Data Integration Research Articles

Retrospective Evaluation of Peri-Implant Marginal Bone Level of Reduced Diameter Implants by Mixed Model Analysis.

The goal of successful implant placement is to maintain a long-term stable marginal peri-implant bone level (MBL). This retrospective study investigated the MBL of reduceddiameter implants (RDI). The implants were analyzed in different indication in the context of various surgical and prosthetic treatment strategies using heterogeneous data from a private practice. Mixed model analysis was used to process the heterogeneous data structure. This method has not been a standard method of data analysis in implantology, and it shows considerable advantages over conventional variance analyses regarding data integration from outpatient practices. 123 patients were treated with 326 implants. 247 implants were diameter-reduced, the remaining 79 implants were standard implants (SDI) as patient related controls. The mean observation time was 24.4 months, and the maximum observation time 76.0 months. The peri-implant bone level of the implants was evaluated, while considering the diameter, as well as the time of implant placement, time of loading, extent of augmentation and localization of the implants. The data were evaluated after restructuring using mixed model analysis. No significant difference was found between the use of RDI or SDI in the analyzed indication. Furthermore, no significant difference was found for the implant placement time, loading time, and the use of two-stage augmentations regarding the stability of the peri-implant bone level. Narrow-diameter implants are a sufficient treatment option in horizontally deficient bone conditions. The use of diameterreduced implants in the posterior region shows promising results; 3.5mm diameter implants may be indicated considering the individual patient situation. The use of mixed model analysis for the evaluation of heterogeneous practice data can lead to a significant increase in the number of retrospective studies and data integration from practices, forming a sound basis for evidence-based dentistry.

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset

The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset—a revised variant of the Digital Database for Screening Mammography (DDSM)—offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes.

Pre-trained models for linking process in data washing machine

Entity Resolution (ER) has been investigated for decades in various domains as a fundamental task in data integration and data quality. The emerging volume of heterogeneously structured data and even unstructured data challenges traditional ER methods. This research mainly focuses on the Data Washing Machine (DWM). The DWM was developed in the NSF DART Data Life Cycle and Curation research theme, which helps to detect and correct certain types of data quality errors automatically. It also performs unsupervised entity resolution to identify duplicate records. However, it uses traditional methods that are driven by algorithmic pattern rules such as Levenshtein Edit Distances and Matrix comparators. The goal of this research is to assess the replacement of rule-based methods with machine learning and deep learning methods to improve the effectiveness of the processes using 18 sample datasets. The DWM has different processes to improve data quality, and we are currently focusing on working with the scoring and linking processes. To integrate the machine model into the DWM, different pre-trained models were tested to find the one that helps to produce accurate vectors that can be used to calculate the similarity between the records. After trying different pre-trained models, distilroberta was chosen to get the embeddings, and cosine similarity metrics were later used to get the similarity scores, which helped us assess the machine learning model into DWM and gave us closer results to what the scoring matrix is giving. The model performed well and gave closer results overall, and the reason can be that it helped to pick up the important features and helped at the entity matching process.

Use of Patient-Reported Outcomes in Risk Prediction Model Development to Support Cancer Care Delivery: A Scoping Review.

The integration of patient-reported outcomes (PROs) into electronic health records (EHRs) has enabled systematic collection of symptom data to manage post-treatment symptoms. The use and integration of PRO data into routine care are associated with overall treatment success, adherence, and satisfaction. Clinical trials have demonstrated the prognostic value of PROs including physical function and global health status in predicting survival. It is unknown to what extent routinely collected PRO data are used in the development of risk prediction models (RPMs) in oncology care. The objective of the scoping review is to assess how PROs are used to train risk RPMs to predict patient outcomes in oncology care. Using the scoping review methodology outlined in the Joanna Briggs Institute Manual for Evidence Synthesis, we searched four databases (MEDLINE, CINAHL, Embase, and Web of Science) to locate peer-reviewed oncology articles that used PROs as predictors to train models. Study characteristics including settings, clinical outcomes, and model training, testing, validation, and performance data were extracted for analyses. Of the 1,254 studies identified, 18 met inclusion criteria. Most studies performed retrospective analyses of prospectively collected PRO data to build prediction models. Post-treatment survival was the most common outcome predicted. Discriminative performance of models trained using PROs was better than models trained without PROs. Most studies did not report model calibration. Systematic collection of PROs in routine practice provides an opportunity to use patient-reported data to develop RPMs. Model performance improves when PROs are used in combination with other comprehensive data sources.

Genetic testing and counseling for hypertrophic cardiomyopathy: An evidence-based practice resource oftheNational Society of Genetic Counselors.

Hypertrophic cardiomyopathy (HCM) is a common hereditary condition affecting approximately 1 in 500 adults. It is characterized by marked clinical heterogeneity with individuals experiencing minimal to no symptoms, while others may have more severe outcomes including heart failure and sudden cardiac death. Genetic testing for HCM is increasingly available due to advances in DNA sequencing technologies and reduced costs. While a diagnosis of HCM is a well-supported indication for genetic testing and genetic counseling, incorporation of genetic services into the clinical setting is often limited outside of expert centers. As genetic counseling and testing have become more accessible and convenient, optimal integration of genomic data into the clinical care of individuals with HCM should be instituted, including delivery via genetic counseling. Drawing on recommendations from recent disease guidelines and systematic evidence reviews, we highlight key recommendations for HCM genetic testing and counseling. This practice resource provides a comprehensive framework to guide healthcare providers in the process of genetic test selection, variant classification, and cascade testing for genetic evaluation of HCM.

Integrated multi-omics revealed that dysregulated lipid metabolism played an important role in RA patients with metabolic diseases

ObjectivesPatients with rheumatoid arthritis (RA) commonly experience a high prevalence of multiple metabolic diseases (MD), leading to higher morbidity and premature mortality. Here, we aimed to investigate the pathogenesis of MD in RA patients (RA_MD) through an integrated multi-omics approach.MethodsFecal and blood samples were collected from a total of 181 subjects in this study for multi-omics analyses, including 16S rRNA and internally transcribed spacer (ITS) gene sequencing, metabolomics, transcriptomics, proteomics and phosphoproteomics. Spearman’s correlation and protein-protein interaction networks were used to assess the multi-omics data correlations. The Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm were used to identify disease-specific biomarkers for RA_MD diagnosis.ResultsOur results found that RA_MD was associated with differential abundance of gut microbiota such as Turicibacter and Neocosmospora, metabolites including decreased unsaturated fatty acid, genes related to linoleic acid metabolism and arachidonic acid metabolism, as well as downregulation of proteins and phosphoproteins involved in cholesterol metabolism. Furthermore, a multi-omics classifier differentiated RA_MD from RA with high accuracy (AUC: 0.958). Compared to gouty arthritis and systemic lupus erythematosus, dysregulation of lipid metabolism showed disease-specificity in RA_MD.ConclusionsThe integration of multi-omics data demonstrates that lipid metabolic pathways play a crucial role in RA_MD, providing the basis and direction for the prevention and early diagnosis of MD, as well as new insights to complement clinical treatment options.

Uncovering Disease Mechanisms through AI and Visual Computing: A Multi-Scale Approach to Biological Data Interpretation

This research focuses on uncovering complex disease mechanisms by employing artificial intelligence (AI) and visual computing to interpret multi-scale biological data. By analyzing data from molecular to organ-level systems, this study aims to bridge the gap between cellular insights and clinical applications, offering new perspectives on diseases such as cancer, neurodegenerative disorders, and cardiovascular diseases. Through a comprehensive approach to data integration, the project compiles omics and imaging data to enable holistic analysis across biological scales. The use of hierarchical neural networks, graph neural networks (GNNs), and convolutional neural networks (CNNs) allows for multi-dimensional modeling and the extraction of disease-related patterns. The findings are validated with clinical data to support actionable insights, laying the groundwork for personalized disease models and precision therapeutic strategies. Expected outcomes include breakthroughs in disease understanding, advancements in precision medicine, and the establishment of a robust AI framework that will contribute to future biomedical applications. This multidisciplinary approach promises to transform diagnostics, prognosis, and treatment, facilitating a deeper understanding of disease mechanisms and their translation into clinical practice. The integration of multi-scale data enables a nuanced view of disease, capturing interactions that may be obscured in single-level analyses. By employing advanced visual computing techniques, the project highlights structural abnormalities within tissue samples, linking cellular changes to systemic disease pathways. This approach not only aids in identifying novel biomarkers but also supports the development of individualized treatment models that predict patient-specific responses. With validation from clinical datasets, the AI framework is poised to enhance clinical decision-making, offering insights tailored to the unique biological profiles of patients. The project’s multi-scale methodology serves as a foundation for future biomedical research, setting a new standard for AI-driven disease analysis.

Multi-omics Data Integration Analysis Identified Therapeutic Targets and Potential Reuse Drugs for Osteoporosis.

To facilitate drug discovery and development for the treatment of osteoporosis. With global aging, osteoporosis has become a common problem threatening the health of the elderly. It is of important clinical value to explore new targets for drug intervention and develop promising drugs for the treatment of osteoporosis. To understand the major molecules that mediate the communication between the cell populations of bone marrow-derived mesenchymal stem cells (BM-MSCs) in osteoporosis and osteoarthritis patients and identify potential reusable drugs for the treatment of osteoporosis. Single-cell RNA sequencing (scRNA-seq) data of BM-MSCs in GSE147287 dataset were classified using the Seurat package. CellChat was devoted to analyzing the ligand-receptor pairs (LR pairs) contributing to the communication between BM-MSCs subsets. The LR pairs that were differentially expressed between osteoporosis samples and control samples and significantly correlated with immune score were screened in the GSE35959 dataset, and the differentially expressed gene in both GSE35959 and GSE13850 data sets were identified as targets from a single ligand or receptor. The therapeutic drugs for osteoporosis were screened by network proximity method, and the top-ranked drugs were selected for molecular docking and molecular dynamics simulation with the target targets. Twelve subsets of BM-MSCs were identified, of which CD45-BM-MSCS_4, CD45-BM- MSCS_5, and CD45+ BM-MSCs_5 subsets showed significantly different distributions between osteoporosis samples and osteoarthritis samples. Six LR pairs were identified in the bidirectional communication between these three BM-MSCs subsets and other BM-MSCs subsets. Among them, MIF-CD74 and ITGB2-ICAM2 were significantly correlated with the immune score. CD74 was identified as the target, and a total of 48 drugs targeting CD47 protein were identified. Among them, DB01940 had the lowest free energy binding score with CD74 protein and the binding state was very stable. This study provided a new network-based framework for drug reuse and identified initial insights into therapeutic agents targeting CD74 in osteoporosis, which may be meaningful for promoting the development of osteoporosis treatment.

AI-Guided Cancer Therapy for Patients with Coexisting Migraines

Background: Cancer remains a leading cause of death worldwide. Progress in its effective treatment has been hampered by challenges in personalized therapy, particularly in patients with comorbid conditions. The integration of artificial intelligence (AI) into patient profiling offers a promising approach to enhancing individualized anticancer therapy. Objective: This narrative review explores the role of AI in refining anticancer therapy through personalized profiling, with a specific focus on cancer patients with comorbid migraine. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar. Studies were selected based on their relevance to AI applications in oncology and migraine management, with a focus on personalized medicine and predictive modeling. Key themes were synthesized to provide an overview of recent developments, challenges, and emerging directions. Results: AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), have become instrumental in the discovery of genetic and molecular biomarkers of cancer and migraine. These technologies also enable predictive analytics for assessing the impact of migraine on cancer therapy in comorbid cases, predicting outcomes and provide clinical decision support systems (CDSS) for real-time treatment adjustments. Conclusions: AI holds significant potential to improve the precision and effectiveness of the management and therapy of cancer patients with comorbid migraine. Nevertheless, challenges remain over data integration, clinical validation, and ethical consideration, which must be addressed to appreciate the full potential for the approach outlined herein.

Predicting Work-in-Process in Semiconductor Packaging Using Neural Networks: Technical Evaluation and Future Applications

This review paper focuses on the application of neural networks in semiconductor packaging, particularly examining how the Back Propagation Neural Network (BPNN) model predicts the work-in-process (WIP) arrival rates at various stages of semiconductor packaging processes. Our study demonstrates that BPNN models effectively forecast WIP quantities at each processing step, aiding production planners in optimizing machine allocation and thus reducing product manufacturing cycles. This paper further explores the potential applications of neural networks in enhancing production efficiency, forecasting capabilities, and process optimization within the semiconductor industry. We discuss the integration of real-time data from manufacturing systems with neural network models to enable more accurate and dynamic production planning. Looking ahead, this paper outlines prospective advancements in neural network applications for semiconductor packaging, emphasizing their role in addressing the challenges of rapidly changing market demands and technological innovations. This review not only underscores the practical implementations of neural networks but also highlights future directions for leveraging these technologies to maintain competitiveness in the fast-evolving semiconductor industry.

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.

Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis

ObjectiveThis systematic review aims to evaluate the quality and accuracy of ML algorithms in predicting ATRX and IDH mutation status in patients with glioma through the analysis of radiomic features extracted from medical imaging. The potential clinical impacts and areas for further improvement in non-invasive glioma diagnosis, classification and prognosis are also identified and discussed.MethodsThe review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic and Test Accuracy (PRISMA-DTA) statement. Databases including PubMed, Science Direct, CINAHL, Academic Search Complete, Medline, and Google Scholar were searched from inception to April 2024. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to assess the risk of bias and applicability concerns. Additionally, meta-regression identified covariates contributing to heterogeneity before a subgroup meta-analysis was conducted. Pooled sensitivities, specificities and area under the curve (AUC) values were calculated for the prediction of ATRX and IDH mutations.ResultsEleven studies involving 1,685 patients with grade I–IV glioma were included. Primary contributors to heterogeneity included the MRI modalities utilised (conventional only vs. combined) and the types of ML models employed. The meta-analysis revealed pooled sensitivities of 0.682 for prediction of ATRX loss and 0.831 for IDH mutations, specificities of 0.874 and 0.828, and AUC values of 0.842 and 0.948, respectively. Interestingly, incorporating semantics and clinical data, including patient demographics, improved the diagnostic performance of ML models.ConclusionsThe high AUC in the prediction of both mutations demonstrates an overall robust diagnostic performance of ML, indicating the potential for accurate, non-invasive diagnosis and precise prognosis. Future research should focus on integrating diverse data types, including advanced imaging, semantics and clinical data while also aiming to standardise the collection and integration of multimodal data. This approach will enhance clinical applicability and consistency.

Comparing Satellite, Reanalysis, Fused and Gridded (In Situ) Precipitation Products Over Türkiye

ABSTRACTPrecipitation is the fundamental source for various research areas, including hydrology, climatology, geomorphology, and ecology, serving essential roles in modelling, distribution, and process analysis. However, the accuracy and precision of spatially distributed precipitation estimates is a critical issue, particularly for daily scale and topographically complex areas. Although many datasets have been developed based on different algorithms and sources are developed for this purpose, determining which of these datasets best reflects actual conditions is quite challenging. This study, hence, aims to compare the 25 global distributed precipitation estimates (gridded, satellite, model, and fused) concerning 221 ground‐based observations based on the ranking of 18 continuous (evaluation statistics), eight categorical (precipitation indices), and two seasonality metric (high and low precipitation). Upon examining the results, gridded and model precipitation data including APHRODITE (Asian Precipitation—Highly‐Resolved Observational Data Integration Towards Evaluation), CPC (Global Unified Gauge‐Based Analysis of Daily Precipitation), ERA5‐Land (ECMWF Reanalysis 5th Generation for Lands), and CFSR (Climate Forecast System Reanalysis) occupy the top four positions in continuous metrics. In contrast, satellite data such as PERSIANN‐PDIR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), CMORPH (Climate Prediction Center morphing method), IMERG (The Integrated Multi‐Satellite Retrievals for GPM), and TRMM‐TMPA (Tropical Rainfall Measuring Mission/Multi‐satellite Precipitation Analysis) dominate in the top four positions in categorical metrics. For seasonality of high and low precipitation, fused, gridded, and reanalyses products such as CPC, MSWEP (Multi‐Source Weighted‐Ensemble Precipitation, version 2), HydroGFD (Hydrological Global Forcing Data), CFSR rank among top four. Based on the first five rankings of all metrics, fused (multiple sourced) and gridded datasets accurately reflect the actual situations compared to other precipitation products. Reanalysis (model) and satellite‐based follow this rank, respectively. The results clearly indicate that fused precipitation derived products from multiple sources offer better accuracy and precision in representing the spatial distribution of precipitation on a daily scale.

Joint inference for telemetry and spatial survey data.

Data integration, the joint statistical analysis of data from different observation platforms, is pivotal for data-hungry disciplines such as spatial ecology. Pooled data types obtained from the same underlying process, analyzed jointly, can improve both precision and accuracy in models of species distributions and species-habitat associations. However, the integration of telemetry and spatial survey data has proved elusive because of the fundamentally different analytical approaches required by these two data types. Here, "spatial survey" denotes a survey that records spatial locations and has no temporal structure, for example, line or point transects but not capture-recapture or telemetry. Step selection functions (SSFs-the canonical framework for telemetry) and habitat selection functions (HSFs-the default approach to spatial surveys) differ in not only their specifications but also their results. By imposing the constraint that microscopic mechanisms (animal movement) must correctly scale up to macroscopic emergence (population distributions), a relationship can be written between SSFs and HSFs, leading to a joint likelihood using both datasets. We implement this approach using maximum likelihood, explore its estimation precision by systematic simulation, and seek to derive broad guidelines for effort allocation in the field. We find that complementarities in spatial coverage and resolution between telemetry and survey data often lead to marked inferential improvements in joint analyses over those using either data type alone. The optimal allocation of effort between the two methods (or the choice between them, if only one can be selected) depends on the overall effort expended and the pattern of environmental heterogeneity. Examining inferential performance over a broad range of scenarios for the relative cost between the two methods, we find that integrated analysis usually offers higher precision. Our methodological work shows how to integrate the analysis of telemetry and spatial survey data under a novel joint likelihood function, using traditional computational methods. Our simulation experiments suggest that even when the relative costs of the two methods favor the deployment of one field approach over another, their joint use is broadly preferable. Therefore, joint analysis of the two key methods used in spatial ecology is not only possible but also computationally efficient and statistically more powerful.

From Data Integration to Precision Medicine: A Value-Based Healthcare Approach for Sarcoma Care

The transformation of healthcare from a fee-for-service model to value-based care is particularly crucial in managing complex and rare diseases like sarcoma, where data fragmentation and variability present significant challenges. This manuscript reviews strategies for structured and harmonized data integration—a critical precursor to precision medicine in sarcoma care. We demonstrate how standardizing data formats, ontologies, and coding systems enable seamless integration of clinical, economic, and patient-reported outcomes across institutions, paving the way for comprehensive predictive analytics. By establishing robust value-based healthcare (VBHC) frameworks through digital transformation and predictive models, including digital twins, we create the foundation for personalized sarcoma treatment and real-world-time clinical decision-making. The manuscript also addresses practical challenges, including the need for system standardization, overcoming regulatory and privacy concerns, and managing high costs. We propose actionable strategies to overcome these barriers and discuss the role of advanced analytics and future research directions that further enhance VBHC and precision medicine. This work outlines the necessary steps to build a cohesive, data-driven approach that supports the transition to precision medicine, fundamentally improving outcomes for sarcoma patients.

Preparation of A Map of Potential Areas For the Installation of A Sanitary Landfill in The Porto Murtinho/MS Region

Objective: The main objective is to prepare a map to identify areas with potential for the installation of a landfill in the region of Porto Murtinho/MS. Theoretical Framework: This study addresses the relevance of the careful selection of locations for the implementation of Sanitary Landfills, with an emphasis on environmental and social aspects, using geotechnologies as fundamental tools for a comprehensive and effective analysis. Method: The mapping was based on the acquisition and processing of geospatial and environmental data. The factors were classified as limiting or not, considering watercourses, areas subject to flooding, roads, land use and occupation, slope, soil and urban proximity. Data integration through map algebra was performed in QGIS software, and WRPLOT View was used for wind analysis. Results and Discussion: The results revealed that the majority of areas with high installation potential are located at a distance of 2 (two) to 5 (five) kilometers from the urban perimeter. The supplementary analysis of Rosa dos Ventos demonstrated that areas located to the east are more suitable for the implementation of sanitary landfills. Research Implications: The multifaceted approach, which includes environmental and operational aspects, makes it possible to identify areas suitable for the implementation of sanitary landfills. Originality/Value: This study demonstrates the relevance of geotechnologies in choosing locations for landfills, where limiting factors exclude unsuitable areas, and non-limiting factors classify the installation potential, being a methodology replicable in other locations for sustainable environmental management.

Digital tools and AI: Using technology to monitor carbon emissions and waste at each stage of the supply chain, enabling real-time adjustments for sustainability improvements

The increasing urgency of addressing climate change has necessitated the adoption of innovative technologies to monitor and mitigate carbon emissions and waste throughout the supply chain. This paper explores the role of digital tools and artificial intelligence (AI) in enabling real-time tracking and analysis of environmental impact at each stage of the supply chain. By integrating IoT devices, big data analytics, and machine learning algorithms, organizations can gain comprehensive insights into their operations, identifying key areas where emissions and waste can be reduced. The use of AI-powered predictive analytics allows companies to model various scenarios, optimizing resource allocation and operational efficiency while minimizing environmental footprints. This research also highlights successful case studies where companies have implemented these technologies, resulting in significant sustainability improvements and cost savings. Furthermore, the paper discusses the challenges associated with data integration, system interoperability, and the need for industry-wide standards to ensure effective monitoring and reporting. The importance of stakeholder collaboration is emphasized, as engaging suppliers, customers, and regulatory bodies is essential for achieving comprehensive sustainability goals. Ultimately, this paper advocates for the strategic implementation of digital tools and AI as pivotal enablers of sustainable supply chain management, providing organizations with the capability to adapt to changing environmental regulations and consumer expectations. By leveraging technology to monitor and reduce carbon emissions and waste, businesses can enhance their competitiveness while contributing to a more sustainable future.

Leveraging Artificial Intelligence and Data Science for Integration of Social Determinants of Health in Emergency Medicine: Scoping Review.

Social determinants of health (SDOH) are critical drivers of health disparities and patient outcomes. However, accessing and collecting patient-level SDOH data can be operationally challenging in the emergency department (ED) clinical setting, requiring innovative approaches. This scoping review examines the potential of AI and data science for modeling, extraction, and incorporation of SDOH data specifically within EDs, further identifying areas for advancement and investigation. We conducted a standardized search for studies published between 2015 and 2022, across Medline (Ovid), Embase (Ovid), CINAHL, Web of Science, and ERIC databases. We focused on identifying studies using AI or data science related to SDOH within emergency care contexts or conditions. Two specialized reviewers in emergency medicine (EM) and clinical informatics independently assessed each article, resolving discrepancies through iterative reviews and discussion. We then extracted data covering study details, methodologies, patient demographics, care settings, and principal outcomes. Of the 1047 studies screened, 26 met the inclusion criteria. Notably, 9 out of 26 (35%) studies were solely concentrated on ED patients. Conditions studied spanned broad EM complaints and included sepsis, acute myocardial infarction, and asthma. The majority of studies (n=16) explored multiple SDOH domains, with homelessness/housing insecurity and neighborhood/built environment predominating. Machine learning (ML) techniques were used in 23 of 26 studies, with natural language processing (NLP) being the most commonly used approach (n=11). Rule-based NLP (n=5), deep learning (n=2), and pattern matching (n=4) were the most commonly used NLP techniques. NLP models in the reviewed studies displayed significant predictive performance with outcomes, with F1-scores ranging between 0.40 and 0.75 and specificities nearing 95.9%. Although in its infancy, the convergence of AI and data science techniques, especially ML and NLP, with SDOH in EM offers transformative possibilities for better usage and integration of social data into clinical care and research. With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. These efforts aim to harness SDOH data optimally, enhancing patient care and mitigating health disparities. Our research underscores the vital need for continued investigation in this domain.

Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield

Maize (Zea mays L.) has been shown to be sensitive to temperature deviations, influencing its yield potential. The development of new maize hybrids resilient to unfavourable weather is a desirable aim for crop breeders. In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. The main objective of this study was to assess if the inclusion of historical weather data, maize growth captured through imagery, and important phenotypic traits would improve the predictive power of an established multimodal deep learning model. Evaluation of the model performance when training from scratch showed its ability to accurately predict ~89% of hybrids with high-yield potential and demonstrated enhanced explanatory power compared with previously published models. Shapley Additive explanations (SHAP) analysis indicated the top influential features include plant density, hybrid placement in the field, date to anthesis, parental line, temperature, humidity, and solar radiation. Including weather historical data was important for model performance, significantly enhancing the predictive and explanatory power of the model. For future research, the use of the model can move beyond maize yield prediction by fine-tuning the model on other crop data, serving as a potential decision-making tool for crop breeders to determine high-performing individuals from diverse crop types.

Embracing Mixed Methods Research in Special Education

This introduction to the special issue on mixed methods research examines guest editor perspectives on the research culture in special education and the broader ecosystem of education research. Articles included in the special issue are used to illustrate how core methodological features and application of a flexible metaparadigm enables mixed methods research to enhance the quality and depth of research findings. Given that the researcher’s role as the instrument is pronounced in mixed methods research, we argue for reflexivity and explicit reporting of how researcher backgrounds shape the questions they ask, methods they use to answer them, and lenses through which they interpret findings. We encourage our colleagues to consider how the integration of numerical and textual data can enable us to ask questions that challenge traditional dichotomies, capture the complexities of human experiences, and generate more nuanced interpretations of findings that acknowledge the inherent limitations of relying on a single methodology or paradigm.