Processing Framework Research Articles

Data-driven distortion compensation for laser powder bed fusion process using Gaussian process regression and inherent strain method

The repeated melting and solidification cycles in the laser powder bed fusion (L-PBF) process lead to significant thermal gradients, resulting in notable distortion in the as-built part. Distortion compensation methods, which pre-deform the part design so the as-built shape aligns with the target, have been widely adopted to mitigate this issue. This research introduces a data-driven distortion compensation framework for the L-PBF process. It employs an experimentally-calibrated inherent strain method to generate a dataset and utilizes Gaussian process regression to create the compensated geometry. Experimental validation shows that the proposed method can reduce the maximum distortion by up to 82.5% for a lattice structure and 77.8% for a canonical part. Furthermore, the compensation results reveal that (1) the lumped layer thickness in finite element models has little impact on simulated distortion reduction but can notably affect the experimental reduction; (2) discrepancies between simulated and experimental compensation performance are largely attributed to the curvy surfaces with sharp transitions in trial and compensated shapes, a result of pre-deforming the design; (3) the number of trial geometries considerably affects the effectiveness of compensation, while the number of deformation states does not have a statistically significant impact.

Mid-Semester Course Evaluations: Meta Evaluation of Higher Education Course Coordinators’ Evaluative Practices

Background: Mid-semester evaluations are gaining traction as a means to gather evaluation data for formative purposes. However, it is not clear if course coordinators who conduct these evaluations are adequately equipped with evaluative knowledge and skills to guide them through their evaluative processes.Objectives: This study is a meta-evaluation of course coordinators’ mid-semester evaluative practices in a Higher Education institution language centre and the extent to which their evaluative practices are attuned to RUFDATA for its preparatory design framework in the evaluative process and Utilization-Focused Evaluation (UFE) for its sets of principles with regards to uses and users and how these impact on the design and use of the evaluation. Research design & subjects: Data was gathered using meta evaluative mixed methods from three groups of participants consisting of course coordinators, academics who taught on their courses and their learners.Results: The results imply that whilst most course coordinators who participated in the study conducted mid-semester evaluations and that their evaluative practices contained some elements from the frameworks, it is problematic to align their evaluative practices and processes against these tools due to the challenges that they encountered in their evaluative processes.Conclusions: As important agents of evaluations in mid semester evaluations, it should be noted that course coordinators are not trained evaluators and conducting evaluations does not constitute a major component of their professional duties and roles. This study has implications for higher education policies and practices, commissioners, and users of evaluations.

Appraising Linguistic and Reading Impairments in Kannada-Speaking Persons With Central Alexia.

Alexia is an acquired condition resulting in impaired abilities to comprehend and/or read aloud written scripts secondary to lesions in the brain involved in reading processes. Just as how linguistic aspects are multi-faceted in persons with aphasia (PWAs), the reading impairments also vary extensively across each PWA depending on the type and nature of the language deficits. Each language has its unique linguistic properties. Whether the impairment is in the spoken form or written form, the dissociations in both aspects across the linguistic distinctions are paramount. Given the diverse orthographic features of Kannada, alongside inquiries into the impairment in oral language skills among individuals with acquired reading difficulties, the study emphasizes distinguishing the concurrent language processes associated with reading impairment. The study aimed to explore the relationship between spoken language and reading abilities in Kannada-speaking post-stroke survivors with acquired alexia. The study recruited 15 Kannada-speaking adults with complaints of reading impairment post-ictus through convenient sampling. The enrolled participants included both males and females with a mean age of 42.2 years (SD=15.58; range=20-68) with 16 years of formal education. The study performed three primary investigations; a) linguistic, b) oral reading, and c) reading comprehension. Linguistic tasks entailed semantics, syntax, and phonological tasks. Oral reading entailed real word and non-word reading tasks. Reading comprehension tasks entailed single-word level, sentence-level, and paragraph-level tasks. Results of overall domain performance across linguistics, oral reading, and reading comprehension revealed superior performance in linguistics (M=71.77, SD=19.18) followed by reading comprehension (M=70.55, SD=24.10) and oral reading (M=41.55, SD=24.66), which was significant (p<0.05). Performance in phonology was weakest (M=58.06, SD=12.44) compared to syntax (M=71.13) and semantics (M=85.33) on comparing PWAs' abilities within the linguistic domain. Reading abilities were compared across oral reading and reading comprehension, which significantly varied (p<0.05), and reading comprehension abilities were superior (M=72.65, SD=24.10). Task-specific variabilities were significant (p<0.05) in both oral reading and reading comprehension, wherein, real word reading (M=58.22) and comprehension at a single level (M=80.22) were better performed. Results of correlation analysis revealed semantics (r=0.494, p<0.05) and phonology (r=0.428, p>0.05) were highly positively correlated to oral reading abilities. Syntax (r=0.412, p>0.05), and semantics (r=0.377, p>0.05) were strongly positively correlated to reading comprehension abilities. The study has convincingly shown that performance on reading-related tasks reflects the functionality of central semantic, phonological, and syntactic processing elements. The literate adults routinely interact with both spoken and written language and a comprehensive assessment framework of language processing must encompass both modalities (linguistic and reading) for individuals with alexia, specific to Indian languages. Owing to the transparent writing system of the Kannada language, a multimodal phonological and lexical-based reading treatment may prove to be beneficial in remediating reading impairments among adult post-stroke survivors.

The future is now: Implementation of standard of care, preemptive pharmacogenomic testing in patients with gastrointestinal cancers.

1546 Background: Preemptive germline pharmacogenomic (PGx) testing provides insight into the safety of high-risk chemotherapy and effective use of supportive care medications in gastrointestinal (GI) cancers. Advances in clinical practice guidelines and clinician support have accelerated the appetite for PGx testing, particularly for drugs with severe adverse reactions. The objective of this study was to assess operational outcomes associated with end-to-end electronic health record (EHR) integration of standard of care, preemptive multigene PGx testing in patients with GI cancers. Methods: Patients in two UCHealth GI oncology clinics were tested for five PGx genes related to 5-fluorouracil (5-FU, DPYD), irinotecan ( UGT1A1), and supportive care therapies ( CYP2C19, CYP2D6, CYP2C9). The PGx test order was embedded in all GI oncology treatment plans. Patient samples were genotyped, and PGx results were returned as discrete data to the EHR. Automated clinical decision support (CDS) tools provided PGx-guided recommendations at the point of prescribing. We leveraged the EPIS process framework and evaluated operational outcomes such as number of patients tested, lab turnaround time, patients with actionable phenotypes, and number of CDS alerts and clinical actions. Results: The analysis included 89 patients who received results for DPYD/UGT1A1 and 83 patients who received results for supportive care genes. Among samples tested, DPYD/UGT1A1 and supportive care gene results were returned to the EHR in an average of 4.7 ± 2 days (range 1-8) and 17.3 ± 3 days (range 9-24), respectively. The number of patients with abnormal phenotypes, defined as poor metabolizers for UGT1A1 and any non-normal phenotypes for the other genes, were UGT1A1, n=10 (11.2%); DPYD, n=4 (4.5%); CYP2C19, n=43 (51.8%); CYP2D6, n=31 (41.3%; 8 uninterpretable results); and CYP2C9, n=19 (22.9%). This equated to 14/89 (15.7%) and 68/83 (81.9%) patients having an abnormal result for DPYD/UGT1A1 and at least one supportive care gene, respectively. CDS alerts fired for 32 drug-gene interactions for 25 distinct patients. Of the alerts, 7 (21.9%) were for 5-FU or irinotecan, which showed for 7/89 patients (7.9%); 25 (78.1%) were for supportive care medications, which showed for 23/83 patients (27.7%). There were 9 clinical actions taken in response to the PGx alerts, 7 (77.8%) for 5-FU or irinotecan and 2 (22.2%) for supportive care drugs. Appropriate clinical actions were taken for 100% of patients with 5-FU or irinotecan drug-gene interactions. Conclusions: End-to-end EHR integration of PGx testing and prescribing guidance facilitated clinician adoption of preemptive, standard of care multigene PGx testing, with test results driving important medication changes. Future studies will evaluate the impact of PGx testing on clinical outcomes and cost effectiveness metrics in this population.

Designing Efficient Data Pipelines: A Framework for Ingestion, Processing, and Enrichment

Designing Efficient Data Pipelines: A Framework for Ingestion, Processing, and Enrichment

Topo: Towards a fine-grained topological data processing framework on Tianhe-3 supercomputer

Big data frameworks are widely deployed in supercomputers for analyzing large-scale datasets. Topological data processing is an emerging approach that focuses on analyzing the topological structures in high-dimensional scientific data. However, incorporating topological data processing into current big data frameworks presents three main challenges: (1) The frequent data exchange poses challenges to the traditional coarse-grained parallelism. (2) The spatial topology makes parallel programming harder using oversimplified MapReduce APIs. (3) The massive intermediate data and NUMA architecture hinder resource utilization and scalability on novel supercomputers and many-core processors.In this paper, we present Topo, a generic distributed framework that enhances topological data processing on many-core supercomputers. Topo relies on three concepts. (1) It employs fine-grained parallelism, with awareness of topological structures in datasets, to support interactions among collaborative workers before each shuffle phase. (2) It provides intuitive APIs for topological data operations. (3) It implements efficient collective I/O and NUMA-aware dynamic task scheduling to improve multi-threading and load balancing. We evaluate Topo's performance on the Tianhe-3 supercomputer, which utilizes state-of-the-art ARM many-core processors. Experimental results of execution time show that compared to popular frameworks, Topo achieves an average speedup of 5.3× and 6.3×, with a maximum speedup of 8.4× and 20×, on HPC workloads and big data benchmarks, respectively. Topo further reduces total execution time on processing skewed datasets by 41%.

Structured replacement policies for a system subject to random mission types

AbstractThis paper optimizes condition‐based replacement policies for a mission‐oriented system. The key challenge in our problem is that the system does not work under a fixed mission type but is subject to an infinite sequence of random types of missions assigned in a Markovian manner, which is realistic in many practical situations. The mission process modulates the deterioration process. Taking advantage of the opportunities when missions are switched, condition monitoring is conducted to support replacement decision‐making. This paper considers two practical scenarios in which the type of the next mission is either available or unavailable at each decision epoch. The objective is to determine the optimal replacement decisions for both scenarios that minimize their long‐run expected average cost rates. The optimization problems are analyzed in the framework of the Markov decision process. The optimal decisions of both scenarios are proven to be of partially monotone control‐limit forms. Near‐optimal policies with multilevel thresholds are provided for more convenient decision‐making. The policy iteration algorithm is modified for efficient optimization. A numerical example is used to demonstrate the feasibility of the proposed approach.

Transcriptional remodeling by OTX2 directs specification and patterning of mammalian definitive endoderm.

The molecular mechanisms that drive essential developmental patterning events in the mammalian embryo remain poorly understood. To generate a conceptual framework for gene regulatory processes during germ layer specification, we analyzed transcription factor (TF) expression kinetics around gastrulation and during in vitro differentiation. This approach identified Otx2 as a candidate regulator of definitive endoderm (DE), the precursor of all gut- derived tissues. Analysis of multipurpose degron alleles in gastruloid and directed differentiation models revealed that loss of OTX2 before or after DE specification alters the expression of core components and targets of specific cellular signaling pathways, perturbs adhesion and migration programs as well as de-represses regulators of other lineages, resulting in impaired foregut specification. Key targets of OTX2 are conserved in human DE. Mechanistically, OTX2 is required to establish chromatin accessibility at candidate enhancers, which regulate genes critical to establishing an anterior cell identity in the developing gut. Our results provide a working model for the progressive establishment of spatiotemporal cell identity by developmental TFs across germ layers and species, which may facilitate the generation of gut cell types for regenerative medicine applications.

A Review on India Traffic Sign Detection Techniques

This abstract delves into the realm of traffic sign detection techniques tailored for India's diverse and dynamic traffic environment. It navigates through traditional methods like color segmentation and template matching, juxtaposing them with the contemporary prowess of deep learning, particularly convolutional neural networks (CNNs). The intricacies of Indian roads, encompassing varied signage designs, fluctuating lighting conditions, and complex infrastructural nuances, are scrutinized in the context of these detection mechanisms. The narrative extends to discuss the amalgamation of IoT devices, real-time processing frameworks, and vehicle-mounted cameras to forge more efficient detection systems. Furthermore, the review underscores the transformative impact of machine learning advancements, spotlighting transfer learning and ensemble techniques as instrumental in augmenting detection accuracy and scalability. This abstract encapsulates a comprehensive exploration of India's traffic sign detection landscape, offering insights into ongoing trends, persistent challenges, and promising avenues for future research and development.

Analysis and Prediction of Risky Driving Behaviors Using Fuzzy Analytical Hierarchy Process and Machine Learning Techniques

Driver behavior plays a pivotal role in ensuring road safety as it is a significant factor in preventing traffic crashes. Although extensive research has been conducted on this topic in developed countries, there is a notable gap in understanding driver behavior in developing countries, such as Pakistan. It is essential to recognize that the cultural nuances, law enforcement practices, and government investments in traffic safety in Pakistan are significantly different from those in other regions. Recognizing this disparity, this study aims to comprehensively understand risky driving behaviors in Peshawar, Pakistan. To achieve this goal, a Driver Behavior Questionnaire was designed, and responses were collected using Google Forms, resulting in 306 valid responses. The study employs a Fuzzy Analytical Hierarchy Process framework to evaluate driver behavior’s ranking criteria and weight factors. This framework assigns relative weights to different criteria and captures the uncertainty of driving thought patterns. Additionally, machine learning techniques, including support vector machine, decision tree, Naïve Bayes, Random Forest, and ensemble model, were used to predict driver behavior, enhancing the reliability and accuracy of the predictions. The results showed that the ensemble machine learning approach outperformed others with a prediction accuracy of 0.84. In addition, the findings revealed that the three most significant risky driving attributes were violations, errors, and lapses. Certain factors, such as clear road signage and driver attention, were identified as important factors in improving drivers’ risk perception. This study serves as a benchmark for policymakers, offering valuable insights to formulate effective policies for improving traffic safety.

A three-dimensional viscoelastic–viscoplastic and viscodamage constitutive model for unidirectional fibre-reinforced polymer laminates

A novel 3D viscoelastic–viscoplastic and viscodamage constitutive model is proposed to predict the viscous effects due to dynamic loading conditions of unidirectional carbon fibre-reinforced polymer laminates at the meso-scale level. The present model is developed under continuum damage mechanics and the thermodynamics of irreversible processes framework. The viscoelastic response is modelled using the generalised Maxwell model, while an overstress model is employed to address the viscoplastic strain. The onset of the viscodamage mechanisms is based on experimental expressions, and their propagation is defined as a function of the corresponding fracture toughness. The mechanical response of the present constitutive model under pure longitudinal shear loading conditions at different strain rates is presented. The higher the strain rate is, the stiffer the responses in the viscoelastic and viscoplastic regions are. Additionally, the onset of viscodamage increases with higher strain rates. Off-axis compressive experimental data at two different strain rates are employed to demonstrate the capabilities of the present model with good predictions being obtained.

Knowledge identification for open innovation: a case study of biotechnology firms

PurposeDespite the increasing studies concerning external knowledge for innovative firms or for innovations, little research demonstrates as what are those capabilities that enable firms to explore and assess high-valuable knowledge. The aim of this study is to examine those capabilities that aid innovative firms to search and identify the potential knowledge for open innovation. This study is an empirical and theoretical examination of knowledge search and identification capabilities of the UK-based biotechnology firms. The literature has been drawn from the research streams into knowledge management, open innovation and dynamic capabilities.Design/methodology/approachForty-five semi-structured interviews with senior-level innovation and R&D managers were completed, and thematic analysis using NVivo 12 was performed.FindingsEvidence from 45 interviews with senior-level innovation and R&D managers in the UK-based biotechnology firms and thematic analysis was used to develop a process framework capturing the key capabilities. By identifying the role and contribution of each of the capabilities in knowledge identification in the practices of open innovation, this study contributes to open innovation and knowledge management literature.Practical implicationsManagerial implications suggest knowledge-oriented leads greatly influence the development of identification capabilities. Hence, firms with knowledge-oriented leads are better crafted approaches to achieve competitive advantages in the practices of open innovation.Originality/valueThis study offers a new theoretical perspective and empirical evidence to view knowledge identification capabilities as the dynamic capabilities. This study offers a process framework of identification capabilities.

Multi-Criteria Decision-Making Methods: A Case of Software Vendor Selection

Various multi-criteria decision-making methods have been utilized in literature to address vendor selection challenges in various contexts. This study reflects on these decision-making techniques for vendor selection in the software ecosystem during software outsourcing. For the same, firstly, a requirement framework for the decision-making process for software vendor selection is proposed. Afterwards, five selected different multi-criteria decision-making techniques are compared and critically analysed against this requirement framework. This study supports software practitioners and decision-makers by providing information about which decision-making method to choose, considering the trade-off between the benefits and drawbacks of adopting each method or using hybrid approaches by combining two or more techniques with fuzzy logic. It is found that decision-making techniques generally lack in modelling the problem itself and handling the conflicts and uncertainties that may prevail during the decision-making process. Furthermore, it has been identified that artificial neural networks (ANN) and analytic network process (ANP) can support the dependencies between alternatives and criteria while handling complex interactions in the software ecosystem. On the other hand, due to its comparative ease of use, analytic hierarchy process (AHP) can serve better in scenarios where decision-makers are not experienced, or past data to train the ANN or ANP models is unavailable.

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for “measuring carbon through electricity”, in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals.

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework.

In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes.

A deep learning framework for quality control process in the motor oil industry

Given the advancements in the modern world, using Multivariate-Multistage Quality Control (MVMSQC) patterns in continuous production industries is deemed crucial and essential. This study examines the importance and necessity of Multivariate-Multistage Quality Control in manufacturing industries, focusing on motor oil production. Motor oil quality significantly influences engine performance. Thus, the primary objective of this research is to enhance accuracy in detecting faults in quality variables, utilizing deep learning algorithms for visual quality control of data, a facet often overlooked in classical statistical methods and reducing process control time. Combining deep learning algorithms such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for controlling numerical variables, and Residual Networks (ResNet) and Dense Convolutional Networks (DenseNet) for controlling image variables have been employed. Additionally, the Honey Bee Mating Optimization Algorithm (GBC) has been utilized to tune the parameters of the LSTM-CNN and ResNet-DenseNet deep learning algorithms. Combining heuristic algorithms and deep learning algorithms enhances the performance of the final models in quality control processes. A case study in the motor oil production industry is examined to demonstrate the real-world application of the proposed model. The proposed LSTM-CNN hybrid algorithm outperforms individual CNN and LSTM algorithms in fault detection, improving performance by 15% and 8%, respectively. Furthermore, in visual components, the proposed ResNet-DenseNet hybrid algorithm has shown higher accuracy than ResNet and DenseNet algorithms, improving performance by 10% and 15%, respectively. This research significantly contributes to developing Multivariate-Multistage Quality Control in manufacturing industries by employing deep learning methods. The primary aim is to identify faults in quality components and utilize deep learning algorithms for visual quality control of data, a facet often overlooked in classical statistical methods. The research significantly advances multivariate-multistage quality control in manufacturing industries by leveraging deep learning methods. It addresses detecting complex nonlinear patterns in large-scale data with high flexibility and minimal time.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities.

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Analyzing group polarization through text emotion measurement and time series prediction: A comparative study across three online platforms

This study investigated the emotional trends of users on social platforms, considering the event of “the Changsha girl jumping off the Lalamove truck” as a case study. It examined the effects of recommendation algorithms and group social comparison attributes on group emotions across three platforms: Zhihu, Weibo, and Bilibili. Through text mining and emotion analysis algorithms, group reviews were analyzed, and an event-based ARIMA robustness detection model was constructed using time series data. Utilizing the theoretical framework of the social comparison process, the study discovered that the “information database” formed by the recommendation algorithms of social platforms fosters the emergence of emotional group polarization among users. Furthermore, the findings revealed that the audience's social comparison attributes play a role in shaping emotional group polarization. High knowledge attributes tend to inhibit emotional group polarization, while low knowledge attributes tend to promote it. Machine learning algorithms were employed to measure user sentiment in social media platform comments, revealing insights into the causes of group polarization through a comparison of social comparison attributes and algorithm techniques. Future studies must focus on measuring technical information entropy.

Community Tourism and Indigenous Territory Defense: The Case of San Miguel del Bala in the Bolivian Amazon

Summary In Bolivia, the Madidi National Park is home to the Tacana Indigenous Community “San Miguel del Bala,” which, within the framework of the plurinational process, obtained its title as a Community Territory of Origin in 2021. Concurrently, during the 1990s, the government began establishing the first protected areas, adopting regulations with a primary focus on biodiversity conservation. In some cases, this led to restrictions and limitations on economic and subsistence activities rooted in ancestral practices. Community-based tourism, implemented by San Miguel del Bala, emerged as one of the few possibilities to alleviate and address these restrictions. Simultaneously, it became a means of revaluing the culture. The overlapping of San Miguel del Bala as a Community Territory of Origin and a protected area involves constitutional rights for shared management, a process that currently has several limitations. Adding to this complex situation is the entrance of extractive projects that contradict conservation regulations and the rights of prior consultation with indigenous populations regarding resource exploitation. As a result, they pose threats to the lives, cultures, and territories of the Tacana. In this context, the development of indigenous community tourism is transforming into a strategy for the defense of ancestral Indigenous territory. Information © The Authors 2024

Towards a social determination of health framework for understanding climate disruption and health-disease processes.

We compare the social determinants of health (SDOH) and the social determination of health (SDET) from the school of Latin American Social Medicine/Collective Health. Whereas SDET acknowledges how capitalist rule continues to shape global structures and public health concerns, SDOH proffers neoliberal solutions that obscure much of the violence and dispossession that influence contemporary migration and health-disease experiences. Working in simultaneous ethnographic teams, the researchers here interviewed Honduran migrants in their respective sites of Honduras, Mexico, and the United States. These interlocutors connected their experiences of disaster and health-disease to lack of economic resources and political corruption. Accordingly, we provide an elucidation of the liberal and dehumanizing foundations of SDOH by relying on theorizations from Africana philosophy and argue that the social determination of health model better captures the intersecting historical inequalities that structure relationships between climate, health-disease, and violence.