Objective: To evaluate the potential effects of a parent-mediated basic skills training program on developmental milestones among autistic preschool children receiving care at a public child psychosocial care center in Brazil. Methods: We conducted a non-randomized controlled pilot study including children with autism spectrum disorder aged 2-6 years and their primary caregivers (n = 18). Families were allocated to an intervention group (parent skills training; n = 9) or a comparison group receiving usual care (n = 9), seeking balance by child age, race/skin color, family income, and caregiver education. Developmental milestones were assessed using the Operant Portage Inventory at baseline (T0) and after 6 months (T1) across five domains (socialization, language, cognition, self-care, and motor). Within-group change was tested with Wilcoxon signed-rank tests and between-group differences with Mann-Whitney tests. Group & times; time effects were examined using two-way repeated-measures ANOVA. Effect sizes were estimated with Hedges' g. Results: In the intervention group, all developmental domains improved over follow-up (all p <= 0.009). At T1, the intervention group scored higher than the comparison group in all domains (all p <= 0.005), with large between-group effect sizes (Hedges' g=1.62-2.42). The comparison group improved significantly only in language (T0 = 1.32 vs T1 = 1.53; p = 0.036). Group & times; time interactions were strongest for socialization (partial eta 2=0.823), cognition (eta 2=0.799), and language (eta 2=0.740). Conclusions: Parent-mediated training delivered within a public child mental health service was associated with developmental gains in autistic preschoolers. These preliminary findings suggest caregiver-mediated interventions as a strategy to expand access to evidence-based care in lowresource public settings.
Urban expansion in subtropical metropolitan regions intensifies land use/land cover (LULC) transformations and alters land surface temperature (LST) dynamics, directly affecting urban climate resilience and environmental sustainability. This study evaluates historical LULC and LST changes (1995–2017) and predicts future scenarios (2028–2039) in Porto Alegre, southern Brazil, using Landsat imagery integrated with a Cellular Automata–Artificial Neural Network (CA-ANN) model implemented in the MOLUSCE platform. LULC maps were generated using supervised Maximum Likelihood Classification. Model validation achieved an overall accuracy of 77.6
Purpose Despite the growing discussion of sustainable business model innovation (SBMI) in theory and practice, the specific sustainability dimensions driving it remain unclear. This study conducts a bibliometric review to map literature on SBMI, identifying thematic trends, key clusters and the field’s evolution while examining what dimensions of sustainability (social, environmental and economic) are prioritized. Design/methodology/approach A bibliometric performance and network analysis was done using articles retrieved from Scopus and Web of Science. A total of 3,437 papers were analyzed using SciMAT and Python. Findings Results show a predominance of the environmental over the social and economic dimensions. The study discovered a strategic diagram depicting 24 clusters related to SBMI, such as Circular Economy, Servitization, Health Care Delivery and Artificial Intelligence. It also presents the thematic evolution structure of SBMI literature in four sub-periods: 1999–2010, 2011–2015, 2016–2020 and 2021–2025. Each subperiod has specifics on the subject. Differences in the sustainability dimensions adopted in the SBMI of developed and developing countries were identified. Practical implications A sustainability maturity matrix is proposed as a benchmarking tool. It can assist managers in assessing a company’s SBMI focus on social, environmental and economic dimensions to pinpoint gaps and advance their maturity level to create, deliver and capture sustainable value. Originality/value The article stresses that SBMI research should deeply explore social sustainability, whose potential to alleviate social discrepancies and injustices can significantly contribute to nations’ sustainable development. This gap opens future research and practice opportunities, detailed in a proposed research agenda.
This study explores smart technology adoption in industrial settings by focusing on the Industrial Internet of Things (IIoT). Despite the rapid growth of IIoT research, there is still a lack of understanding of how different factors work together to influence IIoT adoption, which, in turn, motivates the present study to fill that gap. Our goal, therefore, is to identify and investigate the factors driving IIoT implementation. To do so, we conducted a meta-analysis that synthesizes data from 92 studies, encompassing 391 effect sizes across diverse sectors. We also crafted a theoretical framework that integrates seven foundational theories, namely, diffusion of innovation (DOI) theory, motivation-opportunity-ability (MOA) theory, theory of planned behavior (TPB), technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), technology-organization-environment (TOE) theory, and value-based adoption model (VAM), alongside seven moderators, including continent, domain, firm size, gross domestic product (GDP), industry, period, and systems. In doing so, our findings reveal the impact of more than 20 factors spread across these theories on IIoT adoption. More specifically, we discovered a stronger inclination toward IIoT in manufacturing than retailing and other sectors, larger enterprises than small and medium enterprises (SMEs), post-2018 than pre-2018 period, and industrial than environmental domain. Overall, these insights can equip academics and practitioners alike with a more comprehensive understanding of IIoT backed by a rich pool of meta-analytic evidence.
Salinity has an important effect on stratification in regions affected by large river freshwater inputs. These places show the establishment of the Barrier Layer, which isolates the mixed layer from a deeper layer. As a result, in areas with high vertical salinity stratification, the pycnocline becomes shallower than the thermocline, affecting plankton distribution. This study aims to assess the influence of the low salinity plume from the Amazon River on upper-ocean stratification over the last 30,000 years. Planktonic foraminiferal assemblages from sediment core AMZ-405 were analyzed to reconstruct hydrographic changes in the western tropical Atlantic Ocean during this period. Our findings demonstrate that stratification increased during the Heinrich Stadial 1 event due to a decrease in sea surface salinity, coupled to a simultaneous increase in Amazon River discharge. In addition, the Amazon plume is a source of fertilization in the photic zone, influencing trophic dynamics. The relative abundances of Neogloboquadrina dutertrei, Neogloboquadrina incompta, and Globigerinita glutinata have been previously used to infer changes in mixed layer depth through the RN/Gg proxy. However, G. glutinata primarily relies on diatoms as a food source. Consequently, plume-induced diatom blooms alter relative species abundances, indicating that fertilization interferes with the original interpretation proposed for the proxy. Our results highlight the importance of the Amazon plume in regulating planktonic foraminifera assemblages through both stratification changes and fertilization. This role emphasizes the significance of investigating the connection between low salinity and water column stratification in areas where Barrier Layer formation occurs.
