Profile Analysis Method Research Articles

Latent Profile Analysis of AI Literacy and Trust in Mathematics Teachers and Their Relations with AI Dependency and 21st-Century Skills

Artificial Intelligence (AI) technology, particularly generative AI, has positively impacted education by enhancing mathematics instruction with personalized learning experiences and improved data analysis. Nonetheless, variations in AI literacy, trust in AI, and dependency on these technologies among mathematics teachers can significantly influence their development of 21st-century skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. This study aims to identify distinct profiles of AI literacy, trust, and dependency among mathematics teachers and examines how these profiles correlate with variations in the aforementioned skills. Using a cross-sectional research design, the study collected data from 489 mathematics teachers in China. A robust three-step latent profile analysis method was utilized to analyze the data. The research revealed five distinct profiles of AI literacy and trust among the teachers: (1) Basic AI Engagement; (2) Developing AI Literacy, Skeptical of AI; (3) Balanced AI Competence; (4) Advanced AI Integration; and (5) AI Expertise and Confidence. The study found that an increase in AI literacy and trust directly correlates with an increase in AI dependency and a decrease in skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. The findings underscore the need for careful integration of AI technologies in educational settings. Excessive reliance on AI can lead to detrimental dependencies, which may hinder the development of essential 21st-century skills. The study contributes to the existing literature by providing empirical evidence on the impact of AI literacy and trust on the professional development of mathematics teachers. It also offers practical implications for educational policymakers and institutions to consider balanced approaches to AI integration, ensuring that AI enhances rather than replaces the critical thinking and problem-solving capacities of educators.

Health Risk Behaviors of Adolescents in Europe: A Latent Profile Analysis of Health Behavior in School-Aged Children Survey Data From 43 Countries

PurposeCurrent literature on prevention and intervention programs for European adolescents who engage in health risk behaviors is limited. Few studies have utilized latent profile analysis methods and internationally comparative data on adolescents and their health risk behaviors, highlighting the need for more comprehensive research using internationally comparative data. The current study aimed to examine the health risk behavior profiles and identify subgroups of adolescents with similar patterns of health risk behaviors using a representative sample of European countries. Findings will inform better targeted prevention strategies for this population. MethodsThe study used a quantitative approach and implemented secondary data analysis using the Health Behavior in School-Aged Children survey 2017/2018 from 43 European countries to understand the prevalence of health risk behaviors among European adolescents and yield latent profiles regarding their health risk behaviors by sex. ResultsFive latent profiles of health risk behaviors were identified for boys and six for girls. For both boys and girls, low-risk (83.4% and 85.7%), poor lifestyle, smoking (6.2% and 5.1%), balanced lifestyle, aggressive acts (5.4% and 3.7%), poor lifestyle, high substance use (2% and 1%), moderate lifestyle, and moderate substance use (3% and 0.9%) were common profiles. A moderate lifestyle and low substance use profile (3.6%) was only specific to girls. DiscussionThe current study provides important insights regarding European adolescents' health risk behaviors. The findings suggest the need for better targeted prevention programming.

Latent profile analysis of family adaptation in breast cancer patients-cross-sectional study

When individuals face life pressure or significant family changes, individuals with better family adaptation can better survive the crisis. Although the influencing factors of family adaptation have been investigated, the application of potential profile analysis has yet to be found. This analytical approach can reveal different potential categories of family adaptation, providing new perspectives for theoretical development and interventions. This study used latent profile analysis to explore family adaptation levels in breast cancer patients and identify different latent categories, examining their characteristic differences. A cross-sectional study was conducted in Jinzhou, China, from July 2023 to March 2024. The questionnaire included Sociodemographic and clinical characteristics, Benefit Finding Scale (BFS), Dyadic Coping Scale (DCI), Chinese Perceived Stress Scales (PSS), and Family adaptability and cohesion evaluation scales (FACES). Mplus8.3 and SPSS26.0 software were used for data analysis. The latent profile analysis (LPA) method was used to fit the family adaptations of breast cancer patients. Three latent categories of family adaptation were identified: low-level family adaptation (21.5%), medium level family adaptation (47.8%), and high-level family adaptation (30.6%). All 14 items with high levels of family adaptation scored higher than the other two groups. In particular, out of all the categories, item 9, "The idea of educating children is sound," scored highest. Compared with the low-level group, the influential factors of family adaptation in the high-level group were BFS, DCI, PSS, relapse and personal monthly income; The factors influencing family adaptation at the middle level are DCI, BFS, breast cancer type, family history of breast cancer, and personal monthly income. Compared with the medium level group, PSS and DCI were the influential factors of family adaptation in the high-level group. Family adaptation in breast cancer patients can be divided into three categories: low-level, medium-level, and high-level. There were significant differences among different categories of family adaptation levels in “personal monthly income”, “family history of breast cancer”, “type of breast cancer”, “recurrence”, “dyadic coping”, “benefit finding”, and “perception stress”.

Prediction of blueberry sensory texture attributes by integrating multiple instrumental measurements

Blueberry (Vaccinium spp.) texture contributes to consumer satisfaction, shelf-life, and machine harvestability and is now a critical goal in breeding for new fresh market cultivars. The industry commonly uses instrumental methods to phenotype texture, assuming that instrumental measurements correlate with sensory perceptions. However, the relationship between perceived sensory textures and mechanical parameters is not well established. In this study, we characterized the fruit texture profile of 43 blueberry cultivars using nine sensory descriptors and determined the predictability of the sensory attributes using mechanical parameters. The sensory study was done by a trained descriptive sensory analysis panel, and instrumental analysis was performed using flat probe penetration and texture profile analysis (TPA) methods. Differences in the perceived firmness of blueberries were mainly due to springiness, hardness, and snap/crisp attributes. Among the mechanical parameters, maximum force (FM; a flat probe penetration parameter) and gumminess (a TPA parameter) had the strongest correlations with these three sensory attributes. To develop predictive models for the nine sensory attributes, multivariate statistical methods were used. The highest level of prediction accuracy was achieved when all the penetration or TPA parameters were used for model development. The R2 values increased by up to 0.66 compared to using a single mechanical parameter. Springiness, hardness, and snap/crisp were predictable with R2 > 0.5, regardless of the instrumental method used. TPA parameters were more suitable for predicting juiciness while residual skin was only predictable using penetration parameters. Mealiness was not predictable with any instrumental measure (R2 < 0.05). For most of the sensory attributes, the models were able to effectively discern the cultivars with the highest or lowest intensity scores. This study provides a basis for breeding programs to utilize improved estimations of the sensorial texture using diverse mechanical parameters to enhance selection for desired blueberry textures.

Development of Sourdough Bread Made with Probiotic Lactiplantibacillus plantarum Bacteria Addition

Bread is a staple in the diet of people around the world. A new solution is the addition of selected strains of bacteria to the sourdough to increase the quality of the obtained bread. In the presented research, seven bread samples were baked and analysed, which differed by the selected strain of bacteria of the Lactiplantibacillus plantarum species used in sourdough preparation. The bread was subjected to a 3-day ageing test. The structure of the products was analysed using the texture profile analysis (TPA) method on days 1 and 3 of storage. It was observed that the samples with the addition of selected L. plantarum bacterial strains underwent the process of staling much slower than the control sample. The analysis of the viability of lactic acid bacteria (LAB) in products after 1 and 3 days of storage was also performed. The obtained results indicate the highest survival rate of LAB in the control sample, i.e., the native microflora of baker’s starters, which was at 3.07 log CFU after one-day storage after baking. In the case of other samples, the viability of the bacteria was below 2.74 log CFU, which confirms a certain degree of thermostability of selected bacterial strains. The belonging of the isolated bacteria to the species L. plantarum was proven via genetic identification using the PCR method. A sensory analysis using the quantitative descriptive profile (QDP) method was also performed on the bread immediately after it was baked and cooled down. The analysis showed that the use of L. plantarum strains as starters did not significantly affect the aromatic and taste profiles of the samples compared to the control sample. The overall quality of the bread samples was high, above 6 units, with the control sample having the highest sensory quality of 7.5 units (on a scale of 0–10 units). The presented research suggests that it is possible to produce bread with bacteria that have health-promoting properties and good sensory quality, which enhances the textural features of the final product. Future research will focus on attempting to microencapsulate selected thermostable probiotic bacteria.

Urinary steroid profiling using liquid chromatography-tandem mass spectrometry for the diagnosis of canine Cushing’s syndrome

Serum cortisol measurements by chemiluminescence enzyme immunoassay (CLEIA) are widely used to diagnose hypercortisolism (HC) or Cushing’s syndrome in dogs. However, they are associated with problems such as the need for multiple blood collections under stressful conditions or cross-reactivity between hormones. Therefore, a less invasive and more accurate diagnostic method is required. This study aimed to develop a urinary steroid profile analysis method using liquid chromatography-tandem mass spectrometry (LC/MS/MS) and to evaluate its clinical usefulness. Sixty-five healthy dogs and 38 dogs with suspected HC were included in the study. Using LC/MS/MS, the levels of 11 steroid hormones in the urine were determined. We established the upper limit of the reference interval for each urinary steroid-to-creatinine ratio and evaluated their diagnostic performances. The levels of the five steroid hormones were significantly higher in the 14 dogs with HC than in the 24 dogs with mimicking HC and 65 healthy dogs. The urinary corticosterone-to-creatinine ratio showed the highest diagnostic accuracy (area under the curve, 0.96). A significant correlation was seen between urinary cortisol concentrations measured by LC/MS/MS and CLEIA (rs = 0.88, P <0.001), although the CLEIA measurements were significantly higher than the LC/MS/MS measurements (P <0.001). LC/MS/MS-based urinary steroid profiles are a promising tool for diagnosing canine HC.

Usability of the phenolic profile analysis method developed in RP-HPLC-PDA in natural products

The predominant majority of bioactive compounds in natural products are polyphenols. Reverse Phase High Performance Liquid Chromatography (HPLC) is the most employed analytical method for determining the polyphenol profiles in natural products. Analyses are conducted based on methods validated according to the number and type of phenolic standards used. In this study, validation experiments were performed using HPLC- photodiode array detector for 26 frequently encountered phenolic compounds in bee products and natural substances. The analysis was carried out utilizing a C18 column (250 mm x 4.6 mm, 5 μm; GL Sciences) with a gradient program. The HPLC method was developed, determining the limit of detection within the range of 0.019-0.072 μg/mL, and the limit of quantification within the range of 0.063-0.239 μg/mL. All calibration curves exhibited linear corelations with R² values exceeding 0.994 across the specified range. The developed method has been optimized and validated by assessing detection and quantification limits, accuracy, repeatability, and recovery data suitable for phenolic analysis. It has been concluded that the optimized method allows for the rapid and reliable evaluation of the phenolic content of natural products and their quantitative determination.

Non-coresidential intergenerational relations from the perspective of adult children in China: typology and social welfare implications

The social changes in the new century and the shifting concept of filial piety have jointly shaped the unique intergenerational relationships in China. Intergenerational relationships are closely connected to arrangements for care of the older adults, particularly those who live alone. Based on data from the 2017 China General Social Survey (CGSS), the latent profile analysis method was used to explore the profiles of intergenerational family relationships and their influencing factors in the context of “non-co-residence” based on the five dimensions of intergenerational solidarity theory and related indicators. The study found that family intergenerational relationships can be categorised into five profiles: distant-emotional, proximity-detached, proximity-loose, distant-support, and traditional-reciprocal, which share commonalities with previous studies and have their own characteristics. The results of the multiple regression analysis showed that gender, education level, annual personal income for the previous year, self-rated happiness, and self-rated health had significant effects on the latent profiles of intergenerational relationships. Specifically, adult children who are male, living in a rural area, no legal spouse, a primary school education or less, an annual income above 50,001 RMB in the last year (2016), low or average self-rated happiness, and good self-rated health are more likely to deviate from the traditional filial support track (i.e. they are less likely to be the traditional-reciprocal) than their counterparts in these profiles. In the future, it will be important to increase attention to those families in which older parent live apart from their adult child through relevant legislation, social welfare systems, and social older-care services. This will help ensure that adult children provide intergenerational support and will enable the normal functioning of home- and community-based older adults’ care.

Latent profile analysis of depression in US adults with obstructive sleep apnea hypopnea syndrome.

This study used latent profile analysis to explore the level of depression among US adults with obstructive sleep apnea hypopnea syndrome (OSAHS) symptoms and to identify different latent categories of depression to gain insight into the characteristic differences between these categories. The data of this study were obtained from the National Health and Nutrition Examination Survey (NHANES) database, and the subjects with OSAHS symptoms were aged 18 years and older. The latent profile analysis (LPA) method was used to fit the latent depression categories in subjects with OSAHS symptoms. The chi-square test, rank sum test, and binary logistic regression were used to analyze the influencing factors of depression subgroups in subjects with OSAHS symptoms. Three latent profiles were identified: low-level (83.7%), moderate-level (14.5%) and high-level (1.8%) depression. The scores of 9 items in the high-level depression group were higher than those in the other two groups. Among them, item 4 "feeling tired or lack of energy" had the highest score in all categories. Depression in subjects with OSAHS symptoms can be divided into low-level, moderate-level and high-level depression. There are significant differences among different levels of depression in gender, marital status, PIR, BMI, smoking, general health condition, sleep duration and OSAHS symptom severity.

Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids.

Over the past decade, single-cell transcriptomics has significantly evolved and become a standard laboratory method for simultaneous analysis of gene expression profiles of individual cells, allowing the capture of cellular diversity. In order to overcome limitations posed by difficult-to-isolate cell types, an alternative approach aiming at recovering single nuclei instead of intact cells can be utilized for sequencing, making transcriptome profiling of individual cells universally applicable. These techniques have become a cornerstone in the study of brain organoids, establishing them as models of the developing human brain. Leveraging the potential of single-cell and single-nucleus transcriptomics in brain organoid research, this protocol presents a step-by-step guide encompassing key procedures such as organoid dissociation, single-cell or nuclei isolation, library preparationand sequencing. By implementing these alternative approaches, researchers can obtain high-quality datasets, enabling the identification of neuronal and non-neuronal cell types, gene expression profiles, and cell lineage trajectories. This facilitates comprehensive investigations into cellular processes and molecular mechanisms shaping brain development.

Having Children and Being Married Are Predictors of Burnout and Obesity Among Working Men: Effects of Latent Profile Analysis.

Emergency call-takers and dispatchers' (ECDs) work makes them vulnerable to occupational burnout and health problems. The aim of this research was to apply a Person-Oriented approach in order to examine the relationships between burnout risk factors (having children), personal resources (being married), and health consequences (overweight and obesity) among men working in these positions. The burnout syndrome was assessed among 228 Polish ECDs using The Link Burnout Questionnaire and the method of latent profile analysis (LPA). All ECDs were characterized by high levels of occupational burnout exhibited in 3 out of its 4 dimensions. The LPA allowed us to differentiate 4 patterns of burnout, taking into account socio-demographic variables, the length of employment as ECD and body weight. The need to balance the demands of professional work with having children and marital roles played an important role in predicting the level of professional burnout. There was a relationship between the dimensions of occupational burnout and age, being married, and having children. Being married was associated with a greater intensity of emotional exhaustion, and the level of exhaustion was higher if the employee had children in their care. Health consequences in the form of overweight and obesity can be associated with levels of emotional exhaustion and professional effectiveness. The application of the Person-Oriented approach presents hidden correlations between burnout predictors and health consequences.

XRD as an Alternative Technique for Cation Distribution Characterization of MFe2O4 Magnetic Nanoparticles

This work focuses on utilizing the X‐ray diffraction technique as an alternative method to specialized and high‐cost techniques such as X‐ray photoelectron spectroscopy to obtain the cation distribution in magnetic nanoparticles as MFe2O4 with spinel structure synthesized by thermal decomposition. X‐ray diffraction measurements for MFe2O4 with M = Co2+ as transition cation (inverse spinel CoFe2O4) showed sensitivity to the cation distribution for octahedral and tetrahedral sites in this material with this kind of crystalline structure, being a good option for its determination. The cation distribution was obtained from the X‐ray diffraction intensity ratios for the most sensitive crystalline planes to the cations in octahedral and tetrahedral sites. Besides, the Debye–Scherrer equation and line profile analysis method from the diffraction pattern were used to determine the size distribution and the crystallite size. The size distribution obtained by X‐ray diffraction was from 15 to 18 nm, while transmission electron microscopy was found to be from 10 to 25 nm. The inversion degree x derived from X‐ray diffraction exhibits variability within the interval 0.6 &lt; x &lt; 0.8, not very far from that obtained by X‐ray photoelectron spectroscopy (x = 0.67). This material exhibits a saturation magnetization of 124.88 emu g−1. Consequently, the X‐ray diffraction technique opens the possibility of obtaining reliable information related to nanoparticles’ morphological and chemical properties with spinel structure.

Sugar Profile Method by High-Performance Anion-Exchange Chromatography with Pulsed Amperometric Detection in Food, Dietary Supplements, Pet Food, and Animal Feeds: Interlaboratory Validation Study, Final Action 2018.16.

A method for sugar profile analysis granted First Action 2018.16 was subjected to a multi-laboratory study. Perform a multi-laboratory study with this method to determine the performance parameters of repeatability and reproducibility against the AOAC Standard Method Performance Requirements (AOAC SMPR 2018.001) for Final Action status. Eleven laboratories from three different countries participated in the study. Each laboratory was provided practice materials for successful method setup. Each laboratory then proceeded with analysis of blind duplicates of 10 different products covering the scope of the method. Results were reported to the study directors with any modifications and assessed following the procedures of Appendix D of the AOAC Official Methods of AnalysisSM (guidelines for collaborative study procedures). The majority of results from the study met the SMPR requirements. The data is presented along with any outlying observations or modifications. The method was proven to be flexible across different instrumentation and laboratories, and the method was updated to provide further system suitability and guidelines to maintain the performance of the method across the large scope of matrixes. The results from the collaborative study supported the method for Final Action status. The Expert Review Panel reviewed and voted to move the method forward to Final Action and was followed by review from the Official Methods Board and granted approval. The method was granted Final Action Official Methods status.

Monitoring the reaction process of Fmoc-D-Ala-OH in solid-phase synthesis of peptides with near infrared spectroscopy

Solid phase peptide synthesis (SPPS) has received increasing attention from research laboratories and industry, and a variety of peptides have been successfully constructed in recent years. In this paper, near-infrared spectroscopy was applied to monitor SPPS process, and an analytical model of Fmoc-D-Ala-OH in SPPS solution was established. With the established model the Fmoc-D-Ala-OH content could be predicted rapidly at any time during the reaction. With a help of chemometric method of sampling error profile analysis (SEPA), some pretreatment methods were carefully compared, and the partial least squares method was used to build a model between NIR spectrum and Fmoc-D-Ala-OH content measured by high performance liquid chromatography (HPLC). The results showed that the model had a satisfactory performance, the determination coefficient, root mean square error of the calibration set were 0.9677 and 0.9284 mg/mL, respectively, and the determination coefficient, root mean square error of the prediction set were 0.9561, 1.1208 mg/mL, respectively. These results clearly showed that the model had a good predictive ability. Therefore, it had great potential in the monitoring of SPPS in pharmaceutical production.

Fractional densities and character of dislocations in different slip modes from powder diffraction patterns

Irradiation induced dislocation loops and lattice dislocations produced by plastic deformation have very different dipole characters, dislocation contrasts and range of strain fields. To determine partial dislocation densities of the different dislocation types from X-ray or neutron diffraction patterns, all differences in dislocation properties should be considered. We have extended the convolutional multiple whole profile line profile analysis method to convolve different strain profiles with different effective outer cut-off radii, corresponding to different dislocation type, into a single size profile. The extended procedure is applied to determine the 〈a〉 loop and lattice dislocation densities in a neutron irradiated and tensile deformed Zr-2.5 %Nb alloy and in a proton or neutron irradiated Zircaloy-2 alloy. We show that when the dislocation densities are very different the effective outer cut-off radius of dislocations is a better descriptor of dislocation character than the dislocation arrangement parameter, which was used previously in line profile analysis. Our results show that the combination of line profile analysis and electron microscopy methods provides a detailed and comprehensive description of defect structures in deformed and irradiated materials.

Quantification of individual sugars in tapioca syrups with near-infrared spectroscopy

The sugar profile of a syrup determines its suitability for use in different food applications. The traditional wet chemical method for sugar profile analysis is time-consuming, destructive, and requires several chemical reagents. Therefore, this study aimed to develop a rapid method for determining the concentration of the main sugars (glucose, maltose, maltotriose, and fructose) in tapioca syrups using NIR spectroscopy in the 12000–4000 cm−1 wavenumber region or 800–2500 nm wavelength range. As large spectral variation due to water and liquid glucose was observed, orthogonal projection (OP) was evaluated to reduce those effects. Results showed that for all sugars studied, orthogonal projection prior to partial least squares regression (PLSR) improved the prediction performance in terms of the root mean square error of prediction (RMSEP) by 5–47% and the coefficient of determination for prediction (Rp2) by 3–7%. The developed models had high Rp2 and low RMSEP values, which provides opportunities for industrial monitoring of the sugar profiles of tapioca syrup products.

Changes in fruit texture and cell structure of different pumpkin varieties (lines) during storage

After harvest, the pumpkin fruit has strong respiration and metabolic activities, which easily induce quality deterioration, shortening the storage period and shelf life, which affects the farmers' income. To understand the quality change mechanisms of pumpkins and extend the storage period, the fruit cell wall components, related enzyme activities, and fruit texture of Baimi No. 2, Baimi No. 3, Baimi No. 5, Baimi No. 10 and 2020-X1 pumpkin varieties (lines) were monitored during storage at 18 ± 2 °C after harvest.The pumpkin fruit texture was analyzed via the texture profile analysis (TPA) method, and the cell structure changes in peel and pulp cells were monitored using paraffin sections. The results revealed that the hardness, brittleness, viscosity, and elasticity of the five pumpkin fruits were decreased, while the adhesiveness, cohesion, recovery, and chewiness were first increased before decreasing with the prolongation of storage time. In addition, the protopectin, cellulose, and hemicellulose contents were decreased, while the soluble pectin content was increased. In addition, the polygalacturonase activity was increased, and the β-galactosidase, Cx cellulase, and pectinesterase activities were decreased. The cell wall components and enzymatic activities in Baimi No. 10 had minor changes, implying it is storage resistant. In addition, its peel and pulp cells were small and tightly arranged, with minor cellular changes. The peel cells were nearly square, and the pulp cells nearly round, rendering this variety more storable. Baimi No. 5 pulp cells were large and loosely arranged, with large intercellular spaces, rendering this variety short-lived.

Cantilever beam bending as a potential method to determine the elasticity of cooked Udon noodles

AbstractElasticity is a critical measure of the eating quality of Udon noodles. To characterize the elasticity of Udon noodles, an instrumental method based on the cantilever beam bending test was established. Firstly, the optimum test parameters were determined. Then, texture profile analysis, compression, tension, and cantilever beam bending methods were used to measure the elasticity of 25 commercial Udon noodles with different shapes and sizes, and the correlations between elasticity obtained by the above instrumental methods and sensory evaluation were analyzed. Finally, how the shape and size of Udon noodles influenced their elasticity was discussed in detail. Within the deflection of 2.0 mm, the force increased approximately linearly with increasing deflection, and moderate loading speed (0.5–1.0 mm/s) should be used in the cantilever beam bending experiments to improve the accuracy of results. The bending stiffness obtained by the cantilever beam bending method exhibited a higher coefficient of variation and stronger correlation with the elasticity of sensory evaluation than other instrumental methods. Furthermore, the Udon noodle sample with a higher size, especially the thickness, had higher elasticity, and the Udon noodle sample with a rectangular cross‐section showed higher elasticity than that with a circular cross‐section. In conclusion, the bending stiffness determined by the cantilever beam bending method could be used to characterize the elasticity of cooked Udon noodles.

Understanding farmers’ engagement and barriers to machine learning‐based intelligent agricultural decision support systems

AbstractThe use of intelligent decision support systems (DSS) in precision farming provides an opportunity to improve agricultural recommendations and reduce the impacts of agriculture on the environment. Despite the benefits offered by DDS, many farmers remain skeptical of using these hardware and software tools, and their adoption rates have remained low. A survey of 312 South Dakota farmers examined the barriers and opportunities for their engagement with DSS. Exploratory factor analysis was used to analyze 13 Likert scale survey items that probed farmers’ concerns about DSS. Factor loadings indicated that farmers’ concerns are related to high cost, insufficient knowledge, lack of confidence, and cyber security and privacy. A latent profile analysis (LPA) method was used to classify respondents into profiles or groups based on their dimensions of concerns (cost, knowledge, confidence, and security). Results of the LPA revealed that the sample of farmers could be grouped into four distinct profiles that ranged from low to high confidence in the use of DSS for agronomic decision‐making. Giving attention to farmer comfort/concern profiles allows for a more inclusive and better targeted engagement with farmers and potentially increase the adoption of PA. This knowledge can be vital for technology developers, policymakers, and extension services who are keen to promote PA usage among large‐, medium‐, and small‐scale farmers in the United States.

Microstructure of Gear Steels Treated by Shot Peening by X-Ray Line Profile Analysis Method

Microstructure of Gear Steels Treated by Shot Peening by X-Ray Line Profile Analysis Method