Time Investment Research Articles

Complete Neuron Reconstruction Based on Branch Confidence.

In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time.

Low-Cost Approach to an Instream Water Depth Sensor Construction Using Differential Pressure Sensors and Arduino Microcontrollers.

Accurate hydrological data with high spatial resolution is important for flood risk and water resource management, particularly under the context of climate change. The cost of monitoring networks, as well as the characteristics of the hydrological environment itself, can be a barrier to meeting these data requirements, however. This study covers the design and testing of a low-cost, "build-it-yourself", instream water depth sensor providing an assessment of its potential in future hydrological monitoring projects. The low-cost sensor was built using an Arduino microcontroller, a differential pressure sensor and a thermistor, a real-time clock, and an SD card module. The low-cost logger was deployed in tandem with a factory-calibrated Solinst®LevelLogger® 5 Junior for 6 months in the River Wissey, UK. We found the mean absolute error of the Arduino-based logger relative to the commercial setup to be ±0.69 cm for water depth and ±0.415 °C for water temperature. Economically, the Arduino-based logger offers an advantage, costing a total of £133.35 (USD 168.26 at time of publication) comparative to the industrial comparison's cost of £408 (USD 514.83 at time of publication). This study concludes that the low cost of the Arduino-based logger gives a strong advantage to its incorporation in hydrological data collection, if the trade-offs (i.e., time investment and accuracy) are considered acceptable and appropriate for a project.

Participatory methods used in the evaluation of medical devices: a comparison of focus groups, interviews, and a survey

BackgroundStakeholder engagement in evaluation of medical devices is crucial for aligning devices with stakeholders’ views, needs, and values. Methods for these engagements have however not been compared to analyse their relative merits for medical device evaluation. Therefore, we systematically compared these three methods in terms of themes, interaction, and time-investment.MethodsWe compared focus groups, interviews, and an online survey in a case-study on minimally invasive endoscopy-guided surgery for patients with intracerebral haemorrhage. The focus groups and interviews featured two rounds, one explorative focussing on individual perspectives, and one interactive focussing on the exchange of perspectives between participants. The comparison between methods was made in terms of number and content of themes, how participants interact, and hours invested by all researchers.ResultsThe focus groups generated 34 themes, the interviews 58, and the survey 42. Various improvements for the assessment of the surgical procedure were only discussed in the interviews. In focus groups, participants were inclined to emphasise agreement and support, whereas the interviews consisted of questions and answers. The total time investment for researchers of focus groups was 95 h, of interviews 315 h, and survey 81 h.ConclusionsWithin the context of medical device evaluation, interviews appeared to be the most appropriate method for understanding stakeholder views since they provide a scope and depth of information that is not generated by other methods. Focus groups were useful to rapidly bring views together. Surveys enabled a quick exploration. Researchers should account for these methodological differences and select the method that is suitable for their research aim.

THE END OF TRADITIONAL FOCUS GROUPS?

The key benefits of qualitative research are rich insights and thick data. However, some argue these come at the cost of small sample sizes and low generalisability of findings. With traditional FGDs (FGDs), this could be addressed by conducting multiple groups. However, this requires significant investment of time and manpower. We explored methods to gather thick data quickly, aiming to increase the number of respondents without increasing manpower or lengthening fieldwork while maintaining data quality. In this paper, we detail our experience running a pilot study of a 1.5 hour online discussion with N=100 respondents, to capture in-depth responses at scale. Using pre-programmed questions and artificial intelligence (AI) to provide instant visual analyses of responses and additional probes to respondents live, we ran a full qualitative study with a bigger sample in the same duration required for a typical FGD. The discussion was text-based, with respondents being able to view and give their agreement or disagreement to what others may have said without interaction between them. The data was compared to data collected from a previous study with a similar topic and analysed from an operational aspect of conducting research and gathering insights from both methodologies. While this methodology does not replace traditional FGD, it has proven effective in scaling up qualitative research by gathering large amounts of qualitative data within a short duration, in real-time. The methodology has its limitations, primarily the inability to further nuance responses. Despite this, the pilot study appears to be a successful attempt conceptually, as the AI generated valuable instant insights while the study was ongoing, particularly from open-ended (OE) responses. It may add value to specific use cases such as quick sensing which require both breadth and scalability.

ChatGPT-Enhanced ROC Analysis (CERA): A shiny web tool for finding optimal cutoff points in biomarker analysis.

Diagnostic tests play a crucial role in establishing the presence of a specific disease in an individual. Receiver Operating Characteristic (ROC) curve analyses are essential tools that provide performance metrics for diagnostic tests. Accurate determination of the cutoff point in ROC curve analyses is the most critical aspect of the process. A variety of methods have been developed to find the optimal cutoffs. Although the R programming language provides a variety of package programs for conducting ROC curve analysis and determining the appropriate cutoffs, it typically needs coding skills and a substantial investment of time. Specifically, the necessity for data preprocessing and analysis can present a significant challenge, especially for individuals without coding experience. We have developed the CERA (ChatGPT-Enhanced ROC Analysis) tool, a user-friendly ROC curve analysis web tool using the shiny interface for faster and more effective analyses to solve this problem. CERA is not only user-friendly, but it also interacts with ChatGPT, which interprets the outputs. This allows for an interpreted report generated by R-Markdown to be presented to the user, enhancing the accessibility and understanding of the analysis results.

A Single-Session, 2-Hour Version of Mindfulness-Oriented Recovery Enhancement (One MORE) Improves Chronic Pain Patients' Pain-Related Outcomes Through 3-Month Follow-Up in a Randomized Controlled Trial.

Introduction: Traditional, 8-week, mindfulness-based interventions can effectively treat chronic pain, but require a time and resource investment too burdensome for many patients and providers. The solution to this logistical challenge may be to distill the core, therapeutic elements of an efficacious 8-week, mindfulness-based intervention, Mindfulness-Oriented Recovery Enhancement (MORE), into a 2-h, single-session intervention. Methods: In this study, the authors conducted a waitlist-controlled, randomized clinical trial to assess the impact of a 2-h, single-session adaptation of MORE (i.e., One MORE) on chronic pain patients' (N = 40) pain-related outcomes through 3-month follow-up. Results: Results indicated that One MORE significantly improved chronic pain patients' pain catastrophizing (i.e., primary outcome; F = 9.97, p = 0.002), pain intensity (F = 26.58, p < 0.001), pain interference (F = 39.43, p < 0.001), physical function (F = 16.29, p < 0.001), sleep (F = 16.66, p < 0.001), anxiety (F = 12.54, p < 0.001), and depression (F = 17.48, p < 0.001). One MORE also significantly increased theoretically indicated therapeutic mechanisms through the 3-month follow-up: mindfulness, positive reappraisal, savoring, self-transcendence. Discussion: Study results are promising, and if replicated, would suggest that One MORE is a highly scalable, low-cost (e.g., sustainable), nonpharmacologic treatment for chronic pain. Clinical Trial Registration: NCT05194241.

Evaluation of bromocriptine and plumbagin against the monogenean Rhabdosynochus viridisi: Computational drug repositioning and in vitro approaches

Monogeneans are parasitic platyhelminths that can harm the health of farmed fish. Few treatments are available against monogeneans, and the incentive to develop new antiparasitic agents is similar or even lower than the incentive for neglected parasitic diseases in humans. Considering that searching for and developing new antimonogenean compounds may require enormous investments of time, money, and animal sacrifice, the use of a computer-guided drug repositioning approach is a reasonable alternative. Under this context, this study aimed to evaluate the effectiveness of plumbagin and bromocriptine against adults and eggs of the monogenean Rhabdosynochus viridisi (Diplectanidae). Plumbagin is a phytochemical compound that has recently emerged as a potent antimonogenean; however, further investigation is required to determine its effects on different monogenean species. Bromocriptine was selected through a computational approach that included molecular docking analyses of 77 receptors of monogeneans (putative drug targets) and 77 ligands (putative inhibitors). In vitro experiments showed that bromocriptine does not exhibit mortality at concentrations of 0.1, 1, and 10 mg/L whereas plumbagin at 2 and 10 mg/L caused 100% monogenean mortality after 3 h and 30 min, respectively. The most effective concentration of plumbagin (10 mg/L) did not completely inhibit egg hatching. These findings underscore plumbagin as a highly effective agent against adult monogeneans and highlight the need for research to evaluate its effect(s) on fish. Although computational drug repositioning is useful for selecting candidates for experimental testing, it does not guarantee success due to the complexity of biological interactions, as observed here with bromocriptine. Therefore, it is crucial to examine the various compounds proposed by this method.

Optimal investment and exit decision of venture capitals with multiple heterogeneous Beliefs

Generally, traders hold different beliefs about the same project in venture capital activities. We propose a two-stage decision model with multiple heterogeneous beliefs using real options game theory. Based on this model, we analyse the optimal timing and amount of invested capital for venture capital in stage 1 (investing) and stage 2 (exiting). The results show that different beliefs decrease the optimal amount of invested capital but have little influence on the optimal investment timing, which accounts for the “agree to disagree” phenomenon. This negative effect can be offset by the learning process but can also be exacerbated by the differences in required returns.

Design of Transfer Learning based Deep CNN Paradigm for Brain Tumor Classification

Brain tumor is a fatal illness causing worldwide fatalities. The existing neuroimaging modalities to detect brain tumors are invasive and are observer-biased. Automatic CAD frameworks using sophisticated AI techniques lessen human intervention and can effectively handle large amounts of data. Automatic CAD frameworks using Machine learning techniques require the use of time-consuming and error-prone manual feature extraction procedures. Deep learning techniques involve automatic feature extraction; hence, appreciable classification results are attained quickly. However, training DL models from scratch takes a significant investment of time, money, and large datasets, which are difficult to attain in the medical domain. Therefore, the trade-off is utilizing the well exhaustively learned models like VGG16, VGG19, AlexNet, etc. to design a novel framework for the classification of brain tumors. The paper aims to develop a CNN-based deep learning framework by fine-tuning the pre-trained VGG16 architecture via transfer learning for brain tumor detection. The designed framework employing the transfer-learning technique gives better results with less data in less time. The brain tumor binary classification using brain MR images using transfer learning achieved an appreciable accuracy of 97%. The training and validation accuracy obtained was 100% and 97%, respectively, with 30 epochs. The loss for classification was as low as 0.0059% and the run time of 32ms/step time, much less than the existing models.

Music-evoked emotions classification using vision transformer in EEG signals.

The field of electroencephalogram (EEG)-based emotion identification has received significant attention and has been widely utilized in both human-computer interaction and therapeutic settings. The process of manually analyzing electroencephalogram signals is characterized by a significant investment of time and work. While machine learning methods have shown promising results in classifying emotions based on EEG data, the task of extracting distinct characteristics from these signals still poses a considerable difficulty. In this study, we provide a unique deep learning model that incorporates an attention mechanism to effectively extract spatial and temporal information from emotion EEG recordings. The purpose of this model is to address the existing gap in the field. The implementation of emotion EEG classification involves the utilization of a global average pooling layer and a fully linked layer, which are employed to leverage the discernible characteristics. In order to assess the effectiveness of the suggested methodology, we initially gathered a dataset of EEG recordings related to music-induced emotions. Subsequently, we ran comparative tests between the state-of-the-art algorithms and the method given in this study, utilizing this proprietary dataset. Furthermore, a publicly accessible dataset was included in the subsequent comparative trials. The experimental findings provide evidence that the suggested methodology outperforms existing approaches in the categorization of emotion EEG signals, both in binary (positive and negative) and ternary (positive, negative, and neutral) scenarios.

Real option pricing under the regime-switching model with jumps on a finite time horizon

We consider an irreversible investment decision problem on a finite time horizon where an instantaneous cash flow process of a firm follows a regime-switching jump-diffusion (RSJD) model. The value of a project can be derived from a partial integro-differential equation (PIDE) and then we obtain a closed-form solution of the PIDE. It is proved that the value of the project converges to the solution on an infinite time horizon problem as the lifetime of the project tends to infinity. Moreover, the value function of a real option with a finite expiration date can be evaluated by solving a linear complementarity problem (LCP) under the RSJD model. We apply a 2-step backward differentiation formula (BDF2 method) combined with an operator splitting method to solve the LCP and then it has the second-order convergence rate in the discrete ℓ2-norm with respect to the time and spatial variables. The stability of the proposed method to solve the LCP is also proved in the discrete ℓ2-norm. Finally, a variety of numerical experiments are carried out to determine the optimal investment time and the numerical results on the finite time horizon are analyzed.

A New Model for Volunteerism

ABSTRACT As with many Volunteer Programs, volunteerism at Filoli began in support of a fledgling organization in 1975. Initially a separate nonprofit, Friends of Filoli was created with the purpose of fundraising and other support, but was dissolved in 1989 as it was not self-sufficient. The leadership structure, however, remained and later merged with the Board of Directors, creating confusion and discontent. The program peaked at 1,500 volunteers, and was outsizing service needs, not aligned with Filoli’s strategic direction, and a considerable investment of time and management. Volunteers were largely self-managed, but were not following industry best practices and were unsupportive of needed changes for the organization to be viable. As staff implemented best practices, volunteers responded negatively and publicly. Based on staff investment, diminished returns, and negative culture, the model was unsustainable, so the organization took action to assess the needs through a professional consultant to reframe volunteerism. In 2022 Filoli launched Service Learning, a new project-based program that provides an opportunity for learning and volunteerism. Now, Filoli has no dedicated volunteer coordinator, no docents, and no dedicated volunteer database. Yet there is still a robust program for volunteerism, with 650 participants and 2,400 service hours in 2023 alone.

Differential Effects of Additional Formative Assessments on Student Learning Behaviors and Outcomes

It is well-established that formative assessments with accompanying feedback can enhance learning. However, the degree to which additional formative assessments on the same material further improve learning outcomes remains an open research question. Moreover, it is unclear whether providing additional formative assessments impacts self-regulated learning behavior, and if the benefits of such assessments depend on students' self-regulated learning behavior. The current study, conducted in a real-world blended learning setting and using a Learning Analytics approach, compares 154 students who completed additional formative assessments with 154 students who did not. The results indicate that the additional formative assessments led to an improvement in learning outcomes, but also had both positive and negative effects on students' self-regulated learning behavior. Students who completed additional formative assessments performed better on the assessments but reported lower levels of subjective comprehension and devoted more time to completing exercises. Simultaneously, they devoted less effort to additional learning activities (additional investment), such as class preparation and post-processing. Furthermore, the impact of additional formative assessments on learning success depended on students' self-regulated learning behavior. It was primarily the students who invested above-average time during formative assessments (time investment) who benefited from the additional exercises. Cluster analysis revealed that high-effort students (those with above-average time investment and above-average additional investment) gained the most from the extra exercises. In contrast, low-effort students and those who achieved high performance with relatively low effort (efficient students) did not benefit from additional formative assessments. In conclusion, providing students with additional formative assessments can enhance learning, but it should be done with caution as it can alter self-regulated learning behavior in both positive and negative ways, and not all students may benefit from it equally.

Struggling financially but feeling good? Exploring the well-being of early-stage entrepreneurs

Purpose Many individuals start a new firm each year, mainly intending to become independent or improve their financial situation. For most of them, the first years of operations mean a substantial investment of time, effort and money with highly insecure outcomes. This study aims to explore how entrepreneurs running new firms perform financially compared with the established ones and how this situation influences their well-being. Design/methodology/approach A questionnaire survey was completed in 2021 and 2022 by a representative sample of N = 1136 solo self-employed and microentrepreneurs in the Czech Republic, with dependent self-employed excluded. This study used multiple regressions for data analysis. Findings Early-stage entrepreneurs are less satisfied with their financial situation, have lower disposable income and report more significant financial problems than their established counterparts. The situation is even worse for the subsample of startups. However, this study also finds they do not have lower well-being than established entrepreneurs. While a worse financial situation is generally negatively related to well-being, being a startup founder moderates this link. Startup founders can maintain a good level of well-being even in financial struggles. Practical implications The results suggest that policies should focus on reducing the costs related to start-up activities. Further, policy support should not be restricted to new technological firms. Startups from all fields should be eligible to receive support, provided that they meet the milestones of their development. For entrepreneurship education, this study‘s results support action-oriented approaches that help build entrepreneurs’ self-efficacy while making them aware of cognitive biases common in entrepreneurship. This study also underscores that effectuation or lean startup approaches help entrepreneurs develop their startups efficiently and not deprive themselves of resources because of their unjustified overconfidence. Originality/value This study contributes to a better understanding of the financial situation and well-being of founders of new firms and, specifically, startups. The personal financial situation of startup founders has been a largely underexplored issue. Compared with other entrepreneurs, this study finds that startup founders are, as individuals, in the worst financial situation. Their well-being remains, however, on a comparable level with that of other entrepreneurs.

Cost of start-up activities to implement a community-level opioid overdose reduction intervention in the HEALing Communities Study

BackgroundCommunities That HEAL (CTH) is a novel, data-driven community-engaged intervention designed to reduce opioid overdose deaths by increasing community engagement, adoption of an integrated set of evidence-based practices, and delivering a communications campaign across healthcare, behavioral-health, criminal-legal, and other community-based settings. The implementation of such a complex initiative requires up-front investments of time and other expenditures (i.e., start-up costs). Despite the importance of these start-up costs in investment decisions to stakeholders, they are typically excluded from cost-effectiveness analyses. The objective of this study is to report a detailed analysis of CTH start-up costs pre-intervention implementation and to describe the relevance of these data for stakeholders to determine implementation feasibility.MethodsThis study is guided by the community perspective, reflecting the investments that a real-world community would need to incur to implement the CTH intervention. We adopted an activity-based costing approach, in which resources related to hiring, training, purchasing, and community dashboard creation were identified through macro- and micro-costing techniques from 34 communities with high rates of fatal opioid overdoses, across four states—Kentucky, Massachusetts, New York, and Ohio. Resources were identified and assigned a unit cost using administrative and semi-structured-interview data. All cost estimates were reported in 2019 dollars.ResultsState-level average and median start-up cost (representing 8–10 communities per state) were $268,657 and $175,683, respectively. Hiring and training represented 40%, equipment and infrastructure costs represented 24%, and dashboard creation represented 36% of the total average start-up cost. Comparatively, hiring and training represented 49%, purchasing costs represented 18%, and dashboard creation represented 34% of the total median start-up cost.ConclusionWe identified three distinct CTH hiring models that affected start-up costs: hospital-academic (Massachusetts), university-academic (Kentucky and Ohio), and community-leveraged (New York). Hiring, training, and purchasing start-up costs were lowest in New York due to existing local infrastructure. Community-based implementation similar to the New York model may have lower start-up costs due to leveraging of existing infrastructure, relationships, and support from local health departments.

The effects of seed enhancements on plant establishment in native grasses: A meta‐analysis

AbstractQuestionsNative grasses are widespread with high commercial value and demand across the restoration sector, though their uptake is often hampered by seed‐use challenges. Seed enhancement technologies (SETs) provide a valuable tool for improving plant establishment outcomes for species (such as native grasses) where seed‐based approaches are essential to achieve large‐scale targets. However, due to the increased investment in resources and time associated with application of SETs, their adoption is dependent on the ability of these treatments to consistently provide benefits to one or more demographic life stages, particularly under field conditions. This meta‐analysis addresses the following research question: what are the overall effects of SETs on plant establishment outcomes compared to untreated florets or seeds in native grasses globally?LocationGlobal.MethodsThe effects of six major SET categories (acid treatment, coating, pelleting, flash flaming, priming, and treatment combinations) on germination, emergence, and shoot and root length and biomass were explored. Furthermore, the consistency of the effects of SETs across study types (i.e., laboratory, glasshouse, and field) was investigated.ResultsThe overall effects of SETs on native grasses in this meta‐analysis were positive for germination (16% improvement), emergence (22% improvement), and growth (6.42–8.86 cm and 2.26–2.77 g increase in seedling length and biomass, respectively). However, effects ranged from neutral to positive when data were grouped by SET type, with coating, pelleting and priming delivering consistent benefits across multiple life stages, and acid treatments, flash flaming, and combination treatments having mixed effects. This analysis also provided evidence that benefits observed from SET application in laboratory and glasshouse studies are not translating to field environments.ConclusionsOverall, SETs generally had positive effects and can therefore provide a relatively low‐risk approach to improving seed‐based restoration outcomes in native grasses. Continued SET research focussed on long‐term plant establishment outcomes in field environments is needed to further advance SET uptake.

A novel leaching process of zinc ferrite and its application in the treatment of zinc leaching residue

Zinc ferrite is a refractory phase generated in the pyrometallurgical process of zinc and steel production. Much energy is invested in the decomposition of zinc ferrite to recycle zinc since zinc ferrite is difficult to leach. In this work, a novel leaching process targeted at decomposition of zinc ferrite was proposed to save energy and improve metal recovery efficiency. The key of this novel leaching process was the use of copper powder as the reductant. Leaching of zinc ferrite in the presence of copper powder was investigated. The extraction of zinc was 100% when molar ratio of copper to zinc ferrite was 1.5 while the extraction of zinc was only 19.3% without copper. Effects of leaching temperature, acid concentration, the ratio of liquid to solid and reaction time were studied. Under the conditions: 60 °C, ≥ 70 g·L1 H2SO4, the ratio of liquid to solid ≥ 25 mL/g and the molar ratio of copper to zinc ferrite ≥ 1.5 at the open system, the synthetic zinc ferrite was dissolved completely within 60 min. Besides, the zinc ferrite-bearing zinc leaching residue from a roast-leach-electrowin plant was analyzed and leached under the optimal conditions. It was found that most of zinc ferrite in the zinc leaching residue was removed, remaining unreacted lead sulphate in the leached residue. The leaching efficiency of zinc in zinc leaching residue reached 94.2%. The results suggest that copper powder enhanced facile and efficient zinc extraction from zinc ferrite without concentrated acid, high temperature, long reaction time and specific investment. The recyclability of copper and its intrinsic commodity value showed the potential application in industrial zinc and iron containing wastes.

Efficient Motif Discovery in Protein Sequences Using a Branch and Bound Algorithm.

Identifying motifs within sets of protein sequences constitutes a pivotal challenge in proteomics, imparting insights into protein evolution, function prediction, and structural attributes. Motifs hold the potential to unveil crucial protein aspects like transcription factor binding sites and protein-protein interaction regions. However, prevailing techniques for identifying motif sequences in extensive protein collections often entail significant time investments. Furthermore, ensuring the accuracy of obtained results remains a persistent motif discovery challenge. This paper introduces an innovative approach-a branch and bound algorithm-for exact motif identification across diverse lengths. This algorithm exhibits superior performance in terms of reduced runtime and enhanced result accuracy, as compared to existing methods. To achieve this objective, the study constructs a comprehensive tree structure encompassing potential motif evolution pathways. Subsequently, the tree is pruned based on motif length and targeted similarity thresholds. The proposed algorithm efficiently identifies all potential motif subsequences, characterized by maximal similarity, within expansive protein sequence datasets. Experimental findings affirm the algorithm's efficacy, highlighting its superior performance in terms of runtime, motif count, and accuracy, in comparison to prevalent practical techniques.

Home bias and the returns of strategic portfolios: Neither always so good nor so bad

Home Bias is a phenomenon that has been sufficiently addressed from many different perspectives, such as active management or structural investment constraints. However, there is little work about the economic effects of these situations. This work aims to quantify the impact of this effect on the returns of a set of strategic portfolios with the same allocation between fixed income and equity assets. The statistical information includes the daily values of all indices for the 2004–2021 period. Returns have been calculated for investment time horizons of between 5 and 15 years. Quantile regressions have been used to assess changes in returns in response to changes in portfolio composition. The results suggest that over-investment in local assets is not always positive and dependent on the local index selected.

CBD Oil Concentration and Hemp Flower Drying Preferences of U.S. Consumers

The method used to dry hemp flowers can impact the concentration of cannabidiol (CBD) and other quality characteristics like mold levels in CBD oil. Understanding consumer preferences is vital for hemp producers and processors because there is variation in drying time, energy use, and capital investment between potential drying methods. This research assesses willingness to pay (WTP) for CBD oil at varying CBD content (mg per 1-oz.(30 mL) bottle) and drying methods to better understand consumer preferences and how consumers make tradeoffs between CBD oil attributes. Specifically, the authors elicit preferences toward drying using: i) controlled ambient conditions (air-drying), ii) infrared radiation (IR), and iii) freeze-drying or lyophilization. Few statistically significant WTP differences between CBD oils, when marketed at average attribute levels, were found for higher or lower CBD concentration or drying method. An exception was respondents 55 years of age or older who had a higher WTP for CBD oil with greater CBD content (2,500 mg) and also preferred the freeze dried product over the baseline of a conventionally air-dried product with 1,000 mg of CBD. The lack of distinct preferences among the majority of consumers suggests that producers and processors can choose alternative drying methods based on their cost, time, and energy constraints without risking a WTP penalty. The findings indicate that consumers do not have well-formed preferences for CBD oil as it is a relatively novel product. Yet, the results suggest that consumers may interpret higher prices to reflect higher quality/product safety, thereby offering perceived quality assurance.