Explore the vast range of titles available.

Emerging economies, low- and middle-income countries experiencing rapid population and GDP growth, face the challenge of improving their living standards while stabilizing CO 2 emissions to meet net-zero goals. In this study, we quantify the CO 2 emissions required for achieving decent living standards (DLS) in emerging economies. The results show that, compared to other regions, achieving DLS in emerging Asian and African economies will result in more additional CO 2 emissions, particularly in the DLS indicators of Mobility and Electricity. Achievement of DLS in emerging economies will result in 8.6 Gt of additional CO 2 emissions, which should not jeopardize global climate targets. However, a concerning trend arises as more than half of the emerging economies (62 out of 121) will face substantial challenges in aligning their expected emission growth for achieving DLS with their national emission mitigation targets. Achieving decent living standards for global emerging economies is estimated to lead to an additional 8.6 Gt of CO 2 emission with more than half of emerging economies emitting additional CO 2 more than the value of their emission reduction commitments

Solar home systems (SHSs) are successfully addressing energy access deficits across the globe, particularly when combined with pay-as-you-go (PAYG) payment models, allowing households to pay for energy services in small instalments. To increase energy access, it is vital to understand the PAYG SHS customer journey in depth. To aid this, the paper presents unique data from active customers, consisting of structured interviews (n = 100) and two focus groups (n = 24) across two districts in Rwanda. These results are presented under a novel customer journey framework, which describes all the individual stages a customer might experience, including awareness and understanding, purchase, usage, upgrade, recommendation and retaining or switching energy source. The paper reveals that the customer journey is non-linear and cyclical in nature, acknowledging that a household operates in a social network within which they could influence or be influenced by others. It also highlights the growing importance of SHS recommendations in raising awareness of SHSs, pointing to the shifts in the off-grid energy market environment where customer awareness no longer appears to be a main adoption barrier.

Different methods to incorporate research training during residency are suggested, however, long-term impact is not studied well. This study reports development of a research curriculum with milestones, a long-term outcome and sustainability, and its impact on the overall departmental research culture. The research curriculum that included a research seminar for resident preparation, annual milestones, and structured research mentoring was implemented in our hybrid program in 2012. The research output for five-year period before and after the implementation was evaluated as peer-reviewed publications, presentations, and grant submissions. Further, secondary effects on faculty and medical student research was evaluated. Following implementation, we observed a significant increase in the number of resident presentations (p < 0.05) and higher trends for publications and grant submissions. Medical student research increased significantly in terms of both presentations and publications (p < 0.05). Consequently, we observed a significant improvement in the overall department research productivity. Our resident research curriculum was associated with improved long-term research productivity. It allowed residents to work closely with faculty and medical students leading to more collaboration resulting in an enhanced scholarly environment. •Structured research curriculum and milestones could be implemented and are sustainable over a long period of time.•Proposed curriculum increases surgery residents’ research outcomes and enhances their overall research experiences.•It could become a foundation for improved collaboration among residents, faculty, and students leading to a cultural shift.

Off-grid technologies, such as solar home systems (SHS), offer the opportunity to alleviate global energy poverty, providing a cost-effective alternative to an electricity grid connection. However, there is a paucity of high-quality SHS electricity usage data and thus a limited understanding of consumers’ past and future usage patterns. This study addresses this gap by providing a rare large-scale analysis of real-time energy consumption data for SHS customers (n = 63,299) in Rwanda. Our results show that 70% of SHS users’ electricity usage decreased a year after their SHS was installed. This paper is novel in its application of a three-dimensional convolutional neural network (CNN) architecture for electricity load forecasting using time series data. It also marks the first time a CNN was used to predict SHS customers’ electricity consumption. The model forecasts individual households’ usage 24 h and seven days ahead, as well as an average week across the next three months. The last scenario derived the best performance with a mean squared error of 0.369. SHS companies could use these predictions to offer a tailored service to customers, including providing feedback information on their likely future usage and expenditure. The CNN could also aid load balancing for SHS based microgrids.

Trauma continues to be the leading cause of death and disability in the U.S. for those under the age of 44, making it a prominent public health problem. Recent literature suggests that geographical maldistribution of Trauma Centers (TCs), and the resultant increase of the access time to the nearest TC, could impact patient safety and increase disability or mortality. To address this issue, we introduce the Trauma Center Location Problem (TCLP) that determines the optimal number and location of TCs in order to improve patient safety. We model patient safety through a surrogate measure of mistriages, which refers to a mismatch in the injury severity of a trauma patient and the destination hospital. Our proposed bi-objective optimization model directly accounts for the two types of mistriages, system-related under-triage (srUT) and over-triage (srOT), both of which are estimated using a notional tasking algorithm. We propose a heuristic based on the Particle Swarm Optimization framework to efficiently derive a near-optimal solution to the TCLP for realistic problem sizes. Based on 2012 data from the state of Ohio, we observe that the solutions are sensitive to the choice of weights for srUT and srOT, volume requirements at a TC, and the two thresholds used to mimic EMS decisions. Using our approach to optimize that network resulted in over 31.5% reduction in the objective with only 1 additional TC; redistribution of the existing 21 TCs led to 30.4% reduction.

Background Limited work has been done in predicting discharge disposition in trauma patients; most studies use single institutional data and have limited generalizability. This study develops and validates a model to predict, at admission, trauma patients’ discharge disposition using NTDB, transforms the model into an easy-to-use score, and subsequently evaluates its generalizability on institutional data. Methods NTDB data were used to build and validate a binary logistic regression model using derivation-validation (ie, train-test) approach to predict patient disposition location (home vs non-home) upon admission. The model was then converted into a trauma disposition score (TDS) using an optimization-based approach. The generalizability of TDS was evaluated on institutional data from a single Level I trauma center in the U.S. Results A total of 614 625 patients in the NTDB were included in the study; 212 684 (34.6%) went to a non-home location. Patients with a non-home disposition compared to home had significantly higher age (69 ± 19.7 vs 48.3 ± 20.3) and ISS (11.2 ± 8.2 vs 8.2 ± 6.3); P < .001. Older age, female sex, higher ISS, comorbidities (cancer, cardiovascular, coagulopathy, diabetes, hepatic, neurological, psychiatric, renal, substance abuse), and Medicare insurance were independent predictors of non-home discharge. The logistic regression model’s AUC was 0.8; TDS achieved a correlation of 0.99 and performed similarly well on institutional data (n = 3161); AUC = 0.8. Conclusion We developed a score based on a large national trauma database that has acceptable performance on local institutions to predict patient discharge disposition at the time of admission. TDS can aid in early discharge preparation for likely-to-be non-home patients and may improve hospital efficiency.

Perception of a surgeon based on physical attributes in the operating room (OR) environment has not been assessed, which was our primary goal. A common OR scenario was simulated using 8 different actors as a lead surgeon with combinations of age (<40 vs. >55), race (white vs. black), and gender (male vs. female). One video scenario with a survey was electronically distributed to surgeons, residents, and OR nurses/staff. The overall rating, assessment, and perception of the lead surgeon were assessed. Of 974 respondents, 64.5% were females. There were significant differences in the rating and assessment based upon surgeon's age (p = .01) favoring older surgeons. There were significant differences in the assessments of surgeons by the study group (p = .03). The positive assessments as well as perceptions trended highest towards male, older, and white surgeons, especially in the stressful situation. While perception of gender bias may be widespread, age and race biases may also play a role in the OR. Inter-professional education training for OR teams could be developed to help alleviate such biases. •In addition to widespread phenomenon of gender bias, age bias may also play a role in the operative environment.•Perception towards the lead surgeon changes in operating room especially when situation gets stressful.•Assessment and perception of the surgeon from fellow surgeons vs. OR staff varies significantly; staff being more negative.

The aim of this paper is to highlight limits in the current conceptualisation and implementation of urban Green Infrastructure (GI), particularly in informal settlements. We propose a Multi-Level Perspective (MLP) that helps analyse and identify opportunities to overcome such limits. The article starts by discussing the concept of GI and proposes its definition through the principles of multifunctionality, interlinkages and exchange. Recognising current gaps in implementation in the context of informal settlements, we argue for the better understanding of the range of socio-political conditions which enable or impede GI practices. To reflect on these gaps, the article uses MLP to explore persisting socio-ecological-infrastructural problems in water management, which could be perpetuated through current GI practices. MLP is used as a heuristic framework to analyse influencing factors that exist at multiple interconnected societal and bio-physical levels. The framework is applied to the city of São Paulo in Brazil where traditional water management has resulted in tensions between social and ecological systems between the regime (which encompasses institutional structures) and the niche (where innovations emerge, for example through grassroots movements). Examples of community initiatives are used that demonstrate a disconnection between top-down structures and everyday practices. We conclude that if GI presents the potential to support a transition towards water management that benefits both social and ecological systems, further characterisation of the concept is required.

Background Despite clear linkages between poor Water, Sanitation, Hygiene (WASH) and enteric disease, the design of effective WASH interventions that reduce child enteric infections and stunting rates has proved challenging. WASH factors as currently defined do not capture the overall exposure factors to faecal pathogens through the numerous infection transmission pathways. Understanding the multiple and multifaceted factors contributing to enteric infections and their interconnectedness is key to inform future interventions. This study aimed to perform an in-depth holistic exploration of the environmental, socio-cultural, economic and institutional context surrounding infants to develop an integrated understanding of enteric infection drivers in rural tribal Banswara, in Rajasthan State, India. Methods This study relied on the triangulation of mixed-methods to capture critical influences contributing to infant enteric infection transmission. We conducted structured observations and exploratory qualitative research across 9 rural tribal villages, including transect walks, household observations, interviews with frontline health workers and group discussions with mothers. The emergent social themes and identified factors were mapped based on the scale of agency (individual, family or community-level factor) and on their nature (environmental, socio-cultural, economic and institutional factors). Results Infants aged 5 to 24 months were seen to have constant exposures to dirt via mouthing of soil, soiled hands, soiled objects and food. Rudimentary household environments with dirt floors and domestic animals lacked a hygiene-enabling environment that hindered hygienic behaviour adoption. Several unsafe behaviours failing to interrupt infants’ exposures to pathogens were captured, but caregivers reported a lack of self-efficacy skills to separate children from faecal exposures due to the rural farming environments where they lived. Conceptual mapping helped understand how wider-level societal factors such as socio-economic limitations, caste inequalities, and political corruption may have trickle-down effects on the caregivers’ motivation and perceived self-efficacy for improving hygiene levels around children, highlighting the influence of interconnected broader factors. Conclusions Conceptual mapping proved useful to develop an integrated understanding of the interlinked factors across socio-ecological levels and domains, highlighting the role of wider sociocultural, economic and institutional factors contributing to infant’s enteric infection risks. Future WASH interventions are likely to require similar integrated approaches that account for the complex factors at all levels.