Training Strategies Research Articles

Pre-service teachers’ pedagogical knowledge and attitudes towards slow learner students

The pedagogical knowledge and acceptance of pre-service teachers towards slow learner students contribute to the implementation of inclusive education in regular schools. This study investigates the level of knowledge and acceptance pre-service teachers have in applying effective teaching strategies for students with slower learning needs. The participants of this study were 187 students at a private university in. This research is ex-post facto research. The research instrument used was a pedagogical knowledge test and attitude questionnaire. The data obtained were analyzed using correlation analysis, and t-test, to determine the relationship between pedagogical knowledge and attitudes towards slow learner students. The finding reveals that there was a positive relationship between pedagogical knowledge and attitudes towards slow learners, but the correlation between the two is not strong, in the medium category. Pre-service teachers gain pedagogical knowledge from inclusive education courses. Pre-service teachers also form a positive attitude towards slow learner students because of their experience interacting with slow learner students. A strong correlation between the variables in this study suggests that pre-service teachers' pedagogical knowledge has a limited impact on their attitudes toward slow learners. This highlights the need for improved training in effective teaching strategies for slow learners.

Accentuated Eccentric Loading and Alternative Set Structures: A Narrative Review for Potential Synergies in Resistance Training.

Chae, S, McDowell, KW, Baur, ML, Long, SA, Tufano, JJ, and Stone, MH. Accentuated eccentric loading and alternative set structures: A narrative review for potential synergies in resistance training. J Strength Cond Res 38(11): 1987-2000, 2024-As athletes become adapted to training over time, it becomes more difficult to develop their strength and power. In a conventional resistance training strategy, volume or load may be increased to provide novel stimuli to break through a plateau. However, physiological stress markers increase with increased volume or load, which is an innate shortcoming. In that case, practitioners strive to develop unconventional strategies that could increase training stimuli while adjusting fatigue. Two programming tactics, accentuated eccentric loading (AEL) using eccentric overload and alternative set structures (AS) using intraset rests, have been reported to increase training stimuli and alleviate fatigue, respectively. Importantly, when merging AEL and AS in various contexts, the 2 benefits could be accomplished together. Because AEL and AS cause different outcomes, it is important to deal with when and how they may be integrated into periodization. Moreover, prescribing eccentric overload and intraset rests requires logistical considerations that need to be addressed. This review discusses the scientific and practical aspects of AEL and AS to further optimize strength and power adaptations. This review discusses (a) scientific evidence as to which tactic is effective for a certain block, (b) potential practical applications, and (c) related discussions and future research directions.

Effects of MOOC and video conferencing deliveries on vocabulary usage and learning strategies

While vocabulary learning strategies (VLSs) have primarily been taught in physical classroom settings, the potential for applying strategy training in an online environment remains under-explored. With the rising acceptance of remote education, there is a need to investigate the impacts of applying strategy training in online contexts. Hence, this study explores the effects of online deliveries on VLSs and vocabulary usage in writing among adult English as second language (ESL) learners via massive open online course (MOOC) and video conferencing platforms. A total of 50 pre-intermediate ESL learners completed a 5-week VLSs course on a MOOC while 44 students learnt from the instructor synchronously using Microsoft Teams. The data comprised questionnaires and learners’ compositions, which were collected before and after treatment. The data analysis procedures involved Lextutor’s version of lexical frequency profiling (LFP), descriptive and inferential statistics. The results revealed that there was a significant increase of usage in all categories of the VLSs after the course for both groups. Additionally, the effects of VLSs instruction on vocabulary usage could be reflected in both groups’ writing though the choice of words might have been task-influenced. The findings from this study could yield useful insights on online vocabulary learning for ESL instructors and learners.

A Comprehensive Survey on Brain Tumor Detection and Classification Techniques Using Machine Learning and Deep Learning Models

Abstract: Brain tumor detection and classification have become critical areas of research due to the complexities and challenges involved in accurate diagnosis. With the advancement of machine learning and deep learning techniques, numerous intelligent systems have been proposed to automate and enhance the process of detecting and classifying brain tumors. This survey paper reviews recent approaches and techniques used for brain tumor detection, focusing primarily on deep learning architectures like Convolutional Neural Networks (CNNs) and pre-trained models such as VGG, ResNet, and EfficientNet. In particular, methods using MRI images to classify tumors into categories such as glioma, meningioma, pituitary tumors, and healthy tissue are explored. A detailed analysis of various models, their architectures, training strategies, and evaluation metrics is provided. Moreover, the paper identifies the key challenges faced in the field, including imbalanced datasets, generalization issues, and computational efficiency. By comparing different approaches, we aim to highlight the strengths and limitations of each method and provide insights into how future research can address these challenges. The paper concludes with a discussion on promising future directions in the application of intelligent techniques for brain tumor detection, including the integration of multimodal data and the development of more interpretable models. This survey serves as a comprehensive resource for researchers interested in understanding the current landscape of brain tumor detection using advanced deep learning techniques.

A sales force effectiveness framework for enhancing healthcare access through pharmaceutical sales and training programs

This paper introduces a conceptual framework aimed at enhancing sales force effectiveness within the pharmaceutical industry, specifically focusing on improving healthcare access through optimized sales training programs and customer relationship management (CRM) systems. The proposed framework recognizes the critical role that pharmaceutical sales teams play in facilitating healthcare delivery, particularly during public health crises. It emphasizes the integration of effective training strategies and advanced CRM tools to enhance engagement with healthcare professionals, thereby improving product distribution and patient outcomes. The framework comprises three essential components Sales Force Training Enhancement, which focuses on developing targeted training programs that equip sales representatives with the necessary skills and knowledge to effectively communicate the value of pharmaceutical products to healthcare providers. This includes understanding clinical data, addressing healthcare providers’ concerns, and fostering a consultative selling approach. Secondly, CRM System Implementation, which underscores the significance of adopting robust CRM systems that streamline interactions between sales teams and healthcare professionals. By leveraging data analytics and customer insights, pharmaceutical companies can tailor their approach to meet the specific needs of healthcare providers, thereby enhancing relationship-building and communication. Thirdly, engagement and Outcome Measurement, which highlights the importance of measuring engagement levels and patient outcomes resulting from effective sales strategies. This component advocates for the use of key performance indicators (KPIs) to assess the impact of training and CRM initiatives on product distribution and healthcare access. The framework ultimately aims to foster a more collaborative environment between pharmaceutical sales teams and healthcare professionals, ensuring that patients receive timely access to essential medications and services. By focusing on the strategic integration of training and CRM systems, this framework presents a comprehensive approach to improving sales force effectiveness in the pharmaceutical sector. Future research and practical applications of this framework can further explore its implications for enhancing healthcare access, particularly in times of public health challenges.

Climate-sensitive training for sustainable business in Dickson County with global applications

Purpose This paper aims to examine the implementation and impact of climate-sensitive training programs tailored to the specific climate risks and business needs of Dickson County, Tennessee. It aims to emphasize the importance of integrating climate adaptation strategies into business training and to propose a replicable model that can be adapted to regions facing similar challenges, contributing to global sustainability efforts. Design/methodology/approach The research uses Dickson County’s climate profile as a case study, analyzing its impact on local businesses in sectors such as agriculture, manufacturing and tourism. This paper integrates theoretical frameworks, including organizational learning and adaptive capacity, to provide practical strategies for climate-sensitive training. It further incorporates insights from local institutions, industry partnerships and public data, using a qualitative approach to analyze key themes related to training effectiveness and sustainability. Findings The findings demonstrate that climate-sensitive training significantly enhances the resilience and sustainability of businesses by preparing them to address local climate challenges. Critical factors for successful implementation include stakeholder engagement, industry-specific customization and the continuous evaluation of training outcomes. The model presented shows promise for broader applicability in other regions, especially when localized climate data and collaborative efforts are integrated. Originality/value This paper provides a novel, region-specific approach to climate-sensitive training, distinguishing itself by combining theoretical insights with practical strategies for businesses. By focusing on localized adaptation and stakeholder collaboration, the model addresses an existing gap in the literature on climate adaptation training for smaller, climate-vulnerable regions. This work offers a replicable and scalable framework that contributes to the global discourse on sustainable development and climate resilience in business practices.

Spare Parts Management Strategy of High-Speed Railway Running Department Based on Performance Prediction

Spare parts management is a critical aspect of high-speed train health management, playing a vital role in maximizing in-service time and minimizing maintenance costs. However, traditional spare parts management methods, which rely solely on historical experience and suggest spare parts quantities or ratios in equipment manuals, often lack practicality and fail to meet real-world demands. To address these limitations, this paper proposes a performance prediction-based spare parts management strategy for high-speed trains. The strategy comprises three main components. First, a performance degradation model is developed using performance evaluation results to define a performance degradation envelope. Next, the required quantity or ratio of spare parts for multiple devices in different performance states is determined using the expected performance score method. Finally, the timing of spare parts orders is scientifically optimized by accounting for production and transportation lead times. To demonstrate the effectiveness of the proposed strategy, we conducted experiments using the spare parts management of a specific high-speed train running gear as a case study and compared it with existing spare parts management methods.

Organization of Vaccination Sites in Quebec: Perception of Users, Employees and managers

Abstract Since late 2020, vaccination sites have emerged rapidly in order to protect the population against COVID-19. Health services in Montérégie (Québec) are provided by three organizations. Each adopted a different approach to immunization. The Regional Public Health Department took this opportunity to describe, compare, and explain these three approaches. A mixed methods design through a multiple case study was conducted. Users, employees and managers were interviewed at the end of 2021 on several performance-related topics. Individual interviews with employees and managers in mid-2022 further explored these topics. 38 managers, 360 employees, and 946 users participated in the survey. 9 managers and 10 employees were interviewed. Most participants were satisfied with vaccination organization regardless of the model used, mainly because of high geographic and temporal accessibility of the sites. Further signage to indicate directions could optimize accessibility. High adaptability was essential for managers and employees. Training strategies such as people to refer to and pairing allowed to better deal with frequent changes and staff rotation. The addition of ongoing training could be beneficial. Rapid changes in employee schedules were identified as a challenge. The use of technology-based scheduling tools, accessible to all, brought added flexibility, which was appreciated. Several ideas were used to communicate updates on a daily basis. However, timely implementation of new recommendations remained an ongoing challenge. Dedicated clinics for specific clienteles were successful. Community partnerships were generally beneficial, although establishing more of them could potentially reach other vulnerable communities. To improve accessibility and minimize health social inequities, a greater investment in local partnerships might reach more vulnerable clienteles. Regular communication and innovative employee support are key elements to improve fluidity of operations. Key messages • Reaching out for vulnerable clienteles is essential to minimize health social inequities. • Rapid changes in services needs underlign fluidity communication importance.

Deafness and Inclusion: Perceived Communicative Barriers in the Area of Rehabilitation in Italy

Abstract Introduction Deafness can create a communication deficit/barrier between the Deaf or Hard of Hearing patient and the Healthcare Workers (HWs), with negative outcomes for diagnostic and therapeutic success. As underlined by the COVID-19 pandemic, there is a need to train HWs in effective communication strategies in order to avoid marginalising or excluding the Deaf Community by denying them access to healthcare services. This study highlights the urgent need and desire of Healthcare staff for training in effective communication strategies, such as sign language, to help the Deaf community gain equal access to Healthcare. Materials and methods A cross-sectional survey was carried out between May and July 2022 using the Google Forms platform. An anonymous questionnaire was given to Rehabilitation HWs (RHWs) at a national level to collect information from the HWs of the various areas of rehabilitation, identifying their knowledge, training, and experience with regard to deafness. Data analysis was then carried out using the Google Forms program, SPSS program Windows7 Excel Spreadsheet. Results 252 RHWs belonging to 8 different profiles answered this survey. 89% were women. 46% were younger than 30. 57% of these 252 had deaf patients, but 54.6% of these had no prior training. 33% of the RHWs had followed courses or seminars on deafness at least once. When asked, 94% were interested in courses or training in communication with deaf people. Conclusions HWs recognize the gap in communication and are willing to learn more in the matter for effective communication with their patients to gain a better therapeutic success. They also consider a program that imparts such knowledge and training to be highly important. Key messages • Therapeutic success is increased if there are effective communication strategies, such as sign language, between the Deaf and Healthcare provider. • This study shows the importance of training all health workers in effective communication strategies with deaf people to allow an all-inclusive healthcare.

MW‐SAM:Mangrove wetland remote sensing image segmentation network based on segment anything model

AbstractMangrove wetlands are important ecosystems in tropical and subtropical coastal areas, providing wind and wave attenuation and embankment protection functions. However, mangrove wetlands worldwide are facing severe loss and degradation. Accurate identification of mangrove wetland extent is crucial for their protection, but traditional methods struggle to meet the requirements of large‐scale, high‐precision identification. Furthermore, field data collection and annotation in wetlands in complex intertidal zones pose challenges, and the lack of high‐quality training samples limits the application of deep learning methods in this field. This paper proposes a novel semantic segmentation framework for mangrove wetlands called mangrove wetland remote sensing image segmentation network based on segment anything model (MW‐SAM) to address these issues. MW‐SAM is based on the pre‐trained SAM and achieves cross‐domain adaptation through parameter‐efficient fine‐tuning techniques. It introduces wetland‐specific prompts, auxiliary branches, semi‐supervised training, and iterative optimization strategies to improve accuracy and tackle the problem of sample scarcity. Moreover, on the mangrove wetland dataset constructed in this paper, MW‐SAM significantly outperforms SAM and other traditional methods. MW‐SAM provides a new technology for monitoring and protecting mangrove wetlands, and the constructed dataset will be made publicly available, which is expected to promote the development of mangrove wetland conservation efforts.

PASCL: supervised contrastive learning with perturbative augmentation for particle decay reconstruction

Abstract In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse the hierarchical tree structure, we propose a graph-based deep learning model to infer the tree structure to reconstruct collision events. In particular, we use a compact matrix representation termed as lowest common ancestor generations matrix, to encode the particle decay tree structure. Then, we introduce a perturbative augmentation technique applied to node features, aiming to mimic experimental uncertainties and increase data diversity. We further propose a supervised graph contrastive learning algorithm to utilize the information of inter-particle relations from multiple decay processes. Extensive experiments show that our proposed supervised graph contrastive learning with perturbative augmentation method outperforms state-of-the-art baseline models on an existing physics-based dataset, significantly improving the reconstruction accuracy. This method provides a more effective training strategy for models with the same parameters and makes way for more accurate and efficient high-energy particle physics data analysis.

Lifelong QoT prediction: an adaptation to real-world optical networks

Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.

Research on the Training of Reserve Football Talents at Home and Abroad — On the Strategy of Training Reserve Football on Campus

This research takes the training strategy of reserve football talents on campus as the research object, and takes the literature method and logical analysis method as the research method to analyze and discuss the drawbacks of the existing strategy of reserve football talents training on campus in China and to integrate with the international standards, and to dig deeply into the problems of the current training strategy of reserve football talents under the current mode. Theoretical Significance: Exploring the strategy of combining teaching and sports with training of reserve football talents on campus is to promote the wider, deeper and healthy development of youth football in China, to promote the healthy growth of young students, to consolidate the football foundation of our country, to cultivate more excellent football talents, to provide a theoretical basis for the construction and development of football schools with Chinese characteristics, and to provide a theoretical reference for our schools. The sustainable development of football has laid a good theoretical foundation. Practical Significance: Football itself is an important part of school education and an inevitable requirement to promote students'all-round development. Through the training strategy of reserve football talents on campus, we can develop teenagers' football on campus, cultivate students'interest in football, cultivate students' good will quality and expand students'participation in football. On the basis of the intensity of football training on campus, we find new seedlings of football, further improve their football skills and level, effectively improve the quantity and quality of football reserve talents on campus, train high-quality football talents for the country, and fully demonstrate the practical significance of implementing the strategy of training football reserve talents on campus.

Robust Multimodal Representation under Uncertain Missing Modalities

Multimodal representation learning has gained significant attention across various fields, yet it faces challenges when dealing with missing modalities in real-world applications. Existing solutions are confined to specific scenarios, such as single-modality missing or missing modalities in test cases, thereby restricting their applicability. To address a more general scenario of uncertain missing modalities in both training and testing phases, we propose Robust Multimodal Representation under Uncertain Missing Modalities (RMRU). This framework projects each modality's representation into a shared subspace, enabling the reconstruction of any missing modalities within a unified model. We propose an interaction refinement module that utilizes cross-modal attention to enhance these reconstructions, particularly beneficial in scenarios with limited complete modality data. Furthermore, we introduce an iterative training strategy that alternately trains different modules to effectively utilize both complete and incomplete modality data. Experimental results on four benchmark datasets demonstrate the superiority of RMRU over existing baselines, particularly in scenarios with a high rate of missing modalities. Remarkably, our proposed RMRU can be broadly applied to diverse scenarios, regardless of modality types and quantities.

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

AbstractVirtual reality (VR) technology revitalises rehabilitation training by creating rich, interactive virtual rehabilitation scenes and tasks that deeply engage patients. Robotics with immersive VR environments have the potential to significantly enhance the sense of immersion for patients during training. This paper proposes a rehabilitation robot system. The system integrates a VR environment, the exoskeleton entity, and research on rehabilitation assessment metrics derived from surface electromyographic signal (sEMG). Employing more realistic and engaging virtual stimuli, this method guides patients to actively participate, thereby enhancing the effectiveness of neural connection reconstruction—an essential aspect of rehabilitation. Furthermore, this study introduces a muscle activation model that merges linear and non‐linear states of muscle, avoiding the impact of non‐linear shape factors on model accuracy present in traditional models. A muscle strength assessment model based on optimised generalised regression (WOA‐GRNN) is also proposed, with a root mean square error of 0.017,347 and a mean absolute percentage error of 1.2461%, serving as critical assessment indicators for the effectiveness of rehabilitation. Finally, the system is preliminarily applied in human movement experiments, validating the practicality and potential effectiveness of VR‐centred rehabilitation strategies in medical recovery.

Fault3DNnet: A lightweight 3D seismic fault detection network with bidirectional decoding

Fault detection from seismic data is a critical component of hydrocarbon exploration and production. In recent years, deep-learning techniques have made tremendous achievements and breakthroughs in seismic fault detection. Especially U-nets, represented by FaultSeg3D and its variants, are a dominant method for 3D deep-learning-based seismic fault interpretation. The incorporation of state-of-the-art network modules or training strategies with U-nets continuously improves the performance of fault detection for field seismic data, usually at the cost of increased computational intensity and hardware consumption. We design a lightweight bidirectional decoding network, Fault3DNnet, for fault detection from 3D seismic data volume. To make it simple and concise, our Fault3DNnet is equipped with basic conventional convolution and regular operation modules. The two decoding branches in Fault3DNnet could provide complementary information and improve fault detection performance. The superiority of Fault3DNnet is verified using publicly available synthetic and four field seismic data sets. The experimental results indicate that the method predicts fault structures with improved completeness and continuity compared with FaultSeg3D. Meanwhile, the parameter and computational quantity of the network are only 1/7 and 1/5 of FaultSeg3D, respectively. With the same graphics processing unit memory consumption, Fault3DNnet can handle the size of approximately six times the volume of 3D seismic data during inferencing as that of FaultSeg3D. It helps to improve prediction stability and decrease discontinuous artifacts at the stitching boundaries when predicting the large field data in chunks. In general, our Fault3DNnet delivers superior fault detection results while greatly reducing computational intensity and memory usage.

A new playbook: preparing young team athletes for diverse futures

This article highlights innovative approaches to the development of young athletes in team sports, focusing on strategies that facilitate a successful transition to adulthood. Through a systematic review of the literature in high-impact databases, we investigate the role of positive development, the promotion of transferable skills, the fostering of a growth mindset, and the importance of diversity and inclusion in the sports context. We observe the need for athlete-centered approaches that emphasize autonomy, the development of technical, tactical, and cognitive competencies, and the importance of socio-emotional skills such as leadership, teamwork, and resilience. Another important factor found was the relevance of constructive feedback and effective communication in optimizing learning and improving sports performance. Therefore, the implementation of these innovative strategies in teaching and training contributes to the holistic development of young athletes, preparing them not only for sports success but also for facing future challenges in adult life. This study offers significant insights for researchers, coaches, and educators involved in the sports development of young athletes, highlighting the need for integrated and athlete-centered approaches to promote positive development and a successful transition to adult life.

Analysis of implementation science strategies to train laparoscopic surgical skills among Liberian surgeons using ALL-SAFE.

Significant disparities exist in laparoscopic training opportunities for surgeons in low- and middle-income countries (LMICs). ALL-SAFE is an innovative, low-cost training system for LMIC surgeons' laparoscopic development. However, strategies to implement and scale ALL-SAFE are unstudied. We aimed to assess the impact of implementation science strategies on ALL-SAFE uptake in Liberia, a novel and low-resource context. This study used implementation science approaches to evaluate ALL-SAFE uptake in three Liberian hospitals: John F. Kennedy Medical Center, Phebe, and ELWA. Five validated implementation strategies of program orientation, local champions, network weaving, feedback mechanisms, and practical supervision were piloted. All five strategies were implemented at two hospitals, while four strategies, excluding clinician supervision, were implemented at the final site as a pseudo-control, to evaluate the impact of resource-intensive supervision. Participants included surgical consultants, residents, and medical students. Engagement was assessed with mixed methods including participant number, practice hours, module completion rates, and strategy feasibility through end-user interviews. Across three hospitals, 33 participants used ALL-SAFE for 87.8 total training hours (5,268min). Participant numbers varied across sites (NJFK = 20, 80%; NPhebe = 7, 88%; NELWA = 6, 86%), as did practice times (TotalJFK = 3,060min, Median time/ParticipantJFK = 103min; TotalPhebe = 1,434min, Median time/ParticipantPhebe = 126min; TotalELWA = 774min, Median time/ParticipantELWA = 100min). Sites with practical supervision demonstrated higher engagement than those without (P = 0.042). Interviews (n = 8) revealed positive perceptions toward program orientation, practical supervision, and network weaving. Participants recommended thirteen additional strategies for sustainability including translating ALL-SAFE skills to patient care. Our study is one of the first to evaluate implementation science strategies for laparoscopic training in sub-Saharan Africa. The implementation bundles of five validated strategies were considered feasible by Liberian surgeons in both urban and rural hospitals. Mixed methods suggested a positive association between engagement and the resource-intensive practical supervision strategy. Future studies should focus on quantifying individual strategy contributions with rigorous implementation designs and assessing sustainability strategies.

Research on the Cultivation of Professional Quality for Tourism Management Students

This article aims to explore the cultivation of professional literacy among tourism management students. Through a review of relevant research both domestically and internationally, the connotation and importance of professional literacy are clarified. The current status of professional literacy training for tourism management students is further analyzed, revealing existing problems and deficiencies. Based on this, a series of targeted training strategies and suggestions are proposed, aiming to improve tourism management education, enhance students' professional literacy, and better adapt to the needs of industry development. Finally, empirical research verifies the effectiveness of the proposed strategies, providing a reference for improving tourism management education.

Soil landscapes of the United States (SOLUS): Developing predictive soil property maps of the conterminous United States using hybrid training sets

AbstractDetailed soil property maps are increasingly important for land management decisions and environmental modeling. The US Soil Survey is investing in production of the Soil Landscapes of the United States (SOLUS), a new set of national predictive soil property maps. This paper documents initial 100‐m resolution maps of 20 soil properties that include various textural fractions, physical parameters, chemical parameters, carbon, and depth to restrictions. Many of these properties have not been previously mapped at this resolution. A hybrid training strategy helped increase training data by roughly 10‐fold over previous similar studies by combining commonly used laboratory data with underutilized field descriptions tied to soil survey map unit component property estimates (to help represent within polygon variability) as well as randomly selected soil survey map unit weighted average property estimates. Relative prediction intervals were used to help select which training data sources improved model performance. Conventional and spatial cross‐validation strategies yielded generally strong coefficients of determination between 0.5 and 0.7, but with substantial variability and outliers among the various properties, types of training data, and depths. Internal review of the maps highlighted both strengths and weaknesses of the maps, but most of the critical comments were in areas with high model uncertainty that can be used to guide future improvements. Generally, previously glaciated areas and complex large alluvial basins were harder to model. The new SOLUS 100‐m maps will be updated in the future to address identified issues and feedback as users interact with the data.