Human Resource Management｜AmTRAN Technology Co. Ltd

Workforce Structure

Employee Distribution by Gender and Job Level

	Male	Female	Total
Management Personnel	${dataInfo.area1_block1_type1_value1}	${dataInfo.area1_block1_type1_value2}	${dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type1_value2}
Technical Staff	${dataInfo.area1_block1_type2_value1}	${dataInfo.area1_block1_type2_value2}	${dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type2_value2}
Other Employees	${dataInfo.area1_block1_type3_value1}	${dataInfo.area1_block1_type3_value2}	${dataInfo.area1_block1_type3_value1 + dataInfo.area1_block1_type3_value2}
Subtotal	${dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type3_value1}	${dataInfo.area1_block1_type1_value2 + dataInfo.area1_block1_type2_value2 + dataInfo.area1_block1_type3_value2}	${dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type3_value1 + dataInfo.area1_block1_type1_value2 + dataInfo.area1_block1_type2_value2 + dataInfo.area1_block1_type3_value2}
Percentage (by Gender)	${(dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type3_value1)*100/(dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type3_value1 + dataInfo.area1_block1_type1_value2 + dataInfo.area1_block1_type2_value2 + dataInfo.area1_block1_type3_value2) \| roundToTwo}%	${(dataInfo.area1_block1_type1_value2 + dataInfo.area1_block1_type2_value2 + dataInfo.area1_block1_type3_value2)*100/(dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type3_value1 + dataInfo.area1_block1_type1_value2 + dataInfo.area1_block1_type2_value2 + dataInfo.area1_block1_type3_value2) \| roundToTwo}%	100%

Management
Personnel

Male${(dataInfo.area1_block1_type1_value1 * 100) / (dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type1_value2) | round}%

Female${(dataInfo.area1_block1_type1_value2 * 100) / (dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type1_value2) | round}%

Technical
Staff

Male${(dataInfo.area1_block1_type2_value1 * 100) / (dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type2_value2) | round}%

Female${(dataInfo.area1_block1_type2_value2 * 100) / (dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type2_value2) | round}%

Other
Employees

Male${(dataInfo.area1_block1_type3_value1 * 100) / (dataInfo.area1_block1_type3_value1 + dataInfo.area1_block1_type3_value2) | round}%

Female${(dataInfo.area1_block1_type3_value2 * 100) / (dataInfo.area1_block1_type3_value1 + dataInfo.area1_block1_type3_value2) | round}%

Note: Employee types are explained as follows:
Management Personnel：Management Positions
Technical Staff：Non-Management RD Engineers (e.g., Hardware/Mechanical/Firmware/RF/Safety, etc.)
Other Employees：Non-Management Staff

Age
Distribution

51 years old (inclusive)17%

30-50 years old13%

<30 years old70%

Education
Distribution

Master's and PhD14%

Bachelor's4%

Associate Degree28%

High School and Below54%

Number and Percentage of New Employees in 2023 by Gender and Age Group

	Male	Female	Total
<30 years old	${dataInfo.area1_block2_type1_value1}	${dataInfo.area1_block2_type1_value2}	${dataInfo.area1_block2_type1_value1 + dataInfo.area1_block2_type1_value2}
30-50 years old	${dataInfo.area1_block2_type2_value1}	${dataInfo.area1_block2_type2_value2}	${dataInfo.area1_block2_type2_value1 + dataInfo.area1_block2_type2_value2}
50 years old	${dataInfo.area1_block2_type3_value1}	${dataInfo.area1_block2_type3_value2}	${dataInfo.area1_block2_type3_value1 + dataInfo.area1_block2_type3_value2}
Total	${dataInfo.area1_block2_type1_value1 + dataInfo.area1_block2_type2_value1 + dataInfo.area1_block2_type3_value1}	${dataInfo.area1_block2_type1_value2 + dataInfo.area1_block2_type2_value2 + dataInfo.area1_block2_type3_value2}	${dataInfo.area1_block2_type1_value1 + dataInfo.area1_block2_type2_value1 + dataInfo.area1_block2_type3_value1 + dataInfo.area1_block2_type1_value2 + dataInfo.area1_block2_type2_value2 + dataInfo.area1_block2_type3_value2}
New Hire Rate	${(dataInfo.area1_block2_type1_value1 + dataInfo.area1_block2_type2_value1 + dataInfo.area1_block2_type3_value1) * 100 / (dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type3_value1) \| roundToTwo}%	${(dataInfo.area1_block2_type1_value2 + dataInfo.area1_block2_type2_value2 + dataInfo.area1_block2_type3_value2) * 100 / (dataInfo.area1_block1_type1_value2 + dataInfo.area1_block1_type2_value2 + dataInfo.area1_block1_type3_value2) \| roundToTwo}%	${(dataInfo.area1_block2_type1_value1 + dataInfo.area1_block2_type2_value1 + dataInfo.area1_block2_type3_value1 + dataInfo.area1_block2_type1_value2 + dataInfo.area1_block2_type2_value2 + dataInfo.area1_block2_type3_value2) * 100 / (dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type3_value1 + dataInfo.area1_block1_type1_value2 + dataInfo.area1_block1_type2_value2 + dataInfo.area1_block1_type3_value2) \| roundToTwo}%

Number and Percentage of Employees Who Left in 2023 by Gender and Age Group

	Male	Female	Total
<30 years old	${dataInfo.area1_block3_type1_value1}	${dataInfo.area1_block3_type1_value2}	${dataInfo.area1_block3_type1_value1 + dataInfo.area1_block3_type1_value2}
30-50 years old	${dataInfo.area1_block3_type2_value1}	${dataInfo.area1_block3_type2_value2}	${dataInfo.area1_block3_type2_value1 + dataInfo.area1_block3_type2_value2}
50 years old	${dataInfo.area1_block3_type3_value1}	${dataInfo.area1_block3_type3_value2}	${dataInfo.area1_block3_type3_value1 + dataInfo.area1_block3_type3_value2}
Total	${dataInfo.area1_block3_type1_value1 + dataInfo.area1_block3_type2_value1 + dataInfo.area1_block3_type3_value1}	${dataInfo.area1_block3_type1_value2 + dataInfo.area1_block3_type2_value2 + dataInfo.area1_block3_type3_value2}	${dataInfo.area1_block3_type1_value1 + dataInfo.area1_block3_type2_value1 + dataInfo.area1_block3_type3_value1 + dataInfo.area1_block3_type1_value2 + dataInfo.area1_block3_type2_value2 + dataInfo.area1_block3_type3_value2}
Turnover Rate	${(dataInfo.area1_block3_type1_value1 + dataInfo.area1_block3_type2_value1 + dataInfo.area1_block3_type3_value1) * 100 / (dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type3_value1) \| roundToTwo}%	${(dataInfo.area1_block3_type1_value2 + dataInfo.area1_block3_type2_value2 + dataInfo.area1_block3_type3_value2) * 100 / (dataInfo.area1_block1_type1_value2 + dataInfo.area1_block1_type2_value2 + dataInfo.area1_block1_type3_value2) \| roundToTwo}%	${(dataInfo.area1_block3_type1_value1 + dataInfo.area1_block3_type2_value1 + dataInfo.area1_block3_type3_value1 + dataInfo.area1_block3_type1_value2 + dataInfo.area1_block3_type2_value2 + dataInfo.area1_block3_type3_value2) * 100 / (dataInfo.area1_block1_type1_value1 + dataInfo.area1_block1_type2_value1 + dataInfo.area1_block1_type3_value1 + dataInfo.area1_block1_type1_value2 + dataInfo.area1_block1_type2_value2 + dataInfo.area1_block1_type3_value2) \| roundToTwo}%

	Male	Female	Total
Management Personnel	${dataInfo.area2_block1_type1_value1}	${dataInfo.area2_block1_type1_value2}	${dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type1_value2}
Technical Staff	${dataInfo.area2_block1_type2_value1}	${dataInfo.area2_block1_type2_value2}	${dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type2_value2}
Other Employees	${dataInfo.area2_block1_type3_value1}	${dataInfo.area2_block1_type3_value2}	${dataInfo.area2_block1_type3_value1 + dataInfo.area2_block1_type3_value2}
Subtotal	${dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type3_value1}	${dataInfo.area2_block1_type1_value2 + dataInfo.area2_block1_type2_value2 + dataInfo.area2_block1_type3_value2}	${dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type3_value1 + dataInfo.area2_block1_type1_value2 + dataInfo.area2_block1_type2_value2 + dataInfo.area2_block1_type3_value2}
Percentage (by Gender)	${(dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type3_value1)*100/(dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type3_value1 + dataInfo.area2_block1_type1_value2 + dataInfo.area2_block1_type2_value2 + dataInfo.area2_block1_type3_value2) \| roundToTwo}%	${(dataInfo.area2_block1_type1_value2 + dataInfo.area2_block1_type2_value2 + dataInfo.area2_block1_type3_value2)*100/(dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type3_value1 + dataInfo.area2_block1_type1_value2 + dataInfo.area2_block1_type2_value2 + dataInfo.area2_block1_type3_value2) \| roundToTwo}%	100%

Management
Personnel

Male${(dataInfo.area2_block1_type1_value1 * 100) / (dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type1_value2) | round}%

Female${(dataInfo.area2_block1_type1_value2 * 100) / (dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type1_value2) | round}%

Technical
Staff

Male${(dataInfo.area2_block1_type2_value1 * 100) / (dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type2_value2) | round}%

Female${(dataInfo.area2_block1_type2_value2 * 100) / (dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type2_value2) | round}%

Other
Employees

Male${(dataInfo.area2_block1_type3_value1 * 100) / (dataInfo.area2_block1_type3_value1 + dataInfo.area2_block1_type3_value2) | round}%

Female${(dataInfo.area2_block1_type3_value2 * 100) / (dataInfo.area2_block1_type3_value1 + dataInfo.area2_block1_type3_value2) | round}%

Note: Employee types are explained as follows:
Management Personnel：Management Positions
Technical Staff：Non-Management RD Staff
Other Employees：Non-Management Staff

Age
Distribution

51 years old (inclusive)2%

30-50 years old65%

<30 years old32%

Education
Distribution

Master's and PhD1.1%

Bachelor's18.3%

Associate Degree22.3%

High School and Below58.3%

Number and Percentage of New Employees in 2023 by Gender and Age Group（Suzhou Plant）

	Male	Female	Total
<30 years old	${dataInfo.area2_block2_type1_value1}	${dataInfo.area2_block2_type1_value2}	${dataInfo.area2_block2_type1_value1 + dataInfo.area2_block2_type1_value2}
30-50 years old	${dataInfo.area2_block2_type2_value1}	${dataInfo.area2_block2_type2_value2}	${dataInfo.area2_block2_type2_value1 + dataInfo.area2_block2_type2_value2}
50 years old	${dataInfo.area2_block2_type3_value1}	${dataInfo.area2_block2_type3_value2}	${dataInfo.area2_block2_type3_value1 + dataInfo.area2_block2_type3_value2}
Total	${dataInfo.area2_block2_type1_value1 + dataInfo.area2_block2_type2_value1 + dataInfo.area2_block2_type3_value1}	${dataInfo.area2_block2_type1_value2 + dataInfo.area2_block2_type2_value2 + dataInfo.area2_block2_type3_value2}	${dataInfo.area2_block2_type1_value1 + dataInfo.area2_block2_type2_value1 + dataInfo.area2_block2_type3_value1 + dataInfo.area2_block2_type1_value2 + dataInfo.area2_block2_type2_value2 + dataInfo.area2_block2_type3_value2}
New Hire Rate	${(dataInfo.area2_block2_type1_value1 + dataInfo.area2_block2_type2_value1 + dataInfo.area2_block2_type3_value1) * 100 / (dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type3_value1) \| roundToTwo}%	${(dataInfo.area2_block2_type1_value2 + dataInfo.area2_block2_type2_value2 + dataInfo.area2_block2_type3_value2) * 100 / (dataInfo.area2_block1_type1_value2 + dataInfo.area2_block1_type2_value2 + dataInfo.area2_block1_type3_value2) \| roundToTwo}%	${(dataInfo.area2_block2_type1_value1 + dataInfo.area2_block2_type2_value1 + dataInfo.area2_block2_type3_value1 + dataInfo.area2_block2_type1_value2 + dataInfo.area2_block2_type2_value2 + dataInfo.area2_block2_type3_value2) * 100 / (dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type3_value1 + dataInfo.area2_block1_type1_value2 + dataInfo.area2_block1_type2_value2 + dataInfo.area2_block1_type3_value2) \| roundToTwo}%

Number and Percentage of Employees Who Left in 2023 by Gender and Age Group（Suzhou Plant）

	Male	Female	Total
<30 years old	${dataInfo.area2_block3_type1_value1}	${dataInfo.area2_block3_type1_value2}	${dataInfo.area2_block3_type1_value1 + dataInfo.area2_block3_type1_value2}
30-50 years old	${dataInfo.area2_block3_type2_value1}	${dataInfo.area2_block3_type2_value2}	${dataInfo.area2_block3_type2_value1 + dataInfo.area2_block3_type2_value2}
50 years old	${dataInfo.area2_block3_type3_value1}	${dataInfo.area2_block3_type3_value2}	${dataInfo.area2_block3_type3_value1 + dataInfo.area2_block3_type3_value2}
Total	${dataInfo.area2_block3_type1_value1 + dataInfo.area2_block3_type2_value1 + dataInfo.area2_block3_type3_value1}	${dataInfo.area2_block3_type1_value2 + dataInfo.area2_block3_type2_value2 + dataInfo.area2_block3_type3_value2}	${dataInfo.area2_block3_type1_value1 + dataInfo.area2_block3_type2_value1 + dataInfo.area2_block3_type3_value1 + dataInfo.area2_block3_type1_value2 + dataInfo.area2_block3_type2_value2 + dataInfo.area2_block3_type3_value2}
Turnover Rate	${(dataInfo.area2_block3_type1_value1 + dataInfo.area2_block3_type2_value1 + dataInfo.area2_block3_type3_value1) * 100 / (dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type3_value1) \| roundToTwo}%	${(dataInfo.area2_block3_type1_value2 + dataInfo.area2_block3_type2_value2 + dataInfo.area2_block3_type3_value2) * 100 / (dataInfo.area2_block1_type1_value2 + dataInfo.area2_block1_type2_value2 + dataInfo.area2_block1_type3_value2) \| roundToTwo}%	${(dataInfo.area2_block3_type1_value1 + dataInfo.area2_block3_type2_value1 + dataInfo.area2_block3_type3_value1 + dataInfo.area2_block3_type1_value2 + dataInfo.area2_block3_type2_value2 + dataInfo.area2_block3_type3_value2) * 100 / (dataInfo.area2_block1_type1_value1 + dataInfo.area2_block1_type2_value1 + dataInfo.area2_block1_type3_value1 + dataInfo.area2_block1_type1_value2 + dataInfo.area2_block1_type2_value2 + dataInfo.area2_block1_type3_value2) \| roundToTwo}%

	Male	Female	Total
Management Personnel	${dataInfo.area3_block1_type1_value1}	${dataInfo.area3_block1_type1_value2}	${dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type1_value2}
Technical Staff	${dataInfo.area3_block1_type2_value1}	${dataInfo.area3_block1_type2_value2}	${dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type2_value2}
Other Employees	${dataInfo.area3_block1_type3_value1}	${dataInfo.area3_block1_type3_value2}	${dataInfo.area3_block1_type3_value1 + dataInfo.area3_block1_type3_value2}
Subtotal	${dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type3_value1}	${dataInfo.area3_block1_type1_value2 + dataInfo.area3_block1_type2_value2 + dataInfo.area3_block1_type3_value2}	${dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type3_value1 + dataInfo.area3_block1_type1_value2 + dataInfo.area3_block1_type2_value2 + dataInfo.area3_block1_type3_value2}
Percentage (by Gender)	${(dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type3_value1)*100/(dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type3_value1 + dataInfo.area3_block1_type1_value2 + dataInfo.area3_block1_type2_value2 + dataInfo.area3_block1_type3_value2) \| roundToTwo}%	${(dataInfo.area3_block1_type1_value2 + dataInfo.area3_block1_type2_value2 + dataInfo.area3_block1_type3_value2)*100/(dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type3_value1 + dataInfo.area3_block1_type1_value2 + dataInfo.area3_block1_type2_value2 + dataInfo.area3_block1_type3_value2) \| roundToTwo}%	100%

Management
Personnel

Male${(dataInfo.area3_block1_type1_value1 * 100) / (dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type1_value2) | round}%

Female${(dataInfo.area3_block1_type1_value2 * 100) / (dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type1_value2) | round}%

Technical
Staff

Male${(dataInfo.area3_block1_type2_value1 * 100) / (dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type2_value2) | round}%

Female${(dataInfo.area3_block1_type2_value2 * 100) / (dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type2_value2) | round}%

Other
Employees

Male${(dataInfo.area3_block1_type3_value1 * 100) / (dataInfo.area3_block1_type3_value1 + dataInfo.area3_block1_type3_value2) | round}%

Female${(dataInfo.area3_block1_type3_value2 * 100) / (dataInfo.area3_block1_type3_value1 + dataInfo.area3_block1_type3_value2) | round}%

Note: Employee types are explained as follows:
Management Deputy Section Chief and above L20 (inclusive)
Technical Staff：Non-Management RD (e.g., Hardware/Mechanical/Firmware/RF/Safety, etc.)
Other Employees：Other Staff

Age
Distribution

51 years old (inclusive)1%

30-50 years old46%

<30 years old53%

Education
Distribution

Master's and PhD0.1%

Bachelor's5.8%

Associate Degree1.8%

High School and Below92.3%

Number and Percentage of New Employees in 2023 by Gender and Age Group（Vietnam Plant）

	Male	Female	Total
<30 years old	${dataInfo.area3_block2_type1_value1}	${dataInfo.area3_block2_type1_value2}	${dataInfo.area3_block2_type1_value1 + dataInfo.area3_block2_type1_value2}
30-50 years old	${dataInfo.area3_block2_type2_value1}	${dataInfo.area3_block2_type2_value2}	${dataInfo.area3_block2_type2_value1 + dataInfo.area3_block2_type2_value2}
50 years old	${dataInfo.area3_block2_type3_value1}	${dataInfo.area3_block2_type3_value2}	${dataInfo.area3_block2_type3_value1 + dataInfo.area3_block2_type3_value2}
Total	${dataInfo.area3_block2_type1_value1 + dataInfo.area3_block2_type2_value1 + dataInfo.area3_block2_type3_value1}	${dataInfo.area3_block2_type1_value2 + dataInfo.area3_block2_type2_value2 + dataInfo.area3_block2_type3_value2}	${dataInfo.area3_block2_type1_value1 + dataInfo.area3_block2_type2_value1 + dataInfo.area3_block2_type3_value1 + dataInfo.area3_block2_type1_value2 + dataInfo.area3_block2_type2_value2 + dataInfo.area3_block2_type3_value2}
New Hire Rate	${(dataInfo.area3_block2_type1_value1 + dataInfo.area3_block2_type2_value1 + dataInfo.area3_block2_type3_value1) * 100 / (dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type3_value1) \| roundToTwo}%	${(dataInfo.area3_block2_type1_value2 + dataInfo.area3_block2_type2_value2 + dataInfo.area3_block2_type3_value2) * 100 / (dataInfo.area3_block1_type1_value2 + dataInfo.area3_block1_type2_value2 + dataInfo.area3_block1_type3_value2) \| roundToTwo}%	${(dataInfo.area3_block2_type1_value1 + dataInfo.area3_block2_type2_value1 + dataInfo.area3_block2_type3_value1 + dataInfo.area3_block2_type1_value2 + dataInfo.area3_block2_type2_value2 + dataInfo.area3_block2_type3_value2) * 100 / (dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type3_value1 + dataInfo.area3_block1_type1_value2 + dataInfo.area3_block1_type2_value2 + dataInfo.area3_block1_type3_value2) \| roundToTwo}%

Number and Percentage of Employees Who Left in 2023 by Gender and Age Group（Vietnam Plant）

	Male	Female	Total
<30 years old	${dataInfo.area3_block3_type1_value1}	${dataInfo.area3_block3_type1_value2}	${dataInfo.area3_block3_type1_value1 + dataInfo.area3_block3_type1_value2}
30-50 years old	${dataInfo.area3_block3_type2_value1}	${dataInfo.area3_block3_type2_value2}	${dataInfo.area3_block3_type2_value1 + dataInfo.area3_block3_type2_value2}
50 years old	${dataInfo.area3_block3_type3_value1}	${dataInfo.area3_block3_type3_value2}	${dataInfo.area3_block3_type3_value1 + dataInfo.area3_block3_type3_value2}
Total	${dataInfo.area3_block3_type1_value1 + dataInfo.area3_block3_type2_value1 + dataInfo.area3_block3_type3_value1}	${dataInfo.area3_block3_type1_value2 + dataInfo.area3_block3_type2_value2 + dataInfo.area3_block3_type3_value2}	${dataInfo.area3_block3_type1_value1 + dataInfo.area3_block3_type2_value1 + dataInfo.area3_block3_type3_value1 + dataInfo.area3_block3_type1_value2 + dataInfo.area3_block3_type2_value2 + dataInfo.area3_block3_type3_value2}
Turnover Rate	${(dataInfo.area3_block3_type1_value1 + dataInfo.area3_block3_type2_value1 + dataInfo.area3_block3_type3_value1) * 100 / (dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type3_value1) \| roundToTwo}%	${(dataInfo.area3_block3_type1_value2 + dataInfo.area3_block3_type2_value2 + dataInfo.area3_block3_type3_value2) * 100 / (dataInfo.area3_block1_type1_value2 + dataInfo.area3_block1_type2_value2 + dataInfo.area3_block1_type3_value2) \| roundToTwo}%	${(dataInfo.area3_block3_type1_value1 + dataInfo.area3_block3_type2_value1 + dataInfo.area3_block3_type3_value1 + dataInfo.area3_block3_type1_value2 + dataInfo.area3_block3_type2_value2 + dataInfo.area3_block3_type3_value2) * 100 / (dataInfo.area3_block1_type1_value1 + dataInfo.area3_block1_type2_value1 + dataInfo.area3_block1_type3_value1 + dataInfo.area3_block1_type1_value2 + dataInfo.area3_block1_type2_value2 + dataInfo.area3_block1_type3_value2) \| roundToTwo}%

Note 1: The turnover rate is calculated as "the total number of employees who left in the category for the year" / "the total number of employees in the category as of December 31 of the year."
Note 2: Due to the nature of the work, the turnover rate is higher in the plant locations.

Labor-Management Communication

AmTRAN actively establishes communication bridges with employees, creating a positive work environment that allows the company to understand and appropriately address employees' thoughts and concerns. The main communication channels are as follows：

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Human Rights Protection

Additionally, for specific human rights items, AmTRAN's Taiwan headquarters, Suzhou plant, and Vietnam plant have established additional control measures：

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