Poverty mapping is increasingly important for monitoring Sustainable Development Goal 1 (SDG 1) of the United Nations 2030 Agenda, which aims to end poverty in all its forms everywhere. Yet timely and fine-resolution poverty estimation remains difficult because conventional census- and survey-based approaches are costly, infrequent, and often sparse precisely where deprivation is most severe. As poverty emerges from complex socioeconomic systems shaped by human mobility, social interactions, infrastructure, and economic activities, emerging computational methods and nontraditional data sources have created new opportunities for poverty estimation and mapping. At the intersection of statistical physics, complex systems science, and data science, these approaches enable poverty estimation at finer spatial and temporal resolutions. This review summarizes the main concepts of poverty and the principal frameworks used to measure it, and examines recent advances in poverty estimation and mapping using satellite imagery, mobile phone data, social media data, and multisource data fusion. The review also discusses persistent challenges related to representativeness, transferability across regions, interpretability, and uncertainty quantification. Finally, the review clarifies both the analytical promise and the practical limits of contemporary poverty mapping.
This study investigates the influence of disinfection procedures on the surface chemistry, mechanical integrity and in vitro behavior of Trogamid® CX-based orthodontic hybrid brackets. Brackets were fabricated by injection molding onto micro-structured nickel free stainless-steel inserts and subjected to three disinfection procedures: UV-C irradiation, 70% ethanol immersion or steam autoclaving prior to surface analysis and in vitro testing. Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and atomic force microscopy (AFM) with force spectroscopy were employed to assess changes in the polymer's chemical structure, morphology, and mechanical response. In vitro biocompatibility and bacterial adhesion were also investigated. FTIR spectra indicated that UV irradiation weakened the amide hydrogen-bond network without forming new degradation bands, whereas ethanol disinfection and autoclaving were associated with a reorganization of the hydrogen-bond network. AFM showed a progressive reduction in surface roughness from UV irradiation to ethanol disinfection to autoclaving. Force spectroscopy revealed a lower elastic modulus after UV irradiation (1.2 vs 1.9 GPa for untreated brackets) and higher moduli after ethanol disinfection and autoclaving (2.3 and 2.4 GPa). Streptococcus mutans colonization did not differ significantly between untreated and treated brackets, and UV-disinfected hybrid brackets showed no cytotoxic effect on human gingival cells, comparable to stainless-steel brackets. Overall, none of the treatments left the bracket surface unchanged: UV disinfection best preserved the original topography but softened the surface layer, whereas ethanol and autoclave disinfection smoothed and stiffened the surface, with ethanol causing the smallest change in surface chemistry. The choice of procedure for in vitro studies should therefore be guided by the surface property most relevant to the intended investigation.
Accurately evaluating the annual energy performance of buildings integrated with indirect green façades (IGFs) is challenging because of the coupled interactions among vegetation, cavity airflow, and building heat transfer. This study developed a coupled resistance–capacitance (RC) energy model by adding vegetation and cavity-air nodes to a conventional building RC framework and reformulating the corresponding energy balances. The model’s thermal response was experimentally validated against summer field measurements in Shanghai, with CV-RMSE values below 5.00% for wall-surface and free-running indoor temperatures. Annual heating and cooling predictions for the bare building were benchmarked against EnergyPlus. The model was subsequently applied to investigate the hourly thermal responses of the experimental model buildings during typical weather weeks and to assess the annual energy-saving performance of IGFs across representative building prototypes in Shanghai. Results showed that IGFs exerted a pronounced thermal buffering effect, reducing maximum wall-surface temperatures by 5.43 °C during the day and elevating them by up to 0.75 °C at night in summer. The annual simulations predicted cooling energy reductions of up to 4.44% and heating penalties of up to 1.50%, yielding net annual energy savings of 0.34%–1.44% across all cases. Increasing LAI from 1 to 3 increased the estimated annual total energy-saving rate by approximately 22% to 108%, depending on the building prototype. Furthermore, a regression analysis between wall heat flux differentials and energy savings revealed that older buildings were 3.90 to 7.60 times more sensitive to wall heat flux variations than newer ones.
Monitoring biodiversity in agricultural landscapes is essential for addressing global biodiversity decline. Emerging remote sensing and artificial intelligence technologies enable large-scale, low-impact and cost-effective assessments. This chapter provides a concise review of remote sensing applications for farmland biodiversity monitoring, structured around the Essential Biodiversity Variable framework. We examine plants, animals, fungi and microorganisms, and their habitats in productive and semi-natural areas. Spaceborne and airborne sensors effectively assess agroecosystem structure and functioning, and detect plants and large mammals with distinct spectral or phenological traits. UAV imagery and camera traps offer higher resolution for monitoring smaller or cryptic species. Remote sensing also supports indirect methods, such as species distribution modeling, to infer the occurrence of species that are difficult to detect. Key challenges include technological, methodological and operational limitations. Integrating multi-platform data, establishing shared benchmarks, and broadening taxonomic coverage, supported by policy incentives and farmer-friendly technologies, are essential for scaling biodiversity monitoring and promoting sustainable strategies that reconcile conservation with agricultural production.
Alexithymia, a personality trait marked by difficulties identifying and describing feelings and an externally oriented thinking style, is frequently linked to interpersonal problems and psychopathology. Drawing on the extended process model of interpersonal emotion regulation (IER), we conducted two ecological momentary assessment studies in Germany (N = 128) and in the United States (N = 220) to examine how alexithymia shapes processes of responding to others’ emotions, a potential mechanism underlying social difficulties. Across 5,552 reported social interactions, alexithymia and its dimensions were associated with less emotionally attuned and more emotionally distant IER goals and strategies. Notably, cultural and sample differences emerged, suggesting context may modulate observed associations. In Study 1 (Germany), higher overall alexithymia and externally oriented thinking were linked to disengagement from others’ emotions. In Study 2 (U.S.), alexithymia predicted greater use of counter-hedonic goals and problem-focused strategies along with reduced use of relationship-oriented strategies such as validation and soothing. Disengagement and engagement tendencies varied across alexithymia dimensions and cultural contexts, but both studies indicated a consistent pattern of reduced emotional attunement in IER. These findings extend existing research by showing that alexithymia not only affects self-regulation but also significantly shapes how individuals respond to others’ emotions. IER difficulties may represent a key mechanism linking alexithymia to interpersonal problems and psychopathology. Interventions may benefit from explicitly targeting IER skills, particularly by promoting hedonic regulation goals and relationship-oriented strategies.
