Sequential Method Research Articles

Influence of the synthesis route on Au-Ir/Al2O3 catalysts performing the total propane oxidation reaction at low temperature

The current study highlights synthesis as a key parameter for obtaining Au-Ir/Al2O3 bimetallic catalysts using the deposition-precipitation with urea (DPU) method. Both co-deposition and sequential deposition (first by adding Au and then Ir and vice versa) were employed. These approaches were compared with the sol-gel and impregnation methods, revealing notable differences in the catalytic oxidation of C3H8 at low temperatures. The Au-Ir/Al2O3 catalysts prepared via co-deposition and sequential deposition demonstrated outstanding results, producing nanoparticles as small as 1 nm when thermally reduced in hydrogen at 400 °C; also, these catalysts exhibited greater capacity for oxidizing C3H8 below 280°C than the Ir/Al2O3 and Au/Al2O3-based catalysts. Characterization of the catalysts was conducted by employing various techniques, including in-situ DRIFTS, UV-Vis, H2-TPR, HAADF-STEM-EDS, XPS and surface acidity determinations by pyridine thermo-desorption. This last technique showed differences in total acidity (Brönsted and Lewis) provoked by the Au-Ir interaction in the different catalysts, which significantly influenced their ability to oxidize C3H8 towards CO2. The results emphasize the effectiveness of the co-deposition and sequential deposition methods using the DPU approach compared to the sol-gel and impregnation methods.

“Only some can succeed Here”: A mixed methods study of how faculty unproductive mindsets relate to Gender, Racial, and First-Gen representation in higher education

Gender, racial/ethnic, and socioeconomic disparities pervade academia. Though there are a wealth of established reasons for these gaps, recent research has begun to examine how faculty beliefs contribute to representation outcomes for groups stereotyped as lacking intelligence. Extending prior research and using a sequential explanatory mixed methods approach, the present study investigates how field-specific ability beliefs (beliefs about the importance of brilliance) and fixed mindsets (beliefs about the immutability of intelligence) relate to the representation of women, racial/ethnic minorities, and first-generation students at various levels of academia. In a quantitative study of 1,025 faculty from 83 departments, self-reported beliefs exhibited negative correlations with representation outcomes in the small to very large range (−.16 > r’s > -0.44). Qualitative findings, however, suggest a more complicated relationship. Focus group data from undergraduate and graduate students in two departments differing in faculty beliefs (N = 32), suggested that both explicit and implicit messages from professors, peers, and the learning environment are important contributors to students’ experiences, beliefs, and decisions. The results call for more intentional considerations, both in research and in practice, of the messages students receive from the environments in which they learn.

Drought Adaptation Strategies Among Smallholder Cereal Farmers In Makueni County, Kenya

Increasing frequency and severity of drought increase vulnerability of smallholder cereal farmers globally. Arid and Semi-Arid Lands cover 80% of Kenya and location around the equator are major factors in drought development in the country. Frequent droughts result in fluctuations in cereal yields hence food insecurity in Makueni County. This study evaluated community-based and indigenous knowledge drought adaptations practiced by smallholder cereal farmers in Makueni County, Kenya. This study adopted explanatory sequential mixed methods research design. Household surveys (N=225) and key informant interviews (N=16) were used. Descriptive techniques were used in data analysis. The study found that frequent droughts in Makueni County resulted in adoption of small farm sizes for production of maize, sorghum and finger millet. Majority of the farmers produced between 1 and 10 bags of cereals. Majority (50.2%) of the smallholder cereal farmers who adopted community-based adaptations and indigenous knowledge-based drought adaptations strategies recorded an in increase in cereal yields in Makueni County. Early planting (66.7%), timely weeding (61.4%), manure application (57.0%), improving land tillage (56.1%), seed spacing (55.3%) and planting droughtresilient seeds (44.7%) led to the high annual cereal yields ranging between 1 and 10 bags in Makueni County. The drought adaptation strategies are commendable for upscaling in agro-ecological zones with similar climatic conditions in order to improve food production in the country. Information and data generated by this study is expected to result in improvement in drought adaptation policy formulation and adaptive capacity of smallholder cereal farmers hence, improvement in food production and security.

Evaluation of childbirth readiness and its associated factors and consequences in pregnant women referring to health centers in Tabriz, Iran and providing strategies to promote childbirth readiness: a sequential explanatory mixed method study protocol.

Childbirth readiness encompasses interventions conducted during pregnancy that encourage pregnant women, their families and communities to plan for a healthy pregnancy, childbirth and postpartum period. It enhances women's childbirth experience and diminishes maternal mortality rates. This study seeks to assess the level of childbirth readiness, relevant factors and outcomes among pregnant women who have completed 37 or more weeks of gestational age and are attending health centres in Tabriz, Iran. The objective is to offer strategies for enhancing childbirth readiness. This sequential explanatory mixed-method study comprises three phases. The initial quantitative phase involves a descriptive-analytical longitudinal study focusing on pregnant women at 37 weeks of gestation or beyond. The quantitative data will be gathered by the sociodemographic and obstetric information questionnaire, the childbirth readiness scale, the pregnancy experience scale, the Wijma delivery expectancy/experience questionnaire (version A) (W-DEQ-Version A), the childbirth experience questionnaire-2, the childbirth outcome checklist, the Edinburgh's postnatal depression scale (EPDS), the postpartum specific anxiety scale research short-form, the next pregnancy desire questionnaire and the exclusive breastfeeding checklist. In the subsequent qualitative phase, purposive sampling will be employed, and data will be collected through individual, in-depth and semi-structured interviews featuring open-ended questions. Data analysis will be conducted using conventional content analysis techniques. In the final mixed-method phase, strategies to enhance childbirth readiness will be formulated by integrating findings from the quantitative and qualitative studies, a comprehensive literature review, and employing the Delphi method. This study has received approval from the Ethics Committee of Tabriz University of Medical Sciences in Tabriz, Iran (code number: IR.TBZMED.REC.1401.557). All participants will provide written informed consent before taking part in the study. The outcomes will be shared through articles published in journals, presentations at medical conferences, the validation of a reliable scale for assessing the level of childbirth readiness in pregnant women and the development of a policy brief outlining supportive strategies to improve childbirth readiness. These resources will be valuable for healthcare providers.

Are clinical decision support systems seen as helpful to First Contact Practitioners (FCPs) working in musculoskeletal health?

BackgroundThere is increasing burden on musculoskeletal (MSK) First Contact Practitioners (FCPs) working in primary care. One possible solution is to use digital technologies such as clinical decision support systems (CDSS). The primary objective of this study was to understand the potential for MSK FCPs to use a CDSS to support their practice in the United Kingdom. DesignAn exploratory sequential mixed methods design, using a cross sectional survey questionnaire and a subsequent focus group.Following ethical approval responders were recruited via professional networks to complete an online survey. A subsequent focus group enabled an in-depth exploration of survey results. Descriptive statistics were used to summarise survey data and thematic analysis with normalisation process theory used to describe findings. MethodsA snowball sampling method was used to invite MSK FCPs to complete the survey, using email, adverts and social media. The questionnaire captured responders’ demographic and professional practice characteristics, their knowledge and use of CDSS and their views and experiences regarding CDSS in MSK practice. ResultsThere were 75 responders to the survey and six participants in the focus group. The majority of responders 67% (n = 50/75) reported to be in favour of integrating a CDSS into their practice. Three themes were: 1) ensuring CDSS address efficiency concerns, 2) using CDSS to reduce unwarranted variation in practice, and 3) ensuring CDSS sustainability. ConclusionsCDSSs have potential value for FCPs working in MSK primary care settings. Eight summary recommendations advise future developments of CDSS for FCPs working in MSK primary care practice. Contribution of the Paper•This is the first known study evaluating the usefulness of clinical decision support systems (CDSS) for musculoskeletal (MSK) First Contact Practitioners (FCPs).•CDSS have potential value for MSK FCPs providing they are integrated into existing digital note systems and save clinic consultation time.•Eight recommendations advise future integration of CDSS into MSK FCP practice.

Information FOMO: The Unhealthy Fear of Missing Out on Information-A Method for Removing Misleading Data for Healthier Models.

Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important information within a dataset while ignoring data that are either misleading or bring unnecessary complexity to the surrogate model of choice. Our method improves sample-wise error convergence and eliminates instances where more data lead to worse performance and instabilities of the surrogate model, often termed sample-wise "double descent". We find these instabilities are a result of the complexity of the underlying map and are linked to extreme events and heavy tails. Our approach has two key features. First, the selection algorithm dynamically couples the chosen model and data. Data is chosen based on its merits towards improving the selected model, rather than being compared strictly against other data. Second, a natural convergence of the method removes the need for dividing the data into training, testing, and validation sets. Instead, the selection metric inherently assesses testing and validation error through global statistics of the model. This ensures that key information is never wasted in testing or validation. The method is applied using both Gaussian process regression and deep neural network surrogate models.

Synergizing Generative Pre-Trained Transformer (GPT) Chatbots in a Process-Based Writing Paradigm to Enhance University Students’ Writing Skills

Background: The combination of the process-based writing framework and GPT-based chatbots establishes a dynamic and interactive environment, leading students through the stages of planning, drafting, revising, and editing. This collaborative approach not only elevates writing skills but also cultivates critical thinking and self-reflection, thereby transforming the writing process into a more effective and engaging learning experience. Despite the potential synergy to revolutionize the writing process, there exists a surprising lack of research within the educational domain exploring the impact of this innovative approach. Purpose: This study investigates the influence of a GPT-based chatbot within a process-based writing framework on university EFL students’ writing skills, specifically focusing on components such as organization, content, coherence-cohesion, logical connection, and argumentation. Method: Employing a sequential mixed methods type of research with a pre- and post-test design, 30 university EFL students were selected via purposive sampling technique. They engaged in 10 sessions that incorporated GPT-based chatbots within a process-based writing framework. Data collections were through pre-and post- writing tests, writing quizzes, and semi-structured interviews. Results: The results highlighted substantial improvements in participants’ writing performance, evident through a noteworthy increase in post-writing test scores (x̄=17.03) in comparison to pre-writing test scores (x̄=9.13). The study identified a progressive enhancement in four out of five writing components - organization, content, coherence-cohesion, and argumentation - across the 1st to 4th quizzes. However, the ‘logical connection’ component experienced a temporary decline during the 2nd and 3rd writing quizzes, rebounding significantly in the 4th quiz. Notably, the most improved writing components were ‘content’ and ‘argumentation,’ while the component related to ‘logical connection’ exhibited the least improved one. Qualitative findings further underscored participants’ acknowledgment of the effectiveness of the strategy in facilitating their writing tasks. Conclusion: The integration of chatbots within a writing framework was concluded as a facilitative pedagogical approach, fostering a dynamic, personalized, and effective learning experience, contributing to the multifaceted improvement of their writing skills. As educators and practitioners consider innovative approaches, this study provides a compelling case for the effective utilization of GPT-based chatbots in fostering language proficiency and a more engaging learning experience.

Is there a mental health wake following the COVID-19 pandemic on adolescents in a male juvenile prison?

Lingering effects of the COVID-19 pandemic loom large in the United States but are particularly of concern in prison settings. In the current study, we examine the ongoing mental health consequences of the pandemic among young people incarcerated in a male juvenile prison. Sequential mixed methods data were obtained-78 young people assigned male at birth completed surveys and 19 completed individual interviews. Seven staff were also interviewed about youth experiences during and following the pandemic. Youth survey results indicated racial and ethnic identity exploration, feeling less safe at night, and experiencing staff harassment were significantly associated with some negative mental health concerns, yet lingering COVID-19 policies were not. Triangulation with interviews underscored these findings by highlighting more specific concerns related to COVID-19, such as social isolation, loneliness, and concerns about the indirect effects the pandemic might have on court outcomes and educational goals. Overall, results of the current analysis provide evidence that incarceration continues to be traumatizing and harmful to youth mental health even following the pandemic. To a lesser extent, these results also imply that lingering effects of the pandemic and concurrent cultural and racial tensions have delayed impacts on the mental health of incarcerated youth during this tumultuous time. Taken together, study findings suggests juvenile prisons must implement readiness plans to mitigate these and other harmful effects of juvenile incarceration in the future.

CALCULATION OF FLAT SHELLS BY THE BOUNDARY ELEMENT METHOD

This article describes the iterative process of solving the problem of the stress-strain state of a long flexible cylindrical panel. Stress-strain state and the study of the stability of flat shells taking into account the physical, mechanical properties of the material, the process of deformation change. It is based on sequential approximation methods and the boundary element method (MGE). The results of algorithmic weighting are presented in the form of a table showing what value each band represents. The equations are determined by solving twelve unknown values of functions at the ends of the segment. Linear problems of elasticity theory and plate theory fundamental solutions have a simple form, so the method is widely used here. For flat shells, the matrix of fundamental solutions is determined by complex volumetric expressions, and for flat shells - by special functions. Therefore, there is little research on solving problems in the theory of flat shells by the boundary element method. In this regard, an urgent topic of research is the development of methods of boundary integral equations for solving linear and nonlinear problems in the theory of flat shells based on the application of fundamental solutions defined by simple analytical expressions. The scientific novelty of the work consists in the development of a methodology for assessing the reliability of thin-walled spatial structures using the boundary element method.

A deep learning approach for deriving wheat phenology from near-surface RGB image series using spatiotemporal fusion

Accurate monitoring of wheat phenological stages is essential for effective crop management and informed agricultural decision-making. Traditional methods often rely on labour-intensive field surveys, which are prone to subjective bias and limited temporal resolution. To address these challenges, this study explores the potential of near-surface cameras combined with an advanced deep-learning approach to derive wheat phenological stages from high-quality, real-time RGB image series. Three deep learning models based on three different spatiotemporal feature fusion methods, namely sequential fusion, synchronous fusion, and parallel fusion, were constructed and evaluated for deriving wheat phenological stages with these near-surface RGB image series. Moreover, the impact of different image resolutions, capture perspectives, and model training strategies on the performance of deep learning models was also investigated. The results indicate that the model using the sequential fusion method is optimal, with an overall accuracy (OA) of 0.935, a mean absolute error (MAE) of 0.069, F1-score (F1) of 0.936, and kappa coefficients (Kappa) of 0.924 in wheat phenological stages. Besides, the enhanced image resolution of 512 × 512 pixels and a suitable image capture perspective, specifically a sensor viewing angle of 40° to 60° vertically, introduce more effective features for phenological stage detection, thereby enhancing the model’s accuracy. Furthermore, concerning the model training, applying a two-step fine-tuning strategy will also enhance the model’s robustness to random variations in perspective. This research introduces an innovative approach for real-time phenological stage detection and provides a solid foundation for precision agriculture. By accurately deriving critical phenological stages, the methodology developed in this study supports the optimization of crop management practices, which may result in improved resource efficiency and sustainability across diverse agricultural settings. The implications of this work extend beyond wheat, offering a scalable solution that can be adapted to monitor other crops, thereby contributing to more efficient and sustainable agricultural systems.

An Investigation of the Effects of Dialogic Reading Program Applied in Children s Homes on Children s Language Development

The aim of this study is to investigate the effect of dialogic reading education given to care staff working in children's homes on the language development of children in need of protection and the views of care staff on dialogic reading. The study was designed using the explanatory sequential mixed method in which quantitative and qualitative research methods were used together. The study group consisted of 9 children aged 5-8 years living in children's homes and 6 care staff responsible for their care. Girls and boys live in separate homes, and the average age of the children is 6 years and 7 months. One of the participating care staff members is male, and the remaining five are female. The ages of the participating staff range between 35 and 40. First, dialogic reading training was given to a total of six care staff members working in two different care homes. The training was completed in two sessions. After the training, the Peabody Picture Vocabulary Test was administered to the children in the children’s homes as a pre-test. The care staff who completed the training were asked to read books to the children for four weeks using the dialogic reading technique. After the application, the same test was administered as a post-test. Following the quantitative data collection, qualitative data were gathered through semi-structured interviews with the care staff. As a result of the study, a significant difference was found between the pre-test and post-tests of the children who received dialogic reading. In addition, the participant staff stated that dialogic reading increased children's interest in book, developed various skills in children, and contributed to their professional development. This study recommends that care staff in children's homes receive training on dialogic reading and that this method be integrated into daily practice. Additionally, future studies with larger sample sizes and control groups should be conducted, and early interventions for language development in early childhood should be prioritized.

Time series data analysis to forecast the percentage of on-time graduates at Nusa Cendana university

The existence of information related to the estimated percentage of students graduating on time can certainly be one of the study materials that stakeholders can use to take strategies to increase the percentage of on-time graduates. One way that can be done is to use time series data related to the percentage of on-time graduates in the past year to predict the percentage of on-time graduates in the future. Forecasting can be done using ARIMA modeling. In addition, so that the solution used is also right on target, it is necessary to find out the factors that affect the study period of Undana students. The type of research used is mixed method research with sequential explanatory method where, in the first stage of research using quantitative methods, namely collecting data and quantitative analysis and in the second stage collecting and analyzing qualitative data. The quantitative data used is the percentage of on-time graduates at Nusa Cendana University in the last 5 years, while the qualitative data is the results of interviews with graduates of Nusa Cendana University related to factors that affect the length of study. The results showed that the best model for forecasting the percentage of on-time graduates of undergraduate students at Nusa Cendana University is the ARIMA (2, 2, 1) model with an AIC value of -125.64. The results of forecasting the percentage of on-time for the February, June, and September graduation periods in the next 5 years, namely 2024 to 2028, are 5.31%, 9.84%, 12.79%, 9.05%, 13.08%, 15.91%, 12.74%, 16.35%, 19.06%, 16.39%, 19.63%, 22.22%, 20%, 22.93%, and 25.41% respectively. Meanwhile, the factors that influence the study period of students are the curriculum applied in the study program, facilities and infrastructure, lecturers, and students themselves.

Mabrouk's Measure of Adolescent Optimism: Development and Validation of a New Scale of Adolescent Optimism.

Several measures of adolescent optimism exist. However, none of these measures have been developed in the African setting, and only a few of the available measures have been validated for use in this setting. We aimed to develop and validate a culturally appropriate measure for this context. We employed an exploratory sequential mixed methods design using a five-step approach to tool development, comprising literature reviews, construct clarification, item development, piloting, and scale evaluation. We constructed a six-item scale, the Mabrouk's Measure of Adolescent Optimism (MMAO). In a sample of 1616 adolescents from Kenya, the MMAO demonstrated adequate internal consistency (both Cronbach's alpha and McDonald's Omega > 0.80) and test-retest reliability (ICC = 0.46). Factor analysis supported a unidimensional scale with adequate criterion and divergent validity as well as measurement invariance across sex, age (younger vs. older adolescents), residence (rural vs. urban), schooling status (in-school vs. out-of-school adolescents), and language of administration (Swahili vs. English). We report on the development and validation of a new scale of adolescent optimism that can be used to assess optimism among adolescents in Kenya and similar contexts. Preliminary evidence shows support for this new measure's reliability and validity, although additional tests are needed.

Future Role of Digital Platform of Community Policing in Malawi

Internet evolution and digital platform popularity have revolutionized citizens participation in public security administration. However, digital platform ecosystem and functionality remains underexplored. Study examines the future role of digital platform for citizens participation in community policing in Malawi. Within frame work of exploratory sequential mixed methods, focus group discussion interviews purposively selected four groups of 10 respondents each, and used interview guide for data collection. Survey randomly sampled 432 households using Yamene (1969) formular to distribute structured questionnaires for data collection. Interviews were written on script then coded, later grouped into sub themes and finally into themes which corresponded to research questions aided by NVivo application. The survey was analyzed through SPSS version 20 to come up with descriptive statistics. Using pragmatics philosophical paradigm, broken window, democratic participation and social-disorganization theories, the study revealed that citizens may be encouraged to participate on digital platform in community policing by; (a) providing training to citizens on digital crime prevention and safety measures, (b) offering incentives or rewards to citizens who participate in neighborhood safety and security digital initiatives, (c) using social media and other digital platforms to raise awareness and encourage citizen participation, (d) creating more opportunities for citizen-police collaboration and dialogue through social media. Multinominal logistic regression shows gender may have impact on the future role of digital platform of community policing; (i) gain communications between police and community, (ii) detect a wider variety of insecurities (Transparency), (iii) promote the effective use of limited resources, (iv) move beyond the customary bureaucratic procedures at p=.017, (v) gain participation of women, minorities, and vulnerable population at p=.459, (vi) contribute to empowerment of the community at p=.003. Solutions to challenges of digital platform of community policing use are; (1) awareness (Sensitization) Campaign, (2) capacity building on the usage of ICT tools, (3) make available ICT merchant selling and maintenance points, (4) usage of Solar Powered ICT and (5) Place coordinated governance mechanisms. While previous scholars have argued that digital platform has allowed formal and non-formal wide participation, as well as increase transparency and accountability to police officers in handling policing issues and empowering citizens to take part in security of their times. Current revelation points that harnessing and promotion of digital platform gives chance to revolution for transformation that may influence accountability of public security administration to provide quality public services for better life to the challenges of the time.

Saudi EFL Students’ Perception of the Quality of Teacher-Student Relationships and the Factors that Impact Their Self-Efficacy Beliefs

This study aimed to explore the perceptions of English as Foreign Language (EFL) students in the Preparatory Year Program (PYP) at a Saudi university about emotional quality of teacher-student relationships and the factors that impact their self-efficacy beliefs in EFL classrooms. To accomplish this, an explanatory sequential mixed methods approach was implemented. Starting with a quantitative approach a questionnaire was distributed to 340 EFL students followed by a qualitative approach in terms of 8 semi-structured interviews were conducted to explore the students’ perceptions. The quantitative data were analyzed statistically using SPSS and the transcribed interviews were coded manually after which a thematic analysis was used to extract the emerging themes. The results demonstrated that students have a positive relationship with their teachers in EFL classrooms and they view their English language teachers as reassuring, reliable, and trustworthy. Students also believe that their relationship with their teachers influences both their academic and personal growth. Finally, the outcomes of this study also showed that students believe that relationship with their significant others, teachers- peers- family members, and outcome expectancy impact their self-efficacy greatly in EFL classrooms. This research provides insight into the emotional quality of teacher-student relationships and students’ self-efficacy in EFL classes, and the findings provide insights that can inform teaching strategies, curriculum development, organizations, and learner support systems.

Impelementation of Kurikulum Merdeka (Freedom curriculum) in science learning: A case study in Sekolah Indonesia Kuala Lumpur, Malaysia

Many countries have focused on developing curricula that emphasize competencies within their educational frameworks. In Indonesia, schools have adopted the "Kurikulum Merdeka" (Freedom Curriculum), backed by government initiatives. Indonesian schools abroad have also implemented this curriculum, notably in Kuala Lumpur, Malaysia. This study investigates the implementation of the Kurikulum Merdeka (KM) at the Indonesian School Kuala Lumpur (SIKL), with a specific emphasis on science education. The Indonesian Ministry of Education and Culture developed KM, a novel curriculum that prioritizes the growth of students' competencies and character. This research utilized a mixed-method approach, employing an explanatory sequential method, to collect and analyze both quantitative and qualitative data. The study involved 14 teachers representing various educational levels and subjects, including three dedicated to science. Findings revealed that 71.4% of teachers still needed to establish learning objectives; 64.3% had not created teaching modules; and all used electronic teaching materials. Furthermore, 92.9% participated in P5 activities, albeit not always as implementers, and 42.9% faced difficulties in grasping the concept and execution of P5. Qualitative analysis indicated that all science teachers had integrated the Kurikulum Merdeka into their teaching processes. However, they encountered challenges such as generating teaching ideas, the high cost of project implementation, diverse student abilities, and limited resources.

Fear of progression after cancer recurrence: a mixed methods study.

The recurrence of cancer will significantly impact an individual's quality of life (QoL) as they adjust to living with a condition that is often incurable. Patients remain at risk of further progression following recurrence, but fear of cancer progression (FOP) at this time is not commonly examined. Importantly, these fears are known to reach levels in which there are consequences for QoL. This study sought to explore levels of FOP, health-related QoL, anxiety, and depression in patients after a recurrence of their cancer in a longitudinal manner. With the study taking place throughout the COVID-19 pandemic, an assessment of fears related to cancer and the pandemic was included. A sequential mixed method approach was employed for complementarity and expansion purposes. A questionnaire was administered to 44 participants on three different occasions one month apart. A sub-sample of 10 participants then took part in semi-structured interviews. FOP was present at moderate levels in patients with a cancer recurrence, with over a third of the sample reaching levels considered dysfunctional. Levels of fear were stable over three months and were not predicted by select demographic or clinical factors. On average, depression was low, but anxiety reached mild levels. Challenges to health-related QoL were evident. Low levels of concern about COVID-19 in relation to cancer were reported. Integrated findings provided more nuanced answers to the research questions, including more specific worries about cancer progression. Findings support the development of psychosocial interventions to manage FOP, and future recommendations are provided. Identifying the presence of fears not commonly screened for after cancer recurrence adds to the existing knowledge in this area. Through acknowledging and attending to the psychosocial impact of FOP, healthcare professionals can provide tailored support to enhance the well-being of those with a recurrence of their cancer.

Integrating Social Determinants of Health in Machine Learning-Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study.

The use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes. This study proposes the development of a data-driven decision-support system incorporating interactions between clinical factors and SDoH into an algorithm for prioritizing who receives case management services. The paper presents a design for prediction validation and preimplementation assessment that uses a mixed methods approach to guide the implementation of the system. Our study setting is a large, tertiary care academic medical center in the Deep South of the United States, where SDoH contribute to disparities in diabetes-specific hospitalizations and emergency department (ED) visits. This project will develop an interpretable artificial intelligence model for a population with diabetes using SDoH and clinical data to identify which posthospitalization cases have a higher likelihood of subsequent ED use. The electronic health record data collected for the study include demographics, SDoH, comorbidities, hospitalization-related factors, laboratory test results, and medication use to predict posthospitalization ED visits. Subsequently, a mixed methods approach will be used to validate prediction outcomes and develop an implementation strategy from insights into patient outcomes from case managers, clinicians, and quality and patient safety experts. As of December 2023, we had abstracted data on 174,871 inpatient encounters between January 2018 and September 2023, involving 89,355 unique inpatients meeting inclusion criteria. Both clinical and SDoH data items were included for these patient encounters. In total, 85% of the inpatient visits (N=148,640) will be used for training (learning from the data) and the remaining 26,231 inpatient visits will be used for mixed-methods validation (testing). By integrating a critical suite of SDoH with clinical data related to diabetes, the proposed data-driven risk stratification model can enable individualized risk estimation and inform health professionals (eg, case managers) about the risk of patients' upcoming ED use. The prediction outcome could potentially automate case management referrals, helping to better prioritize services. By taking a mixed methods approach, we aim to align the model with the hospital's specific quality and patient safety considerations for the quality of patient care and the optimization of case management resource allocation. DERR1-10.2196/56049.

Approximate sequential optimization for informative path planning

We consider the problem of finding an informative path through a graph, given initial and terminal nodes and a given maximum path length. We assume that a linear noise corrupted measurement is taken at each node of an underlying unknown vector that we wish to estimate. The informativeness is measured by the reduction in uncertainty in our estimate, evaluated using several Gaussian process-based metrics. We present a convex relaxation for this informative path planning problem, which we can readily solve to obtain a bound on the possible performance. We develop an approximate sequential method where the path is constructed segment by segment through dynamic programming. This involves solving an orienteering problem, with the node reward acting as a surrogate for informativeness, taking the first step, and then repeating the process. The method scales to very large problem instances and achieves performance close to the bound produced by the convex relaxation. We also demonstrate our method’s ability to handle adaptive objectives, multimodal sensing, and multi-agent variations of the informative path planning problem.

Soil phosphorus fractionations as affected by cropping systems in the central mid-hills region of Nepal.

Soil plays a critical role as the primary reservoir of phosphorus (P) in terrestrial ecosystems. Sequential fractionation has been extensively utilized to gain insights into the characteristics and dynamics of soil P. However, there is a knowledge gap regarding the different P pools in Nepalese soils. Therefore, this study aimed to investigate the impact of cropping systems on soil P fractions in the central mid-hills of Nepal. The study focused on four cropping systems: vegetable, fruit, rice, and maize-based systems, which exhibited variations in nutrient management, topography, and cropping intensity. A total of 240 soil samples (60 samples from each cropping system) were collected from multiple sites within the central mid-hill region. Standard analytical methods were used to determine the general parameters of the soils, while the sequential fractionation method was employed to assess the organic and inorganic P pools. The results indicated that the effect of cropping systems on soil pH, calcium carbonate (CaCO3) content, and the proportion of sand, silt, and clay was not statistically significant in terms of general parameters. However, significant differences were observed among the different cropping systems in organic matter (OM), electrical conductivity (EC), cation exchange capacity (CEC), and available phosphorus. Similarly, in terms of inorganic phosphorus fractions, loosely bound P (LB-P), aluminum bound P (Al-P), iron bound P (Fe-P), and reductant soluble P (RS-P) were significantly affected, while calcium bound P (Ca-P) did not show a significant difference. Furthermore, in terms of organic phosphorus fractions, labile organic P (L-Po), fluvic acid organic P (FA-Po), and non-labile organic P (NL-Po) exhibited significant differences, whereas moderately labile organic P (ML-Po) and humic acid organic P (HA-Po) did not show a significant difference. Additionally, reductant soluble P showed a significant difference, while total P did not differ significantly. The vegetable-based system exhibited higher levels of the majority of P fractions, followed by the fruit-based, maize-based, and rice-based systems. These findings emphasize the importance of considering cropping systems and their response to different phosphorus pools, as this knowledge can contribute to the development of improved soil phosphorus management strategies and promote sustainable agricultural practices in the region.