Bamboo is one of the noble plant species in Ethiopia. Household (HH) income and construction role of highland bamboo (Oldeania alpina (K. Schum.) Stapleto) stands were assessed at Masha district, Southern Ethiopia. Three peasant associations (PAs), Yepo, Yina and Gada, 7–15 key informants and 68, 46, 31 households, respectively were interviewed about the cost and income of bamboo to compare with woody climbers, honey, and mushroom in 2021. Bamboo was one of the main sources of income in all PAs, at least for fencing or house construction. In Yepo, Yina and Gada bamboo accounts 0.7%, 28.1%, 16.3% of the HH NTFP income, respectively. The local people responded that bamboo constructed houses and fences were durable for 15–30 and 2–10 years, respectively. In constructing a 2.44–4.27 m radius local house in Yepo, Yina and Gada 2.4–6 m³, 4.1–5.82 m³ and 3.1–4.3 m³ bamboo culms were harvested at 15, 20, and 30 years interval, respectively by each HH. Bamboo young shoots were also seasonally used for food. Although bamboo provides multiple uses, like substitute for wood and environmental services, it was facing different problems of deforestation. Therefore, policy attention is highly important for bamboo sustainable utilization.

Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learning under connectionism to improve the intelligent level of cartography. This paper discusses three problems about the proposition of “deep learning + cartography”. One is the consistency between the deep learning method and the map space problem solving strategy, based on gradient descent, local correlation, feature reduction and non-linear nature that answer the feasibility of the combination of “deep learning + cartography”; the second is to analyze the challenges faced by the combination of cartography from its unique disciplinary characteristics and technical environment, involving the non-standard organization of map data, professional requirements for sample establishment, the integration of geometric and geographical features, as well as the inherent spatial scale of the map; thirdly, the entry points and specific methods for integrating map making and map application into deep learning are discussed respectively.

In order to understand the finishing effect of Waterborne Acrylic Paint under different painting methods and amount, bamboo-laminated lumber for furniture was coated with waterborne acrylic paint, then the effects of different painting methods and amount on the drying rate, smoothness, hardness, adhesion and wear resistance of the paint film were investigated. Further, the mechanism of film formation was described by thermal property analysis using thermogravimetry and differential scanning calorimeter. The results show that different painting methods have little effect on film properties, the drying time of primer and topcoat are not affected by them, which is 8/8.5 min for primer surface/solid and 6.5/7 min for topcoats. The film surface hardness and adhesion can reach B and 0 grade, the best wear resistance of the film is 51.24 mg·100 r⁻¹ when using one-layer primer one-layer topcoat. Different coating amount has great influence on film properties, the drying speed of the film increases with the increase of the painting amount. The film properties reach the best when the painting amount is 80 g/m², while too little painting amount leads to the decrease of hardness, and too much leads to the wear resistance weaken. Thermal analysis of the primer and topcoat show that water decomposition occurs at 100 ℃ and thermal decomposition of organic components occur at 350 ℃. Topcoats have better thermal stability than primers higher than that of topcoat, the topcoat displayed better thermal stability than the primer.

This study aims to elucidate the digital transformation process in Tunisian companies, identify its driving factors, and explain its key success factors. We examine a sample of 70 companies across various economic sectors using a Multinomial Logistic regression to assess the impact of digital strategy, corporate culture, and leadership on digital transformation success. The dependent variable “digital maturity” is categorized into low, medium, and high, with medium serving as the reference category. The results indicate a significant and positive effect of digital strategy on digital transformation success. Leadership influences companies at a low level of digital maturity but does not significantly impact those at a high maturity level. Corporate culture does not significantly affect digital transformation. Digital strategy is crucial for the success of digital transformation in Tunisian companies, while leadership plays a role primarily at lower maturity levels. Corporate culture, however, does not significantly contribute to digital maturity. The study provides insights for Tunisian companies and policymakers to focus on developing robust digital strategies and leadership qualities to enhance digital transformation efforts. This research expands the theoretical base on digital transformation in the Tunisian context, identifying critical success factors and barriers, and confirming the significant role of digital strategy in successful digital transformations.

This study scrutinizes the allocation of financial aid for climate change adaptation from OECD/DAC donors, focusing on its effectiveness in supporting developing countries. With growing concerns over climate risks, the emphasis on green development as a means of adaptation is increasing. The research explores whether climate adaptation finance is efficiently allocated and what factors influence OECD/DAC donor decisions. It examines bilateral official development assistance in the climate sector from 2010 to 2021, incorporating climate vulnerability and adaptation indices from the ND-GAIN Country Index and the IMF Climate Risk Index. A panel double hurdle model is used to analyze the factors influencing the financial allocations of 41,400 samples across 115 recipient countries from 30 donors, distinguishing between the decision to select a country and the determination of the aid amount. The study unveils four critical findings. Firstly, donors weigh a more comprehensive range of factors when deciding on aid amounts than when selecting recipient countries. Secondly, climate vulnerability is significantly relevant in the allocation stage, but climate aid distribution does not consistently match countries with high vulnerability. Thirdly, discerning the impact of socio-economic vulnerabilities on resource allocation, apart from climate vulnerability, is challenging. Lastly, donor countries’ economic and diplomatic interests play a significant role in climate development cooperation. As a policy implication, OECD/DAC donor countries should consider establishing differentiated allocation mechanisms in climate-oriented development cooperation to achieve the objectives of climate-resilient development.