This study analyses the dynamic development of soybean (Glycine max (L.) Merr.) breeding in Russia, particularly examining its historical development, status, and future predictions. With the global demand for vegetable protein rising, understanding Russia’s potential contribution becomes crucial. This research provides valuable insights, offering precise data that may be unfamiliar to international researchers and the private sector. The authors trace the history of soybean selection in Russia, emphasizing its expansion from the Far East to other regions in Russia. The expansion is primarily attributed to the pioneering work of Soviet breeder V. A. Zolotnitsky and the development of the soybean variety in the Amur region in the 1930s. The study highlights the main areas of soybean variety originators, with approximately 40% of foreign varieties registered. The Krasnodar and Amur regions emerge as critical areas for breeding soybean varieties. In Russia, the highest yield potential of soybeans is in the Central Federal District. At the same time, the varieties registered in the Volga Federal District have higher oil content, and the Far Eastern Federal District has high protein content in the registered soybean varieties. The research outlines the state’s pivotal role in supporting soybean breeding and fostering a competitive market with foreign breeders. The study forecasts future soybean breeding development and the main factors that can influence the industry.

Against the backdrop of anti-globalization rhetoric, this paper summarizes our joint book entitled Going Beyond Aid (Lin and Wang, 2017a) and discusses the prospects for development finance in the broad context of Belt and Road Initiative (BRI). Based on the New Structural Economics (Lin, 2010; 2011), here we focus on China’s demonstrated comparative advantages in infrastructure, e.g. in hydropower and high-speed railways (HSR). In addition, long-term orientation (LTO) and patient capital are latent comparative advantages that many Asian economies possess, and are critical for the Belt and Road Initiative. Only if these comparative advantages are utilized can these economies cooperate to potentially achieve win-win.

This research implements sustainable environmental practices by repurposing post-industrial plastic waste as an alternative material for non-conventional construction systems. Focusing on the development of a recycled polymer matrix, the study produces panels suitable for masonry applications based on tensile and compressive stress performance. The project, conducted in Portoviejo and Medellín, comprises three phases combining bibliographic and experimental research. Low-density polyethylene (LDPE), high-density polyethylene (HDPE), and polypropylene (PP) were processed under controlled temperatures to form a composite matrix. This material demonstrates versatile applications upon cooling—including planks, blocks, caps, signage, and furniture (e.g., chairs). Key findings indicate optimal performance of the recycled thermoplastic polymer matrix at a 1:1:1 ratio of LDPE, HDPE, and PP, exhibiting 15% deformation. The proposed implementation features 50 × 10 × 7 cm panels designed with tongue-and-groove joints. When assembled into larger plates, these panels function effectively as masonry for housing construction, wall cladding, or lightweight fill material for slab relieving.

Prepolymers containing isocyanates must be prevented from curing when exposed to moisture, which can be achieved by blocking the isocyanate groups with a suitable agent. The study carefully examines several blocking agents, including methyl ethyl ketoxime (MEKO), caprolactam, and phenol, and concludes that methyl ethyl ketoxime is the best choice. Spectroscopic and thermal analyses, as well as oven curing studies, are conducted with various blocking agents and isocyanate prepolymer to castor oil ratios, revealing MEKO to be the most effective blocking agent which gets unblocked at higher temperatures.

This paper provides insight into innovation energy, its five working mechanisms, and innovative work behaviour (IWB). Although human energy is often mentioned as an important factor in theories about motivation, it is still an unexplored theme in literature. The management of organisations often focuses on the innovation content and neglects the process aspects. Strategic and operational HRM involvement is needed to realising the essential conditions for the innovation energy of innovative employees. An abductive case study on innovation energy took place in five educational departments of one academy at Saxion University of Applied Sciences in the Netherlands. We interviewed 21 innovating lecturers and their five team leaders individually and organised five focus groups with a total of 17 team members. Innovation energy converts individual innovation properties (creativity, psychological empowerment, and optimism) into IWB. Organisations must pay attention to these properties and four other working mechanisms (autonomy, teamwork, leadership, and external contacts) that influence this conversion process. HRM professionals should be involved with innovation processes to realise the right conditions for innovation energy, together with line management. The construct of innovation energy with five working mechanisms gives more insight into the IWB process from the perspective of the engaged employee with IWB. This research contributes to the body of knowledge on IWB, (human) innovation energy, and engagement in relation to HRM.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.