This paper aims at exploring the local background of and solutions to the forest conflict in upland areas inhabited by ethnic minorities, who are called hill tribes, in northern Thailand. A so-called hill tribe problem has been officially identified as a result of the slash-and-burn cultivation and other perceived problems, such as opium poppy cultivation, illegal immigration, and the suspicion of disloyalty to the state. This has created distrust and tension between the groups and authorities. The local conflict has recently been related to the dilemma of conserving the forest from all human interference, while many people live and make their livelihood within and adjacent to the protected areas. Furthermore, as the results imply, strictly protected areas and reforestation have also increased the competition over land and natural resources and, thereby, the likelihood of local conflicts. The scarcity and pollution of water, illegal logging, and poor fire control have contributed to the conflicts between local communities. The conflicts between the local communities and officials have been nourished by political and public discussions. Using definitions and terms with negative connotations and ignoring the heterogeneity between the groups or labeling some groups as malevolent have increased distrust and strengthened existing stereotypical images. Conflict resolution starts with efforts toward better mutual understanding, and changes in structures and attitudes are necessary. Local cooperation, utilization of traditional methods, and local institutions are central to conflict solving.
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http://dx.doi.org/10.1007/s00267-008-9239-7 | DOI Listing |
Medicine (Baltimore)
January 2025
Department of Otolaryngology, Hangzhou Red Cross Hospital (Zhejiang Hospital of Integrated Traditional Chinese and Western Medicine), Hangzhou, Zhejiang, China.
T-helper 17 (Th17) cells significantly influence the onset and advancement of malignancies. This study endeavor focused on delineating molecular classifications and developing a prognostic signature grounded in Th17 cell differentiation-related genes (TCDRGs) using machine learning algorithms in head and neck squamous cell carcinoma (HNSCC). A consensus clustering approach was applied to The Cancer Genome Atlas-HNSCC cohort based on TCDRGs, followed by an examination of differential gene expression using the limma package.
View Article and Find Full Text PDFNurs Health Sci
March 2025
School of Nursing and Health, Zhengzhou University, Zhengzhou, People's Republic of China.
To explore the chain mediating role of social support and trust in between decision self-efficacy and decision conflict of stroke caregivers. Convenient sampling was used to select stroke caregivers who were admitted to the department of neurology, neurosurgery and rehabilitation in a four-3A hospital in Henan Province from September 2023 to April 2024. General information questionnaire, Decisional Self-efficacy Scale, Social Support Scale, Wake Forest Physician Trust Scale and Decisional Conflict Scale were adopted.
View Article and Find Full Text PDFArch Microbiol
January 2025
Andaman and Nicobar Regional Centre, Botanical Survey of India, Haddo, Port Blair, 744102, India.
During recent survey for the investigation of foliar fungi in Kerala, India, a new species of foliicolous hyphomycete, Paramyrothecium kamalii was discovered on living leaves of Matourea azurea (Plantaginaceae) based on morpho-cultural characteristics and multigene (ITS, LSU, cmdA, tub2, and rpb2) phylogenetic analysis; is described, illustrated and discussed. In vitro Pathogenicity tests were performed and confirmed the pathogenic nature of the fungus, thereby fulfilling Koch's postulates. Phylogenetically, P.
View Article and Find Full Text PDFTheor Appl Genet
January 2025
State Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China.
Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic architecture of complex traits in other crops, its application in cotton has been limited. Here, we applied five machine learning models-AdaBoost, Gradient Boosting Regressor, LightGBM, Random Forest, and XGBoost-to identify loci associated with fiber quality and flowering days in cotton.
View Article and Find Full Text PDFAm J Dermatopathol
February 2025
Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC; and.
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