Time Investment Research Articles

Current experience and future potential of facilitating access to digital NHS primary care services in England: the Di-Facto mixed-methods study.

Current National Health Service policy in England encourages enhanced digital access in primary care service provision. In this study, we investigate 'digital facilitation' - that range of processes, procedures and personnel which seeks to support National Health Service primary care patients in their uptake and use of online services. Identify, characterise and explore the potential benefits and challenges associated with different models of digital facilitation currently in use in general practice which are aimed at improving patient access to online services in general practice in England. Use the resulting intelligence to design a framework for future evaluations of the effectiveness and cost effectiveness of such interventions. Explore how patients with mental health conditions experience digital facilitation and gauge their need for this support. Observational mixed-methods study (literature review, surveys, ethnographic observation and interviews); formal synthesis of findings. General practice in four regions of England. Practice survey: 156 staff. Patient survey: 3051 patients. Mental health survey: 756 patients. General practitioner patient survey: 3 million responders. Ethnographic case-studies: 8 practices; interviews with 36 staff, 33 patients and 10 patients with a mental health condition. Stakeholder interviews: 19 participants. Digital facilitation as undertaken in general practice. Patient and practice staff reported use of, and views of, digital facilitation. Surveys, qualitative research; national General Practitioner Patient Survey (2019-22). Scoping-review methodology applied to academic and grey literature published 2015-20. While we did find examples of digital facilitation in routine practice, these often involved using passive or reactive modes of support. The context of COVID, and the necessary acceleration (at that time) of the move to a digital-first model of primary care, shaped the way digital facilitation was delivered. There was lack of clarity over where the responsibility for facilitation efforts lay; it was viewed as the responsibility of 'others'. Patients living with mental health conditions had similar needs and experiences regarding digital facilitation to other patients. The context of the COVID pandemic placed limitations on the project. Fewer practices responded to the practice survey than anticipated; reconfiguration of general practices to support COVID measures was a key consideration during non-participant observation with social distancing and other measures still in place during fieldwork. Digital facilitation, while not a widely recognised concept, is important in supporting the move to a National Health Service with enhanced digital opportunities and enhanced digital access. General practice staff are allocating resources to provide such efforts in general practices in England. The establishment of clear lines of responsibility, the development of digital tools and platforms that work for patients and practice staff, and investment in staff time and training are needed if digital facilitation is to support the intended digital revolution. We did not find one single dominant or preferred model of digital facilitation which might reasonably be considered to form the basis of an intervention to be tested. Rather, there is a need to co-develop such an intervention with patients, general practice staff and relevant policy experts. We outline a framework for a future evaluation of such an intervention. This study is registered as ResearchRegistry6523 (www.researchregistry.com/browse-the-registry#home/?view_2_search=Di-Facto&view_2_page=1) and PROSPERO CRD42020189019 (www.crd.york.ac.uk/prospero/display_record.php?RecordID=189019). This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR128268) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 32. See the NIHR Funding and Awards website for further award information.

Impact of Parental Time Investment on Child Development among Dual Income Family

This cross-sectional study investigates how early childhood development is impacted by parental time commitment in the Horizon Hill dual-income family community. This study employs quantitative method approach to examine how changes in parental engagement dynamics affect children's behavior, mental health, and cognitive functioning both before and after the intervention. The results demonstrate that families have a great involvement rate, with 10 out of 11 demonstrating a strong dedication to enhancing their children's developmental experiences through the program's seamless integration into their daily routines. The results also show that advances in children's cognitive, social, emotional, and behavioral dimensions positively correspond with higher parental investment. These findings highlight how important parental participation is in helping Horizon Hill neighborhood children have great early experiences. This study has ramifications that go beyond improving child well-being and encouraging good parenting techniques through community-based interventions. By comprehending how parental time investment affects child development, policymakers and practitioners can create focused initiatives that enable parents to create caring environments that support their kids' development and success. This study adds to our understanding of the dynamics of parent-child relationships and emphasizes how important it is to spend quality time raising children to promote positive developmental outcomes.

A real options-based framework for multi-generation liquid air energy storage investment decision under multiple uncertainties and policy incentives

Liquid Air Energy Storage (LAES) is a promising energy storage technology renowned for its advantages such as geographical flexibility and high energy density. Comprehensively assessing LAES investment value and timing remains challenging due to uncertainties in technology costs and market conditions. This study introduces a real options-based framework to evaluate investments in multi-generation LAES systems in China's power market by considering uncertain factors, such as investment cost and electricity price. Additionally, the impact of various incentives, including subsidies and preferential taxation policies, on LAES investment is investigated. The proposed investment decision framework is applied in a case study of a 10 MW-class multi-generation LAES system. Results show that the current economic environment is insufficient to trigger immediate LAES investment. The recommended optimal investment times are 2029 for Guangdong, 2031 for Jiangsu, and 2036 for Beijing and Qinghai without incentives. The levelized cost of storage at the optimal investment time is 0.105-0.174$/kWhe, and the optimal investment value is 882-9269k$. It is also found that preferential taxation policies can increase the LAES investment value. Increasing investment subsidies and technology learning effects have limited impacts on stimulating investments. In contrast, discharge subsidies are more favorable for promoting the deployment of LAES systems.

Comparative study of foundry patterns created using conventional methods and additive manufacturing

Pattern is a very vital tool in the foundry industry, and the traditional method of pattern creation using wood or metal working tools is time-consuming, labor-intensive, and prone to quality variations. This study explores additive manufacturing (3d-printing) as an effective alternative to the conventional pattern making process. Two patterns design were fabricated using 3d printing (Fused Deposition Modelling (FDM) technology) and conventional process and they were compared in terms of their dimensional accuracy, lead time, and cost investment. The result showed that the patterns produced using additive manufacturing have higher dimensional accuracy, lower costs and lead-times, suggesting that the integration of additive manufacturing into the foundry industry could substantially improve production efficiency and quality.

Comparative Study of Machine Learning Based Diabetes Predictive System

Diabetes is one of the most lethal diseases in the world. It is also a precursor to various other disorders such as coronary failure, blindness, and kidney diseases. Patients often need to visit diagnostic centers to get their reports after consultation, which requires a significant investment of time and money. However, with the growth of machine learning methods, we now have the ability to address this issue. Advanced systems utilizing information processing can forecast whether a patient has diabetes or not. Furthermore, early prediction of the disease can provide patients with critical interventions before it fully develops. Data mining techniques can extract hidden information from large datasets of diabetes-related information. The aim of this research is to develop a system that can predict the diabetic risk level of a patient with higher accuracy. The model development is based on classification methods such as K-Nearest Neighbors, Decision Tree, and Support Vector Machine (SVM) algorithms. For K-Nearest Neighbors, the models achieve an accuracy of 71%, 78% for SVM, and 70% for the Decision Tree algorithm. The outcomes demonstrate a significant accuracy of these methods.

Effect of parenting centers on caregiver mental health ——Evidence from a large scale randomized controlled trial in rural China

In the context of economic development, the escalating prevalence of depression, anxiety and stress poses substantial challenges, not only impacting individuals' physical and mental well-being but also exerting a profound influence on future human capital. This study conducts an exploratory analysis of the effects of a center-based early childhood development intervention on the mental health of the caregivers, utilizing data from a cluster randomized controlled trial involving 1652 caregivers in rural China. Over the course of two years, caregivers and children in 50 villages received individualized parenting training, one-on-one parenting classes, and access to open play spaces in village parenting centers. Our findings reveal sustained and significant intervention effects on caregivers' anxiety and stress symptoms. The primary mechanisms driving these effects include increased parenting time investment, alterations in parenting styles, and changes in the level of trust in others. Furthermore, our research demonstrates that the positive treatment effects on mental health outcomes are more pronounced among caregivers of children who initially lower development skills. The intervention exhibits a stronger impact on caregivers with lower educational attainment, particularly when the caregivers are grandmothers. These results underscore the importance of governmental attention to mental health issues in rural China, suggesting avenues for intervention through the provision of parenting knowledge and spaces facilitating caregiver-child interactions. This research contributes valuable insights into addressing mental health challenges in rural settings, promoting effective strategies for future policy implementation.

ROGER: A method for determining the geothermal potential in urban areas

Shallow geothermal energy is increasingly adopted for heating and cooling purposes because of the short pay-back time of initial installation investments. As a result, a relevant concentration of Ground Heat Exchangers is being experienced in urban areas. Planning issues thus arise to manage interferences and optimize the use of underground heat resources without depletion, harm to the environment nor efficiency losses on heat pumps or plant oversizing. This study provides a rational approach to optimise geothermal resources based on the use of Geographic Information Systems and transient 3D Thermo-Hydro numerical models. An optimised semi-analytical formula for the assessment of Borehole Heat Exchangers geothermal potential in hydrodynamic conditions is developed through a parametric numerical study. The long-term performances of BHE subjected to groundwater velocity in the range of 0 to 1 m/day were analysed with multiple aquifer thermal parameters. This analytical expression allows a fast and accurate assessment of the potential even in large areas without leading to excessively conservative evaluations. This may serve designers in the preliminary sizing of installations and city planners in the development of appropriate policies for the promotion and management of shallow geothermal resources. An example of the application to the central district of the city of Turin (Italy) is also shown.

Magnetic biochar conjugated quaternary ammonium salt-constructed hemoperfusion adsorbent for viral removal with rapid and high-performance

Due to variations in virus structures and their high variability, treating viral infections remains challenging. Moreover, developing vaccines and medications for newly emerged viruses demands a significant time investment. Efficient methods are required to prevent dangerous infections caused by unknown pathogens. Hemoperfusion, with its low cost, can effectively remove toxins from the blood quickly, thus achieving therapeutic effects. The core of the hemoperfusion technology lies in the adsorbent. Therefore, the development of a viral adsorbent aiming to combat viruses through hemoperfusion was envisioned, following general principles to effectively save lives and buy time for the research and development of vaccines and medications. Towards this goal, a facile, green, and low-priced magnetic biochar/quaternary ammonium salt (Fe3O4/C-2) was constructed using the hydrothermal carbonization (HTC) method. HIV lentiviruses were used as a model pathogen to quantify the adsorption capacity and elucidate the adsorption mechanism. Benefiting from a macroporous structure and electrostatic interactions, fabricated Fe3O4/C-2 has a high adsorption capacity (965 ng g−1) and strong suppression of infectious activity (99.99%). Moreover, a low hemolysis ratio (<2%), high cell viability (>100%), high activated partial thromboplastin time (APTT) (67.29 s), and low protein adsorption (<5%) indicate outstanding biocompatibility and good anticoagulant activity. Therefore, Fe3O4/C-2 is expected to serve as a simple and efficient pathogen sorbent for viral diseases treatment.

How Market Turmoil Affects Investors' Investment Psychology

Using bibliometric research methodology, this study provides insights into the impact of market turmoil on investors' investment psychology, revealing the significant influence of financial markets on investment decisions. The findings of the study emphasize that investors mostly exhibit fearful, extremely risk-averse emotions when faced with market downturns. Overall, the study advocates for a diverse approach to investment decision-making that combines different methods of analyzing market situations and controlling emotions to increase the stability of an individual's investment in times of market turbulence and reduce financial losses, leading to a more inclusive and prosperous economic landscape.With every arrival of financial crisis causing bankruptcy or severe financial losses for a large number of investors, finance exists for everyone. There has been a great deal of literature on what factors influence investors' investment psychology, but these studies do not do a good job of linking the influencing factors to a specific context, such as the severe impact of the 2020 New Crown Epidemic on the financial markets. The world landscape is ever-changing, and the crises that investors have to deal with are beginning to be complex, specific, and diverse. This paper collects and analyzes relevant literature to focus on the relationship between specific crisis environments and investor psychology, and divides crisis periods into two categories to illustrate how they affect investors' psychological activities.

Comparison between evapotranspiration estimate methods in the state of Rio Grande do Sul

The characterization of evapotranspiration requires time and financial investment, but from meteorological data, it is possible to estimate the values of this phenomenon by means of indirect methods. The Penman-Monteith FAO (PM) method is considered the most accurate by the Food and Agriculture Organization of the United Nations (FAO), which recommends its use because it takes into account solar radiation, temperature, relative humidity, and wind speed, data that are not always available in some locations in Brazil, making it necessary to use more simplified methods. Therefore, the objective of this work is to compare the evapotranspiration estimated by the Penman-Monteith FAO method with the evapotranspiration estimated by the methods of Penman-Monteith Simplified (PMS), Priestley-Taylor (PT), and Hargraves-Samani (HS) for the 10 weather stations of the National Institute of Meteorology (INMET) distributed in the Pampa biome in the state of Rio Grande do Sul. The results obtained indicated some divergences between the compared methods. However, the PT method showed more accurate results, with the best performance among the proposed methods. This indicates that this method can be used in future studies in the region, especially in cases of a lack of meteorological data.

Feasibility and Educational Value of Clinical Cases Generated Using Large Language Models.

In medical education, case-based learning (CBL) is a fundamental method for training healthcare professionals across different levels of expertise. This approach hinges on using authentic or fabricated clinical cases to bridge the gap between theoretical knowledge and its practical application. It fosters active engagement and knowledge application among learners in healthcare domains. While creating effective cases demands substantial clinical understanding and time investment, the integration of Generative Artificial Intelligence (AI) presents a promising solution to this challenge. AI can efficiently analyze extensive medical data to generate diverse and realistic clinical scenarios, continuously updating case content based on emerging medical literature and guidelines. This study explores AI-generated cases' feasibility and educational value in continuing medical education, focusing on COVID-19 scenarios tailored for the MENA region. Results indicate the potential of AI-generated cases to foster engagement and critical thinking among learners, suggesting their suitability for different levels of education. This study highlights the advantages of integrating AI into CBL and emphasizes the need for future efforts to tackle obstacles and facilitate its successful adoption.

Remote sensing reveals trends in vegetative recovery and land cover transformation post-reclamation at tar creek superfund site

The harmful effects of mining waste on human and ecosystem health make reclamation of former mine sites an environmental management priority. However, field-based monitoring of reclamation requires significant investments of time, labor, and money. Remote sensing offers a less expensive alternative to field-based monitoring, but work is still needed to determine which metrics can be reliably estimated using remote sensing techniques. This study uses remote sensing to examine over 20 years of reclamation efforts at Tar Creek Superfund Site and to assess revegetation after site restoration. Using the Normalized Difference Vegetation Index (NDVI), we quantify key factors affecting vegetation recovery and stability after reclamation across 123 surface mining waste cleanup locations within Tar Creek. Leveraging long-term imagery from Landsat and high-resolution PlanetScope imagery, we combine time series analysis of vegetation regrowth and landcover change detection for a comprehensive picture of recovery at each site. Across all reclamation sites, the average recovery duration was 3.5 years, and average recovery rate was 0.1 NDVI year−1. After vegetative growth had plateaued, reclaimed sites had an average NDVI of 0.70. All reclamation areas were converted from majority barren landcover to vegetated landcover classes after reclamation, and vegetative stability at reclamation sites was high (70% of reclaimed area saw continuous vegetative cover from 2017 to 2023). These results demonstrate a strong potential for multi-method remote sensing techniques in tracking and explaining vegetation recovery after reclamation and represent a cost-effective approach for real-time monitoring of reclamation progress and outcomes.

Improving electronic health record documentation and use to promote evidence-based pediatric care.

Electronic health records (EHRs) often lack the necessary functionalities to support the full implementation of national clinical guidelines for pediatric care outlined in the American Academy of Pediatrics Bright Futures Guidelines. Using HealthySteps (HS), an evidence-based pediatric primary care program, as an exemplar, this study aimed to enhance pediatric EHRs, identify facilitators and barriers to EHR enhancements, and improve data quality for delivering clinical care as part of HS implementation and evidence building. Three HS sites-each differing in location, setting, number of children served, and mix of child insurance coverage-participated in the study. Sites received technical assistance to support data collection and EHR updates. A comprehensive evaluation, including a process evaluation and outcomes monitoring, was conducted to gauge progress toward implementing study data requirements over time. Data sources included administrative records, surveys, and interviews. All sites enhanced their EHRs yet relied on supplemental data systems to track care coordination. Sites improved documentation of required data, demonstrating reductions in missing data and increases in extractable data between baseline and follow-up assessments. For example, the percentage of missing social-emotional screening results ranged from 0% to 8.0% at study conclusion. Facilitators and barriers to EHR enhancements included organizational supports, leadership, and capacity building. With significant investment of time and resources, practices modified their EHRs to better capture services aligned with HS and Bright Futures. However, more scalable digital solutions are necessary to support EHR updates to help drive improvements in clinical care and outcomes for children and families.

Jupyter widgets and extensions for education and research in computational physics and chemistry

Interactive notebooks are a precious tool for creating graphical user interfaces and teaching materials. Python and Jupyter are becoming increasingly popular in this context, with Jupyter widgets at the core of the interactive functionalities. However, while packages and libraries which offer a broad range of general-purpose widgets exist, there is limited development of specialized widgets for computational physics, chemistry and materials science. This deficiency implies significant time investments for the development of effective Jupyter notebooks for research and education in these domains. Here, we present custom Jupyter widgets that we have developed to target the needs of these communities. These widgets constitute high-quality interactive graphical components and can be employed, for example, to visualize and manipulate data, or to explore different visual representations of concepts, clarifying the relationships existing between them. In addition, we discuss with one example how similar functionality can be exposed in the form of JupyterLab extensions, modifying the JupyterLab interface for an enhanced user experience when working with applications within the targeted scientific domains.

The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright (m peak ≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We present BTSbot, a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find that BTSbot is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. BTSbot has been integrated into Fritz and Kowalski, ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May, BTSbot selected 609 sources in real time, 96% of which were real extragalactic transients. With BTSbot and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan.

Start-Up and Implementation Costs for the Trust Based Relational Intervention.

Capturing costs associated with prevention activities related to substance use disorders (SUD) and mental health (MH) is critical. In this study, Trust Based Relational Intervention (TBRI®), an attachment-based, trauma-informed intervention, is conceptualized as a preventive intervention to reduce substance and opioid use among youth involved with the legal system. When implemented alongside community reentry, TBRI leverages family systems as youth transition from secure residential care into communities through emotional guidance and role modeling. Activity-based cost (ABC) analysis was used to guide cost data collection and analysis for both start-up and implementation of the TBRI intervention. Start-up costs were estimated using data across eight sites during their start-up phase. All components, activities, personnel involved, and time associated with implementation of TBRI sessions according to protocol were defined. National wages were extracted from O*NET and utilized to calculate total costs for each TBRI component. Total and average TBRI intervention costs were calculated with a breakdown by TBRI sessions and number of staff and participants. A sensitivity analysis was conducted to estimate TBRI implementation costs with travel. The total cost for the TBRI intervention, representing 42 sessions, ranges from $6,927, without travel expenses or $12,298, with travel expenses. The average per family cost ranges from $1,385 (without travel) to $2,460 (with travel). Costs are primarily generated by time investments from primary interventionists. The sensitivity analysis shows costs for responsive coaching would double with travel costs included. Results aim to show that using ABC for prevention activities, like TBRI, to understand cost drivers can facilitate future intervention sustainability.Clinical Trail.gov ID: NCT04678960.

Leak localization in District Heating Networks integrating physical model-based and data driven-based methods: Impact of dataset construction on model performance

Leaks in district heating networks result in waste of water and heat, and even threaten personal safety. Various machine learning-based methods have been proposed to realize leak localization and gain satisfying performance based on a large amount of leak samples. However, the creation of such samples demands a substantial investment of time and computational resources, presenting challenges for real-time applications. Previous studies have rarely addressed the influence of leak dataset construction and efficient creation methods. To fill this limited area, this paper conducts a comparative analysis on the variables in leak dataset and proposes an effective method for sample dataset generation, complemented by a novel accuracy evaluation that incorporates the topological relationship among pipelines. A multi-source looped heating network is employed to do the case study. The results demonstrate that the proposed method achieves satisfactory leak localization performance with a significantly reduced training set. Furthermore, by segmenting the physical pipeline into multiple virtual pipelines and using the adjacent evaluation metric, the leak localization method achieves a 75 m error with an accuracy of 0.998.

Something for the young and old: A natural experiment to evaluate the impact of park improvements

Given the significant time and financial investment required to improve parks, evaluations of the effectiveness of park improvements are crucial to inform future investment and design to benefit people of all ages. This natural experiment study examined the impact of park improvements on park visitation and park-based physical activity (PA) in two suburban parks (Park A and Park B) compared to a control park with no improvements. Park A underwent substantial improvements with wide range of facilities, including an all-abilities large adventure-style playground, outdoor fitness area designed for older adults, walking paths and other amenities. Park B received relatively minor improvements that included a playground for young children, outdoor fitness equipment for older adults, and a picnic area. Direct observations were conducted using the System for Observing Play and Recreation in Communities at three timepoints; before (T1–2020) and after (T2–2021, and T3–2022) the improvements. At Park A, there was a significant increase in the total number of park visitors at both timepoints, and those engaged in moderate-to-vigorous physical activity (MVPA) from T1 to T3, relative to the control park. There were also significant increases in active park visits among children, adults, and older adults. At Park B, there were no significant changes in the total number of park visitors or those engaged in MVPA at either timepoint relative to the control park. These findings suggest the extent of improvements and the diversity of facilities included can influence the success of the intervention. The study highlights that including challenging and diverse play equipment suitable for various age groups and abilities, as well as other recreational features such as walking paths and outdoor fitness equipment can increase park visitation and physical activity across different age groups. The findings can inform future park management and planning decisions.

NJmat: Data-Driven Machine Learning Interface to Accelerate Material Design.

Machine learning techniques have significantly transformed the way materials scientists conduct research. However, the widespread deployment of machine learning software in daily experimental and simulation research for materials and chemical design has been limited. This is partly due to the substantial time investment and learning curve associated with mastering the necessary codes and computational environments. In this paper, we introduce a user-friendly, data-driven machine learning interface featuring multiple "button-clicking" functionalities to streamline the design of materials and chemicals. This interface automates the processes of transforming materials and molecules, performing feature selection, constructing machine learning models, making virtual predictions, and visualizing results. Such automation accelerates materials prediction and analysis in the inverse design process, aligning with the time criteria outlined by the Materials Genome Initiative. With simple button clicks, researchers can build machine learning models and predict new materials once they have gathered experimental or simulation data. Beyond the ease of use, NJmat offers three additional features for data-driven materials design: (1) automatic feature generation for both inorganic materials (from chemical formulas) and organic molecules (from SMILES), (2) automatic generation of Shapley plots, and (3) automatic construction of "white-box" genetic models and decision trees to provide scientific insights. We present case studies on surface design for halide perovskite materials encompassing both inorganic and organic species. These case studies illustrate general machine learning models for virtual predictions as well as the automatic featurization and Shapley/genetic model construction capabilities. We anticipate that this software tool will expedite materials and molecular design within the scope of the Materials Genome Initiative, particularly benefiting experimentalists.

Revitalizing Electrochemical Science and Engineering Education to Create a Clean Energy Workforce

Electrochemical science, engineering, and technology education and workforce development are central to the ongoing clean energy transition. In particular, I will highlight battery, hydrogen, and microelectronics industries as drivers. I then highlight a unique Electrochemistry-Technology-focused Graduate program launched through the Oregon Center for Electrochemistry in 2020. At the master’s level, the accelerated program consists of six core electrochemistry science and engineering lecture, laboratory, and project-based courses over either two or three academic quarters. In addition to core electrochemistry knowledge spanning thermodynamics, transport, kinetics, and device technology, emphasis is placed on problem solving, data analysis, programming (particularly in Python) and professional development and this additional content is embedded throughout all the core lecture and laboratory courses. Students are also introduced to continuum-level electrochemical simulations, with an emphasis on understanding the effects of boundary conditions on the simulation output. They also complete a team-based applied project sponsored by industry partners. The program ends with a nine-month paid internship leading to a master’s degree and career in the electrochemistry industry after 15 months.The coursework is also open to PhD students and postdoctoral scholars who go on to serve as near peer mentors and teaching assistants in the program in future years, directly synergistic with their research, and building community around electrochemistry. A new BS-MS program leveraging the above has launched in 2023, allowing University of Oregon students to complete coursework for both BS and MS in just four years yielding an incredibly high return on tuition and time investment in terms of career potential and earnings. Key to the success of the programs is very low administrative overhead with technical faculty/staff leading all efforts and a transparent budget model to fund the program based on tuition return.