Learning Approach Research Articles

Young People’s voices on social justice: a participatory storytelling and action learning approach

ABSTRACT South Africa is home to a large youth population, defined in South Africa as aged between 18 and 34 years. In working to transform our unequal society, we need to take diverse youth voices, including young people at university, into account regarding what matters to them. To enable their voices, the paper recounts a participatory storytelling research project with students in the educational space of one South African university. Using individual and collective digital storytelling, participants produced an archive of everyday stories, which then comprised a data base for them to analyse social in/justice issues and themes and co-create knowledge from the ground-up, with their emergent ideas categorised in a social injustice map. Through the iterative action learning and reflective process, participants developed their critical awareness of injustices and enhanced their sense of their own epistemic agency to contribute to change. Both the project process and the formation of intersecting capabilities for the co-creation of substantive reparative knowledge about social injustices and fairness were advanced through the project. Finally, it is suggested that these two capabilities might be applied to young people globally as crucial for contributions to epistemic agency and for making more just future societies.

Meshwork choreography: a pragmatic approach to collaborative embodied learning in South African higher education

ABSTRACT The diversity of the South African higher educational student population requires innovative pedagogical strategies that facilitate transformative and collaborative embodied learning. This article demonstrates how the choreographic process emerges from the context of multiculturalism through a pragmatic learning approach called meshwork choreography. Meshwork suggests an interwoven relation that emphasises the ever-evolving nature of relationships within an environment. Choreography as meshwork is where the choreographer does not impose preconceived ideas on the dancers but rather facilitates a collaborative and exploratory process. The research employed a conceptual framework of phenomenography, embodied and collaborative learning to frame the discussion towards a model of how choreography functions as meshwork. Composition and design processes in and of themselves embrace transformative learning through collaborative, embodied, and relational practices and are bolstered when they converge within the creation of choreographic work. This article reflects on students’ experiences in the Performing Arts Dance Stream at Tshwane University of Technology in conceptualising choreography as meshwork. Phenomenography was adopted as the research methodology, where data was analysed within phenomenographic categories. The article offers a South African perspective, informed by Ubuntu, of a meshwork model for the benefits of applying transformative learning through choreography within a higher education dance context.

GraphPI: Efficient Protein Inference with Graph Neural Networks.

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled data sets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a graph neural network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein data sets with pseudolabels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating data set-specific training, our research illustrates that GraphPI, due to the well-normalized nature of Percolator features, exhibits universal applicability without data set-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test data sets and deliver significantly reduced computation times compared to common protein inference algorithms.

Implementation of research, education and leadership placements into Operating Department Practitioner training: A 4-pillar practice-based learning approach.

Practice-based learning has traditionally focused on clinical practice in pre-registration courses. However, recent national strategies emphasise the importance of incorporation of all four pillars, clinical, education, leadership and research, into practice-based learning (placements). This article details the introduction of practice-based learning in research, education and leadership alongside clinical placements for BSc Operating Department Practice students at Sheffield Hallam University. It provides insights into the benefits of this approach, outlines the components of each placement with examples of completed projects, shares feedback from students and practice partners and addresses challenges. The authors advocate for adoption of this approach across all pre-registration Operating Department Practice courses, to ensure a workforce capable of meeting evolving health care needs and driving the Operating Department Practice profession forwards.

Prediction and feature selection of low birth weight using machine learning algorithms

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.

GWO-SVM: AN APPROACH TO IMPROVING SVM PERFORMANCE USING GREY WOLF OPTIMIZER IN INTELLECTUAL DISABILITY CLASSIFICATION

Intellectual disability (ID) is a neurodevelopmental disorder that requires early and accurate diagnosis. This study aims to improve the efficiency of ID diagnosis using a machine learning approach. A Support Vector Machine (SVM) model optimized with Grey Wolf Optimizer (GWO) was developed and trained using data from questionnaires completed by 101 families/guardians of ID patients at RSUD Dr. Soetomo Surabaya. The features used include family history, cognitive abilities, and adaptive behaviors. The results showed that the GWO-SVM model achieved an accuracy of 95% in classifying ID patients, an improvement of 5% compared to the conventional SVM. The GWO algorithm successfully optimized the parameters in SVM, resulting in a model with the best performance. These findings indicate the potential of GWO-SVM as an effective and efficient tool for assisting in the diagnosis of ID.

Prediction of electron-solid interaction parameters using machine learning.

Electron backscattering coefficient and electron-stopping power are essential concepts in many disciplines, from radiation to materials science, semiconductor manufacturing, and space exploration. They enable precise calculations, measurements, and simulations of electron interactions with matter, which contribute to advancing science, technology, and safety in a variety of applications. The availability of these data is fundamental to scientific research to validate hypotheses, conduct experiments, and explore new theories. A relatively novel machine learning approach has demonstrated notable success in enhancing data quality and completeness, significantly contributing to the facilitation of data discovery. Using fundamental material property data, the stacking ensemble machine learning (EML) technique was established in this study to generate electron-solid interaction parameters for any target material over a wide range of energies. The final stacking EML was built using the base and meta learners bagging regressor (BR), K-nearest neighbors (k-NN), random forest (RF), support vector regression (SVR), and eXtreme Gradient Boosting (XGB). In this study, two publicly available databases with a total of 4030 data points were used. Training datasets have 785 and 525 data points for electron backscattering coefficient and stopping power, respectively, whereas testing datasets contain 262 and 175 data points. Fivefeatures were used as input variables to train different individual algorithms and their combinations. On both the training and test datasets, the model was evaluated using different error metrics, including R-squared (R2), mean-absolute-error (MAE), root-mean-squared-error (RMSE), and mean-absolute-percentage-error (MAPE). Our model evaluation tests revealed that combining RF and XGB with a k-NN meta-learner outperformed other algorithms. The analysis of error metrics demonstrated a very close fit to all samples in each training dataset. Furthermore, predictions made by the model on unseen test data indicated accurate estimations of new backscattering and stopping power data. The developed model achieved high prediction accuracy for various target materials across the broad electron energy spectrum. The outcomes demonstrate the effectiveness of machine learning methodology and the chosen models' suitability for addressing substantial physics challenges.

A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification

In breast cancer research, diverse data types and formats, such as radiological images, clinical records, histological data, and expression analysis, are employed. Given the intricate nature of natural phenomena, relying on the features of a single modality is seldom sufficient for comprehensive analysis. Therefore, it is possible to guarantee medical relevance and achieve improved clinical outcomes by combining several modalities. The presen study carefully maps and reviews 47 primary articles from six well-known digital libraries that were published between 2018 and 2023 for breast cancer classification based on multimodal deep learning fusion (MDLF) techniques. This systematic literature review encompasses various aspects, including the medical modalities combined, the datasets utilized in these studies, the techniques, models, and architectures used in MDLF and it also discusses the advantages and limitations of each approach. The analysis of selected papers has revealed a compelling trend: the emergence of new modalities and combinations that were previously unexplored in the context of breast cancer classification. This exploration has not only expanded the scope of predictive models but also introduced fresh perspectives for addressing diverse targets, ranging from screening to diagnosis and prognosis. The practical advantages of MDLF are evident in its ability to enhance the predictive capabilities of machine learning models, resulting in improved accuracy across diverse applications. The prevalence of deep learning models underscores their success in autonomously discerning complex patterns, offering a substantial departure from traditional machine learning approaches. Furthermore, the paper explores the challenges and future directions in this field, including the need for larger datasets, the use of ensemble learning methods, and the interpretation of multimodal models.

Doctoral rhythms within an EdD: the case of group supervision

ABSTRACT This paper explores the application and use of group supervision with doctoral candidates studying a professional doctorate, the EdD, at a teaching intensive university. Group supervision is applied as a pedagogical strategy during the module stage of the award with candidates who were advanced practitioners in their fields with several working at the Post’92 university in question. Group supervision is an emergent idea has not been well described in the literature either as a concept or a practice. This paper aims to contribute to understandings of group supervision and its potential value as a doctoral learning approach. Rhythmanalysis is used to explore students’ experiences of group supervision particularly the advantages for advanced professionals from education who are at an early stage in their research career. The paper highlights the importance of the exploration and production of ideas within the group supervision process such as the containing of anxiety, development of identities, grappling with theories and methodologies and the value of peer learning and mattering. Group supervision itself could also be described as a meta-supervision approach with the supervisor stepping in and out of the doctoral learning to articulate and justify what was happening in relation to future learning demands.

The Application of Culturally Responsive Teaching (CRT) Approach In Learning The Indonesian Language

This research was conducted to reflect on teachers' performance and analyze the improvement of the quality of student learning in Indonesian language subjects. This research aims to explore the use of poetry learning strategies through CRT. The focus is on improving the quality of Indonesian language learning by implementing CRT in the students' environment. This research design uses a case study. Based on the implementation of Field Experience Practices, it was found that students looked sleepy and easily bored while learning the Indonesian language. This case was because Indonesian learning displays too much long text. So, the Culturally Responsive Teaching (CRT) approach was implemented to solve the problem. Teaching using the CRT approach in the Indonesian language subject was carried out for eight weeks with two sessions of meetings in class X.1 of State Senior High School 1 Jember. The results showed that students actively participate in the learning process. Besides, learners' learning activities support the existence of learning products. The Indonesian language learning product produced is a musicalization video of poetry. The product also received feedback from the teacher in the form of appreciation, enthusiasm, and motivation for students. The findings of this research show that improving the quality of learning can be achieved by utilizing CRT as a context in the development of texts and their instruction. The meaningfulness of the learning can be seen from the students' deep understanding, enabling them to grasp the benefits and functions of studying texts in Indonesian. This study recommends implementing Culturally Responsive Teaching in Indonesian language learning subjects to increase student learning activities. One of the activities of Culturally Responsive Teaching is creating appropriate activities for students' cultural backgrounds in the form of musicalization poetry.

Evaluation of Advances in Battery Health Prediction for Electric Vehicles from Traditional Linear Filters to Latest Machine Learning Approaches

In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles.

Constructivist Approach to Language Learning: Linking Piaget's Theory to Modern Educational Practice

This study aims to explore the application of the constructivism approach in language learning by connecting Jean Piaget's theory of cognitive development with modern educational practices. Constructivism theory emphasizes that learning is an active process in which students build knowledge through experience and interaction with the environment. In the context of language learning, this approach is considered relevant to increase student engagement and support more meaningful learning. This study uses a qualitative approach with a case study design, involving in-depth interviews, classroom observations, and document analysis. Participants consisted of 10 teachers and 20 students who were selected by purposive sampling based on their experience in applying constructivist learning methods. The results show that the constructivism approach can increase students' active participation, improve critical thinking skills, and provide a more contextual and relevant language learning experience. Teachers who apply Piaget's principles, such as experiential learning, reflection, and collaboration, report increased student learning motivation. However, challenges in implementing this method include time constraints, a rigorous curriculum, and a lack of support resources for project-based learning. The study concludes that although constructivist approaches have great potential in language learning, further support is needed in terms of curriculum flexibility and resources. Recommendations for educators include increased use of technology and project-based teaching strategies to support more interactive and participatory language learning.

Analisis Perbandingan Pembelajaran Individu dengan Pembelajaran Berkelompok Melalui Model Pembelajaran Kontekstual Mata Pelajaran Ekonomi terhadap Hasil Belajar di SMPN 1 Borbor

The problem in this study is to find out how individual learning compares with group learning through the contextual learning model of economics subjects on learning outcomes at SMPN 1 Borbor on various materials of human needs and fulfillment tools with a sample of 60 students. The research method in this study is used with a quantitative method. The students' scores in individual learning through contextual learning have an average score of 78.7586 with a standard deviation of 6.836 and group learning through the contextual learning model has an average score of 88.6667 with, so it can be concluded that there is a significant difference between the variables X1 (individual learning through the contextual learning model) X2 (group learning through contextual learning). As a study conducted by Suhprihtin Siti, et al, the influence of economic learning with a contextual teaching and learning (CTL) approach on economic learning outcomes is reviewed from the initial ability of students

Thawed Gaussian Wavepacket Dynamics with Δ-Machine-Learned Potentials.

A method for performing variable-width (thawed) Gaussian wavepacket (GWP) variational dynamics on machine-learned potentials is presented. Instead of fitting the potential energy surface (PES), the anharmonic correction to the global harmonic approximation (GHA) is fitted using kernel ridge regression─this is a Δ-machine learning approach. The training set consists of energy differences between ab initio electronic energies and values given by the GHA. The learned potential is subsequently used to propagate a single thawed GWP by using the time-dependent variational principle to compute the autocorrelation function, which provides direct access to vibronic spectra via its Fourier transform. We applied the developed method to simulate the photoelectron spectrum of ammonia and found excellent agreement between theoretical and experimental spectra. We show that fitting the anharmonic corrections requires a smaller training set as compared to fitting total electronic energies. We also demonstrate that our approach allows to reduce the dimensionality of the nuclear space used to scan the PES when constructing the training set. Thus, only the degrees of freedom associated with large-amplitude motions need to be treated with Δ-machine learning, which paves a way for reliable simulations of vibronic spectra of large floppy molecules.

Effective Hard Negative Mining for Contrastive Learning-based Code Search

Background. Code search aims to find the most relevant code snippet in a large codebase based on a given natural language query. An accurate code search engine can increase code reuse and improve programming efficiency. The focus of code search is how to represent the semantic similarity of code and query. With the development of code pre-trained models, the pattern of using numeric feature vectors (embeddings) to represent code semantics and using vector distance to represent semantic similarity has replaced traditional string matching methods. The quality of semantic representations is critical to the effectiveness of downstream tasks such as code search. Currently, the state-of-the-art (SOTA) learning method uses the contrastive learning paradigm. The objective of contrastive learning is to maximize the similarity between matching code and query (positive samples) and minimize the similarity between mismatched pairs (negative samples). To increase the reusing of negative samples, prior contrastive learning approaches use a large queue (memory bank) to store embeddings. Problem. However, there is still a lot of room for improvement in using negative examples for code search: ① Due to the random selection of negative samples, semantic representations learned by existing models cannot distinguish similar codes well. ② Since semantic vectors in the memory bank are reused from previous inference results and then directly used for loss function calculation without gradient descent, the model cannot effectively learn the negative sample semantic information. Method. To solve the above problems, we propose a contrastive learning code search model with hard negative mining called CoCoHaNeRe: ❶ To enable the model to distinguish similar codes, we introduce hard negative examples into contrastive training, which are negative examples in the codebase that are most similar to positive examples. As a result, hard negative examples are most likely to make the model make mistakes. ❷ To improve the learning efficiency of negative samples during training, we add all hard negative examples to the model's gradient descent process. Result. To verify the effectiveness of CoCoHaNeRe, we conducted experiments on large code search datasets with six programming languages, as well as similar retrieval tasks code clone detection and code question answering. Experimental results show that our model achieves SOTA performance. In the code search task, the average MRR score of CoCoHaNeRe exceeds CodeBERT, GraphCodeBERT, and UniXcoder by 11.25%, 8.13%, and 7.38%, respectively. It has also made great progress in code clone detection and code question answering. In addition, our method performs well in different programming languages and code pre-training models. Furthermore, qualitative analysis shows that our model effectively distinguishes high-order semantic differences between similar codes.

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

Eau de co-operation: An exploration of the essence of co-operative learning

At the Co-operative College we conducted action research to explore co-operative learning and what this means to others. Over a period of four months, some of our learners, members, College team, and participants from the further and higher education sectors all took part in a variety of interactive workshops using co-operative learning approaches. We used a qualitative research method to gather participant thinking, which was underpinned by a metaphorical framework: the art of distilling fragrances. Fragrances are created through the process of blending a variety of different ingredients (notes) that produce both range and depth; these top, base, and heart notes are distilled together to produce a unique fragrance. What is more, each fragrance profile is endlessly adaptable, changing its character through time, space, and context. Co-operative learning too is built on a blend of ingredients, such as individual and co operative values, lived experience, and a range of participatory learning theories. The findings suggested that top notes happen before the learning takes place, while heart notes occur during learning, and the base notes come about after the learning process. Equally, throughout the whole learning process it became clear that for co-operative learning to be effective, each stage is blended through common themes which were identified. Analysis of the data collected identified that six consistent learning influences are present within the top, heart, and base notes of learning, namely social, environmental, equitable, cultural, behavioural, and developmental influences.

An integrative predictive model for orthokeratology lens decentration based on diverse metrics

PurposeTo develop a predictive model for orthokeratology (Ortho-K) lens decentration 1 month after wear.MethodsThis study included myopic children who were fitted with Ortho-K lenses at Fujian Provincial Hospital between December 2022 and May 2024. Corneal topography parameters and other relevant metrics were collected pre- and post-treatment. Feature selection was conducted using univariate logistic regression and Lasso regression analysis. A machine learning approach was used to develop multiple predictive models, including Decision Tree, Logistic Regression, Multilayer Perceptron, Random Forest, and Support Vector Machine. Model performance was evaluated using accuracy, sensitivity, specificity, ROC curves, DCA curves, and calibration curves. SHAP values were employed to interpret the models.ResultsThe Logistic Regression model demonstrated the best predictive performance, with an AUC of 0.82 (95% CI: 0.69–0.95), accuracy of 77.59%, sensitivity of 85%, and specificity of 61.11%. The most significant predictors identified were age, 8 mm sag height difference, 5 mm Kx1, and 7 mm Kx2. SHAP analysis confirmed the importance of these features, particularly the 8 mm sag height difference.ConclusionsThe Logistic Regression model successfully predicted the risk of Ortho-K lens decentration using key corneal morphological metrics and age. This model provides valuable support for clinicians in optimizing Ortho-K lens fitting strategies, potentially reducing the risk of adverse outcomes and improving the quality of vision for patients. Further validation in clinical settings is recommended.

Optimizing Distributed Energy Resources in Microgrid SCUC through Seq2Seq Scheduling Algorithms

In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. Significant gains in economic efficiency, ecological viability, and compliance with security constraints have been shown. This study lays the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization.

US federal resource allocations are inconsistent with concentrations of energy poverty.

Recent data from the US Energy Information Administration reveals that nearly one in three households in the United States report experiencing energy poverty, and this number is only expected to rise. Federal assistance programs exist, but allocations across states have been nearly static since 1984, while the distribution of energy poverty is dynamic in location and time. We implement a LASSO-based machine learning approach using sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. We then compare the allocation to states from the Low Income Home Energy Assistance Program to an optimized allocation. We allocate funds to the most burdened households, providing them with enough assistance to reduce their energy expenditures so that their household energy burden is equal to a new maximum allowable energy burden. This markedly shifts funds from the northern cold-weather states to the southern warm-weather states.