increased by around 0.47°C. The highest temperature from 2001-2020 occurred in 2016, with the highest temperature of 36°C.

Air gap flux density measurement of PMSLM based on TMR sensing external stray magnetic field and CNN-LSTM

This paper presents a new non-invasive air gap flux density measurement method for permanent magnet synchronous linear motor (PMSLM) using tunneling magnetoresistance (TMR) sensor and convolutional neural networks-long short-term memory (CNN-LSTM) regression modeling. First, the analytical and finite element models of the air gap magnetic field of PMSLM are established as the data basis. Second, TMR sensor is used to measure the external stray magnetic density. Gramian Angular Field method combined with image similarity matching technology are used to obtain the optimal measurement position of the TMR sensor. Then, a new deep learning regression method as CNN-LSTM is introduced to establish a high-precision mapping model to realize non-invasive high-precision measurement of the air gap magnetic density by “substituting external to internal.” Finally, PMSLM prototype experimental platform with TMR sensor hardware acquisition circuit is built. Comparison confirmatory experiments with Gauss meter can verify the effectiveness and superiority of the proposed method.

Intelligent vineyard blade density measurement method incorporating a lightweight vision transformer

Under the new demand model of Agriculture 4.0, automated spraying is a very complex task in precision agriculture, which needs to be combined with a computerized vision perception system to distinguish the plant leaf density and execute the spraying operation in real-time accordingly. Aiming at the accurate determination of grape leaf density, an image leaf density determination method based on the lightweight Vision Transformer (ViT) architecture is proposed, which designs a fusion data augmentation method containing a dual augmentation spatial extension and weather data augmentation method, where the former adopts the pixel augmentation and spatial augmentation for the original image processing, and the latter realizes the data augmentation from the empirical point of view adapted to the agricultural operation environment, and fuses the two in order to expand the sample capacity of the grape leaf density image, which then enhances the model's generalization ability and robustness. The lightweight ViT model has self-attention that can automatically and efficiently extract high-frequency local feature representations and use the two-branch structure to mix high-frequency and low-frequency information to form grapevine-leaf density features in the region of interest. The semantic analysis of the feature extraction layer is parsed using t-SNE and histogram methods, which improves the transparency of the model from the multidimensional with frequency domain distribution space. The experimental results show that the fusion data augmentation method can effectively improve the model recognition accuracy, and the accuracy of comparing the included data augmentation methods is improved by 0.55 % and 3.46 %, respectively. The accuracy of recognizing all four types of grape leaf densities exceeded 94 %, and the MCC reached 90.39 %. In addition, the proposed lightweight ViT improves the accuracy by at least 0.34 % with FLOPs of only 0.6 G compared to the popular MobileViT. The proposed method of this work has high recognition speed and accuracy, which can provide practical technical support for plant protection spraying robots and improve the profitability of growers based on the reduction of pesticide residues.

Uncertainty in measuring the role of climate change on debris-flow triggering on volcanoes - bulk-density, temperature and moisture analysis at Unzen Volcano (Japan)

As climate change creeps into the 21st century, the intensity of debris flows due to heavy and concentrated rainfall has increased in mountainous regions of Japan and East Asia. However, the relationship between climate change and an increase in debris flows is likely to be non-linear. Rainwater infiltrates more quickly into porous material, and the lack of vegetation cover increases evaporation and the temperature of surface sediments. In addition, periodic gully collapses bring fresh layers of porous material that increase the distance between the surface and the vadose zone. Therefore, to understand the relationship between volcanic debris flows (or lahars), parameters such as density, porosity, temperature, and moisture retention must be captured in detail in both time and space. The aim of this paper is to assess the role of loose, coarse, and porous sediments in lahar triggering. The present study was conducted at Unzen volcano in the Tansandani Guly between 31 May and 1 June 2023, 30 years after the last eruption. The dacite material is composed of a matrix of sand and coarse sand mixed with larger fractions and blocks, therefore traditional density measurement methods could not be applied, and a photogrammetric based method was used. In the field, sets of SfM-MVS photographs were taken before and after digging a hole in the sediments so that the measured mass could be compared to the volume of the hole in the sediments. After the sediments were dried, the dry and wet density, bulk density and porosity of the sample were calculated. When compared to the temperature data collected in the field, the following results were obtained: (1) The porosity of the volcanic material was highest in the lower reaches, followed by the upper reaches, and lowest in the middle reaches. This may be because fine sand washed out of the upstream area by rainfall is currently stored in the midstream area, which may facilitate debris flow generation. In addition, the downstream area has a high porosity, which may be due to the surrounding vegetation preventing the influx of new fine sand from the channel wall. (2) Because of the higher porosity and the lack of organic matter and vegetation cover, the increase in temperature acts more directly on the decrease in water content than in mountainous areas. Consequently, empirical equations for the potential for mudslides in volcanic areas with respect to surface temperature and soil moisture need to be developed for hazard and disaster risk management purposes.

High-precision alkali-atom density measurement and control methods using light absorption for dual-beam SERF magnetometers

High-precision alkali-atom density measurement and control methods using light absorption for dual-beam SERF magnetometers

Automated Density Measurement of Weft Knitted Fabrics Using Backlight Imaging

This paper proposes a new density measurement algorithm to address the issues of poor applicability and inaccurate results associated with the automatic density measurement algorithm for weft-knitted fabrics. The algorithm involves collecting the transmitted light image of the knitted fabric, calculating the tilt angle using the skewing correction algorithm, and rotating the image to correct the weft skew present therein. The pre-rotated and post-rotated images are then projected vertically and horizontally in grayscale, and the obtained projection curves are used to represent the distribution of loops in vertical and horizontal rows. This study proposed a wave peak coordinate verification algorithm that calculates the coursewise densities and walewise densities of the knitted fabric. In experiments, the proposed density measurement method is found to exhibit an accuracy above 98% when compared with the manual mode.

Deep learning assisted microwave-plasma interaction based technique for plasma density estimation

The electron density is a key parameter to characterize any plasma. Most of the plasma applications and research in the area of low-temperature plasmas (LTPs) are based on the accurate estimations of plasma density and plasma temperature. The conventional methods for electron density measurements offer axial and radial profiles for any given linear LTP device. These methods have major disadvantages of operational range (not very wide), cumbersome instrumentation, and complicated data analysis procedures. The article proposes a deep learning (DL) assisted microwave-plasma interaction-based non-invasive strategy, which can be used as a new alternative approach to address some of the challenges associated with existing plasma density measurement techniques. The electric field pattern due to microwave scattering from plasma is utilized to estimate the density profile. The proof of concept is tested for a simulated training data set comprising a low-temperature, unmagnetized, collisional plasma. Different types of symmetric (Gaussian-shaped) and asymmetrical density profiles, in the range 1016–1019 m−3, addressing a range of experimental configurations have been considered in our study. Real-life experimental issues such as the presence of noise and the amount of measured data (dense vs sparse) have been taken into consideration while preparing the synthetic training data-sets. The DL-based technique has the capability to determine the electron density profile within the plasma. The performance of the proposed DL-based approach has been evaluated using three metrics- structural similarity index, root mean square logarithmic error, and mean absolute percentage error. The obtained results show promising performance in estimating the 2D radial profile of the density for the given linear plasma device and affirms the potential of the proposed machine learning-based approach in plasma diagnostics.

Density and surface tension measurements of molten Al–Si based alloys

AbstractThis study is part of a series of studies aimed at measuring the thermophysical properties of molten phase change material-type metallic thermal energy storage materials near 873 K (600°C). The target material is Al–Si based alloys. First, as a feasibility study, density measurements of the molten state of three Al–Si binary alloys (Al–12.2Si, Al–50Si and Al–90Si in atomic%) were performed. A highly accurate non-contact density measurement method based on the static magnetic field superposition electromagnetic levitation (EML) method was employed as the density measurement method. The validity of this experimental method was confirmed, and density of molten Al–Si base alloys (ADC12 and Al–5.9mass%Si–1.6mass%Fe) were measured as a function of temperature with an expanded uncertainty of 1.2%. In addition, the surface tension of the alloys was measured by the droplet oscillation method using the EML technique. The surface tension was successfully obtained as a function of temperature with expanded uncertainty of 2.3%.

A sheath correction method for electron density measurements with the microwave resonant curling probe

A correction method accounting for plasma sheath effects that appear when performing an electron density measurement with a microwave resonant probe immersed in plasma is described. The diagnostic is the novel curling probe (CP) that has already shown promising capabilities in various plasma sources. The correction method is based on the evaluation of two effects relative to the resonant probe operation and its interface with the plasma. First, the characteristic decay length of the electromagnetic field emitted by the CP, which defines the probed volume, has been characterized for the different harmonic resonance modes. Second, a semi-analytical model has been adapted to describe the plasma structure forming near the probe, which quantitatively describes the electron density profiles across the sheath structure. The correction method is then developed uniquely from numerical simulations and is independent of other diagnostics. Experimental results inside two plasma sources, an inductively coupled plasma and an electron cyclotron resonance plasma thruster, are presented and discussed. The validity of the method is assessed (i) by comparing CP corrected densities with Langmuir probe results, (ii) by varying the probe orientation to the expanding plasma flow, and (iii) by using two harmonics of the probe. The method is shown to significantly improve the accuracy of electron density measurements. Possible improvements to the method are also discussed.

Automated nailfold capillary density measurement method based on improved YOLOv5

Nailfold capillary density is an essential physiological parameter for analyzing nailfold health; however, clinical images of the nailfold are taken in many situations, and most clinicians subjectively analyze nailfold images. Therefore, based on the improved “you only look once v5” (YOLOv5) algorithm, this study proposes an automated method for measuring nailfold capillary density. The improved technique can effectively and rapidly detect distal capillaries by incorporating methods or structures such as 9mosaic, spatial pyramid pooling cross-stage partial construction, bilinear interpolation, and efficient intersection over union. First, the modified YOLOv5 algorithm was used to detect nailfold capillaries. Subsequently, the number of distal capillaries was filtered using the 90° method. Finally, the capillary density was calculated. The results showed that the Average Precision (AP)@0.5 value of the proposed approach reached 85.2 %, which was an improvement of 4.93 %, 5.24 %, and 107 % compared with the original YOLOv5, YOLOv6, and simple-faster rapid-region convolutional network (R-CNN), respectively. For different nailfold images, using the density calculated by nailfold experts as a benchmark, the calculated results of the proposed method were consistent with the manually calculated results and superior to those of the original YOLOv5.

Ion current density on the substrate during short-pulse HiPIMS

A probe method for measuring the ion current density and theoretical calculations of the dynamics of neutral and charged plasma particles using the ionization region model (IRM) is used to study short and ultra-short pulse high-power impulse magnetron sputtering (HiPIMS). This paper studies reasons for the increase in the average ion current density on the substrate at shorter pulses, when the average discharge power does not change. HiPIMS pulses are applied to the copper target at constant values of average discharge power (1000 W) and peak current (150 А), respectively, while the pulse time of the discharge voltage ranges from 4 to 50 µs. A power supply with low output inductance is designed to generate ultra-short pulses. It is shown that shorter discharge pulses lead to a multiple growth (from 2 to 7 mA cm−2) in the average ion current density on the substrate and a growth in the peak intensity of Ar+, Cu+ and Cu2+ recorded by optical emission spectroscopy. A theoretical model of this effect is based on the spatially averaged IRM, which considers afterglow effects. According to theoretical calculations, the increase in the average ion current density on the substrate is determined by the plasma dissipation in the ionized region after the pulse ends. Also, a decrease in the copper deposition rate from 180 to 60 nm min−1 with decreasing pulse time from 40 to 4 µs is explored. A comparison of experimental data with those obtained earlier shows that the suggested dependences of the ion current density and deposition rate on the HiPIMS pulse time are typical for discharge systems with different cathode materials and configurations, i.e., for single- and dual-magnetron systems. This indicates a common nature of the phenomena observed and additionally confirms the results obtained.

A Simple Low-cost Method for Accurate Canopy Density Evaluation of Citrus

The use of canopy density for crop production is a useful tool for evaluating management practices for informed decision-making and predicting crop yields. Traditional methods for analyzing canopy density include expensive equipment that requires advanced software and costly repairs, such as leaf area index analyzers, or equipment that can only be used during optimal weather conditions, such as light meters, that quantify photosynthetically active radiation (PAR), thus making it difficult for researchers to maximize the time needed for field research evaluations and data collection. Digital image analyses using technologically advanced cameras, such as smartphone cameras, have allowed new ways of collecting data without the need to purchase specialized instruments. Using a combination of smartphone cameras and ImageJ software, canopy density can be measured in any weather conditions for a much lower cost than that of traditional equipment. This low-cost, digital image analysis method was compared with traditional PAR measurements for ‘Valencia’ sweet orange [Citrus sinensis (L.) Osb.] trees with varying levels of canopy density. A strong positive correlation between the digital image analysis method and standard canopy density measurement method using PAR measurements (r = 0.79; P<0.0001) was found, indicating that this method can be used as an alternative to the PAR method. The digital image analysis method was also consistent when used during different weather conditions, whereas the PAR method was highly variable when quantifying the canopy when clouds were present in comparison with clear sky conditions. This novel method provides researchers and growers with an easy, flexible, consistent, and low-cost option for analyzing canopy density.

A novel constraint-based method for density measurement in cased hole

In recent years, thru-casing measurement has shown high application values in controlling risk and improving production efficiency. However, in cased-hole applications, the investigation depth of gamma density logging is significantly confined due to the impacts of casing and cement, resulting in decreased accuracy of density measurement. Furthermore, the common uncertainty associated with cement bonding quality increases the difficulty of quantitative density evaluation. In order to overcome these challenges, a new thru-casing density measurement method is proposed. The method consists of three aspects: 1) casing correction; 2) simultaneous formation and cement density calculation based on cement constraint characterization; 3) cement thickness correction. The key to the method is step 2), where cement is defined by a set of physical constraints. e.g., volumetric fractions of contaminants such as water, mud, gas. The identification of constraints enables efficient sampling in the constrained phase space and provides the optimal solution to both formation density and cement density. To obtain precise and scenario-based cement constraints, Naive Bayes classifier combined with sliding window technique is used to provide prior information on cement quality. Geant4 is employed to simulate gamma density tools and obtain their responses in various formations, casings, and cements. These models are capable of meeting statistical error requirements. Since the simulation parameters are well-defined, it is useful for validating the proposed method using Monte Carlo simulation data under varying casing, cement, and formation conditions. The method can effectively obtain formation and cement density, as indicated by the calculation error of less than 0.02 g/cm³ and 0.025 g/cm³, respectively. The method is then applied to a well in the Bohai oilfield, and the measured data is processed using the proposed method. A comparison of open-hole and cased-hole density in a 48-m well interval shows that the error of each section increases as the well condition deteriorates. The maximum RMSE is 0.027 g/cm³ in the unstable borehole section (>13.6), while minimum RMSE is 0.016 g/cm³ in the section with good cementing quality and stable borehole. The mean relative error of the entire section is 0.91% and the RMSE of cased-hole density measurement is 0.0245 g/cm³. These results demonstrate the feasibility of the proposed method in the field by effectively correcting casing and cement impacts.

Real-time nondemolition measurement method for alkali vapor density and its application in a spin-exchange relaxation-free co-magnetometer.

This study presents a novel method for measuring the number density of K in K-Rb hybrid vapor cells using circularly polarized pump light on polarized alkali metal atoms. This proposed method eliminates the need for additional devices such as absorption spectroscopy, Faraday rotation, or resistance temperature detector technology. The modeling process involved considering wall loss, scattering loss, atomic absorption loss, and atomic saturation absorption, with experiments designed to identify the relevant parameters. The proposed method is real-time, highly stable, and a quantum nondemolition measurement that does not disrupt the spin-exchange relaxation-free (SERF) regime. Experimental results demonstrate the effectiveness of the proposed method, as the longitudinal electron spin polarization long-term stability increased by 204% and the transversal electron spin polarization long-term stability increased by 44.8%, as evaluated by the Allan variance.

Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods.

To investigate the differences of size and density measurements in assessing pure ground-glass nodules (pGGNs) growth, and compare the growth rates and growth proportions of the two methods during follow-up period. Ninety patients with at least 3 consecutive thin-section chest CTs and confirmed 103 pGGNs on baseline CT were enrolled retrospectively. Using the two definitions of size and density to evaluate pGGNs growth with semi-automated segmentation. Then, the two methods were compared to assess differences in pGGNs growth. For the size and density methods to assess nodule growth, 50.5% and 26.2% showed interval growth at the last CT (p < 0.001). Among the 19 nodules that grew in both size and density, the volume doubling time (VDT) of solid component (mean, 317.1; standard deviation, 224.8days) was shorter than total VDT (median, 942.8; range, 400.1-2315.9days) (p < 0.001). Of the 27 growth pGGNs assessed by the density method, the growth rates at years 1 and 2 were 25.9% and 63.0%, while the growth rates of 52 growing nodules assessed by size method were 11.5% and 48.1%, respectively. Twenty of 103 (19.4%) nodules were classified into category 4A lesions, and 7 (6.8%) were 4B lesions. Compared to size measurements, observed density increases have a higher proportion of early growth and faster growth rates in growing nodules. Clinicians need to pay close attention to the nodules of new solid components and make timely decision management.

Ultrasonic Wave Mode-Based Application for Contactless Density Measurement of Highly Aerated Batters.

An ultrasonic wave mode-based method for density measurement in highly foamed batters was developed. Therefore, a non-contact ultrasonic sensor system was designed to generate signals for batch-wise processes. An ultrasonic sensor, containing a piezoelectric ceramic at the fundamental longitudinal frequency of 2 MHz, was used to take impedance measurements in pulse-echo mode. The ultrasonic signals were processed and analysed wave-mode wise, using a feature-driven approach. The measurements were carried out for different mixing times within a container, with the attached ultrasonic sensor. Within the biscuit batter, the change to the ultrasonic signals caused by density changes during the batter-mixing process was monitored (R2 = 0.96). The density range detected by the sensor ranges between 500 g/L and 1000 g/L. The ultrasonic sensor system developed also shows a reasonable level of accuracy for the measurements of biscuit batter variations (R2 > 0.94). The main benefit of this novel technique, which comprises multiple wave modes for signal features and combines these features with the relevant process parameters, leads to a more robust system as regards to multiple interference factors.

Phytochemical Constituents Isolated From Distichochlamys citrea M.F. Newman Rhizomes and Their Antioxidant and α-glucosidase Inhibitory Activities

Objective/Background: Distichochlamys citrea M.F. Newman (Zingiberaceae) is an endemic plant of Vietnam and has been used as a folk medication for treatments of infection-related diseases, diabetes, and used as spice. This study aimed to characterize the antioxidant and α-glucosidase inhibitory activities of isolated compounds from D. citrea rhizomes. Methods: Compounds have been isolated from D. citrea rhizomes by different chromatographic techniques. Their chemical structures were determined based on mass spectrometry and nuclear magnetic resonance spectroscopy in comparison with those reported in the literature. The antioxidant effect was evaluated by 2,2-diphenyl-1-picrylhydrazyl-free radical neutralization whereas α-glucosidase inhibitory activity was determined by using the method of optical density measurement. Results: Seven compounds including 5-hydroxy-3,7,4'-trimethoxyflavone (1), kaempferol (2), platyphyllone (3), 5- O-caffeoylquinic acid (4), a mixture of phytosterols ( β-sitosterol (5) and stigmasterol (6)), and glycerol monostearate (7) were isolated and identified from D. citrea rhizomes. Compounds 1 and 2 simultaneously exhibited antioxidant activities (IC50 = 794.71 ± 2.68 µM, 14.08 ± 5.60 µM, respectively) and α-glucosidase inhibitory effects (IC50 = 189.40 ± 1.55 µM, and 31.86 ± 5.38 µM, respectively). Meanwhile, compound 3 and the mixture of 5 and 6 singly inhibited the enzyme α-glucosidase (IC50 = 69.41 ± 6.11 µM, 1204.82 µM, respectively), and showed weak antioxidant effects. On the contrary, compound 4 only had antioxidant activity (IC50 = 30.20 ± 1.86 µM). Compound 7 did not exhibit either antioxidant or enzymatic inhibitory effects. Conclusion: This is the first time that all of the seven compounds (1-7) and their biological activities were reported from D. citrea. Moreover, the α-glucosidase inhibitory effect of platyphyllone (3) is reported for the first time in the literature. This study revealed that flavonoids, phenolics, sterols, and monoglycerides in D. citrea rhizomes could be responsible for the antioxidant property and α-glucosidase inhibitory effect of this Vietnamese endemic medicinal plant.

High-efficiency Monte Carlo simulation based on CADIS method for Gamma Density Measurement

Formation density measurement is one of the most important part in reservoir hydrocarbon evaluation, which relies on accurate acquisition of the detector energy deposition spectra. Monte Carlo method is usually used to solve such problems, while huge computational time will be consumed due to the accuracy requirements of simulations. Therefore, this work aims to improve simulation efficiency based on the CADIS methods in Geant4 and achieve accurate density calculations. To obtain energy deposition spectra, this manuscript introduces a scoring method to assign weight windows according to energy group in Geant4 and to collect energy deposition spectra according to particle weights. This method is verified in simulated spectra using a dual-detector gamma density tool. Five test pits with measurement data are selected to demonstrate the accuracy of the calculated densities. The results show that the energy deposition spectra obtained by CADIS method can effectively improve the accuracy of density calculation.

Density estimation method of mature wheat based on point cloud segmentation and clustering

The sustainable development of agriculture needs to rely on precision agricultural technology. The premise and key to realizing precision agriculture is the research on the characteristics of crops in the field. For wheat plants, the ear is the flower or fruit part at the top of the wheat stem, which is one of the important components of yield, and its density can be said to be one of the most important traits. Its number is arguably-one of the most important phenotypic traits. The traditional method of density measurement is manual, which is time-consuming and laborious. Therefore, an efficient and convenient wheat density estimation scheme is needed to provide data support for crop yield monitoring, to achieve a better production management system. Stereo vision is a 3D imaging method that allows rapid measurement of plant structures, and point cloud segmentation is the key to studying the 3D spatial characteristics of plants. In this paper, the three-dimensional point cloud of wheat reconstructed by stereo vision technology is used for segmentation and clustering, and a method for clustering dense wheat rows is proposed. Firstly, a binocular camera was used to record video to reconstruct the wheat point cloud; then, point cloud pre-processing was used to remove noise; then, the octree splitting algorithm and voxel mesh merging algorithm were used to divide the dense wheat, and then clustering algorithm was used to get the point cloud of wheat ears; finally, the relationship model between the number of wheat ears point clouds and the number of wheat plants was established by linear regression analysis with R2 of 0.97. To verify the effectiveness of the algorithm, the actual field measurements and the predicted values of the algorithm were compared, with R2 of 0.93. The density estimation method provides a new method for the study of phenotypic information of crop population information and also provides a reference for nondestructive crop measurements.

Inhibitory effect of garlic and omam extracts on Mucor circinelloides, a fungus causing mucormycosis: An in vitro study.

Mucor circinelloides is reported to be the second among the most common causative agents of emerging mucormycosis. It is intrinsically resistant to most known antifungals. Further the use of antifungals cause side effects. Traditional knowledge system for treating various ailments is stronger in India and it also backs deriving various bioactive compounds from herbal sources, in the modern system of medicine. Therefore, two most commonly used culinary herbal materials viz., ginger and omam were studied in vitro against M. circinelloides, as an alternate to antifungal drugs. To explore the traditional herbal resources as alternate to Amphotericin B to use against M. circinelloides, a fungus causing mucormycosis. Aqueous extracts of garlic and omam were prepared and tested against M. circinelloides, at different concentrations. A positive control with Amphotericin B and negative control without any supplements were also maintained. The inhibitory effect was assessed by adopting optical density (OD) measurement method in SD broth and SD Agar Well Plate using spore suspension as inoculum. Paired student T test was employed using SPSS Version 1.6. Both garlic and omam extracts were found to inhibit the M. circinelloides and their MICs were 600 and 700 μL/mL. It is comparable with the MIC of Amphotericin B, 200 μg/mL. Thus, the regular use of garlic and omam can reduce the risk of possible mucormycosis and these herbs can be explored for drug formulations against M. circinelloides.