diff --git a/Dockerfile b/Dockerfile
index 25250c40..fef69f45 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -12,6 +12,7 @@ RUN apt-get update && apt-get install -y \
     curl
 
 # Create rstudio user
+# default accout 'rstudio'; password: rstudio123
 ENV USER=rstudio
 ENV PASSWORD=rstudio123
 RUN useradd -m $USER && \
diff --git a/R/zzz.R b/R/zzz.R
index 180768ec..725ff14c 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -19,42 +19,105 @@
 #' @importFrom reticulate py_module_available
 #' @importFrom reticulate py_install
 #' @keywords internal
+# .onAttach <- function(...) {
+#   # Determine Python command
+#   python_cmd <- if (Sys.info()["sysname"] == "Windows") "python" else "python3"
+#   python_path <- Sys.which(python_cmd)
+#
+#   # Check Python path
+#   if (python_path == "") {
+#     packageStartupMessage(paste("Cannot locate the", python_cmd, "executable. Ensure it's installed and in your system's PATH. flaiR functionality requiring Python will not be available."))
+#     return(invisible(NULL))  # Exit .onAttach without stopping package loading
+#   }
+#
+#   # Check Python versio Try to get Python version
+#   tryCatch({
+#     python_version <- system(paste(python_path, "--version"), intern = TRUE)
+#     if (!grepl("Python 3", python_version)) {
+#       packageStartupMessage("Python 3 is required, but a different version was found. Please install Python 3. flaiR functionality requiring Python will not be available.")
+#       return(invisible(NULL))  # Exit .onAttach without stopping package loading
+#     }
+#   }, error = function(e) {
+#     packageStartupMessage(paste("Failed to get Python version with path:", python_path, "Error:", e$message, ". flaiR functionality requiring Python will not be available."))
+#     return(invisible(NULL))   # Exit .onAttach without stopping package loading
+#   })
+#
+#   # Check if PyTorch is installed
+#   check_torch_version <- function() {
+#     # torch_version_command <- paste(python_path, "-c 'import torch; print(torch.__version__)'")
+#     torch_version_command <- paste(python_path, "-c \"import torch; print(torch.__version__)\"")
+#     result <- system(torch_version_command, intern = TRUE)
+#     if (length(result) == 0 || result[1] == "ERROR" || is.na(result[1])) {
+#       return(list(paste("PyTorch", paste0("\033[31m", "\u2717", "\033[39m"), sep = " "), FALSE))
+#     }
+#     # Return flair version
+#     return(list(paste("PyTorch", paste0("\033[32m", "\u2713", "\033[39m") ,result[1], sep = " "), TRUE, result[1]))
+#   }
+#
+#   # Check if flair is installed
+  # check_flair_version <- function() {
+  #   # flair_version_command <- paste(python_path, "-c 'import flair; print(flair.__version__)'")
+  #   flair_version_command <- paste(python_path, "-c \"import flair; print(flair.__version__)\"")
+  #   result <- system(flair_version_command, intern = TRUE)
+  #   if (length(result) == 0 || result[1] == "ERROR" || is.na(result[1])) {
+  #     return(list(paste("flair", paste0("\033[31m", "\u2717", "\033[39m"), sep = " "), FALSE))
+  #   }
+  #   # Return flair version
+  #   return(list(paste("flair", paste0("\033[32m", "\u2713", "\033[39m"),result[1], sep = " "), TRUE, result[1]))
+  # }
+
+#   flair_version <- check_flair_version()
+#   torch_version <- check_torch_version()
+#
+#   if (isFALSE(flair_version[[2]])) {
+#     packageStartupMessage(sprintf(" Flair %-50s", paste0("is installing from Python")))
+#
+#     commands <- c(
+#       paste(python_path, "-m pip install --upgrade pip"),
+#       paste(python_path, "-m pip install torch"),
+#       paste(python_path, "-m pip install flair"),
+#       paste(python_path, "-m pip install scipy==1.12.0")
+#     )
+#     command_statuses <- vapply(commands, system, FUN.VALUE = integer(1))
+#
+#     flair_check_again <- check_flair_version()
+#     if (isFALSE(flair_check_again[[2]])) {
+#       packageStartupMessage("Failed to install Flair. {flaiR} requires Flair NLP. Please ensure Flair NLP is installed in Python manually.")
+#     }
+#   } else {
+#     packageStartupMessage(sprintf("\033[1m\033[34mflaiR\033[39m\033[22m: \033[1m\033[33mAn R Wrapper for Accessing Flair NLP\033[39m\033[22m %-5s", paste("\033[1m\033[33m", flair_version[[3]], "\033[39m\033[22m", sep = "")))
+#   }
+# }
+
+
+
 .onAttach <- function(...) {
-  # Determine Python command
-  python_cmd <- if (Sys.info()["sysname"] == "Windows") "python" else "python3"
-  python_path <- Sys.which(python_cmd)
-
-  # Check Python path
-  if (python_path == "") {
-    packageStartupMessage(paste("Cannot locate the", python_cmd, "executable. Ensure it's installed and in your system's PATH. flaiR functionality requiring Python will not be available."))
-    return(invisible(NULL))  # Exit .onAttach without stopping package loading
-  }
+  # 总是先清除环境变量，避免冲突
+  Sys.unsetenv("RETICULATE_PYTHON")
 
-  # Check Python versio Try to get Python version
+  # 使用 reticulate 的 py_discover_config 来检测 Python
   tryCatch({
-    python_version <- system(paste(python_path, "--version"), intern = TRUE)
-    if (!grepl("Python 3", python_version)) {
-      packageStartupMessage("Python 3 is required, but a different version was found. Please install Python 3. flaiR functionality requiring Python will not be available.")
-      return(invisible(NULL))  # Exit .onAttach without stopping package loading
-    }
+    python_config <- reticulate::py_discover_config()
+    python_path <- python_config$python
   }, error = function(e) {
-    packageStartupMessage(paste("Failed to get Python version with path:", python_path, "Error:", e$message, ". flaiR functionality requiring Python will not be available."))
-    return(invisible(NULL))   # Exit .onAttach without stopping package loading
+    # 如果 py_discover_config 失败，尝试找系统 Python
+    python_cmd <- if (Sys.info()["sysname"] == "Windows") "python" else "python3"
+    python_path <- Sys.which(python_cmd)
   })
 
-  # Check if PyTorch is installed
-  check_torch_version <- function() {
-    # torch_version_command <- paste(python_path, "-c 'import torch; print(torch.__version__)'")
-    torch_version_command <- paste(python_path, "-c \"import torch; print(torch.__version__)\"")
-    result <- system(torch_version_command, intern = TRUE)
-    if (length(result) == 0 || result[1] == "ERROR" || is.na(result[1])) {
-      return(list(paste("PyTorch", paste0("\033[31m", "\u2717", "\033[39m"), sep = " "), FALSE))
-    }
-    # Return flair version
-    return(list(paste("PyTorch", paste0("\033[32m", "\u2713", "\033[39m") ,result[1], sep = " "), TRUE, result[1]))
+  # 检查 Python 路径
+  if (!file.exists(python_path)) {
+    packageStartupMessage("Cannot locate Python. Please ensure Python 3 is installed.")
+    return(invisible(NULL))
   }
 
-  # Check if flair is installed
+  # 设置 Python 路径并检查版本（全部静默执行）
+  suppressWarnings({
+    Sys.setenv(RETICULATE_PYTHON = python_path)
+    reticulate::use_python(python_path, required = TRUE)
+  })
+
+
   check_flair_version <- function() {
     # flair_version_command <- paste(python_path, "-c 'import flair; print(flair.__version__)'")
     flair_version_command <- paste(python_path, "-c \"import flair; print(flair.__version__)\"")
@@ -66,113 +129,35 @@
     return(list(paste("flair", paste0("\033[32m", "\u2713", "\033[39m"),result[1], sep = " "), TRUE, result[1]))
   }
 
-  flair_version <- check_flair_version()
-  torch_version <- check_torch_version()
+  flair_version <- suppressMessages(check_flair_version())
 
   if (isFALSE(flair_version[[2]])) {
     packageStartupMessage(sprintf(" Flair %-50s", paste0("is installing from Python")))
-
     commands <- c(
       paste(python_path, "-m pip install --upgrade pip"),
       paste(python_path, "-m pip install torch"),
       paste(python_path, "-m pip install flair"),
       paste(python_path, "-m pip install scipy==1.12.0")
     )
-    command_statuses <- vapply(commands, system, FUN.VALUE = integer(1))
 
-    flair_check_again <- check_flair_version()
+    tryCatch({
+      for (cmd in commands) {
+        system(cmd, intern = TRUE)
+      }
+    }, error = function(e) {
+      packageStartupMessage(paste("Failed to install packages:", e$message))
+    })
+
+    flair_check_again <- suppressMessages(check_flair_version())
     if (isFALSE(flair_check_again[[2]])) {
       packageStartupMessage("Failed to install Flair. {flaiR} requires Flair NLP. Please ensure Flair NLP is installed in Python manually.")
     }
   } else {
-    packageStartupMessage(sprintf("\033[1m\033[34mflaiR\033[39m\033[22m: \033[1m\033[33mAn R Wrapper for Accessing Flair NLP\033[39m\033[22m %-5s", paste("\033[1m\033[33m", flair_version[[3]], "\033[39m\033[22m", sep = "")))
+    packageStartupMessage(sprintf("\033[1m\033[34mflaiR\033[39m\033[22m: \033[1m\033[33mAn R Wrapper for Accessing Flair NLP\033[39m\033[22m %-5s",
+                                  paste("\033[1m\033[33m", flair_version[[3]], "\033[39m\033[22m", sep = "")))
   }
 }
 
 
-# .onAttach <- function(...) {
-#   # Docker 環境檢查及 Python 設置
-#   Sys.unsetenv("RETICULATE_PYTHON")
-#   in_docker <- file.exists("/.dockerenv")
-#
-#   if (in_docker) {
-#     # Docker 環境使用固定路徑
-#     python_path <- "/opt/venv/bin/python3"
-#     Sys.setenv(RETICULATE_PYTHON = python_path)
-#   } else {
-#     # 非 Docker 環境
-#     python_cmd <- if (Sys.info()["sysname"] == "Windows") "python" else "python3"
-#     python_path <- Sys.which(python_cmd)
-#   }
-#
-#   # 檢查 Python 路徑
-#   if (python_path == "") {
-#     packageStartupMessage("Cannot locate Python executable. Ensure Python is installed and in PATH.")
-#     return(invisible(NULL))
-#   }
-#
-#   # 檢查 Python 版本
-#   tryCatch({
-#     python_version <- system(paste(python_path, "--version"), intern = TRUE)
-#     if (!grepl("Python 3", python_version)) {
-#       packageStartupMessage("Python 3 is required.")
-#       return(invisible(NULL))
-#     }
-#   }, error = function(e) {
-#     packageStartupMessage(paste("Failed to check Python version:", e$message))
-#     return(invisible(NULL))
-#   })
-#
-#   # Version check functions
-#   check_torch_version <- function() {
-#     torch_version_command <- paste(python_path, "-c \"import torch; print(torch.__version__)\"")
-#     result <- system(torch_version_command, intern = TRUE)
-#     if (length(result) == 0 || result[1] == "ERROR" || is.na(result[1])) {
-#       return(list(paste("PyTorch", paste0("\033[31m", "\u2717", "\033[39m"), sep = " "), FALSE))
-#     }
-#     return(list(paste("PyTorch", paste0("\033[32m", "\u2713", "\033[39m"), result[1], sep = " "), TRUE, result[1]))
-#   }
-#
-#   check_flair_version <- function() {
-#     flair_version_command <- paste(python_path, "-c \"import flair; print(flair.__version__)\"")
-#     result <- system(flair_version_command, intern = TRUE)
-#     if (length(result) == 0 || result[1] == "ERROR" || is.na(result[1])) {
-#       return(list(paste("flair", paste0("\033[31m", "\u2717", "\033[39m"), sep = " "), FALSE))
-#     }
-#     return(list(paste("flair", paste0("\033[32m", "\u2713", "\033[39m"), result[1], sep = " "), TRUE, result[1]))
-#   }
-#
-#   flair_version <- check_flair_version()
-#   torch_version <- check_torch_version()
-#
-#   if (isFALSE(flair_version[[2]])) {
-#     packageStartupMessage(sprintf(" Flair %-50s", paste0("is installing from Python")))
-#
-#     # 安裝命令
-#     if (in_docker) {
-#       commands <- c(
-#         paste(python_path, "-m pip install --no-cache-dir numpy==1.26.4"),
-#         paste(python_path, "-m pip install --no-cache-dir torch"),
-#         paste(python_path, "-m pip install --no-cache-dir flair"),
-#         paste(python_path, "-m pip install --no-cache-dir scipy==1.12.0")
-#       )
-#     } else {
-#       commands <- c(
-#         paste(python_path, "-m pip install --upgrade pip"),
-#         paste(python_path, "-m pip install torch"),
-#         paste(python_path, "-m pip install flair"),
-#         paste(python_path, "-m pip install scipy==1.12.0")
-#       )
-#     }
-#
-#     command_statuses <- vapply(commands, system, FUN.VALUE = integer(1))
-#
-#     flair_check_again <- check_flair_version()
-#     if (isFALSE(flair_check_again[[2]])) {
-#       packageStartupMessage("Failed to install Flair. Please install manually.")
-#     }
-#   } else {
-#     packageStartupMessage(sprintf("\033[1m\033[34mflaiR\033[39m\033[22m: \033[1m\033[33mAn R Wrapper for Accessing Flair NLP\033[39m\033[22m %-5s",
-#                                   paste("\033[1m\033[33m", flair_version[[3]], "\033[39m\033[22m", sep = "")))
-#   }
-# }
+
+
diff --git a/README.Rmd b/README.Rmd
index cc19893a..14938d8f 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -106,26 +106,24 @@ __Intel/AMD Processors:__
 ```r
 # Pull image
 docker pull ghcr.io/davidycliao/flair-rstudio:latest
-```
 
-```r
 # Run container
-docker run -p 8787:8787 ghcr.io/davidycliao/flair-rstudio:latest
+docker run -d -p 8787:8787 --user root --name flair-rstudio ghcr.io/davidycliao/flair-rstudio:latest
 ```
 
 
 __Apple Silicon (M1/M2 Mac):__
 
 ```r
-# Pull and run with platform specification
+# Pull image
 docker pull --platform linux/amd64 ghcr.io/davidycliao/flair-rstudio:latest
-```
 
-```r
-docker run --platform linux/amd64 -p 8787:8787 ghcr.io/davidycliao/flair-rstudio:latest
+# Run container
+docker run -d -p 8787:8787 --platform linux/amd64 --user root --name flair-rstudio ghcr.io/davidycliao/flair-rstudio:latest
 ```
 
-After running these commands in terminal (or powershell), open your browser and navigate to [`http://localhost:8787`]() to access RStudio.
+
+After running these commands in terminal (or powershell), open your browser and navigate to [`http://localhost:8787`](http://localhost:8787) to access RStudio.
 For detailed installation instructions, please visit [Quick Start Guide](https://davidycliao.github.io/flaiR/articles/quickstart.html#flair-installation).
 
 </div>
diff --git a/README.md b/README.md
index 09c1a8b0..b2fd8b2a 100644
--- a/README.md
+++ b/README.md
@@ -92,27 +92,25 @@ library(flaiR)
 ``` r
 # Pull image
 docker pull ghcr.io/davidycliao/flair-rstudio:latest
-```
 
-``` r
 # Run container
-docker run -p 8787:8787 ghcr.io/davidycliao/flair-rstudio:latest
+docker run -d -p 8787:8787 --user root --name flair-rstudio ghcr.io/davidycliao/flair-rstudio:latest
 ```
 
 **Apple Silicon (M1/M2 Mac):**
 
 ``` r
-# Pull and run with platform specification
+# Pull image
 docker pull --platform linux/amd64 ghcr.io/davidycliao/flair-rstudio:latest
-```
 
-``` r
-docker run --platform linux/amd64 -p 8787:8787 ghcr.io/davidycliao/flair-rstudio:latest
+# Run container
+docker run -d -p 8787:8787 --platform linux/amd64 --user root --name flair-rstudio ghcr.io/davidycliao/flair-rstudio:latest
 ```
 
 After running these commands in terminal (or powershell), open your
-browser and navigate to [`http://localhost:8787`]() to access RStudio.
-For detailed installation instructions, please visit [Quick Start
+browser and navigate to [`http://localhost:8787`](http://localhost:8787)
+to access RStudio. For detailed installation instructions, please visit
+[Quick Start
 Guide](https://davidycliao.github.io/flaiR/articles/quickstart.html#flair-installation).
 
 </div>
diff --git a/vignettes/quickstart.Rmd b/vignettes/quickstart.Rmd
index 1eade630..969331d9 100644
--- a/vignettes/quickstart.Rmd
+++ b/vignettes/quickstart.Rmd
@@ -99,7 +99,13 @@ __Intel/AMD Processors:__
 ```bash
 # Pull and run
 docker pull ghcr.io/davidycliao/flair-rstudio:latest
-docker run -p 8787:8787 ghcr.io/davidycliao/flair-rstudio:latest
+docker run -d \
+  -p 8787:8787 \
+  --user root \
+  -e USER=rstudio \
+  -e PASSWORD=rstudio123 \
+  --name flair-rstudio \
+  ghcr.io/davidycliao/flair-rstudio:latest
 ```
 
 - Apple Silicon (M1/M2 Mac):
@@ -107,12 +113,19 @@ docker run -p 8787:8787 ghcr.io/davidycliao/flair-rstudio:latest
 ```bash
 # Pull and run with platform specification
 docker pull --platform linux/amd64 ghcr.io/davidycliao/flair-rstudio:latest
-docker run --platform linux/amd64 -p 8787:8787 ghcr.io/davidycliao/flair-rstudio:latest
+docker run -d \
+  -p 8787:8787 \
+  --platform linux/amd64 \
+  --user root \
+  -e USER=rstudio \         
+  -e PASSWORD=rstudio123 \
+  --name flair-rstudio \
+  ghcr.io/davidycliao/flair-rstudio:latest
 ```
 
 __Access RStudio Server:__
 
-- Open browser: `http://localhost:8787`
+- Open browser: [`http://localhost:8787`](http://localhost:8787)
 - Username: `rstudio`
 - Password: `rstudio123`
 
@@ -220,6 +233,50 @@ If you encounter any problems or have questions:
 </div>
 
 
+__Troubleshooting for Docker__
+
+1. If you encounter any issues:
+
+```
+# Try using a different port (e.g., 8788)
+docker run -d \
+  -p 8788:8787 \
+  --platform linux/amd64 \
+  --user root \
+  --name flair-rstudio \
+  ghcr.io/davidycliao/flair-rstudio:latest
+```
+Then access via [`http://localhost:8788`](http://localhost:8788)
+
+2. Container already exists:
+
+```
+# Remove existing container
+docker stop flair-rstudio
+docker rm flair-rstudio
+```
+
+Then retry the run command.
+
+3. Check container status:
+
+```
+# View running containers
+docker ps
+# View all containers including stopped ones
+docker ps -a
+# View container logs
+docker logs flair-rstudio
+```
+
+
+
+
+
+
+
+
+
 
 -----
 
diff --git a/vignettes/tutorial.Rmd b/vignettes/tutorial.Rmd
index c81e6df1..8466f2c5 100644
--- a/vignettes/tutorial.Rmd
+++ b/vignettes/tutorial.Rmd
@@ -2315,9 +2315,9 @@ print(confidence)
 
 <div style="text-align: justify;">
 
-`ConText` is a fast, flexible, and transparent framework for estimating context-specific word and short document embeddings using [the 'a la carte' embeddings regression](https://github.com/prodriguezsosa/EmbeddingRegression), implemented by Rodriguez et al.
+`ConText` is a fast, flexible, and transparent framework for estimating context-specific word and short document embeddings using [the 'a la carte' embeddings regression](https://github.com/prodriguezsosa/EmbeddingRegression), implemented by [Rodriguez et al (2022)](https://www.cambridge.org/core/journals/american-political-science-review/article/embedding-regression-models-for-contextspecific-description-and-inference/4C90013E5C714C8483ED95CC699022FB) and [Rodriguez et al (2024)](https://www.cambridge.org/core/journals/political-analysis/article/multilanguage-word-embeddings-for-social-scientists-estimation-inference-and-validation-resources-for-157-languages/F5DE16FA784CA81481150715967BD9FC).
 
-In this case study, I'll demonstrate how to use the conText package alongside other embedding frameworks by working through the example provided in Rodriguez et al.'s [Quick Start Guide](https://github.com/prodriguezsosa/conText/blob/master/vignettes/quickstart.md). While ConText includes its own cross-lingual ALC Embeddings, this tutorial extends its capabilities by integrating it with flaiR. Through this tutorial integration, this tutorial shows how to:
+In this case study, we will demonstrate how to use the conText package alongside other embedding frameworks by working through the example provided in Rodriguez et al.'s [Quick Start Guide](https://github.com/prodriguezsosa/conText/blob/master/vignettes/quickstart.md). While ConText includes its own cross-lingual ALC Embeddings, this tutorial extends its capabilities by integrating it with flaiR. Through this tutorial integration, this tutorial shows how to:
 
 </div>
 
@@ -2329,9 +2329,9 @@ In this case study, I'll demonstrate how to use the conText package alongside ot
 
 <div style="text-align: justify;">
 
-I'll be following the example directly from [Rodriguez et al.'s Quick Start Guide]() as this case study. It's important to note that results obtained using alternative embedding frameworks may deviate from the original implementation, and should be interpreted with caution. These comparative results are primarily intended for reference and educational use.
+We will be following the example directly from [Rodriguez et al.'s Quick Start Guide]() as this case study. It's important to note that results obtained using alternative embedding frameworks may deviate from the original implementation, and should be interpreted with caution. These comparative results are primarily intended for reference and educational use.
 
-First of all, when loading the conText package, you'll find three pre-loaded datasets: `cr_sample_corpus`, `cr_glove_subset`, and `cr_transform.` These datasets are used in the package's tutorial to demonstrate preprocessing steps. For this exercise, I'll use `cr_sample_corpus` to explore other embedding frameworks, including:
+First of all, when loading the conText package, you'll find three pre-loaded datasets: `cr_sample_corpus`, `cr_glove_subset`, and `cr_transform.` These datasets are used in the package's tutorial to demonstrate preprocessing steps. For this exercise, we only use `cr_sample_corpus` to explore other embedding frameworks, including:
 
 </div>