The management of subterranean termite pests remains a major challenge in Southeast Asia, where these pests cause significant structural and economic damage. Termite baiting has emerged as an effective option to conventional soil termiticides, offering a safer pest management approach with reduced chemical input into the environment. In this paper, we review the history of termite research in Southeast Asia, highlighting the turning points of termite research, from agriculture and plantations to buildings and structures, and the transformative impact of termite baiting on the pest management industry in the region over the last 25 yr. We also discuss the outcome of a survey of pest management professionals on their baiting practices, bait performance, and reinfestation rates. All bait products eliminated termite colonies. There were significant differences in terms of the baiting period to colony elimination, with Xterm outperforming Sentricon, Exterra, and Exterminex. Above-ground (AG) baiting was preferred over in-ground (IG) baiting due to construction constraints and low IG station interception rates. While bait effectively controlled Coptotermes spp., challenges persist in managing fungus-growing termites such as Macrotermes gilvus Hagen. Reinfestation occurred in < 10% of baited premises.

This study aims to construct essential information on the pests attacking Cnidium officinale Makino, which is one of the most important medicinal plants in Korea and neighboring countries. Based on the current survey, a total of 12 species were identified, including three above-ground pests attacking flowers, leaves, and stems, as well as ten soil pests attacking roots. In the vertical distribution of damaged roots, the dominant species is bulb mite (Rhizoglyphus robini) followed by onion maggot (Delia antiqua). Based on this study and the previous literature, the total number of species of pests reported to attack C. officinale is 36, including 3 on flowers, 16 on leaves, 6 on stems, and 11 on roots. We also investigated and compiled a list of natural enemies based on all available information and the current study, totaling 14 species. Parasitus sp., Macrocheles glaber, and Smicroplectrus sp. were identified as candidate natural enemies of root pests.

The European earwig F. auricularia L. is an omnivore that has only recently been identified as a direct, fruit-feeding pest of citrus. Here, we start to build the basic tools needed for integrated pest management for this species. We introduce a time-efficient sampling method based on small wooden boards placed on the ground, and we use them in a 2-yr survey of 93 commercial citrus blocks in California's San Joaquin Valley. Insecticides were not applied targeting F. auricularia in any of these citrus blocks. We find that F. auricularia populations are very low or undetectable in most blocks, with higher densities occurring only sporadically. To know when control measures should be implemented, we used video-monitoring of citrus tree trunks to characterize the timing of F. auricularia movement from their soil nests into the tree canopy. Movement of earwigs along the tree trunks was observed throughout our sampling period (22 March to 18 June), suggesting that control measures (sticky bands placed on trunks, or insecticides applied to trunks and surrounding soil surface) should be applied early, well before petal fall when fruit are susceptible to F. auricularia herbivory. Sticky barriers effectively reduced the vertical movement of 2 crawling arthropods, F. auricularia and the Fuller rose beetle Napactus godmanni, along citrus trunks. We failed to find any relationship between estimated F. auricularia densities and damage to maturing or harvested fruit. This highlights a set of important and still unresolved questions about the biology of this species, underscoring the need for additional research.

Revegetation following human-induced damage to vegetation is now a common phenomenon in many ecologically fragile areas around the globe. However, more attention has been paid to climate and ecological engineering factors as influences on the effectiveness of vegetation restoration, while the extent to which socioeconomic factors influence vegetation restoration remains a question that has not been clearly answered. In this study, socio-economic data were obtained through field and household surveys, and then the extent to which socio-economic factors influence the effects of vegetation restoration and their mechanisms of action were assessed using a generalized linear mixed effects model, a partial least squares variable projection significant indicator approach, and a partial least squares path modeling approach. It was found that among the socioeconomic factors, variables such as percentage of cars, conservation awareness, and agricultural practices significantly influenced vegetation restoration (the R2 values are 0.21, 0.15 and 0.15). In terms of importance analysis, economic factors ranked first in terms of importance, followed by psychological factors, agricultural system factors, cultural factors, and natural factors in that order. From the comprehensive impact analysis, economic factors, cultural factors, and agricultural system factors positively affect vegetation recovery (the path coefficients are 0.26, 0.06 and 0.08), and psychological factors negatively affect vegetation recovery (the path coefficient is -0.31). To summarize, in addition to ecological engineering, the remaining socio-economic factors are also important and cannot be ignored for their influence on the effectiveness of vegetation restoration.

Permafrost, a major component of the cryosphere, is undergoing rapid degradation due to climate change, human activities, and other external disturbances, profoundly impacting ecosystems, hydroclimate, engineering geological stability, and infrastructure. In Northeast China, the thermal dynamics of Xing'an permafrost (XAP) are particularly complex, complicating the accurate assessment of its spatial extent. Many earlier mapping efforts, despite significant progress, fall short in accounting for some key local geo-environmental factors. Thus, this study introduces a new approach that incorporates four key driving factors-biotic, climatic, physiographic, and anthropogenic-by integrating multisource datasets and in situ observations. Four machine learning (ML) models [random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGB)] are applied to simulate permafrost distribution and probability, as well as to evaluate their performance. The results indicate that models' accuracy, ranked from highest to lowest, is as follows: RF (area under the curve (AUC) =0.88 and accuracy =0.81), XGB (0.86 and 0.77), LR (0.81 and 0.73), and SVM (0.76 and 0.66), with RF emerging as the most effective model for permafrost mapping in Northeast China. Analysis of the relationships between predictors and permafrost occurrence probability (POP) indicates that vegetation and snow cover exert nonlinear effects on permafrost, while human activities significantly reduce POP. Additionally, finer soil textures and higher soil organic matter content are positively correlated with increased POP. The modeling results, combined with field survey data, also show that permafrost is more prevalent in lowlands than in uplands, confirming the symbiotic relationship between permafrost and wetlands in Northeast China. This spatial variation is influenced by local microclimates, runoff patterns, and soil thermal properties. The primary sources of model error are uncertainties in the accuracy of multisource datasets at different scales and the reliability of observational data. Overall, ML models demonstrate great potential for mapping permafrost in Northeast China.

Relevance. Engineering-geological surveys are an integral part of mining operations for various purposes. The quality of soil core sampling has an important impact on the results of engineering geological surveys. At the same time, obtaining a frozen rock core is complicated by an increase in the bottomhole zone temperature, which arises as a result of drilling. As the temperature rises, the physical and mechanical properties of frozen soils change, which leads to a transformation of the mechanism of their destruction and an increase in the likelihood of drilling emergencies. A core obtained under conditions of rising temperature does not allow for a reliably accurate assessment of the properties and structure of soils in their natural conditions. Therefore, there is a need to develop technological and technical means that help maintain the temperature regime of a rock mass under mechanical effect on it. The analysis of the conditions of core drilling in frozen rocks showed that, along with technological reasons, the design of the rock-cutting tool affects the increase in bottom-hole temperature. The article reveals the dependence of the temperature change at well bottom when drilling on the design features of the core rock-cutting tool. Aim. To study the impact of the design features of a drilling core tool on the nature of destruction of frozen soils, represented by loose sedimentary rocks as the most susceptible to changes in physical and mechanical properties with increasing temperature. The study was based on frozen soils that make up the of Yakutia, a large industrial region that requires frequent geotechnical surveys for its development. Objects. Core drilling tool design, mechanism of frozen rocks destruction, conditions for core sampling in frozen soils. Methods. Analytical method, experimental method, production test method. Results. The authors have determined the main directions for the development of core tools for high-quality core sampling in frozen soils. They derived the dependence of the magnitude of the temperature increase at well bottom on the orientation and size of the cutters reinforcing the rock-cutting tool.

Climate change is causing significant damage to crop production in the central plateau zone of Rwanda, particularly affecting sorghum, food, and the incomes of smallholder farmers. Understanding farmers' perceptions and the factors impacting their responses is crucial for improving sorghum production policies and programs. Therefore, a study was conducted to assess sorghum farmers' perceptions of climate change and the factors determining their adaptation strategies. A multistage sampling method and a cluster random selection were utilized to select 345 respondents from five districts of the study area. The data were analyzed using descriptive statistics and a multivariate probit model. The results showed that 98.8% of farmers were aware of climate change, with deforestation being the main anthropogenic activity causing it. Consequently, 95.7% and 84.3% of farmers experienced grain yield reductions, and over 20 sorghum varieties disappeared. To address these impacts, farmers adopted five adaptation strategies: early maturing sorghum varieties (67%), adjusting planting dates (50.1%), drought-tolerant varieties (46.7%), soil conservation practices (38.3%), and crop diversification (32.8%). The multivariate probit model results showed the age and literacy level of the household head, access to extension services, access to information, access to credit, farming experience, and land size as the important factors influencing at least one of the climate change adaptation strategies. The study concluded that sorghum farmers are aware of the impacts of climate change and are acting to address its negative effects. The results suggest that the government and stakeholders should support farmers in strengthening their adaptation strategies for sustainable sorghum production.

As terrestrial resources and energy become increasingly scarce and advancements in deep space exploration technology progress, numerous countries have initiated plans for deep space missions targeting celestial bodies such as the Moon, Mars, and asteroids. Securing a leading position in deep space exploration technology is critical, and ensuring the successful completion of these missions is of paramount importance. This paper reviews the timelines, objectives, and associated geotechnical and engineering challenges of recent deep space exploration missions from various countries. Extraterrestrial geotechnical materials exist in unique environments characterized by special gravity, temperature, radiation, and atmospheric conditions, and are subject to disturbances such as meteoroid impacts. These factors contribute to significant differences from terrestrial geotechnical materials. Based on a thorough literature review, this paper investigates the transformation of geomechanical properties of extraterrestrial geological materials due to the distinctive environmental conditions, referred to as the four unique characteristics and one disturbance, and their distinct formation processes. Considering current deep space mission plans, the paper summarizes the geotechnical challenges and research advancements addressing specific mission requirements. These include unmanned exploration and in-situ mechanical testing, construction of extreme environment test platforms, the mechanical properties of geotechnical materials under extreme conditions, the interaction between engineering equipment and geotechnical materials, and the in-situ utilization of extraterrestrial geotechnical resources. The goal is to support the successful execution of China's deep space exploration missions and to promote the development of geomechanics towards extraterrestrial geomechanics.

Ouagadougou, the capital city of Burkina Faso, is facing significant economic and social damages due to recurring floods. This study aimed to develop a flood susceptibility map for Ouagadougou using a logistic regression (LR) model and 14 flood conditioning factors, including elevation, slope, aspect, profile curvature, plan curvature, topographic position index (TPI), topographic roughness index (TRI), flow direction, topographic wetness index (TWI), distance to river, rainfall, land use/land cover (LULC), normalized difference vegetation index (NDVI) and soil type. A historical flood inventory map was created from household survey data, identifying 1026 flooded sites which were divided into a training dataset (70%) and a validation dataset (30%). The factors that had a statistically significant influence (p-value 1.96) at the 95% confidence level were, in order of importance, elevation, distance to river, rainfall, plan curvature and NDVI. The receiver operating characteristic (ROC) curve method was used to validate the model. The area under the curve (AUC) values of the model were 81% for the prediction rate and 82% for the success rate indicating its effectiveness in identifying areas susceptible to flooding. The results showed that 18.48% of the city is very high susceptible to flooding, 18.99% has high susceptibility, 18.43% has moderate susceptibility, and 19.98% and 24.18% have low and very low susceptibility, respectively. This research provides valuable information for policy makers to develop effective flood prevention and urban development strategies.

The Earth is currently experiencing severe economic and social consequences as a result of frequent floods. This study is crucial for effective risk management and mitigation, protecting lives and property from potential flood damage in the Deme watershed. This study endeavors to assess the efficacy of a logistic regression model in generating a flood susceptibility map for the Deme watershed in Ethiopia. Fourteen factors contributing to flooding were considered, including digital elevation model, slope, aspect, profile curvature, plane curvature, Topographic Position Index (TPI), Topographic Roughness Index (TRI), flow direction, Topographic WetnessIindex (TWI), distance to the river, rainfall, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), and soil type. The receiver operating characteristic (ROC) curve method was employed to validate the model. The area under the curve (AUC) values for the model were determined to be 81% for the training dataset and 82% for the validation dataset, indicating its effectiveness in delineating flood-prone areas. The findings revealed that 18% of the watershed is very highly susceptible to flooding, 19% exhibits high susceptibility, 18% shows moderate susceptibility, while 20 and 24% have low and very low susceptibility, respectively. This research provides insights into comprehensive flood prevention and urban development strategies. HIGHLIGHTS center dot Flood susceptibility is determined by historical flood patterns and their influencing factors. center dot Logistic regression can be used to map flood-susceptible areas in a small watershed. center dot A multicollinearity test is necessary to ensure a linear relationship in flood conditioning factors. center dot Factors with high multicollinearity should be removed from models to improve prediction accuracy.