Mapping - Science topic

Dr Murtadha Shukur thank you for your contribution to the discussion

The three issues you raised are familiar to me either as practitioner, researcher or educator in Africa (including Kenya).

"Scoping" is standard practice in [Strategic] Environmental Impact assessments (SEA/EIA). You may have a look at my two pertinent publications on ResearchGate whether this may help you in your field of expertise as well. In short, scoping includes literature review, but goes beyond it. Especially by identifying the relevant unknows that are not covered in the literature. This requires a wider level of expertise than literature review.

"Literature review" is part of all research, from design, project proposal to publication. In technical reports and MSc theses an explicit section on literature reviews may be included. But this is unusual in papers published in journals. There are also specific review papers in most journals.

"Map reviews", in the sense of critically reviewing one map or a set of related maps, is rather rare compared with the above categories. You may enjoy a look at our recent paper on comparing vegetation/landscape maps of Namibia and a much older paper exploring the issue on a global and conceptual level.

Your research on Covid, HIV and Malaria is very thorough and relevant. Therefore, I am willing to share my unpublished "field" experiences on all three with your institution.

Remote Sensing technology aids in monitoring soil health, assessing agricultural viability, and guiding land-use planning. Remote sensing (RS) technologies have been widely used to investigate soil degradation as it is highly efficient, time-saving, and broad-scope. Remote sensing aids soil mapping by assessing types, moisture, and fertility. In rock mapping, it identifies geological features and mineral deposits. In the environment, it monitors deforestation; land cover changes, water quality, and supports disaster management. Remote sensing plays a vital role in assessing and monitoring geological hazards such as landslides, earthquakes, and volcanic eruptions. By analyzing changes in land surface features and topography, geologists can identify areas prone to hazards and assess their potential impacts. Remote sensing helps in locating potential groundwater reservoirs by mapping subsurface geological structures and identifying areas with high groundwater potential. This valuable information supports sustainable groundwater management and prevents overexploitation of this vital resource.Remote sensing provides multi-spectral, and multi temporal satellite images for accurate mapping. Land cover/Land use mapping provide basic inventory of land resources. This mapping can be local or regional in scope; it depends on user's objective and requirement. For land use and land cover mapping, remote sensing gives a synoptic picture and multi-temporal data. The use of remote sensing and GIS tools to map LULC and detect changes is a cost-effective means of gaining a detailed understanding of the land cover change processes and their repercussions.Remote sensing technique provides a powerful systematic tool to monitor, map and model the different vegetation cover and provides a precise and accurate road map for many aspects. Band ratioing extracts vegetation from heterogeneous surface features and reduces the spectral biasness also. GIS can store the voluminous amount of spatial (maps) and non spatial (tabular data) information. It has potential uses in land resource management and inventory. The collection of remotely sensed data facilitates the synoptic analyses of Earth. Remote sensing can show how land has changed over time, including areas of soil erosion, vegetation density, and other markers used to inform conservation strategies. Land managers can use this data to identify areas with the highest risks and develop plans to address them. Some of the known RS applications are: monitoring of forest, water courses, agriculture area, regional and urban planning, land use and land cover changes, air and water quality, mineral exploration, natural and manmade hazards etc.Remote sensing is widely used to monitor biological species, habitat and species distribution, and landscape ecosystems. Biodiversity Conservation Priority Areas (BCPA) are core areas of conservation. Remote sensing monitoring of these areas will allow for continuous management of them.

Dear Catherene Julie Aarthy C

I am pleased to help you.

you can contact me.

Dear Mr. Frisk, I'm attaching my CV. Could you please take a look at it?

Hooman Soudmand Mapping the breast cancer patient journey in a process map format can be an instrumental tool in understanding the various stages and experiences that patients undergo throughout their treatment and care. Drawing from my own experiences in healthcare research and patient advocacy, I can offer guidance on how to create an effective and comprehensive patient journey map for breast cancer.

First and foremost, it is essential to gather insights from various stakeholders, including patients, healthcare providers, caregivers, and support staff, to gain a holistic understanding of the breast cancer patient experience. Conducting in-depth interviews, focus groups, and surveys can provide valuable firsthand perspectives that can inform the development of the patient journey map.

Identifying key touchpoints and milestones in the patient's journey, from initial diagnosis to treatment, recovery, and survivorship, is critical. Carefully documenting each stage, including medical appointments, diagnostic tests, treatment modalities, and supportive care interventions, will enable a comprehensive visualization of the patient's trajectory through the healthcare system.

Incorporating patient perspectives, emotional challenges, and psychosocial support needs into the journey map can provide a more nuanced understanding of the holistic care required for breast cancer patients. This may involve highlighting the importance of patient education, emotional support services, and community resources that contribute to the patient's overall well-being and quality of life.

Additionally, collaborating with multidisciplinary healthcare teams and engaging in interdisciplinary discussions can enrich the patient journey map by integrating insights from various specialties, including oncology, radiology, surgery, and psychology, among others.

I recommend leveraging visual tools and software applications specifically designed for process mapping to create a comprehensive and visually appealing representation of the breast cancer patient journey. Ensuring the accessibility and user-friendliness of the map can facilitate effective communication and collaboration among healthcare professionals and stakeholders involved in the patient's care.

By fostering a patient-centered approach and emphasizing the importance of empathy, compassion, and comprehensive support, we can develop patient journey maps that not only capture the medical aspects of breast cancer care but also acknowledge the emotional and psychological dimensions that significantly impact the patient's well-being.

At community level, official data are often non-existent, and those that exist are far less useful than information collected from the people themselves.

There are often local health workers who can advise you on which households have people in the target age group. Community nurses, for example, or traditional birth attendants. You can compile a sample frame from this information and then sample households. This often will yield more than one eligible participant per household, but you can use robust estimation of variance to account for clustering within household.

Another reason for using trusted community workers is that they can then introduce the survey (pay them for this!) and help to create a bridge of trust between participants and the research team.

Clearly, getting community participation should be the goal - I hardly have to remind you to involve them as much as possible in the design and conduct of your study. But I mention it because much LMIC research is carried out by white idiots jumping out of a 4x4 with clipboards.

Belhadj Maha Attached you can find some articles about! Good luck in your research!

Navoda Wijesuriya

Thank you for reaching out about this genomics analysis challenge. Assessing copy number variation of SSR loci through read mapping is an excellent technique. Let me offer some guidance based on best practices:

First, align your sample reads against a reference genome using a tool like BWA-MEM. This creates a BAM file mapping each read. Then use software like SAMtools to identify reads mapping to your target SSR coordinates.

The read depth or coverage at an SSR locus indicates copy number. A consistent depth close to the genome-wide average implies a single copy locus. Significantly higher coverage suggests multiple copies. You can statistically compare depth distributions to identify outliers.

There are robust tools like CNVnator that can automate and streamline the analysis process for you. But manually inspecting alignments in a genome browser is also insightful for small-scale assessment.

Feel free to reach out if you need help interpreting results or finding suitable software. Optimizing these bioinformatic techniques does involve some initial learning. But you are pursuing a very fruitful approach to characterize your SSRs.

Thanks for th information Jens Kleb

The simple answer is "you can do whatever you want', the complex part is the burden you have explaining why you undertook this particular approach and further, why it would be of interest to others. That goes to the context you provide, the expected consumer of your research, and what the goal is. there is almost infinite number of types, and even more techniques and methods, as long as your upfront and honest, and not make any overly broad claims.

Consider medical research: https://news.harvard.edu/gazette/story/2004/04/scientists-discuss-experiments-on-self/ "Examples of self-experimentation range from physician Santorio Santorio’s 30-year, daily measurements of his weight, food intake, and bodily waste in the 16th century, to physician and physiologist Werner Forssmann’s experiments in 1929 and 1935 in which he inserted a catheter into a vein in his arm and pushed the tube up into his heart (he later won a Nobel Prize), to less harrowing contemporary practices such as blood draws, knee MRIs, and urine analyses."

Google Scholar is your friend: https://scholar.google.com/ ... if you look through the references of the top search results, and the 'cited by', you will most likely find what folks are using currently.

Utilizing the LIDAR data can be beneficial since you can separate the ground and the open pits. Understanding the DSM and DTM will be of importance in this area.

Crop models are a formal way to present quantitative knowledge about how a crop grows in interaction with its environment. Using weather data and other data about the crop environment, these models can simulate crop development, growth, yield, water, and nutrient uptake. Crop growth model is a very effective tool for predicting possible impacts of climatic change on crop growth and yield. The tests were made to reflect the model response when used to predict yield under changing climate condition and different field parameters than those encountered during model formulation.Crop model simulations are subject to considerable uncertainties with respect to model implementations and process representation, and thus vary significantly at field and global scale. On a global scale, detailed data are often not available on basic management options, such as sowing dates and variety selection. Crop weather analysis model : These models are based on the product of two or more factors each representing the functional relationship between a particular plant response i.e., crop yield and the variations in selected weather variables at different crop development stages. Remote sensing can be used to monitor the health and growth of crops by analyzing spectral data obtained from satellites, airborne sensors, or ground-based instruments. This information can help farmers identify areas of their fields that may need additional attention or water, fertilizer, or pest management. Remote sensing provides multi-spectral, and multi temporal satellite images for accurate mapping. Land cover/Land use mapping provide basic inventory of land resources. This mapping can be local or regional in scope; it depends on user's objective and requirement. Remote sensing provides multi-spectral, and multi temporal satellite images for accurate mapping. Land cover/Land use mapping provide basic inventory of land resources. This mapping can be local or regional in scope; it depends on user's objective and requirement. Vegetation extraction from remote sensing imagery is the process of extracting vegetation information by interpreting satellite images based on the interpretation elements such as the image color, texture, tone, pattern and association information, etc.

1. ASTER L1B数据是需要做几何校正的，参考数据通常为OLI数据。而ASTER L1T数据是不需要做几何校正的。

2. ENVI 打开ASTER L1B数据时，软件会自动做辐射定标。而ENVI 处理ASTER L1T数据时，需要单独做辐射定标。

3. 你可以去我的主页参考我的论文。

Yes it is interesting but your wording is incorrect it makes title to long so reword it

Thank you sir

Yes, there are privacy and security considerations that need to be addressed when using AI for laboratory mapping or any application involving the collection, processing, and analysis of data, especially in sensitive environments like research laboratories. Here are some key considerations:

Addressing these privacy and security considerations is critical to ensure the responsible and secure use of AI for laboratory mapping. It helps protect sensitive research data, maintain the integrity of research findings, and safeguard the privacy of individuals involved in the research process. Additionally, involving data privacy and cybersecurity experts in the planning and implementation of AI projects in laboratory settings is advisable to mitigate risks effectively.

Dear Dave, 1 second is equal to 1000 milliseconds. Note that rounding errors may occur due to that you must put in your formula: 500x0.001,

, so always check the results, 500 x 0.001 x 7700 ---> 3,850 ft.

I will help you with your other questions, Cheers, Mario

Hello Alwielland Q. Bello

Information Mapping is a strategy or methodology used to organize and present information clearly and in a structured way. Robert E. Horn developed it in the 1960s to improve the communication of complex information. The main goal of information mapping is to make information more accessible, understandable, and usable for the intended audience.

The strategy involves breaking down complex information into smaller, manageable chunks and arranging them logically and hierarchically. This hierarchy typically involves a top-down approach, presenting overarching concepts first, followed by supporting details and specific information.

For more information, kindly go through these articles:

Best Regards!

Ali YOUNES

Here's how you can process and visualize Raman mapping data using position mapping:

By using position mapping, you can gain deeper insights into the spatial distribution of vibrational modes across your sample, which can be particularly useful for understanding complex materials or heterogeneous structures. Keep in mind that the specific steps and tools you use might vary based on your experimental setup, equipment, and software preferences.

For discussion it's better to see your results.

Yes, without GCPs and RTK/PPK, it is highly possible to obtain wrong or inaccurate geometric information in UAV-based photogrammetric mapping. When dealing with large areas, relying solely on the drone's GNSS information can lead to distortions in the 3D model or orthomosaic. GCPs not only add global georeferenced accuracy but also help decrease errors in the scale of the results, making them essential for reliable measurements.

The accuracy of UAS-based photogrammetric mapping depends on several factors, including Ground Control Points (GCPs), flight height, camera resolution, GNSS accuracy of the device, weather conditions, processing software, user experience, etc. While it is possible to obtain satisfactory 3D models without GCPs or RTK/PPK, for precise measurements such as surface area or volume calculations, GCPs are essential. For more in-depth information, you can refer to my MSc thesis and the following articles. In the following studies, you can find comparisons of processing with and without GCPs as well.

MSc Thesis:

Good luck on your journey of exploration and innovation!

you can follow these general steps:

Keep in mind that the specific steps and options in Origin may vary depending on the version of the software you are using. The above steps provide a general guide to creating a 2D graph for your temperature-dependent PL measurement data. For more detailed instructions and tutorials, refer to the official Origin documentation or user guides, or explore online resources and tutorials specific to your version of Origin.

Appana Kiran Kumar

To prove the fixed-point theorem using different types of contraction mappings, it's essential to understand what a contraction mapping is and how it relates to the fixed-point theorem. A contraction mapping is a function on a metric space that shrinks the distance between two points. Specifically, a function f: X → X, where X is a metric space with distance metric d, is called a contraction mapping if there exists a constant k (0 < k < 1) such that:

d(f(x), f(y)) ≤ k * d(x, y) for all x, y ∈ X.

The fixed-point theorem states that every contraction mapping on a complete metric space has a unique fixed point, i.e., a point x ∈ X such that f(x) = x.

Different types of contraction mappings can be used to prove the fixed-point theorem, and here are a few examples:

Ciric (Ćirić) Contraction: A function f: X → X is a Ciric contraction if there exists a constant k (0 < k < 1) such that:

d(f(x), f(y)) ≤ k * [d(f(x), x) + d(f(y), y)] for all x, y ∈ X.

Rus-Contractions: A function f: X → X is a Rus contraction if there exists a constant k (0 < k < 1) such that:

d(f(x), f(y)) ≤ k * max[d(f(x), x), d(f(y), y)] for all x, y ∈ X.

F-Contraction (F-Map): A function f: X → X is an F-contraction if there exists a function g: [0, ∞) → [0, ∞) such that g(0) = 0 and:

d(f(x), f(y)) ≤ g(d(x, y)) for all x, y ∈ X.

Suzuki Contraction: A function f: X → X is a Suzuki contraction if there exists a constant k (0 < k < 1) such that:

d(f(x), f(y)) ≤ k * [d(x, f(x)) + d(y, f(y)) + d(x, y)] for all x, y ∈ X.

These are just a few examples of different types of contraction mappings. There are other variations and generalizations that researchers have explored.

When you want to prove the fixed-point theorem using any of these contraction mappings, you'll typically show that the mapping satisfies the required contraction condition, and then you can invoke the fixed-point theorem to conclude the existence of a unique fixed point.

Remember that the fixed-point theorem and its applications have significance in various fields, including functional analysis, optimization, computer science, and various areas of mathematics.

Dear friend Cheshini Malavipathirana

Ah, air quality mapping through remote sensing, an intriguing endeavor indeed! I am here to shed some light on this captivating topic.

To utilize remote sensing data for air quality mapping, we can deploy various techniques and indices. Here's how we can go about it:

1. Satellite Imagery: Satellites equipped with sensors can capture data from different regions of the Earth's atmosphere. By analyzing this data, we can obtain valuable insights into various air pollutants, such as particulate matter, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide.

2. Spectral Bands: Remote sensing instruments often use specific spectral bands that are sensitive to certain air pollutants. By analyzing the reflectance or absorption patterns in these bands, we can estimate pollutant concentrations in the atmosphere.

3. Aerosol Optical Depth (AOD): AOD is a common index used to assess particulate matter concentrations. It measures the attenuation of sunlight by aerosols in the atmosphere. High AOD values indicate higher levels of particulate matter, indicating poorer air quality.

4. Nitrogen Dioxide (NO2) Tropospheric Columns: Remote sensing can estimate the vertical column density of NO2 in the troposphere. Elevated NO2 levels are associated with urban pollution and traffic emissions.

5. Total Ozone Mapping Spectrometer (TOMS): TOMS instruments onboard satellites can monitor the total ozone content in the atmosphere. Changes in total ozone levels can be indicative of air pollution events or ozone layer depletion.

6. Thermal Infrared Sensors: These sensors can help detect heat anomalies associated with industrial emissions, wildfires, or other sources of air pollution.

7. Multispectral Data: Combining data from multiple sensors can provide a comprehensive view of various pollutants and their spatial distribution.

By leveraging these remote sensing techniques and indices, we can create detailed air quality maps, identify pollution hotspots, monitor changes over time, and implement targeted mitigation strategies. It's a powerful tool in the battle for cleaner and healthier air for all!

Please note that while I can present this information, the implementation and accuracy of remote sensing for air quality mapping may vary depending on the specific technology, data sources, and analysis methods used.

Some useful articles are:

samples are the key points in molecular biology and higher technologies. did you check for quality of your samples before going to library preparation. low quality (RIN) will give low target assignment.

you can also check after sequencing quality using multiQC tools but the worst is done.

fred

In addition to the excellent approach mentioned by Enda William O'Brien, which involves statistical adjustments based on available data, it is also possible to adopt approaches that take into account the scarcity of available data. One such approach is spatial interpolation, which is highly useful for estimating precipitation values in locations with insufficient data. By utilizing information from nearby stations with more complete records, it is possible to fill in the data gaps of recently installed stations.

Furthermore, historical data analysis from nearby stations with longer precipitation records can be employed to infer precipitation patterns in the area and fill in the data gaps of the new station, allowing for a better understanding of the climatic context in which the Standardized Precipitation Index (SPI) is being calculated.

very interesting!!

There is an Australian soil carbon accreditation scheme

Dear Mr. Ghosh,

Thank you for your kind response. Your answers were very helpful. However, if possible, could you please share a sample report, article, or any real-life experiences that you have had in this area? I am eager and looking forward to hearing from you.

Greetings, Naser Dehghanian,

Indeed, flood risk mapping in urban and rural areas is feasible by integrating diverse data sources, innovative tools, and advanced techniques.

Several data sources such as topographic maps, hydrological models, rainfall data, land use and land cover data, and historical flood data can be used with remote sensing, Geographic Information Systems (GIS) and field observations to generate accurate and comprehensive flood risk maps.

Furthermore, community engagement and participation hold significant importance in flood risk mapping as local knowledge and expertise can offer valuable insights into a particular area's specific flood risks and vulnerabilities.

I recommend reviewing the literature to enhance your understanding and knowledge in this field. Numerous high-quality research papers provide insightful perspectives on urban flood risk mapping and assessment.

Please find below a selection of potential sources for your consideration:

Ali YOUNES,

Aahed Alhamamy Please refrain from copy-pasting AI generated responses.

Floods have become common natural disasters that lead to destruction to the infrastructure and natural environment.With the striking climate change and weather variability, it might be impossible to prevent floods. However, flood prevention and mitigation can be facilitated by flood susceptibility mapping. The map can be produced based on the AHP methodology which is an multi-criteria decision analysis. Several influencing factors are considered for mapping the flood susceptible areas within the study area,i.e. elevation, slope, geology, drainage density, flow accumulation, land-use/cover, and soil. It is very similar to the groundwater potential mapping and landsilde susceptibility mapping. I have experiences in groundwater potential mapping and landsilde susceptibility mapping using AHP and SVM. If you don't mind, I will do your project with you using your spatial data like DEM and sentinel-2.

It is true that some ML use statistical techniques for learning, but the statistical approaches that I mentioned comprise much simpler calculations for specifying weights to the sub-classes of landslide conditioning factors. For instance frequency ratio uses a simple division. You can refer to some of my works to see details.

Dear Aahed Alhamamy

Salam, thanks a lot,

But I think the PRISMA level2C images are atmospherically corrected in the best way.

An optional radiometric calibration can do for them. (Cross-track illumination correction for pushbroom sensors)

In my opinion there is only a mandatory correction for this level that's a geometric correction (Orthorectification) ,I'm looking for details, I will be very grateful to know your idea and I welcome your suggestions.

1. This reference genome can either be from the same strain of bacteria or from a closely related strain. In terms of the source of heterogeneity, it can be due to various factors, including genetic regulation, genomic mutations, and horizontal gene transfer. Horizontal gene transfer is not the only source of genomic diversity in bacteria. Regulation and genome mutations can also contribute to heterogeneity within a bacterial population. Single-cell sequencing of bacteria can provide insights into the heterogeneity at the level of individual cells and can help distinguish between these factors.

2. Culture vs. original sample: Many scRNA-seq platforms require the bacteria to be cultured in the laboratory before sequencing. This can introduce biases and may not reflect the natural state of the bacteria in their original environment. In some cases, it may be possible to perform single-cell sequencing directly on environmental samples, but this can be technically challenging and may require specialized methods. Bulk sequencing can also be used to study bacterial communities without the need for single-cell isolation.

3. Polycistronic mRNA is a type of mRNA molecule that contains multiple coding regions, each encoding a different protein. These regions are separated by non-coding regions called intergenic regions. There are methods that can help to address this issue of polycistronic mRNA, such as using barcoding or unique molecular identifiers (UMIs) to label individual transcripts.

4. Single-bacteria genome sequencing can provide valuable information about the genetic makeup of individual bacteria, but it may not be suitable for all research questions.

Muhammad Ali

While mapping geomorphic features using deep learning approach, there could be several possible problems.

One of the main challenges is the availability of high-quality training data, which is essential for the deep learning model to learn the features accurately.

Another challenge is the complexity of the terrain, which can lead to difficulties in identifying and classifying different features

The deep learning model may also be sensitive to variations in the data, such as changes in illumination, weather conditions, and sensor noise

Additionally, the model may require significant computational resources and time to train and optimize, which can be a limitation for large-scale mapping projects

The model's performance may be affected by the choice of hyperparameters, such as the learning rate, batch size, and network architecture, which need to be carefully tuned to achieve optimal results

I recommend the python-cmethods package: https://github.com/btschwertfeger/python-cmethods

There are multiple methods available for 1- and 3-dimensional time-series climate data.

And the documentation includes descriptions and formulas as well as links to articles that deal with these methods: https://python-cmethods.readthedocs.io/en/stable/src/introduction.html

For fast bias corrections on large data sets I recommend the BiasAdjustCXX command-line tool: https://github.com/btschwertfeger/BiasAdjustCXX. There is also a brand new article describing about this tool (https://doi.org/10.1016/j.softx.2023.101379).

Both tools are free, open source and made by me.

Muhammad Firdaus

The choice of LS factor calculation method for RUSLE mapping depends on a variety of factors, such as the scale and resolution of your data, the terrain characteristics of your study area, and the specific research question you are trying to answer.

One way to determine which LS factor calculation method is most suitable for your study is to compare the results of different methods and assess their accuracy and reliability. You can do this by validating your results against field data or other independent sources, and by assessing the sensitivity of your results to different input parameters.

In general, the Wischmeier & Smith (1978) method is widely used in RUSLE mapping and is recommended by many researchers as a reliable and accurate method for calculating LS factors. This method is based on the slope gradient and length of overland flow, and takes into account the influence of vegetation cover and surface roughness on runoff and erosion.

However, other methods such as the Moore & Nieber (1989) or Desmet & Gobers (1996) methods may be more suitable for certain types of terrain or environmental conditions. For example, the Moore & Nieber method is better suited for areas with high relief and steep slopes, while the Desmet & Gobers method takes into account the effects of land use and soil texture on runoff and erosion.

Ultimately, the choice of LS factor calculation method will depend on your specific research question, the characteristics of your study area, and the available data and resources. It is important to carefully evaluate and validate your results, and to document your methods and assumptions clearly in order to ensure the reproducibility and transparency of your research.

If possible applie Quantile Mapping in daily value of precipitation, fir extreme value. For mean regimen use delta method

The criterion to establish whether two hyperspectral images are similar or not depends on the specific application and the goals of the analysis. Some common approaches include comparing the spectral signatures of the pixels in the two images, analyzing the spatial patterns and textures, and comparing statistical features such as mean, variance, and correlation.

In the context of SAM, the Spectral Angle Mapper, the similarity criterion is based on the angle between the spectral vectors of the pixels in the two images. The smaller the angle, the more similar the spectra are considered to be. However, the exact angle threshold for considering two spectra to be similar may depend on the specific application and the desired level of accuracy.

I recommend the following article as a good introduction to hyperspectral image analysis with SAM:

"Review of spectral imaging technology in biomedical engineering: achievements and challenges" by Rongxin Li, Jie Yang, et al. in Journal of Biomedical Optics, vol. 23, no. 9, 2018.

This article provides a comprehensive review of the applications and challenges of hyperspectral imaging in biomedical engineering, including an overview of the SAM algorithm and its applications. It also discusses various approaches for hyperspectral image analysis, including feature extraction, classification, and visualization.

You can process MODIS data using GIS. You can do research on Landsat8 data, one of the band on Landsat8 too may give you temperature data. Processing the data to generate maps is very easy using GIS.

Hi Hazrin.

Thank you very much.

Am in the mining space and did not know that both Trimble and Topcon have HD mapping solutions and maybe their solutions might be cheaper than NVIDIA. Thanks again and this will be helpful.

I appreciate your assistance.

Thanks again.

Johanna Mae Ga Absolutely, multiple research have utilized OCO-2 and Tropomi satellite data to trace CO2 and CH4 trends.

Saunois et al. (2020) published "The global methane budget 2000-2017," which used Tropomi satellite data to estimate worldwide methane emissions and trends. Another research, "A decade of GOSAT proxy satellite CH4 measurements," by Parker et al. (2018), examined changes in global atmospheric methane concentrations using OCO-2 and other satellite data.

Furthermore, Zhang et al. (2018) used both OCO-2 and Tropomi satellite data in "Mapping and quantifying methane emissions from oil and gas production in the Barnett Shale region using satellite observations" to map and quantify methane emissions from oil and gas production in the Barnett Shale region.

Overall, the use of satellite data such as OCO-2 and Tropomi is becoming increasingly significant in mapping and monitoring greenhouse gas changes, and many more research employing these data sources are planned in the future.

Hi Mauro,

The Abaqus manual for SIGINI says: "You should ensure that the initial stress field is in equilibrium with the applied forces and distributed loads by using a static step or a geostatic step to check the equilibrium of the initial stress field before starting the response history".

Regards,

Simon

Dear Dr. Ajay... I use Tassel program to analyse the SNP panel although this software only able to analyse with GLM and MLM methods. But, this software able to inform us the genetic diversity every snps marker. For mè, GAPIT is an accessories software. It can provide interesting graphics to support our finding...

Aahed Alhamamy Thank you so much for your wonderful response. You provided exactly the kind of guidance I was seeking.

Thanks, and have a nice day.

Sathish Sathasivam

Thank you for the lead Aahed, I'm sorry I'm having trouble locating the database and having only been able to find the NOAA 3D geoelectric hazard data, would it be possible for you to share the link for the dataset you mentioned? Thank you again!

Hi,

Did you check for instance: https://earth.esa.int/eogateway/news/gosat-2-on-line-dataset-now-available.

I asked your question on the platform https://platform.openai.com/

After less than a minute, the GPT chat replied:

Judge for yourself if this answer is useful to you.

How can I conduct specificity and selectivity of validation?

The specificity and selectivity of a validation method refer to its ability to accurately determine the presence or absence of a specific analyte in a sample, without detecting other substances that may be present. To conduct specificity and selectivity testing, you can follow these steps:

Prepare standards of the analyte of interest, as well as standards of any substances that may interfere with the analysis.

Analyze the standards using the validation method.

Evaluate the results of the analysis to determine if the method is specific for the analyte of interest, meaning it does not produce false positive results for other substances. This can be done by comparing the response of the analyte to the responses of the interfering substances.

Evaluate the selectivity of the method, meaning its ability to distinguish the analyte of interest from similar compounds. This can be done by analyzing mixtures of the analyte with structurally similar compounds and comparing the response of the analyte to the responses of the similar compounds.

Finally, it is important to verify the accuracy of the method by analyzing real samples that are known to contain the analyte and comparing the results to a reference method or a known value.

It is important to note that specificity and selectivity are complex and interrelated concepts and may depend on the specific application, sample matrix, and measurement conditions. The results of the specificity and selectivity testing should be thoroughly evaluated and documented to ensure that the validation method is appropriate for its intended use.

VMD has a great plugin named TopoTools, which has a function called mergemols. Look at the example in the link below.

Here period doubling means route to chaos. If you want to show chaotic behaviour, then either find period 3 cycle that represents chaos or determine Lyapunov exponents which should be positive. There are also some more quantities can show about existence of chaotic behaviour.

See any book on chaos and fractals.

Antonio Luca Conte There are various software choices available for creating a climate change risk map for an oak ecosystem. Some common choices are:

1. ArcGIS is a geographic information system (GIS) program that may be used to produce comprehensive maps and analyze data. It is capable of analyzing climatic data and creating danger maps for specific ecosystems.

2. QGIS is a free and open-source geographic information system (GIS) program that may be used to build and analyze maps. It shares many of ArcGIS's features and may be used to generate risk maps for specific environments.

3. R is a statistical computing and graphics programming language and software environment. It's commonly used for data analysis and visualization, as well as risk mapping. Many R packages, such as 'raster' and 'dismo,' may be used to construct risk maps.

4. Google Earth Engine is a cloud-based platform for accessing and processing huge volumes of geographical data, including climate data. It may be used to generate risk maps for individual environments.

It should be noted that developing a risk map for a given ecosystem would need an understanding of GIS, data analysis, and modeling. If you lack such information, it may be preferable to seek advice from professionals in the subject, such as ecologists, climatologists, or geologists.

Intan Putri The process of building a model that captures the fundamental properties of a chemical substance that are responsible for its biological action is known as pharmacophore modeling. The "Pharmacophore Generation" module in Discovery Studio may be used to do pharmacophore modeling.

There might be numerous reasons why the pharmacophore-generating module in Discovery Studio cannot produce the pharmacophore mappings to the ligand. Here are some possible solutions to the problem:

1. Examine the input data: Ascertain that the ligand structure used as input is valid and appropriately formatted. To guarantee that the ligand is appropriately identified by the module, you may need to alter the input choices or parameters.

2. Examine the output settings: To build pharmacophore maps, ensure that the output parameters are appropriately specified. You may need to select the map type (e.g., 2D or 3D) as well as the level of detail.

3. Examine the following pharmacophore generation parameters: The pharmacophore generation module contains a variety of settings that may be adjusted to affect the module's behavior. If the default parameters do not produce the desired results, you may need to experiment with alternative parameter settings to discover a combination that works better for your ligand.

4. Check for error messages: If the pharmacophore generation module fails to generate a result, there may be errors or warning messages in the log that might help you figure out what's wrong. The log may be seen by selecting the "Log" tab in the pharmacophore creation module.

I hope this was helpful! Please let me know if you have any queries or require any other support.

Dear Jardel,

Try using the FlattenLakes tool available in the Whitebox. If your water body polygons are lakes, this will be a very useful tool. https://www.whiteboxgeo.com/manual/wbt_book/available_tools/hydrological_analysis.html#FlattenLakes

There are many variables that can be considered when mapping forest depletion or degradation, but some of the most important ones include the extent and type of forest cover, the rate at which trees are being removed or lost, the causes of forest loss (e.g. logging, fires, development), and the impact of forest loss on the surrounding ecosystem and local communities. Other factors that can be taken into account include the age and health of the remaining trees, the presence of protected areas or conservation efforts, and the availability of data and technology to monitor and track changes in forest cover over time. Ultimately, the best variables to consider will depend on the specific goals and context of the mapping effort.

Hi,

There is a long list of optimization algorithms that you can use to tune the hyperparameters of machine learning models. Regarding metaheuristic optimization, a genetic algorithm has been implemented in the library sklearn-genetic-opt to optimize hyperparameters, such as the number of trees, learning rate, and depth of the tree. The library scikit-opt implements other metaheuristic algorithms, such as particle swarm optimization and simulated annealing.

All these hyperparameter strategies can be used to tune gradient boosting models (or any machine learning model). In this sense, you can use these strategies to compute a set of hyperparameters that maximize the model performance.

Other optimization algorithms are widely applied in hyperparameter optimization tasks, for instance, Bayesian optimization (python libraries: scikit-optimize and hyperopt).

Is your expectation that soil salinity has some distinctive signature you will be able to extract directly from the image's bands? Perhaps a more detailed description of your methodology will yield a helpful answer - especially if you provide some citations / references for your analysis.

Blue carbon sequestration in mangrove ecosystems can be calculated using ArcGIS by assessing the aboveground biomass of the mangrove forests, as well as the amount of carbon stored in the soils and sediments of the mangrove forests. This can be done by collecting high-resolution remote sensing data, such as LiDAR, to measure the canopy height of mangrove forests and calculate the aboveground biomass. Additionally, ground truthing can be used to identify and measure the carbon stored in the soils and sediments of mangrove forests, and this data can then be used to estimate the amount of carbon sequestered in the mangrove ecosystem.

I too am looking for such studies, but I have not found anything

So you want the code for Kriging using gstat. A simple Google search shows plenty of tutorials. You just need to apply the principles showed on these tutorials to your data.

I would advise you to start with Kriging and then move to co-Kriging.

For this case, the use of multiple CFD programs can be useful, such as coupling the towing models with one program and meshing with the simulation using another program.

You can use PCRaster, see link https://pcraster.geo.uu.nl/

Dear Colleague,

Please, be so kind as to write your (probably very important) remark in English. The translator tool I have access to does not provide a meaningful interpretation.

Thank you very much, and kind regards,

Imre Horvath

𝗠. 𝗔𝗹𝘃𝗶𝗼𝗹𝗶

The derivation is in PDF file that I upload.

Aleksi Sihvonen What's the name of the article?

Thank you Patrick for your source of information

Is there any software or code can directly construct these dynamical matrix?

Can you share these with me please! I am looking for .cry for α-Al12(Fe,Mn)3Si and α-Al15(Fe,Mn)3Si2 as well as β-Al6(Fe,Mn)

Dear Shaid,

This is the short-wave infrared range in which you can observe molecular bindings (i.e. H-OH, Al-OH, Fe-OH, Mg-OH, CO3). As a geologist, this is a perfect range for mineral mapping especially hydrothermal alteration minerals. Therefore, the most important application would be mineral exploration (e.g. porphyry copper deposits). However, identification is a routine thing that almost many geologists/geoscientists do with the SWIR spectra but if you are aiming for publication you must go further and answer a specific research question that has not been addressed before.

There are a lot of valuable links but in the following, you can find the most well-known papers:

1. Hunt, G. R. (1977). Spectral signatures of particulate minerals in the visible and near infrared. Geophysics, 42(3), 501-513.‏

2. Pontual, S., Merry, N., & Gamson, P. (1997). Spectral interpretation field manual, G-MEX. Spectral Analysis Guides for Mineral Exploration. Ferny Creek: AusSpec Int. Pty. Ltd: Victoria, Australia, Volume 1, 1169.

Please also make sure that you have applied sufficient corrections on your hyperspectral images as most of the time they are noisy and require pre-processing.

Kind regards

Fardad