Learning Approach Research Articles

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.

Integrated analysis of gene expressions and targeted mirnas for explaining crosstalk between oral and esophageal squamous cell carcinomas through an interpretable machine learning approach.

This study explores the bidirectional relation of esophageal squamous cell carcinoma (ESCC) and oral squamous cell carcinoma (OSCC), examining shared risk factors and underlying molecular mechanisms. By employing random forest (RF) classifier, enhanced with interpretable machine learning (IML) through SHapley Additive exPlanations (SHAP), we analyzed gene expression from two GEO datasets (GSE30784 and GSE44021). The GSE30784 dataset comprises 167 OSCC samples and 45 control group, whereas the GSE44021 dataset encompasses 113 ESCC samples and 113 control group. Our analysis led to identification of 20 key genes, such as XBP1, VGLL1, and RAD1, which are significantly associated with development of ESCC and OSCC. Further investigations were conducted using tools like NetworkAnalyst 3.0, Single Cell Portal, and miRNET 2.0, which highlighted complex interactions between these genes and specific miRNA targets including hsa-mir-124-3p and hsa-mir-1-3p. Our model achieved high precision in identifying genes linked to crucial processes like programmed cell death and cancer pathways, suggesting new avenues for diagnosis and treatment. This study confirms the bidirectional relationship between OSCC and ESCC, laying groundwork for targeted therapeutic approaches. This study helps to identify shared biological pathways and genetic factors of these conditions for designing personalized medicine strategies and to improve disease management.

The Miniaturized Isolation of Neutrophil Granules (MING) method allowed a deep proteome mapping of human neutrophil granules.

Neutrophils are the innate immune system's first line of defense, and their storage organelles are essential to their function. The storage organelles are divided into three different granule types named azurophilic, specific, and gelatinase granules, besides a fourth component called secretory vesicles. The isolation of neutrophil's granules is challenging, and the existing procedures rely on large sample volumes, about 400 mL of peripheral blood, precluding the use of multiple biological and technical replicates. Therefore, the aim of this study was to develop a miniaturized isolation of neutrophil granules (MING) method, using biochemical assays, mass spectrometry-based proteomics and a machine learning approach to investigate the protein content of these organelles. Neutrophils were isolated from 40 mL of blood collected from three apparently healthy volunteers and disrupted using nitrogen cavitation; the organelles were fractionated with a discontinuous 3-layer Percoll density gradient. The isolation was proven successful and allowed for a reasonable separation of neutrophil's storage organelles using a gradient approximately 37 times smaller than the methods described in the literature. Moreover, mass spectrometry-based proteomics identified 368 proteins in at least 3 of the 5 analyzed samples, and using a machine learning strategy aligned with markers from the literature, the localization of 50 proteins was predicted with confidence. When using markers determined within our dataset by a clusterization tool, the localization of 348 proteins was confidently determined. Importantly, this study was the first to investigate the proteome of neutrophil granules using technical and biological replicates, creating a reliable database for further studies.

Automated identification and localization of interictal epileptiform discharges: leveraging morphological analysis, five-criterion fulfillment, and machine learning approach

Automated identification and localization of interictal epileptiform discharges: leveraging morphological analysis, five-criterion fulfillment, and machine learning approach

EEMD-MST-Resnet: a hybrid deep learning approach for predicting passenger flow in urban transportation hubs

EEMD-MST-Resnet: a hybrid deep learning approach for predicting passenger flow in urban transportation hubs

Large-scale identification of social and behavioral determinants of health from clinical notes: comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

Abstract Background Social and behavioral determinants of health (SBDH) are associated with a variety of health and utilization outcomes, yet these factors are not routinely documented in the structured fields of electronic health records (EHR). The objective of this study was to evaluate different machine learning approaches for detection of SBDH from the unstructured clinical notes in the EHR. Methods Latent Semantic Indexing (LSI) was applied to 2,083,180 clinical notes corresponding to 46,146 patients in the MIMIC-III dataset. Using LSI, patients were ranked based on conceptual relevance to a set of keywords (lexicons) pertaining to 15 different SBDH categories. For Generative Pretrained Transformer (GPT) models, API requests were made with a Python script to connect to the OpenAI services in Azure, using and models. Prediction of SBDH categories were performed using a logistic regression model that included age, gender, race and SBDH ICD-9 codes. Results LSI retrieved patients according to 15 SBDH domains, with an overall average PPV $$\ge$$ ≥ 83%. Using manually curated gold standard (GS) sets for nine SBDH categories, the macro-F1 score of LSI (0.74) was better than ICD-9 (0.71) and GPT-3.5 (0.54), but lower than GPT-4 (0.80). Due to document size limitations, only a subset of the GS cases could be processed by GPT-3.5 (55.8%) and GPT-4 (94.2%), compared to LSI (100%). Using common GS subsets for nine different SBDH categories, the macro-F1 of ICD-9 combined with either LSI (mean 0.88, 95% CI 0.82-0.93), GPT-3.5 (0.86, 0.82-0.91) or GPT-4 (0.88, 0.83-0.94) was not significantly different. After including age, gender, race and ICD-9 in a logistic regression model, the AUC for prediction of six out of the nine SBDH categories was higher for LSI compared to GPT-4.0. Conclusions These results demonstrate that the LSI approach performs comparable to more recent large language models, such as GPT-3.5 and GPT-4.0, when using the same set of documents. Importantly, LSI is robust, deterministic, and does not have document-size limitations or cost implications, which make it more amenable to real-world applications in health systems.

Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests

Background Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pathogens (CP), considering Whole Genome Sequencing (WGS) and antimicrobial susceptibility testing (AST). Methods The search covered databases including PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO, from their inception until June 2024. The review protocol was officially registered on PROSPERO (CRD42024543099). Results The review included 26 papers, analyzing data from 104,141 microbial samples. Random Forest (RF), XGBoost, and logistic regression (LR) emerged as the top-performing models, with mean Area Under the Receiver Operating Characteristic (AUC) values of 0.89, 0.87, and 0.87, respectively. RF showed superior performance with AUC values ranging from 0.66 to 0.97, while XGBoost and LR showed similar performance with AUC values ranging from 0.83 to 0.91 and 0.76 to 0.96, respectively. Most studies indicate that integrating WGS and AST data into ML models enhances predictive performance, improves antibiotic stewardship, and provides valuable clinical decision support. ML shows significant promise for predicting AMR by integrating WGS and AST data in CP. Standardized guidelines are needed to ensure consistency in future research.

Enhancing thermal management systems: a machine learning and metaheuristic approach for predicting thermophysical properties of nanofluids

Abstract In thermal engineering, predicting nanofluid thermophysical properties is essential for efficient cooling systems and improved heat transfer. Traditional methods often fall short in handling complex datasets. This study leverages machine learning (ML) and metaheuristic algorithms to predict key nanofluid properties, such as specific heat capacity (SHC), thermal conductivity (TC), and viscosity. By utilizing Artificial Neural Networks (ANN), Support Vector Regression (SVR), Gradient Boosting (GB), and Linear Regression (LR), alongside metaheuristic models like Differential Evolution (DE) and Particle Swarm Optimization (PSO), we achieve superior prediction accuracy compared to traditional models. The integration of these computational techniques with empirical data demonstrates their effectiveness in capturing the complex dynamics of thermofluids. Our results validate the precision of ML and metaheuristic models in predicting nanofluid properties and underscore their potential as robust tools for researchers and practitioners in thermal engineering. This work paves the way for future exploration of ML algorithms in thermal management, marking a significant advancement in optimizing nanofluid applications in industry and research.

Dynamic collaborative truck-drone delivery with en-route synchronization and random requests

Coordinated truck and drone delivery is gaining popularity in logistics as it can greatly reduce operation costs. However, existing studies on related operations management problems typically ignore the following important features: (i) the random appearance of requests, which require operators to dynamically respond to the requests; and (ii) the decisions of optimal launch and retrieval locations for trucks and drones instead of fixed to customer locations, which can significantly impact the overall time costs. To tackle these challenges, this study investigates the dynamic collaborative truck-drone routing problem with randomly arriving requests and synchronization on routes. We model the problem as a Markov Decision Process (MDP) and solve the MDP via a reinforcement learning (RL) approach. The proposed RL approach determines: (i) whether each request should be serviced upon arrival, (ii) which truck or drone should be assigned for the request, and (iii) the optimal en-route take-off and landing positions for paired trucks and drones. We further employ a framework of decentralized learning and centralized dispatching in RL to increase performance. Numerical experiments are conducted to assess the proposed solution approach on instances generated based on both the Solomon dataset and real-world operational data of a logistics operator in Singapore over several benchmark algorithms under various battery endurance levels of drones and distinct transportation scenarios including node-based dynamic collaborative truck-drone routing problem, dynamic non-collaborative truck and drone routing problem, and dynamic vehicle routing problem. The results show that our RL solution outperforms the benchmark algorithm in total profit by an average of 28.03 %, and our en-route takeoff and landing scenario outperforms the benchmark scenarios in total profit by an average of 8.43 % in multi-day instances. Additionally, compared to the traditional node-based landing scenario, employing our en-route takeoff and landing strategy can save 0.9 h/(drone*day) of waiting time on average.

Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach.

Cognitive functioning is associated with various factors, such as age, sex, education, and childhood adversity, and is impaired in people with psychosis. In addition to specific effects of the disorder, cognitive impairments may reflect a greater exposure to general risk factors for poor cognition. To determine the extent that impairments in cognition in psychosis reflect risk factor exposures. This cross-sectional study examined the relationship between exposures and cognitive function using data from the Bipolar-Schizophrenia Network on Intermediate Phenotypes studies 1 and 2 across 6 sites. Participants included healthy controls; patients with schizophrenia, schizoaffective disorder, or bipolar I disorder with psychosis; and relatives of patients. Predictive modeling was performed using extreme gradient boosting regression to train a composite cognitive score prediction model with nested cross-validation. Shapley additive explanations values were used to examine the relationship between exposures and cognitive function. Exposures were chosen based on associations with cognition previously identified: age, sex, race and ethnicity, childhood adversity, education, parental education, parental socioeconomic status, parental age at birth, substance use, antipsychotic dose, and diagnosis. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. A total of 3370 participants were included: 840 healthy controls, 709 patients with schizophrenia, 541 with schizoaffective disorder, 457 with bipolar I disorder with psychosis, and 823 relatives of patients. The mean (SD) age was 37.9 (13.3) years; 1887 were female (56%) and 1483 male (44%). The model predicted cognitive scores with high accuracy: out-of-sample Pearson correlation between predicted and observed cognitive composite score was r = 0.72 (SD = 0.03). Individuals with schizophrenia (z = -1.4), schizoaffective disorder (z = -1.2), and bipolar I disorder with psychosis (z = -0.5) all had significantly worse cognitive composite scores than controls. Factors other than diagnosis and medication accounted for much of this impairment (schizophrenia z = -0.73, schizoaffective disorder z = -0.64, bipolar I disorder with psychosis z = -0.13). Diagnosis accounted for a lesser proportion of this deficit (schizophrenia z = -0.29, schizoaffective disorder z = -0.15, bipolar I disorder with psychosis z = -0.13), and antipsychotic use accounted for a similar deficit across diagnostic groups (schizophrenia z = -0.37, schizoaffective disorder z = -0.33, bipolar I disorder with psychosis z = -0.26). This study found that transdiagnostic factors accounted for a meaningful share of the variance in cognitive functioning in psychosis. A significant proportion of the cognitive impairment in psychosis may reflect factors relevant to cognitive functioning in the general population. When considering interventions, a diagnosis-agnostic, symptom-targeted approach may therefore be appropriate.

Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review

Organic solar cells (OSCs), renowned for their lightweight, cost efficiency, and adaptability nature, stand out as a promising option for developing renewable energy. Improving the power conversion efficiency (PCE) of OSCs is essential, and researchers are delving into novel materials to achieve this. Traditional approaches are often laborious and costly, highlighting the need for predictive modeling. Machine learning (ML), especially via quantitative structure–property relationship (QSPR) models, is streamlining material development, with a goal to exceed a 20% PCE. In this review, the application of ML in OSCs is explored, and recent studies utilizing ML approaches for PCE prediction are reviewed, encompassing empirical functions, ML algorithms, self‐devised ML frameworks, and the combination with automated experimental technologies. First, the benefits of ML in predicting PCE for OSCs are addressed. Second, the development of high‐efficiency predictive models for both fullerene and nonfullerene acceptors is delved into. The impact of various ML algorithm models on PCE prediction is then assessed, taking into account the construction of predictive models. Moreover, the quality of databases and the selection of descriptors are considered. Databases and descriptors based on experimental studies are further categorized. Finally, prospects for the future development of OSCs are proposed.

An End-to-End Underwater Acoustic Target Recognition Model Based on One-Dimensional Convolution and Transformer

Underwater acoustic target recognition (UATR) is crucial for defense and ocean environment monitoring. Although traditional methods and deep learning approaches based on time–frequency domain features have achieved high recognition rates in certain tasks, they rely on manually designed feature extraction processes, leading to information loss and limited adaptability to environmental changes. To overcome these limitations, we proposed a novel end-to-end underwater acoustic target recognition model, 1DCTN. This model directly used raw time-domain signals as input, leveraging one-dimensional convolutional neural networks (1D CNNs) to extract local features and combining them with Transformers to capture global dependencies. Our model simplified the recognition process by eliminating the need for complex feature engineering and effectively addressed the limitations of LSTM in handling long-term dependencies. Experimental results on the publicly available ShipsEar dataset demonstrated that 1DCTN achieves a remarkable accuracy of 96.84%, setting a new benchmark for end-to-end models on this dataset. Additionally, 1DCTN stood out among lightweight models, achieving the highest recognition rate, making it a promising direction for future research in underwater acoustic recognition.

Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired NeuralNetworks for the 3D microstructure evolution of materials via Cahn-Hilliard flow

Abstract A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions as described by the Cahn-Hilliard equation. A specialized, physics-inspired architecture is proven to provide close accordance between the predicted evolutions and the ground truth ones obtained via conventional integration schemes. The method can accurately reproduce the evolution of microstructures not represented in the training set at a fraction of the computational costs. Extremely long-time extrapolation capabilities are achieved, up to reaching the theoretically expected equilibrium state of the system, consisting of a layered, phase-separated morphology, despite the training set containing only relatively-short, initial phases of the evolution. Quantitative accordance with the decay rate of the Free energy is also demonstrated up to the late coarsening stages, proving that this class of Machine Learning approaches can become a new and powerful tool for the long timescale and high throughput simulation of materials, while retaining thermodynamic consistency and high-accuracy.

Meningkatkan Keterampilan Berpikir Kritis Siswa Menggunakan Model Project Based Learning Berbantuan Media Audio Visual Fase C Kelas VI SDN 033913 Kalang Baru

This research aims to apply the Project Based Learning model to improve critical thinking skills of Phase C Class VI students of Elementary School UPT SD Negeri 033913 Kalang Baru. This research process uses a classroom action research (CAR) method assisted by audio-visual media involving 5 Phase C class VI students in the 2024/2025 academic year. The results of the analysis show a significant increase in students' critical thinking skills in the application of the Project Based Learning model in each cycle for two meetings. The implementation of the PjBL model steps that have been applied such as determining basic statements, preparing project plans, preparing schedules, monitoring, testing results, and evaluating experiences. The results of the observation show that each meeting gets a good average score. The overall achievement of activities at the first meeting reached 95% for the assessment of observer 1 and 96.25% for the assessment of observer 2. The overall achievement of activities at the second meeting reached 97.5%. The teacher's ability to Determine Basic Questions in cycle I increased by 50% to 83.3% with an increase of 66.7%. Teachers' ability in Making Project Design in cycle I increased by 80% to 90% with an increase of 12.5%. Teachers' ability in Monitoring Project Progress in Cycle I increased by 83.3% to 100% with an increase of 20%. Teachers' ability in Assessing Results in cycle I increased by 80% to 90% with an increase of 12.5%. Teachers' ability in Evaluating Experience in cycle I increased by 50% to 100% with an increase of 100%. Students' ability in Providing Simple Explanations in cycle I increased by 30% to 60% with an increase of 100%. Students' ability in Building Basic Skills increased by 26.7% with an increase of 100%. Students' ability in Making Inferences increased by 40% with an increase of 100%. Students' ability in Providing Further Explanations increased by 26.7% with an increase of 100%. Students' ability in Organizing Strategies and Tactics increased by 33.3% with an increase of 100%. The recommendations of this study include facilitating teachers in implementing an effective Project Based Learning model and expanding the project-based and interactive learning approach model.

Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such as fly ash and ground granulated blast furnace slag (GGBS). The accurate prediction of their compressive strength is crucial for optimizing their mix design and reducing experimental efforts. We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. Our results indicate that the SCA-RF model achieved a superior performance with a root mean square error (RMSE) of 1.562 and a coefficient of determination (R2) of 0.987, compared to the HHO-RF model, which obtained an RMSE of 1.742 and an R2 of 0.982. Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. This research underscores the potential of hybrid machine learning models in advancing sustainable construction materials through precise predictive modeling, paving the way for more environmentally friendly and efficient construction practices.

Integrating digital and interactive approaches in adolescent health literacy: a comprehensive review

Adolescent health literacy is critical for navigating the complex landscape of modern healthcare and making informed decisions that influence long-term health outcomes. This comprehensive review synthesizes current research on integrating digital tools and interactive learning approaches to enhance health literacy among adolescents. We explore the use of digital technologies, such as mobile apps and virtual reality, which cater to the preferences of this tech-savvy generation, offering personalized and accessible health information. The effectiveness of interactive learning methodologies, including simulations and role-playing, is also examined, highlighting their potential to increase engagement and retention of health-related knowledge. We address the importance of mental health literacy and the role of social media and peer education in disseminating health information effectively. Our review identifies gaps in the current literature, particularly the need for studies that consider long-term outcomes and the impact of socioeconomic and cultural factors on health literacy initiatives. We propose a multidimensional approach to health literacy education that incorporates innovative technologies and interactive methods to meet the diverse needs of adolescents in various contexts. The findings suggest that an integrated approach, including digital and critical health literacy, is essential for developing comprehensive health education programs that are both informative and engaging for adolescents.

Neutron-Alpha Reaction Cross Section Determination by Machine Learning Approaches

Neutron-Alpha Reaction Cross Section Determination by Machine Learning Approaches

Impact of Autoscaling on Application Performance in Cloud Environments

The current paper studies the impact of Autoscaling on application performance in Cloud computing environments. Cloud computing is one of the most encouraging innovations due to its vast applications. Predictive autoscaling is an advanced technique that aims to address the challenges in the autoscaling trends for large-scale systems. To account for the Quality of Service to the customer, features like load balancing according to the workload demand, understanding resource allocation and utilization, and dynamic decision-making are vital to any cloud computing application. This paper interprets these challenges and reviews a meta-reinforcement learning approach for predictive autoscaling in cloud environments. A novel RL-based predictive autoscaling approach on a popular large-scale digital payment platform system, Alipay, is compared with the existing models such as Autopilot and FIRM. The aim is to conduct a detailed analysis of performance metrics before and after autoscaling actions, aiming to identify optimal scaling strategies that minimize response time and maximize resource utilization without over- provisioning.

AKT and EZH2 inhibitors kill TNBCs by hijacking mechanisms of involution.

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and has the highest rate of recurrence1. The predominant standard of care for advanced TNBC is systemic chemotherapy with or without immunotherapy; however, responses are typically short lived1,2. Thus, there is an urgent need to develop more effective treatments. Components of the PI3K pathway represent plausible therapeutic targets; more than 70% of TNBCs have alterations in PIK3CA, AKT1 or PTEN3-6. However, in contrast to hormone-receptor-positive tumours, it is still unclear whether or how triple-negative disease will respond to PI3K pathway inhibitors7. Here we describe a promising AKT-inhibitor-based therapeutic combination for TNBC. Specifically, we show that AKT inhibitors synergize with agents that suppress the histone methyltransferase EZH2 and promote robust tumour regression in multiple TNBC models in vivo. AKT and EZH2 inhibitors exert these effects by first cooperatively driving basal-like TNBC cells into a more differentiated, luminal-like state, which cannot be effectively induced by either agent alone. OnceTNBCs are differentiated, these agents kill them by hijacking signals that normally drive mammary gland involution. Using a machine learning approach, we developed a classifier that can be used to predict sensitivity. Together, these findings identify a promising therapeutic strategy for this highly aggressive tumour type and illustrate how deregulated epigenetic enzymes can insulate tumours from oncogenic vulnerabilities. These studies also reveal how developmental tissue-specific cell death pathways may be co-opted for therapeutic benefit.

Forecasting public transit ridership amidst COVID-19: a machine learning approach

Forecasting public transit ridership amidst COVID-19: a machine learning approach