Cambodia Development Resource Institute

The type of AI used to design video game enemies greatly affects the gameplay experience of speed, difficulty, and enjoyment. In most cases, the majority of developers who create 2D platforming games will choose to implement a simple but efficient AI design over an advanced AI model that learns based on experience. One of these simpler AI models that is frequently utilized by 2D platforming game developers is the Finite State Machine (FSM) model. The FSM model creates an organization of the enemy's actions into a limited number of well-defined behaviours, while also indicating how these behaviours relate to one another. We look at how AI uses the FSM method in the 2D platform game "Kirby: Nightmare in Dreamland," which first came out on the Game Boy Advance. The analysis of FSM models enemy AI behaviors and how those behaviors change and when they do so affects how hard the game is. Simulated experiments were conducted on how state time is spread out and how to make things harder by changing the attack cooldown. The results show that FSM-based AI is easy to control, doesn't need a lot of processing power, and has behavior that can be predicted. This makes it a good choice for platform games that are easy to get into. The results show that FSMs are still important in AI research and game design today.

Our world is rapidly urbanizing, while 2.5 billion people are projected to shift from rural to urban areas by 2050, with close to 90% occurring in Asia and Africa. In Southeast Asia, particularly, the Phnom Penh capital city of Cambodia is experiencing this rapid urbanization, facing significant challenges in improving the quality of urban life and achieving social sustainability. Hence, this research aims to assess this capital on social sustainability dimensions to find out the strong and weak points of its 14 districts in order to reveal the improvement potential. The research developed and applied an urban social sustainability index based on national development priorities, SDG11, the New Urban Agenda, and other SDGs that related to human wellbeing and social inclusiveness. The AHP was used to prioritize indicators to develop a priority index, while the standard score was used to apply the index to assess the 14 districts of Phnom Penh. The data for this index application were sourced from Phnom Penh’s commune database. The results showed that the highest-scoring district for urban social sustainability was Chamkarmon, followed by Boeng Keng Kang and Doun Penh. The findings revealed that Prek Pnov was weak in income generation and welfare, while Kamboul was weak in gender inclusion compared to other districts. Prampir Makara was strong, and Sen Sok was weak in resilience to vulnerability. Boeng Keng Kang was strong in welfare and sanitation, while Kamboul was weak in sanitation. Doun Penh was strong, and Mean Chey was weak in water supply.

Climate change has negatively affected agricultural productivity, threatening the incomes and livelihoods of rural farmers. These pressures have led to both permanent and temporary migration. Migration trends are often internal or international, and their patterns vary significantly between men and women. Drawing on primary data from 377 migrant individuals across Kratie, Mondulkiri, and Stung Treng provinces, this paper explores the relationship between climate-related impacts and migration using the Exposure-Sensitivity- Adaptability (ESA) framework, with an emphasis on the gender dimension of vulnerability that induces migration. The findings show that climate hazards are a major driving force, with women being more likely than men to migrate when affected. Key factors include regional household differences, farmland size, and migration type. To address the impacts of climate-induced migration, the paper proposes policy recommendations involving ensuring legal and social protections, promoting climate-resilient agricultural practices, focusing on large-farm households, implementing community awareness programmes on climate adaptation, providing women’s leadership training, and improving infrastructure and livelihood diversification, especially in Stung Treng province.

ABSTRACT Understanding how actors perceive and anticipate future states of the world is gaining traction in climate change governance scholarship and related calls for sustainability transformations. However, smallholder farmers, indigenous groups, and local communities, who are expected to bear disproportionate burdens of loss and damage from climate change, remain underrepresented in future‐oriented research. In this paper, we outline key barriers to integrating bottom‐up perspectives on visions of the future in sustainability transformation research and develop a framework to support transdisciplinary research to more productively engage with diverse perspectives on agrarian‐climate futures. The framework is derived from (a) literature on climate change, loss and damage, anticipatory climate governance, and critical agrarian studies; (b) interviews, focus group discussions, and a stakeholder workshop in Ratanakiri province, Cambodia; and (c) an analysis of Cambodian climate change policies. It consists of five dimensions: (i) desirability; (ii) viability; (iii) agency; (iv) heuristics; and (v) responsibility. The framework aims to support researchers in better accounting for complex rural‐agrarian realities and reorienting future framingsfield from loss towards nurturing a “brighter” side of transformation. Integrating diverse rural‐agrarian perspectives on climate futures is critical for fostering and co‐creating more just and locally contingent pathways.

Context Rapid agrarian changes in north-west Cambodia have led to extensive deforestation for upland crop cultivation. Aims This study assessed the impact of four land-use types on soil chemical and biological properties and indicators of soil health. Methods The four land uses were fallow (16 years since forest conversion), maize–cassava (14 years), mango (16 years), and mango–cassava intercropping (15 years). Soil organic carbon (C) and nitrogen (N) stocks, chemical properties, and macro-/meso-fauna were measured at four depths. Soil health indicators were assessed at 0–10 cm: permanganate oxidisable carbon (POXC), aggregation, water infiltration, and visual evaluation of soil structure. Key results Agricultural lands showed lower topsoil C than fallow, with mango–cassava intercropping exhibiting a C depletion rate of −1.42 Mg C ha−1 y−1. Significant N depletion occurred under maize–cassava and mango–cassava (−250 and −260 kg N ha−1 y−1, respectively). Maize–cassava had a higher C/N ratio due to lower N concentrations. Water aggregate stability and POXC were significantly lower under mango–cassava and maize–cassava. Soil fauna abundance and richness were reduced in agricultural lands. Conclusions The forest conversion to agriculture negatively impacted topsoil C (38% loss), labile C (35–70% loss), soil aggregation, and soil fauna. Implications These findings highlight the need to evaluate the costs of soil fertility depletion and explore and promote alternative management practices such as conservation agriculture and agroforestry, alongside financial mechanisms for soil restoration.

The increasing frequency and intensity of flooding, driven by climate change, are having a detrimental impact on Phnom Penh’s urban ecosystem. Disaster managers and decision-makers increasingly recognize the value of tools that can directly translate flood hazards—specifically, the extent of inundation during flood events—into estimates of expected socio-economic impacts, including affected populations and economic losses. While previous studies have often focused on broad-scale flood impacts at local or regional levels, this paper presents a straightforward approach for estimating impacts at a finer, more localized scale within the affected area, using flood hazard data and damage functions. This study aims to assess flood inundation areas and the direct economic losses resulting from flood events in the southern and southwestern parts of Phnom Penh, the capital city of Cambodia. The methodology involves applying Google Earth Engine (GEE) with Sentinel-1 synthetic aperture radar (SAR) data to assess flooding, including the generation of flood extent and depth maps. These maps are then used as input for an impact assessment module, which translates the estimated flood extents and depths into quantitative assessments of socio-economic impacts. The results indicate that the inundation areas in the southern and southwestern urban areas of Phnom Penh reached 1,638 ha, with flood depths ranging from 0.5 to 6 m, during the October 2020 flood event. The total estimated economic damage was approximately USD 20 million. Agricultural areas were the most affected, followed by residential and commercial areas. This study provides a valuable tool for rapid assessment of urban flood impacts, offering crucial data on flood extent, depth, and economic consequences for urban policymakers and planners.

Artificial Intelligence (AI) has transformed systems that support data-driven decision-making processes and added new functionalities and capabilities in different sectors, such as fisheries and health. Data bias in AI systems can produce results that influence decision-making and could even cause already-existing disparities. The ripple effect of data bias on AI-enabled decision-making was examined in this study. This study was conducted by using the IBM 360 AI Fairness toolkit (AIF 360) and traditional machine learning models to analyse Diabetes and Fisheries data to know the bias, skewness, and risk factors. Our research presented empirical findings that emphasise the need for diverse and representative datasets to use AI decision-making to ensure equitable and sustainable outcomes and to avoid inequalities and injustices in the results. The results revealed that the accuracy of datasets increased from 82 to 89% and from 51 to 72% after using the reweighted algorithm of the AIF 360 tool, which balanced the bias to avoid misdiagnosis and misallocation of resources. This research was conducted on the relevance of obtaining balanced and constituency-balanced data to create AI systems that would be able to make effective decisions with fairness whilst serving the needs of diverse groups. Future research should focus on developing tools to mitigate bias while improving decision-making.

Pendidikan memainkan peran penting dalam kehidupan anak, yang bertujuan untuk membantu mereka mengembangkan kemampuan melalui pembelajaran di kelas. Motivasi adalah faktor kunci dalam mencapai tujuan pendidikan, yang secara signifikan mempengaruhi kinerja siswa, termasuk mereka yang berkebutuhan khusus dalam pendidikan inklusif. Di Indonesia, kebijakan pendidikan inklusif memastikan kesempatan pendidikan yang setara bagi semua, termasuk anak-anak berkebutuhan khusus. Penelitian ini mengeksplorasi bagaimana keterampilan kreatif dapat meningkatkan motivasi belajar anak berkebutuhan khusus di SLB Negeri 1 Pagaruyung. Dengan menggunakan metode kualitatif, penelitian ini mengamati interaksi kelas dan mewawancarai guru untuk memahami metode dan tantangan pengajaran yang efektif. Temuan menunjukkan bahwa kegiatan kreatif, seperti kerajinan tangan dan puisi, secara signifikan meningkatkan motivasi dan kepercayaan diri pada siswa berkebutuhan khusus. Guru memainkan peran penting dalam mengidentifikasi dan memelihara bakat siswa melalui dukungan berkelanjutan dan penguatan positif. Studi ini menekankan pentingnya pendekatan pengajaran yang inovatif dan lingkungan belajar yang mendukung dalam menumbuhkan motivasi dan pertumbuhan pribadi di antara anak-anak berkebutuhan khusus. Wawasan ini berkontribusi untuk mengembangkan strategi yang efektif untuk pendidikan inklusif, memastikan semua anak dapat mencapai potensi penuh mereka.

Abstract This study analyses industry‐specific training programmes catering to the garment industry in Bangladesh, Cambodia and Sri Lanka. Employing a historical‐institutionalist perspective and using mainly qualitative data, it focuses on programmes that are considered particularly relevant by the industry and discuss their characteristics as well as factors that have had an impact on their development. The article finds that such programmes are usually offered beyond formal upper secondary vocational education and training, mostly at postsecondary and tertiary level, and in many cases by private providers. However, the case of Sri Lanka shows that public investment in the training of employees at different skill levels can make an important contribution to the development of a high‐skill formation regime—but that it depends on the right timing.