Roughness - Science topic

How are changes in snow cover related to changes in the snow line? Do thermal changes affect environmental climate change and water changes?

Glacier mass balance is a key measure for determining the contribution of glaciers to regional hydrology and global sea level. As the number of glaciers with direct mass balance measurements (using the glacial method) is limited (e.g., and their representativeness for larger mountain ranges is often unknown), it is attractive to derive glacier mass changes from remotely sensed data. In larger areas, the difference of digital elevation models (DEMs) from two points in time (geodetic method) has been widely used with approximately decimal resolution to determine the total volume and mass changes of glaciers. However, for several geological and socio-economic applications, the availability of regional-scale glacier mass changes with annual resolution would be beneficial. Direct measurements show a high correlation of glacier mass balance with (a) snow cover on a glacier and (b) snowline height (SLA), while mapping snow cover (SC) on glaciers from satellite imagery provides a proxy. For glacier mass balance (e.g., the remotely sensed snow cover ratio (SCR) is considered as a proxy for the accumulation area ratio (AAR) (12,13,15) and snowline height at the end of the erosion period. A proxy for the equilibrium line height (ELA) is used to compensate for local variations in elevation. ELA is mathematically defined as the height of the where the vertical mass balance profile obtained by direct measurements crosses zero. The SC extent map has also been used to reconstruct missing mass balance measurements based on manually selected satellite scenes coinciding with the end of the erosion season, showing a high correlation between satellite-derived snow lines and field-measured ELAs. Snow and glacier ice (clean) together can be easily mapped from multispectral satellite images using different band ratios such as near infrared (NIR), shortwave infrared (SWIR), red (SWIR) or normalized difference snow index (NDSI) (which is given as green) / (SWIR-green) and a manually selected threshold. In contrast, snow on glaciers is difficult to classify with these band ratios, because the shape of the spectral curves of ice and snow are very similar and the ratios consequently occur at approximately the same values ​​for both. A robust and widely used approach for SC mapping on glaciers is based on the use of The threshold is on single-band reflectance values, the preferred values ​​in the NIR to avoid saturating the snow with readily available albedo data, or products. Therefore, mapping snow extent on very long glaciers is It has been. The path, from the 1970s with early experiments using contrast-enhanced Landsat Multispectral Scanner (MSS) satellite images, to the recent use of near-daily MODIS data to extract time series of surface albedo and SC maps for even smaller glaciers (e.g., for repeatable applications over large areas), would be useful to automatically extract the snow-covered area on glaciers, including determining the snowline height (SLA) (near the end of the erosion season) as a proxy for the ELA. This is challenging for the following reasons: (1) Given the strong influence of the terrain orientation (defined by its normal vector) on surface reflectance in mountain topography, extracting any SC measure from the reflectance requires performing topographic normalization using a DEM. SC on glaciers is often patchy and snowlines are not generally parallel to contour lines. And highly variable atmospheric conditions (e.g., due to cloud shadows) mean that the same threshold may not be applicable to all glaciers. Glaciers do not perform equally well. Therefore, it is crucial for any automated algorithm to find a threshold that is adjusted to local conditions, i.e., for each glacier individually, and consequently, for smaller glacier samples, snow lines are often manually digitized (e.g., for SC mapping on small glaciers such as the Alps and for analysis over long time periods, Landsat data with a resolution of 30 m are the best choice because they cover the period from 1984 to the present and have a higher spatial resolution than MODIS data (250 m). On the other hand, Landsat data have the disadvantage of much coarser temporal coverage (every 16 days), so autumn snowfall and cloud cover can make it difficult to obtain usable images of the end of the erosion period in certain years. As the surface elevation of glaciers changes over time, multitemporal DEMs are needed to correctly determine SLA values ​​for long time series. In this study, we present fully automated snow mapping on natural glaciers (ASMAG) to (a) map SC on glaciers, and (b) SLA from a 30-year time series of multitemporal Landsat and DEM data. This method is applied to glaciers in the southern Alps, as three glaciers with long-term mass balance measurements are located there—Hintereisferner, Kasselwandferner, and Vernachtferner—providing data for tool validation. As explained above, specific challenges for an automated approach include distinguishing snow from glacier ice, Considering clouds (or their shadows) affecting glacier parts, ground shadow, the often patchy nature of snow cover, debris cover on the glacier surface, and methodological limitations such as the effect of a time-invariant DEM. Our approach to address this The case is designed and automatically finds a glacier-specific reflectance threshold to separate snow from ice. As an introduction to our methodological approach, we present the workflow, discuss the main challenges, and provide a comprehensive assessment of the accuracy of the approach using three independent validation methods. We also present results for the study area of ​​the SC ratio and SLA time series compared to field data and explore potential reasons for the deviations. Snow cover plays an important role in the Earth's energy balance due to its high whiteness and affects the climate (Akyurk et al., 2010, 3728). Many studies have been conducted on the study of snow cover and its changes. For example, Khadka et al. (2014) used MODISTRA data for the period 2000 to 2009 to analyze the snow trend in the Tamakoshi basin in the Himalayan mountains. The findings of these researchers showed that during the ten years under study, there was a decreasing trend in the area of ​​snow areas in the spring and winter seasons. This is while the area of ​​snow areas has increased in the autumn season (Khadka et al., 2014, 51-54). Sharma et al. (2012) used the snow trend in the sub-basins of the Jhelum in the northwestern Himalayas used MODIS data for the years 2000 to 2011. The findings showed that there was a trend of decreasing snow cover in all sub-basins; but the rate of decreasing snow cover was highest in the Banihal sub-basin (Sharma et al., 2012, 863). In another study, Maski et al. (2011) examined the trend of snow cover in and around Nepal for the years 2000 to 2008. For this purpose, they used MODIS data. The analysis showed that in January, there was a trend of decreasing snow cover for three altitude belts below 6000 m and in March, there was a trend of increasing snow cover for two altitude belts above 5000 m. In the autumn season, a trend of increasing snow cover was observed for four altitude belts above 4000 m (Maskey et al., 2011, 391). However, much less studies have been conducted on the subject of snowline. The snowline is the boundary that separates snow-covered areas from snow-free areas (Siddal et al., 1997; Kor et al., 2010). The snowline is the most sensitive metameter that can be used to monitor the climatic behavior of all elements of the ice sheet. Changes in the snowline time series are considered the response of snow to climate change and will allow predicting snow behavior in the future. Changes in the snowline height following the increase or decrease of snow areas have a significant impact on the availability of river water in watersheds (McFadden et al., 2011; Although various studies have been conducted in Iran on changes in snow cover and the number of snow-covered days (Masoudian and Kaykhosravi Kiani 2017; Azizi et al. 2017; Mirmousavi and Sabour 2014), no comprehensive and systematic studies have been conducted on the identification of snowline behavior in the country, and the present study seeks to fill this gap. Annual global temperatures have increased by 0.74°C over the past century and 1.5°C above pre-industrial levels (Fasnacht et al. 2016; IPCC 2018). This temperature increase has particularly affected high-altitude areas, such as mountains. Snow and glaciers are severely affected by increasing temperatures due to their proximity to melting conditions (Barnett et al. 2005; (Benniston et al. 2011). 4 Key State Information Engineering Laboratories in Mapping, Surveying and Remote Sensing,Wuhan University, Wuhan, 430079 China. Snowline rise is clearly the most sensitive factor to climate change, which is due to the ratio of snow and ice accumulation to erosion, which largely depends on temperature and precipitation (Huang et al. 2017). Climate change has become a major concern for Iran due to its geographical location, which encompasses both Mediterranean and dry subtropical climate regimes. This has led to further changes in climate in the past decades, as shown by (Kowsari et al. 2011; Modarres and Sarhadi 2009; Rahimzadeh et al. 2009). Another factor that also contributes to climate change is the fact that Iran is located in a mountainous region, an area with an altitude of more than 2000 m (Biskop et al. 2012), which in turn is associated with severe impacts on snow cover and water resources as a result of seasonal climate changes (Arsalani et al. 2015). According to previous studies, the climate of northwestern Iran is changing, such that the total annual precipitation of the Zagros region has decreased during the years 1997-2010 and the highest annual maximum temperature was recorded in 2010 (Arsalani et al. 2015). Fortunately, advances in remote sensing approaches have provided the necessary tools for in-depth analysis in various applications, including, but not limited to, snow cover monitoring (Gafurov and Bardosi 2009; Surman et al. 2019). In fact, much research has been conducted on the use of satellite data to monitor snow areas (SCA) in northwestern Iran, considering various parameters such as distance (in km2) and snow cover duration (SCD) (using passive and active microwave remote sensing and optical remote sensing) (Che et al. 2014; Hall and Riggs 2007;Linz et al. 2014; Snepier et al. 2019; Zhang et al., 2012. 2019). As can be inferred from previous studies, remote sensing can be an effective alternative to traditional ground-based snow depth observations to study and assess changes in snow area, especially in mountainous areas. In this way, (Huang et al., 2017) proposed the use of remote sensing snow data to study the impact of climate and elevation changes on snow cover in the Tibetan Plateau. Their results indicated a decrease in the average snow cover area (SCA) in the study area, as well as a further decrease in snow-covered days (SCD) and snow water equivalent (SWE), especially at high altitudes, as a result of reduced snowfall and increased precipitation and temperature from 2001 to 2014. In addition, (Shokal et al., 2017) investigated the changes in snow cover area in the upper reaches of the Satluj River Basin in India using MODIS Terra (MOD10A2) images for the period 2001-2014. According to their results, the annual mean snow cover for the Satluj River Basin varies between 44 and 56 percent with an average of 48 percent for the given area (16,650 km2). MODIS data was also used by (Sharma et al. 2014) to analyze the distribution of snow cover in the northern regions of the Himalayas. The results indicated a decrease in snow cover in the northwest direction and also a greater decrease in the river basins. It was also shown that an increase in altitude above 4,500 m causes a decrease in the annual mean snow cover areas. Other studies have also reported similar results for the Himalayan snow line (Kur et al. 2016; Shafiq et al., 2018; Singh et al., 2018. (Zhang et al., 2014) conducted a spatiotemporal analysis of snow cover changes using MODIS data for the Qinghai-Tibet Plateau from 2003 to 2014. They also reported a decreasing trend in snow cover area for the future region. MODIS data were also used by (Dieu et al., 2014) to analyze snow cover changes with respect to temperature and precipitation changes for a given 10-year period.The mountains of northwestern Iran are home to some of the rarest remaining glaciers and an abundant source of water. The size of the glaciers is rapidly decreasing, highlighting the need to study the effects of climate change as a major factor in snow cover changes. Therefore, the present study seeks to (1) explain the main causes of changes in snow cover area in the northwestern mountains of Iran using monthly optical remote sensing snow products from 2003 to 2020. (2) describe the effects of climate change on the temporary snow line height (TSLE). (3) finally clarify the changes in land surface temperature (LST). Considering how snow-covered areas are the main source of water supply, it is very important to monitor them continuously. Monitoring TSLE as well as its changes can also provide further insight. In Iran, Kiani and Masoudian (2017) investigated the role of land surface temperature (LST) in the distribution of snow cover in Iran using MODIS satellite data. Their findings indicate that in areas of Iran where the annual average land surface temperature is less than 30 °C, conditions for snow cover are favorable. They link snow-covered days and land surface temperature They also state that the number of snow-covered days is maximum in areas where the temperature is zero degrees Celsius. Masoudian and Kaykhosravi Kiani, 2017, evaluated the changes in snow-covered days in the altitudinal classes of the Zayandeh Rood basin in a study. In this study, MODIS Terra and Aqua sensor data were used daily for the period 2003 to 2020 at a spatial resolution of 500 meters. Their studies showed that in the months of Farvardin and Ordibehesht, the number of snow-covered days in the high altitudinal belts of the basin shows a decreasing pattern. In the months of Aban and Azar, the number of snow-covered days in many altitudinal belts is increasing, but in the months of Dey and Bahman, the number of snow-covered days in many altitudinal belts is decreasing. Azizi et al., 2017, studied the temporal-spatial changes in snow cover on the southern slopes of the central Alborz. For this purpose, they used hourly and monthly data from regional stations and MODIS satellite images. Their results show that snow cover is increasing in early autumn and late winter and is gradually decreasing in spring. In terms of altitude, all Altitude classes, especially those above 3000 meters, show snow cover. Mohammadi et al., 2019, studied changes in snow cover in the Zagros Mountains using daily data from the MODIS sensor. They studied changes in snow cover area (SCA) and snow line (SL) in months with snow cover from 2003 to 2020. Their results show that most of the research conducted on snow cover in mountainous areas mainly focuses on changes in snow cover area in different seasons or the height of the snow line in these areas in different time periods. However, less attention has been paid to studying the snow cover phenomenon in different topographic directions of the earth in different time periods. Since various components such as absolute altitude, slope, slope direction, and local roughness play a significant role in the distribution of energy on the Earth's surface, the asymmetric distribution of energy on these surfaces can lead to various biological, hydrological, climatological, and environmental changes in general.

In my study, I developed two membrane materials: one without a ZnO coating and another with a ZnO coating. The diameter of the ZnO-NPs is approximately 30 nm. Using atomic force microscopy, I measured the average surface roughness (Ra) of both materials and found that the difference in Ra values between the coated and uncoated membranes is approximately 30 nm. This Ra difference closely matches the diameter of the ZnO-NPs.

Does this observation imply that the ZnO-NPs are arranged in a single layer on the membrane surface? If so, what additional characterization techniques would be most suitable to confirm this single-layer arrangement? Are there any potential exceptions or considerations. Thanks in advance

Is surface roughness of direct resin composite restorations material and polisher-dependent?

Good morning all,

Are you aware of equations (or a set of equations) that could be used to estimate the carbon sequestration rates in different marine habitats per unit area?

I understand the question is quite broad. In fact, I am looking at what data source would be need (and what is available in online platform such as Copernicus) and try to couple different factors together to get a very rough estimation.

Thank you very much.

Cheers,

why kinnow is not propagated directly through seed rather than propagate through budding. Is their chance of growing rough lemon if we propgate kinnow citrus through seed?

I am studying the efficiency of the Tesla turbine as my graduation project. However, I couldn’t find the mathematical formulation for the gap between the discs, disc thickness, and roughness ratio. Is there a direct relationship with the shaft’s torque or angular velocity?

I am trying to assess wind environment around high-rise buildings at pedestrian level height (2m in full scale). My geometric scale is 1/300, which gives me PL (Pedestrian Level) height of 6.67 mm in reduced scale (rounded to 6 mm). Now, the issue is that as per the CFD recommendations, I need to have atleast 3 cells below PL, however as per the experiment, aerodynamic ground roughness (Zo = 0.0002 m), which is giving me sand grain roughness height, Ks = 1.95 mm (using Ks = 9.793*Z0/Cs) at Cs = 1 which is approximately equal to 2 mm, i.e., the first cell height if I were to divide PL height of 6 mm uniformly into 3 cells and I want Ks to be atleast half of 2mm. Now, ANSYS Fluent has by default restriction on the roughness constant Cs, i.e., 0<=Cs<=1, so I wish to bypass it using a User Defined Function (UDF).

To achieve this, I have read several research papers on how to form this UDF which ultimately led me to Master's Thesis of Monticelli pertaining to Generalized Wall Function (GWF) and I've also been reading ANSYS Fluent UDF manual, however, the issue is that I just need to bypass a constant so I am beginning to think that DEFINE_WALL FUNCTIONS macro may not be of use as I'm not trying to modify a wall function but just bypass a constant (I am working with Standard Wall Function) and figuring out how to do it by going through UDF manual is proving be a daunting task. I have already spent much time on this subject due to which rest of my work is getting delayed. Hence if anybody can offer any help, I would be deeply grateful.

I want to know the average surface roughness for the AlTiSiN-coated carbide insert once the coating is deposited on the substrate.

I'm looking for tips/best practices or equipment's to polish the the gauge area for the fatigue samples. The main purpose is to measure the surface roughness (0.2um).

I made 2 membrane samples of 91% CuO and 93% CuO using the sol-gel method. The SEM image of the grain is obtained evenly but the surface is slightly rough

📷

How can the soil roughness coefficient be measured from the size distribution of soil aggregates or other physical properties?

Are there simpler ways to measure this feature when we have not measured the flow depth and flow velocity?

A resolution is correlate with roughness of thin film. I expected some like FWHM(2th) for XRD. It can be calculated from configuration of device. What's here?

I want to quantify some roughness parameters using AFM data. I tried to quantify some of them by Gwyddion software but its HHCF doesn’t have a fitting function contemplating all parameters I need.

Fit function that I found in my Gwyddion version:

y = 2sigma^2 * ( 1 - exp ( - ( r/E )^2 ))

, where, sigma is the interface width and E is the lateral correlation lenght.

Fit funtion that I was looking for:

y = 2sigma^2 * ( 1 - exp ( - ( r/E )^(2a) ))

, where a is the Hurst exponent.

I also tried to export data to origin and tried to set a new fitting function, but I got stuck on how to build it. Something went wrong since my limit bounds aren't being followed.

If anyone could help me with some insights I will be really grateful.

I have prepared Copper thin films by magnetron sputtering and the samples have been annealed at 100, 200 and 300 degree celsius. The variation of the rms roughness and all other AFM height related parameters like roughness exponent, lateral correlation length etc. are similar. They increase first when annealed at 100 degree and then starts decreasing. What can be the reason for such a reversal in behaviour? An adequate explanation or some good references would be really helpful.

Thank you.

Other two questions are:

1) Are the conversion tables (grit-microns) found om internet reliable?

2) Where does the equation to convert grit in microns come from?

1 ton charcoal fixes 3,7 t CO2. The global charcoal production is 54 mio tons, in which 200 mio t CO2 are fixed. 200 times the currently produced charcoal will fix the entire CO2 emitted globally per year!!! CO2 emission about 30 bln t per year: means this will be fixed in 10 bln t charcoal.

The entire annually CO2 can be fixed in a volume 2,7 km * 2,7 km*2,7 km and could be dumped in e.g. open pit mining sites where the coal was exploited (give it back).

Rapid growing biomass may be used. Charcoal production cost may be assumed the in the order of 200$ per ton, results in an annual investment of 2000 bln $ globally. THIS IS THE SAME AS WHAT ARE MILITARY WEAPON EXPENDITURES.

This is possible...in principle!

Please tell me where I am wrong in my rough brainstorming.

I need to convert 120 and 320 grit surface roughness into micron unit

I am measuring the surface roughness of 3D printed tensile specimens, that have different raster angles and raster widths.

Calibration and proper fixture of the specimens are set up to conduct the experimentation. However, upon measuring and re-measuring the surface roughness of all 30 specimen, R squared values yield 0.91, adjusted, 0.85, but predicted, 0.47.

I measure 4 raw values across the width of the tensile specimen before utilising the average for generating the ANOVA r squared values. I have even tried using one set (instead of the average of all 4) of raw values but to not much success on the predictive front. So it's made me curious.

What are some useful techniques for instance when going about identifying potential specimen outliers, when it's difficult to know which surface roughness value is 'correct' from the raw values obtained?

I've attached a snapshot of my Fit Statistics from the ANOVA using Design Expert 13 software. I know I can reduce the model using techniques like backward elimination but, I'm trying to get high R squared values natively without doing that.

Any advice will be most appreciated!

Can the contact angle increase with the increase of surface roughness in hydrophilic surface?

A polymere surface treated with plasma, as a resulte the centact angle decreased. Can we affirme that the surface roughness was increased?

is there a constant relationship between surface rougness and contact angle.

I am currently producing membranes with a rough thickness of 15-40 µm and want to measure and compare them reliably. My desired accuracy is < 1 µm (e.g., 0.3 µm) or something in that region. I am looking for a tool/method/device that is not overly expensive, like SEM, but still gets the job done.

What is Your suggestion?

Dear reader:

Thank you very much for looking into this question!

I am testing a dry etching process that transfers the photoresist profile into silicon. The target is using O2/SF6 reactive ion etching (RIE) to achieve a 1:1 selectivity (Si etching rate: photoresist etching rate 1:1). But when I am close to the 1:1 selectivity by: changing O2/SF6 ratio; changing the power; changing the pressure, I always see a dark, rough surface.

I notice that this phenomenon might be due to the SiO2 mask layer forming on the wafer surface, so I am adding CHF3 for Si etching, but similar things happen when selectivity is close to 1:1. So would there be any other reasons for this phenomenon? How can I improve the roughness? I can only tune the power, pressure, and gas mixture, without control for temperature and DC bias.

Thank you very much if you can give me any clue!

EOR: Contact Angle

1. Dynamic contact angle remains equal to the static contact angle, only when, the flow velocity remains to be very small. How easy would it remain to figure out such a scenario in a typical petroleum reservoir?

OR

Under what circumstances, will we be forced to consider contact angle to remain to be a function of the flow velocity? If so, how exactly, should we go ahead with dynamic-wetting associated with ‘spontaneous imbibition with dynamic contact angle’ (contact angle varying with flow velocity)? How to deduce the initial imbibition rate?

2. Whether Capillary Imbibition as described by Lucas-Washburn equation (which considers a fixed value of contact angle) can be modified in order to accommodate the variations associated with (a) the minerals on the solid grain surfaces; (b) the degree of roughness; and (c) the degree of interaction among the solid grain particles in the fluid – towards estimating dynamic contact angle, during an imbibition process?

3. Whether the measured dynamic contact angle would remain to be larger than the static contact angle during an imbibition process? If so, what should be the respective time-frame, over which, a sensible relationship between dynamic contact angle and the imbibition velocity of the fluid could get established?

4. For a petroleum reservoir with a relatively smaller pore radii, can we afford to ignore the effect of gravity on spontaneous imbibition, even during the later stage of spontaneous imbibition?

On the other hand, for a petroleum reservoir with a relatively larger pore radii, can we afford to ignore the effect of gravity on spontaneous imbibition, even at the initial stage of spontaneous imbibition?

5. How easy would it remain to deduce and segregate the magnitude of forces resulting from (a) gravity of the fluid (no-wetting phase fluid); (b) the frictional-resistance between fluid (non-wetting phase fluid) and capillary surface; and (c) the capillary forces – associated with a typical spontaneous imbibition process @ laboratory-scale?

Whether such laboratory-scale observation could be up-scaled directly to a larger field-scale scenario?

Also, feasible to define the intensity of friction between fluid and capillary surface, if the fluid remains associated with transition regime?

6. What are the typical circumstances, under which, the imbibition velocity remains to have (a) a negative linear correlation; and (b) a positive linear correlation - with the imbibition time in a log-log plot (for an imbibition with a constant contact angle)?

What is the physical significance of the imbibition distance being (a) positively; and (b) negatively correlated with the imbibition time in a log-log plot?

Hi

Does anyone know what would be the drag coefficient value for a horizontally submerged cylinder submerged? I found as per API (American Petroleum Institute), it's like below:

"API recommends the following drag coefficient values for unshielded circular cylinders: Smooth cylinders: 0.65 and for Rough cylinders:1 .05." However, I think these are for vertically submerged cylinders. Can I use those for my case as well i.e. horizontally submerged pipe?

Thanks.

We assume that the Nabla^2 expression in 3D geometry is quite old and its lifespan is almost expired.

B-matrix chains suggest adding a fourth dimension (mainly time t) woven into the 3D geometric space to form a 4D unit space for two fundamental reasons:

i- The classic expression in 1D,

Nabla^2 Y(x)={Y(x+ h)-2 Y(x)+Y(x-h)}/2 h^2

and similar for 2D and 3D,

is a rough approximation because it only uses 3 geometric points and requires a small interval h.

On the other hand, the same expression suggested by the statistical matrix-B chains is much more precise and uses as many geometric points “free nodes” as necessary with small or large intervals h.

ii- What is quite surprising is that the physical expression of Nabla^2 also turns out to be a differential and integral operator.

Single, double and triple finite integrals can be realized via a modern 4D expression[1].

1-Effective unconventional approach to statistical differentiation and statistical integration, Researchgate, IJISRT journal, Nov 2022.

Manuscript Title: Impact of Machining Parameters on Surface Roughness and Machining Forces in Al7075 Turning with MQL and Cold Fluid

My problem: sequencing result is low-quaility and dirty. Most sequence is matched with expected one, but there are some sites having two bases' signal, and the overall base signal is not strong, but around 60th bp position, there is a very high peak.

My rough experiment flow: purified plasmid DNA, Big Dye v3.1 protocol to do sequencing PCR, FastGene Dye terminal removal kit, then sequence in machine (using Medium-seq).

Primer is 100 bp ahead of my target region, primer annealing region is a fragment of GFP protein.

In fact I have tried DNA amount of 100ng, 200ng, 500ng, 1000ng, but they all did not have clear result. Only in 500ng, only one of the sample showed a bit good result and matched with expected result, but other samples were just so-so or bad whatever the DNA amount is.

Please help me! I can give more information if needed.

Hi guys,

I am interested in conducting a longitudinal study investigating the development of within-connectivity of the DMN in autistic and non-autistic children. Scans will take place at 8, 13 and 18.

My dependent variable will be DMN within connectivity calculated by averaging ROI-to-ROI connectivity and pairwise correlation between time series of regions within two hemispheres and between the hemispheres will be averaged. These averages will then be averaged to get a within-connectivity.

My fixed variables will be time and autism as well as their interaction. My random effects will be subject-specific intercepts and slopes and my covariates will be gender and education (I am expecting to add more covariates).

I am curious as to how I calculate a sample size a priori for this. I know I need to define an effect size which I have a rough idea of and of course power and alpha. Can this be done on G*Power (to my knowledge I dont think so, but I may be missing something).

Any help would be useful.

Thanks in advance

Inputs-Laser power Layer thickness Scan speed Hatch spacing

Output- Relative density, UTS, Hardness, Yield strength, Surface roughness , % Elongation

Hello everybody.

I'm trying to study HPHT synthetic diamond plates with low (first nm roughness). When i taking 100×100 μm photo by AFM, you can see a lot of flakes/dirt on them.

The question is how can i remove this dirt? I already tryed ultrasonic cleaning in ethanol, but it doesn't remove dirt at all.

Is it possible that a nanostructured thin film surface area can be found using surface roughness data from AFM

Thanks in advance

hello

I want to use experimental and semi-empirical models to estimate soil moisture, but I don't have the surface roughness parameter. Is there a satellite or experimental relationship that can obtain the surface roughness value or should this parameter be taken in the study area. Thank you for guiding me

What is the relationship between crystallite size, contact angle and roughness for metal oxides?, and can I connect this relationship with the results of the electrical characterization (hall effect)?.

Thanks in advance.

Hello everyone,

I'm simulating a simplified bolt connection between two parts. I made the bolt without a thread, as it was done in most forum posts here. However, I do not have a bolt-nut connection, but I screw the bolt directly into a hole (the hole is also simplified without a thread).

What interactions do I have to make with the screw shaft and the hole? I tried it at the beginning with tie-constraints, but I got errors because there was an overlap with the bolts pre-tensioning. In addition, I tried contact, where I defined the tangential behavior as rough. Unfortunately, without success.

Any of you have an idea for this problem?

Thank you very much for your efforts!

Fatigue fracture surfaces of broken high strength materials exhibit rough conoidal cracks at the vertex of which are located inclusions or heterogeneities

Experimental: The observations refer to Sakai et al. (2002), Abdesselam et al. (2018), Stinville et al. (2018) ... These cracks have been named “fish-eye marks” by two former authors and their formations have been divided into three stages: (i) formation of the characteristic area as a fine granular area (FGA); (ii) crack propagation to form the fish-eye (i.e. according to us “rough conoidal crack”); (iii) rapid crack propagation to cause the catastrophic fracture.

This subject is important because evidence of conoidal rough cracks is observed experimentally on various macrographs of broken specimens, under fatigue for instance. Our recent works (see below in answers) provides associated physical quantities.

I deposited TiN on SS 201 substrate by reactive magnetron sputttering. After the sputtering process patches appeared on the surface of my target - its surface got rough

What could be a possible reason for these patches??

Hello everyone,

I'm experiencing an issue regarding surface roughness calculation and I could use some help in troubleshooting.

Here's what I've done so far:

This leads me to a couple of doubts:

a) Are the coordinate data provided by the Wyko vision software in ASCII format for Ra and Rq calculations the actual raw data, or has it been processed in some way by the software before the calculations are performed?

I'd appreciate any guidance on this issue, specifically on understanding the data processing done by Wyko software and how I can ensure my Matlab script is accurately calculating the roughness parameters.

I've also attached the ASCII file below for reference.

Thank you in advance for your help!

Reservoir Engineering: Contact Angle Measurement

1. To what extent, the ‘gravitational force’ plays a crucial role:

(a) when the fluid flow remains to be perfectly horizontal;

(b) when the fluid flow remains to be perfectly vertical;

(c) when the fluid flows at an angle towards gravity; and

(d) when the fluid flows at an angle against gravity; - towards dictating whether

(i) the cohesive forces between the liquid molecules remain to be stronger than the adhesive forces between the solid and liquid molecules (and as a result, the liquid balls up and tending to avoid contact with the surface); or,

(ii) the solid/liquid adhesion remaining stronger than the cohesion within the liquid molecules (and as a result, no drop forms and the liquid tending to spread on the surface)?

Whether such role of ‘gravity’ would remain to be similar both @ laboratory-scale as well as @ field-scale?

If not, how would ‘contact angle’ measured @ laboratory-scale would be able to provide ‘an inverse measure of wettability’ @ field-scale?

2. In the absence of an atomically flat and chemically homogenous ideal surface – associated with a real field reservoir scenario; (for that matter, even with laboratory-scale experimental conditions), whether force per unit length OR energy per unit area would remain to be more apt towards the determination of contact angles (where, the formation of a perfect spherical cap geometry remains ruled out)?

3. Which one of the following essentially controls the wettability @ laboratory-scale? (a) the advancement of solid/liquid interface to a certain area; OR (b) the advancement of three-phase contact line to a certain length; upon placing a sessile droplet on a substrate?

4. Which one of them plays a very crucial role in ceasing the apparent contact angle @ field-scale?

(a) the surface roughness;

(b) the chemical heterogeneity of surface; or

(c) physical/chemical properties of reservoir fluids.

5. Even, if we manage to measure advancing, receding and static apparent contact angles along with the details of contact angle hysteresis – towards determining the properties of surface; whether the nature of surface roughness, chemical heterogeneity and shear hydrophobicity @ laboratory-scale and @ field-scale would more or less remain to be the same?

6. Why does apparent contact angle remain to be significantly different from (a) advancing contact angle;

(b) receding contact angle; and

(c) local contact angle?

7. How important is the ratio of drop-size to wavelength towards contact angle measurement?

8. How exactly could we get rid-off stick-slip movement of drops; and what exactly happens when the drop volume exceeds 10 micro-liters – towards contact angle measurement?

I have read in a article that a norm that defines the technique, the parameters and filters to use in the measurment of dental implants surface roughness was in development but I wasn,'t able to find it.

I've done an indepth literature serach on the subject, but at the moment the only document that comes close to the definition of these parameters that i was able to find is the guidelines published by "Wennberg et al" in 2000.

Can speckle-based application benefit from the potential benefits of vortex beams?

What can be the meaning of this sentence?

"Because the real area of a rough surface is bigger than its nominal area."?

Hello colleagues,

I have been facing a DRIE (Deep Reactive Ion Etching) problem lately. I start with a silicon wafer, and deposit a 50 nm Al2O3 film as a hard mask. Then, I use photolithography to create a pattern and etch away part of the Al2O3 mask using BCl3/Ar dry etching. Next, I strip the resist, leaving only Al2O3 on silicon as a hard mask. Before DRIE, I conduct one more cleaning step of 5 min oxygen plasma and 30 s silicon oxide etching. The DRIE tool we use in our facility is Unaxis 770. However, after 50-100 loops, sometimes the etched area becomes very rough with many small holes. Can anyone give me some hints about why the etching is not uniform and what causes the holes on the silicon surface? I would really appreciate any suggestions. Thank you!

Dear researchers

I have some doubts about biofilm roughness

Biofilm roughness provides a measure of how much the thickness of the biofilm varies, and is an indicator of biofilm heterogeneity.

so does increased biofilm roughness means the biofilm is patchy in some areas?

also what is the unit of measuring biofilm roughness?

Comstat does not provide an exact unit of measurement for biofilm roughness

BiofilmQ provides mentions the unit as (a.u.). So what is a.u. ??

Magnesium alloys for medical applications.

To date, I have only encountered academic literature pertaining to the Digital Image Correlation (DIG) of 2D or 3D smooth surfaces. Regrettably, I have yet to find any research publications regarding DIG analysis of rough surfaces.

I tried to etch the oxide layer using RIE(4sccm:26sccm, O2: CF4, 50W, 15 mins), however i obtained uneven etched oxide layer. Thus, there is gradient of etch profile (thickness, and roughness) from edge to the middle. If there is any method that suitable, that will be really helpful. thanks!

We are trying to compare these two systems in the process of purchasing one of them. Our applications revolve around surface roughness quantification, mineral wettability evaluation, and surface force measurements on rock samples. I would appreciate your expert views.

I have used Moody charts for estimating pipe run pressure drops throughout my career generally with good success. Until recently, I had not checked that the friction values from the operating system matched the values used in the design.

As part of the upgrade to a 75 mm diameter pneumatic conveying system, some line pressure drops were measured with only air flowing. When I plotted friction factors versus Reynolds number, rather than following a line of constant relative wall roughness the data cut across two of the lines suggesting that the roughness varied with Reynolds number.

Has anyone made a similar observation? Are the pressure drop data in error? Or does a Moody chart not match all possible internal pipe surface conditions?

Discontinuites

I need to determine the surface roughness of a curved or cylindrical item created using a 3D printer.

I used the software Mountains8 of the SEM JSM-IT 500 (JEOL company) to create the 3D reconstructed images. Because my field is in MIcrobiology and for first time I tried to measure roughness I have some questions:

1. In the software there are three option to create the 3D reconstructed from 1 SEM image, 2 SEM images and 4 SEM images. I do not know for reconstruction 3D images form 2 or 4 images I have to have 2 or 4 different SEM images from a sample surface or these 2 or 4 images should be the same?

2. I took the SEM images without any title but in the software there is an option to select the tilt that starts from 1°, with change of angle the roughness aslo is changed. How can I select the angle? and what is for?

3. What are the meaning of Sa, Sq and Sz?

Thank you.

I have attached the roughness data and the profile.

I am having a thin film of Cr Au of 30nm (total) on Fused Silica deposited by sputtering. I wasn't able to obtain any Kikuchi pattern. My surface roughness is around 2.5nm. I could not find any surface preparation techniques for thin films.

Let me know how to do EBSD.

For comparison study of experiment and numerical work, I have to create rough surface structure modeling. which implies my numerical model would be more accurate equal to the experimental work.

But I don't know how to create a rough surface on all the inner walls of the channels (see the attachment).

Approximate dimension details:

Channel width 0.5 mm and height: 1mm

Ra: 300 micrometer

Please help me with how to do that.

I found difficult to quantify the heated affected zone visually from optical microscope.

Are there any other technique (calculating surface roughness, color temperature or any other technique that can give more accurate results

Please describe in context of metals.

Hi everyone!

I am modeling a Spar Structure and did everything like ANSYS tutorials!

have 2 questions if anyone kindly can help me on them!

1. In ANSYS AQWA, for Point Mass Input, I saw for a regular ship there is a formula for Kxx,Kyy and Kzz calculation, 0.34*beam,0.25 Length and 0.26 Length respectively. I am just wondering to know is there any specific or rough idea that I can use for Spar Structures?

2. when I modeled my spar structure, in Analysis of the Time Domain Response when I consider the use cable dynamic as yes I encounter with an error such as bellow,

" CABIN4:CONV. FAILED STAGE#6 - ERRN=2.49E-10 LINE#4" is there any idea how can I fix this error. if you can help me it would be highly appreciated.

kind regards to everyone! and thanks in advance for reading my questions!

cheers, Sal

Hi, trying to figure out if this cell culture looks above or below 50% confluent. If anybody can give me a rough estimate? ImageJ says this is only about 32% confluent which seems a bit off to me.. but since I am fairly new, all the input is appreciated.

I have a published phylogenetic tree but no access to the rough data used to generate the tree. I want to compare among clades in terms of morphological characters. How do I include the phylogenetic correction? Should I just measure the distance between clades and use this as my phylogenetic correction? The phytools package in R assumes I have the rough tree data, and I don´t. Some suggestions will be most welcomed.

Hello everyone, I'm a graduate student. Now I'm doing GaN etch with Cl2/BCl3 gas (ICP-RIE) with GXR601

When the etch depth is under 100nm, the surface (PR X) is clean but up to 200~300nm the surface become very rough and some mark on it. I want to know reason of this... I do soft bake 90C 1min, PEB 110C 1min, Hard Bake 110C 1min 30sec.

Thank you for answer.

Hi dear Researcher,

I found that the roughness of Co3O4 thin film increase strongly with doping by Sn.

Have any one an idea about this effect?

Best regards

Hi everyone,

Beginning my research journey and looking for some assistance finding some research tied to the above question (rough). Would also appreciate your additional thoughts on this question.

I believe there may be some positive outcomes for PLCs within government schools, where rituals and routines are utilized or embedded within the process.

I’m interested to read more about how this might positively impact on ideas such as teacher identity (or intersections between identities- eg. teacher, teacher-leader, facilitator, knowledgeable other or teacher-researcher), collective efficacy and relationships between members of the community.

I would also be interested to read research related to rituals and routines embedded within classrooms.

Thanks,

Callum Shaw.

I am trying to spin coat single walled carbon nanotube (in DMF) and Gold nanoparticles (in Chloroform) on rough PMMA surface, but I am getting very non-uniform spread of the nanoparticles. There are random chunks/ islands of the nanoparticles after the solvent is evaporated. What are the possible reasons behind this phenomena? Thanks in advance.

Hello. I have a series of images of morphology created by the hydrothermal

I have grown Ge on glass with in-situ annealing. firstly the rms roughness and height decreases then increases with temperature while grain size increases for all temperature.

I am quite new to AFM imaging and I'm having struggles in getting accurate topography images to measure surface roughness. My issue is that even though I it seems that the trace and retrace profiles match I am unsure if I accurately traced the surface or the image is an artifact. I'd like to ask for any tips or suggestions?

The afm machine is bruker multimode afm.

The mode I normally use is air tapping mode.

I would like to perform surface roughness of natural fibers by either using AFM or Laser micrsocpy, what are the main sample preparation steps involved for better results and accurate measurement?

We need to understand the relationship between two dependent variables (Frictional Noise and Coefficient of Friction) and three independent variables (Material Hardness, Surface Roughness, and Sliding Frequency). I need help as to which software to use and how to go about it.

tapping mode tips are too stiff to measure extracellular matrix roughness. I am looking to find the right cantilever type with a stiffness (k-value) that is able to do so in solution.

Thank you in advance

The most important source of animals in wolkite are natural pasture,concentrated feeds/roughes.

I want you to help me if/not their animal feeds sources............

While X-Ray reflectivity fitting from Globalfit software, intermediate layer of SiOx between the Silicon substrate and the film is showing higher roughness value than its thickness.

Do we need a smoother thin film of Piezoelectric membrane material in the PMUT device performance? is there any dependence of resonant frequency on the roughness of piezoelectric membrane in PMUT?

Hello,

How can we plot the "Roughness length plot of wind" in "GrADS"?

Current I have done ANOVA analysis. Pulse on time, Pulse current and Gap voltage is input factor and output factor is Material removal rate and Surface roughness. during analysis pulse current is most significant factor in place of pulse on time. workpiece material is stainless steel.

Hello,

I am doing some tribological tests with a reciprocal sliding tribological test.

To calculate the wear rate, the wear volume is needed with high accuracy. Do you have any accurate and easy method(s) to evaluate that wear volume? I have already tried some imaging with 3D digital microscopy but the initial roughness of the substrate makes the measurement difficult.

Thank you in advance,

Alexandre