Abstract Barriers to dog vaccination in Tanzania in general, and in Kilosa District in particular, are not well understood. Therefore, this study sought to address the following research question: What are the multilevel barriers to the vaccination of dogs against rabies in Kilosa District? Guided by a socioecological model, the study explored the barriers to dog vaccination at multiple levels within the Kilosa District context. A case study design employing a qualitative research approach was used. Data were collected through focus group discussions (FGDs) and in-depth interviews to investigate contextual barriers to dog vaccination. The collected data were analyzed using a thematic analysis approach. The results are organized according to the levels of the socioecological model. At the individual level, barriers include limited literacy about rabies and dog rabies vaccination; low perceived risk of rabies transmission; low prioritization of dog vaccination in household financial decisions; and uncertainty regarding the cost of dog vaccination. At the organizational/health system level, barriers include fragmented provision of rabies-related health education, reactive rather than proactive sensitization efforts, and limited communication channels, primarily relying on loudspeaker announcements. At the community level, barriers include misconceptions about protection against rabies and mistrust in political leadership. In conclusion, Rabies vaccination uptake in Kilosa District is constrained by multilevel barriers. Individually, limited knowledge, low risk perception, and financial uncertainty reduce prioritization of dog vaccination. Organizational barriers include fragmented health education, weak cross-sector coordination, and reactive communication. At the community level, logistical challenges, absence of By-Laws, persistent misconceptions, and political mistrust further limit vaccination coverage.
Environmental sciences, Public aspects of medicine
Erik de Lima Andrade, D. C. da Cunha e Silva, Eligelcy Augusta de Lima
et al.
Environmental noise has been growing in recent years, causing numerous health problems. Highly sensitive environments such as hospitals deserve special attention, since noise can aggravate patients’ health issues and impair the performance of healthcare professionals. This work consists of a systematic review of scientific articles describing environmental noise measurements taken in hospitals between the years 2015 and 2020. The researchers started with a consultation of three databases, namely, Scopus, Web of Science, and ScienceDirect. The results indicate that for the most part, these studies are published in journals in the fields of medicine, engineering, environmental sciences, acoustics, and nursing and that most of their authors work in the fields of architecture, engineering, medicine, and nursing. These studies, which are concentrated in Europe, the Americas, and Asia, use as reference values sound levels recommended by the World Health Organization. Leq measured in hospital environments showed daytime values ranging from 37 to 88.6 dB (A) and nighttime values of 38.7 to 68.8 dB (A). Leq values for outdoor noise were 74.3 and 56.6 dB (A) for daytime and nighttime, respectively. The measurements were taken mainly inside hospitals, prioritizing more sensitive departments such as intensive care units. There is a potential for growth in work carried out in this area, but research should also include discussions about guidelines for improvement measures aimed at reducing noise in hospitals.
Designing for sufficiency is one of many approaches that could foster more moderate and sustainable digital practices. Based on the Sustainable Information and Communication Technologies (ICT) and Human-Computer Interaction (HCI) literature, we identify five environmental settings categories. However, our analysis of three mobile OS and nine representative applications shows an overall lack of environmental concerns in settings design, leading us to identify six pervasive anti-patterns. Environmental settings, where they exist, are set on the most intensive option by default. They are not presented as such, are not easily accessible, and offer little explanation of their impact. Instead, they encourage more intensive use. Based on these findings, we create a design workbook that explores design principles for environmental settings: presenting the environmental potential of settings; shifting to environmentally neutral states; previewing effects to encourage moderate use; rethinking defaults; facilitating settings access and; exploring more frugal settings. Building upon this workbook, we discuss how settings can tie individual behaviors to systemic factors.
Global climate warming and air pollution pose severe threats to economic development and public safety, presenting significant challenges to sustainable development worldwide. Corporations, as key players in resource utilization and emissions, have drawn increasing attention from policymakers, researchers, and the public regarding their environmental strategies and practices. This study employs a two-way fixed effects panel model to examine the impact of environmental information disclosure on corporate environmental performance, its regional heterogeneity, and the underlying mechanisms. The results demonstrate that environmental information disclosure significantly improves corporate environmental performance, with the effect being more pronounced in areas of high population density and limited green space. These findings provide empirical evidence supporting the role of environmental information disclosure as a critical tool for improving corporate environmental practices. The study highlights the importance of targeted, region-specific policies to maximize the effectiveness of disclosure, offering valuable insights for promoting sustainable development through enhanced corporate transparency.
Generative AI holds significant potential for ecological and environmental applications such as monitoring, data analysis, education, and policy support. However, its effectiveness is limited by the lack of a unified evaluation framework. To address this, we present the Environmental Large Language model Evaluation (ELLE) question answer (QA) dataset, the first benchmark designed to assess large language models and their applications in ecological and environmental sciences. The ELLE dataset includes 1,130 question answer pairs across 16 environmental topics, categorized by domain, difficulty, and type. This comprehensive dataset standardizes performance assessments in these fields, enabling consistent and objective comparisons of generative AI performance. By providing a dedicated evaluation tool, ELLE dataset promotes the development and application of generative AI technologies for sustainable environmental outcomes. The dataset and code are available at https://elle.ceeai.net/ and https://github.com/CEEAI/elle.
Unsupervised Domain Adaptation~(UDA) focuses on transferring knowledge from a labeled source domain to an unlabeled target domain, addressing the challenge of \emph{domain shift}. Significant domain shifts hinder effective knowledge transfer, leading to \emph{negative transfer} and deteriorating model performance. Therefore, mitigating negative transfer is essential. This study revisits negative transfer through the lens of causally disentangled learning, emphasizing cross-domain discriminative disagreement on non-causal environmental features as a critical factor. Our theoretical analysis reveals that overreliance on non-causal environmental features as the environment evolves can cause discriminative disagreements~(termed \emph{environmental disagreement}), thereby resulting in negative transfer. To address this, we propose Reducing Environmental Disagreement~(RED), which disentangles each sample into domain-invariant causal features and domain-specific non-causal environmental features via adversarially training domain-specific environmental feature extractors in the opposite domains. Subsequently, RED estimates and reduces environmental disagreement based on domain-specific non-causal environmental features. Experimental results confirm that RED effectively mitigates negative transfer and achieves state-of-the-art performance.
Abstract The research on work-life balance of teachers highlights the significant challenges faced by educators in balancing their professional and personal lives. The study emphasizes the evolving role of teachers, the impact of technology, and the increasing demands placed on them. It also underscores the importance of achieving a healthy work-life balance to reduce stress and improve job satisfaction. This study critically examines research papers and articles related to work-life balance of teachers published between 2014–2024 in the Scopus database. Bibliometric analysis was done with the help of a biblioshiny package of Rstusio and VoS viewer. Out of 554 articles, 112 were located after restricting the search to the English language and publications related to social science, psychology, business, management, and economics for the selected time (2014–2024). The research on the work-life balance of teachers has seen significant growth, especially during the COVID-19 pandemic. The findings highlight that the USA in countries, Fokkens-Bruinsma, M. in authors and Frontiers in Psychology in sources have the highest contribution of articles in related field. Key topics include “human,” “teaching,” “work-life balance,” “questionnaire,” and “education. This study provides a comprehensive overview of the research trends, key contributors, and important topics in the field of work-life balance of teachers. Moreover, it also highlights the growing interest in this area and the need for further research to address the challenges faced by educators. The findings suggest that implementing work-life policies and initiatives tailored to the teaching profession can help address these challenges and promote better well-being among teachers. Overall, the study calls for a greater focus on supporting teachers to ensure they can effectively manage their work and personal responsibilities.
Noémie Letellier, Caitlin G. Jones-Ngo, Michael W. Cheung
et al.
Background: As the health burden attributable to wildfire activity increases under climate change, it is crucial to determine which subgroups face heightened vulnerability to wildfire smoke. Marginalized communities may experience disproportionate risk from overlapping individual and community vulnerability factors. We leverage recent developments in machine learning methods for high-dimensional settings to construct detailed profiles of California communities disproportionately impacted by wildfire smoke across 27 potential effect modifiers. Methods: We used daily 2006–2019 data on hospital admissions and emergency department (ED) visits for cardio-respiratory diseases in California. We applied a time-stratified case-crossover study design to analyze the effect of wildfire-specific fine particulate matter (PM2.5) on cardio-respiratory diseases. Then, we investigated heterogeneous effects using a generalized random forest approach, which can handle a large set of individual-level (age, sex, race/ethnicity) and area-level (e.g., poverty level, racial/ethnic segregation) factors to construct vulnerability profiles for each Air Basin, representing areas with similar meteorological and geographic conditions. Results: A 10 µg/m3 increase in wildfire PM2.5 concentration (2-day moving average) was associated with an increased risk of hospital admissions and ED visits related to respiratory diseases (OR = 1.014, 95 % confidence interval = 1.012–1.016). No association was found for cardiovascular diseases. Associations between exposure to wildfire PM2.5 and respiratory diseases varied strongly by individual- (age, sex, race/ethnicity) and area-level factors (such as A/C prevalence, Black/White dissimilarity index). The importance of these effect modifiers, and vulnerability profiles, changed across Air Basins. Conclusions: Machine learning can characterize the complex heterogeneity in wildfire smoke-related health impacts.
Hasanah Hasanah, Wulandari wulandari, Afriani afriani
et al.
Porphyridium cruentum has advantages in pigments and exopolysaccharides, so it has the potential to be developed in the food, pharmaceutical, nutraceutical, and cosmetic industries. Porphyridium cruentum cultivation media uses synthetic and indoors, causing high costs. This study aims to determine the growth performance of Porphyridium cruentum in different mediums and method of cultivation. The research methods used cultivating Porphyridium cruentum for 7 days with four treatments, namely indoors Guillard media (IGM), outdoors Guillard media (OGM), indoors fertilizer media (IFM), and outdoors fertilizer media (OFM. Each treatment had three replications. Growth was identified using an optical density spectrophotometer uv-vis, gravimetrically dried weight of biomass and pH adjustment during 7 days of cultivation. The result shows the best growth performance in IFM, followed by IGM, OFM, and OGM. Fertilizer media is better for the growth of Porphyridium cruentum than Guillard media. pH growth Porphyridium cruentum during cultivation 7-7.5. Indoor cultivation is better than outdoor cultivation. The highest biomass was IFM, which had the best performance in IFM with OD 0.247, pH 7, and biomass 79.6%.
Katharine Hopkins, Annarita Antelmi, Jakob Dahlin
et al.
Fibromyalgia is a chronic syndrome characterized by pain, fatigue, and cognitive disturbances. The increased prevalence of contact allergy to gold in individuals with fibromyalgia when compared with the general population has previously been described. Gold contact allergy can manifest as a systemic contact dermatitis, with cutaneous and extracutaneous manifestations presenting upon systemic administration of gold. This study aimed to establish whether gold allergy is of significance in the fibromyalgia population. Prior to patch testing with the Swedish baseline series and an extended dental series, 119 females with fibromyalgia answered questionnaires including details of past medical history, dental history, and previous cutaneous and mucous membrane intolerance to metals. Prevalence of allergy to gold sodium thiosulphate (2.0% and 5.0%) was 33.6% (40 individuals). There was a statistically significant overrepresentation of gold allergy among individuals who experienced cutaneous symptoms upon direct contact with gold (p = 0.010). Contact allergy to gold was more frequent among patients with oral symptoms (p = 0.024). This study demonstrates concordance between reported cutaneous symptomatology related to gold exposure and gold allergy in the fibromyalgia population. Whether individuals with oral symptoms and gold allergy have objective oral clinical findings and relevant gold exposure is the focus of ongoing study.
Even if the Periodic Table of Chemical Elements is relatively well defined, some controversial terms are still in use. Indeed, the term “heavy metal” is a common term used for decades in the natural sciences, and even more in environmental sciences, particularly in studies of pollution impacts. As the use of the term appears to have increased, we highlight the relevance of the use of the term “Potentially Toxic Element(s)”, which needs more explicit endorsement, and we illustrate the chemical elements that need to be considered.
Kyriakos Flouris, Anna Volokitin, Gustav Bredell
et al.
The autoencoder model typically uses an encoder to map data to a lower dimensional latent space and a decoder to reconstruct it. However, relying on an encoder for inversion can lead to suboptimal representations, particularly limiting in physical sciences where precision is key. We introduce a decoder-only method using gradient flow to directly encode data into the latent space, defined by ordinary differential equations (ODEs). This approach eliminates the need for approximate encoder inversion. We train the decoder via the adjoint method and show that costly integrals can be avoided with minimal accuracy loss. Additionally, we propose a $2^{nd}$ order ODE variant, approximating Nesterov's accelerated gradient descent for faster convergence. To handle stiff ODEs, we use an adaptive solver that prioritizes loss minimization, improving robustness. Compared to traditional autoencoders, our method demonstrates explicit encoding and superior data efficiency, which is crucial for data-scarce scenarios in the physical sciences. Furthermore, this work paves the way for integrating machine learning into scientific workflows, where precise and efficient encoding is critical. \footnote{The code for this work is available at \url{https://github.com/k-flouris/gfe}.}
Christopher Tyler Cox, Jakob Haynes, Christopher Duffey
et al.
Understanding of the formation and evolution of the Solar System requires understanding key and common materials found on and in planetary bodies. Mineral mixing and its implications on planetary body formation is a topic of high interest to the planetary science community. Previous work establishes a case for the use of Optical PhotoThermal InfraRed (O-PTIR) in planetary science and introduces and demonstrates the technique's capability to study planetary materials. In this paper, we performed a measurement campaign on granular materials relevant to planetary science, such as minerals found in lunar and martian soils. These laboratory measurements serve to start a database of O-PTIR measurements. We also present FTIR absorption measurements of the materials we observed in O-PTIR for comparison purposes. We find that the O-PTIR technique suffers from granular orientation effects similar to other IR techniques, but in most cases, is is directly comparable to commonly used absorption spectroscopy techniques. We conclude that O-PTIR would be an excellent tool for the purpose of planetary material identification during in-situ investigations on regolith and bedrock surfaces.