Article
Chemistry, Multidisciplinary
Jemin Lee, Sihyeong Park, Taeho Kim, Hyungshin Kim
Summary: The increasing number of notifications from smartphones and wearable devices causes mental burdens, decreases productivity, and wastes energy. Previous notification management systems have difficulty handling new notifications, hence a long-term study was conducted to investigate behavioral changes and improve accuracy through an online learning windowing method.
APPLIED SCIENCES-BASEL
(2022)
Article
Ecology
Celso Augusto Guimaraes Santos, Gleycielle Rodrigues do Nascimento, Camilo Allyson Simoes de Farias, Richarde Marques da Silva, Manoranjan Mishra
Summary: This study presents a methodology that improves streamflow forecasting by using wavelet neural networks to relate streamflows with rainfall data. The methodology performs well in long-term forecasts, especially in the Mahanadi River basin, and can be applied in other catchments.
ECOLOGICAL INFORMATICS
(2023)
Article
Chemistry, Multidisciplinary
Stefan Ungureanu, Vasile Topa, Andrei Cristinel Cziker
Summary: In the current trend of consumption, electricity consumption will become a high cost for end-users. This study proposes a deep learning method for accurately forecasting industrial electric usage, automate the prediction process, and optimize the operation of power systems.
APPLIED SCIENCES-BASEL
(2021)
Article
Thermodynamics
Radek Svoboda, Vojtech Kotik, Jan Platos
Summary: The development of natural gas consumption forecasting tools is crucial for forecasting models, with research efforts focused on creating a new dataset with relevant data features. A forecasting methodology is proposed to evaluate statistical and machine learning algorithms in the time series forecasting domain, along with the availability of the new dataset for research use.
Article
Engineering, Electrical & Electronic
Paulina Bartkowiak, Mariapina Castelli, Alice Crespi, Georg Niedrist, Damiano Zanotelli, Roberto Colombo, Claudia Notarnicola
Summary: In this article, a new method is presented to predict satellite-derived land surface temperature under cloudy skies over vegetated areas in the Alps. The method utilizes ground-measured temperature data and other geo-biophysical variables in conjunction with the ESRA radiation model to establish predictive models. The results show the feasibility and reliability of the method in heterogeneous ecosystems.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Engineering, Civil
Songhua Huan
Summary: In this study, a novel IDCPSO-ELM model is proposed to improve long-term water quality prediction ability. The model uses various decomposition methods and optimization algorithms to reconstruct and extract features, and achieves better performance compared to other popular models in short-term and long-term prediction.
JOURNAL OF HYDROLOGY
(2023)
Article
Medicine, General & Internal
Vida Abedi, Venkatesh Avula, Durgesh Chaudhary, Shima Shahjouei, Ayesha Khan, Christoph J. Griessenauer, Jiang Li, Ramin Zand
Summary: Machine-learning models were trained to predict long-term stroke recurrence using patient-level data and interpretable algorithms, identifying important clinical features such as age, body mass index, and laboratory variables. Model performance could be optimized through different strategies to improve the balance between specificity and sensitivity.
JOURNAL OF CLINICAL MEDICINE
(2021)
Article
Engineering, Civil
Ather Abbas, Minji Park, Sang-Soo Baek, Kyung Hwa Cho
Summary: Accurate estimation of harmful algal blooms is crucial for protecting surface water. Recent studies have shown the potential of deep learning models for hydrological and Chl-a simulations. However, past researches were primarily focused on developing site-dependent models, limiting their applicability. This study proposes a DL-based framework that can be used across different sites and compares the performance of six state-of-the-art DL methods. The results demonstrate that the attention-based IA-LSTM method outperforms others in predicting Chl-a concentration.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Civil
Francesco Granata, Fabio Di Nunno, Giovanni de Marinis
Summary: Prediction of river flow rates is a challenging task due to the high uncertainty associated with basin characteristics, hydrological processes, and climatic factors. This study compares two different daily streamflow prediction models and finds that they have comparable forecasting capabilities. The stacked model based on the Random Forest and Multilayer Perceptron algorithms outperforms the bi-directional LSTM network model in predicting peak flow rates, but is less accurate in forecasting low flow rates. The prediction accuracy of both models decreases as the forecast horizon increases. The length of the time series and the presence of outliers in the data can also affect the accuracy of the prediction models.
JOURNAL OF HYDROLOGY
(2022)
Article
Construction & Building Technology
Sheng Liu, Aaron Zeng, Kevin Lau, Chao Ren, Pak-wai Chan, Edward Ng
Summary: This study on future long-term monthly electricity demand in Hong Kong found that the Gradient Boosting Decision Tree (GBDT) method performed the best in terms of accuracy, generalization ability, and time-series stability, while the Artificial Neural Network (ANN) method exhibited the lowest accuracy and lower generalization ability.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Computer Science, Artificial Intelligence
Yingying Zhu, Minjeong Kim, Xiaofeng Zhu, Daniel Kaufer, Guorong Wu
Summary: Neuroimaging investigations for early detection and diagnosis of Alzheimer's disease have been driven by the significant social and economic cost. Current computational approaches applied to longitudinal imaging data in subjects with Mild Cognitive Impairment aim to increase sensitivity for detecting changes and potentially serve as a diagnostic biomarker for AD. However, the lack of robust predictive power in current brain imaging diagnostic methods limits their utility in clinical practice.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Water Resources
Chien Pham Van, Huu Duy Nguyen, Quoc-Huy Nguyen, Quang-Thanh Bui
Summary: The objective of this study was to develop an advanced method using LSTM, SVM, and RF to predict streamflow in the Mekong Delta in Vietnam, which is crucial for food security. Water level and flow data from 2014 to 2018 were used as input for the prediction model. The results showed that the SVM and RF models improved the performance of the LSTM model, with R-2 > 80%. LSTM was found to be a robust technique for characterizing and predicting time series behaviors in hydrology applications.
JOURNAL OF WATER AND CLIMATE CHANGE
(2023)
Article
Computer Science, Artificial Intelligence
Guido Perboli, Ehsan Arabnezhad
Summary: In this paper, the focus is on improving the accuracy of bankruptcy prediction for small and/or medium enterprises in the short term using machine learning techniques, while also making accurate mid- and long-term predictions. Extensive computational tests in Italy demonstrate the efficiency and accuracy of the developed method, showing its potential as a tool for developing strategies and policies for the entire economic system. Additionally, the study discusses how the method can be utilized for large-scale policy-making in the context of the recent COVID-19 pandemic.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Environmental Sciences
Roberto Cazzolla Gatti, Arianna Di Paola, Alfonso Monaco, Alena Velichevskaya, Nicola Amoroso, Roberto Bellotti
Summary: Tumours have become the second leading cause of death after cardiovascular diseases. Recent research suggests that environmental pollution is one of the main triggers, but governments and institutions have not prioritized the study of cancer's environmental connections. A detailed study shows a correlation between environmental pollution and cancer mortality, with higher mortality rates in highly polluted areas despite healthier lifestyles. The quality of air plays the most important role in influencing cancer mortality rates.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Computer Science, Artificial Intelligence
Samarra Badrouchi, Abdulaziz Ahmed, Mohamed Mongi Bacha, Ezzedine Abderrahim, Taieb Ben Abdallah
Summary: This study proposes a machine learning framework to predict graft survival after five years of kidney transplantation and determines the most influential parameters. The XGBoost algorithm was found to be the best model with high AUC and sensitivity. The framework can serve as a decision support system for Nephrologists to provide safer treatment recommendations and achieve positive kidney transplant outcomes.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Editorial Material
Psychology, Clinical
Matthew J. Hirshberg, Simon B. Goldberg, Melissa Rosenkranz, Richard J. Davidson
PSYCHOLOGICAL MEDICINE
(2023)
Article
Education & Educational Research
Karen Kurotsuchi Inkelas, Blake A. Colaianne, Matthew J. Hirshberg, Mark T. Greenberg, Richard J. Davidson, John D. Dunne, David Germano, Robert W. Roeser
Summary: This study investigates whether variability in the implementation of an undergraduate course on human flourishing is differentially associated with student outcomes. The findings suggest that despite differences in teaching methods and student engagement, the outcomes of the students are similar. Institutions interested in offering this course can make limited adaptations without concerns of altering its impact on students.
JOURNAL OF AMERICAN COLLEGE HEALTH
(2023)
Article
Psychology, Developmental
Elizabeth M. Planalp, Kristin N. Dowe, Andrew L. Alexander, H. Hill Goldsmith, Richard J. Davidson, Douglas C. Dean III
Summary: This study examines the neural correlates of negative emotions in infants and found that the microstructure of white matter tracts at 1 month of age is associated with the expression of fear later in infancy. Specifically, the left stria terminalis, a tract connecting frontal and tempo-parietal regions, showed differential associations with the level and change in infant fear. These findings suggest the unique neurobehavioral characteristics of fear as early as 1 month of age and contribute to our understanding of affective development.
DEVELOPMENTAL SCIENCE
(2023)
Article
Education & Educational Research
Matthew J. J. Hirshberg, Richard J. J. Davidson, Simon B. B. Goldberg
Summary: Educator mental health is connected to several urgent educational issues. A study found that during the COVID-19 pandemic, a majority of school system employees experienced clinically significant anxiety and depressive symptoms. Lower family income was associated with higher stress levels, more severe depressive symptoms, and decreased intentions to stay in the same job, contributing to the current staffing shortages in schools. Supporting the mental health of educators should be prioritized as a policy.
EDUCATIONAL RESEARCHER
(2023)
Article
Psychology, Clinical
Sin U. Lam, Kevin M. Riordan, Otto Simonsson, Richard J. J. Davidson, Simon B. B. Goldberg
Summary: Despite the well-documented psychological benefits of meditation practice, limited research has examined factors associated with meditation practice persistence. This study explored rates and correlates of meditation persistence using a population-based sample in the USA. The findings provide insights into factors that may promote persistence with meditation, which can guide the delivery of meditation training.
Article
Clinical Neurology
P. A. Rowley, M. J. Paukner, L. B. Eisenmenger, A. S. Field, R. J. Davidson, S. C. Johnson, S. Asthana, N. A. Chin, V. Prabhakaran, B. B. Bendlin, B. R. Postle, H. H. Goldsmith, C. M. Carlsson, M. A. Brooks, N. H. Kalin, L. E. Williams, H. A. Rowley
Summary: This study investigated 16,400 brain MRIs and found that incidental findings are common, ranging from trivial to life-threatening. Formal neuroradiologist interpretation yielded more reliable results compared to spontaneous detection by nonradiology scanning staff.
AMERICAN JOURNAL OF NEURORADIOLOGY
(2023)
Article
Neurosciences
Olivia Surgent, Jose Guerrero-Gonzalez, Douglas C. Dean III, Gregory R. Kirk, Nagesh Adluru, Steven R. Kecskemeti, Andrew L. Alexander, Brittany G. Travers
Summary: This study investigated the associations between variations in grip strength and white matter microstructure of lateral grasping, proprioception input, and cortico-cerebellar modification networks among 70 children using high resolution, multi-shell diffusion and quantitative T1 imaging. The results revealed that stronger grip strength was associated with higher fractional anisotropy and R1 values in the lateral grasping and proprioception input networks, indicating stronger microstructural coherence and increased myelination. No relationships were found in the cerebellar modification network. These findings provide a neurobiological mechanism for grip behavior in children and suggest that increased myelination of cortical sensory and motor pathways is associated with stronger grip.
Article
Multidisciplinary Sciences
Wendy C. Crone, Pelin Kesebir, Beverly Hays, Shilagh A. Mirgain, Richard J. Davidson, Susan C. Hagness
Summary: The mental health crisis in graduate education highlights the importance of engineering graduate programs providing effective methods to promote well-being. Mindfulness-based training has been found to improve emotional well-being and research capacity. A study conducted on engineering graduate students showed significant improvements in emotional health, neuroticism, positive affect, negative affect, and mindfulness after participating in a mindfulness training program.
Article
Psychology, Social
Qinggang Yu, Stacey M. Schaefer, Richard J. Davidson, Shinobu Kitayama
Summary: The study examined the moderating role of behavioral adjustment on the relationship between neuroticism and brain structure. Findings indicated that behavioral adjustment significantly moderated the effect of neuroticism on total brain volume (TBV), with a negative association between neuroticism and TBV only observed when behavioral adjustment was low.
JOURNAL OF PERSONALITY
(2023)
Article
Education & Educational Research
Matthew J. Hirshberg, Blake Colaianne, Karen Kurotsuchi Inkelas, Godwill Oke, Natalia Van Doren, Richard J. Davidson, Robert W. Roeser
Summary: The objective of this study was to evaluate the impact of the COVID-19 pandemic on college student mental health. The study found that anxiety, depression, and well-being of college students did not significantly worsen during the pandemic compared to before. Additionally, more frequent in-person social interactions were associated with lower anxiety and depressive symptoms, higher well-being, but also less compliance with handwashing and face mask-wearing.
JOURNAL OF AMERICAN COLLEGE HEALTH
(2023)
Article
Clinical Neurology
Daniel Y. Chu, Nagesh Adluru, Veena A. Nair, Timothy Choi, Anusha Adluru, Camille Garcia-Ramos, Kevin Dabbs, Jedidiah Mathis, Andrew S. Nencka, Carson Gundlach, Lisa Conant, Jeffrey R. Binder, Mary E. Meyerand, Andrew L. Alexander, Aaron F. Struck, Bruce Hermann, Vivek Prabhakaran
Summary: This study examined the association between white matter connectivity abnormalities in temporal lobe epilepsy and neighborhood disadvantage. The findings suggest that while epilepsy has a larger impact on brain connectivity, neighborhood disadvantage does have a modest relationship with white matter structure and integrity in epilepsy patients.
Article
Psychology, Clinical
Simon B. B. Goldberg, Zishan Jiwani, Daniel M. M. Bolt, Kevin M. M. Riordan, Richard J. J. Davidson, Matthew J. J. Hirshberg
Summary: Bidirectional associations between alliance and distress were found in a 4-week smartphone-based meditation intervention, similar to results from in-person psychotherapy. Alliance may play an important role in promoting engagement and effectiveness within unguided mobile-health interventions. Measuring alliance in unguided mHealth tools may improve their acceptability and effectiveness.
CLINICAL PSYCHOLOGICAL SCIENCE
(2023)
Article
Psychology, Clinical
Christian A. Webb, Matthew J. Hirshberg, Oscar Gonzalez, Richard J. Davidson, Simon B. Goldberg
Summary: There is limited understanding of the mechanisms underlying the beneficial effects of psychosocial interventions. This study demonstrates the importance of considering individual differences and subgroup-specific mediators in understanding the mechanisms of change. By identifying baseline characteristics that predict differential response, we can gain insights into why certain interventions work for specific subgroups and inform personalized interventions.
JOURNAL OF CONSULTING AND CLINICAL PSYCHOLOGY
(2023)
Review
Psychology
Laura D. Kubzansky, Eric S. Kim, Julia K. Boehm, Richard J. Davidson, Jeffrey C. Huffman, Eric B. Loucks, Sonja Lyubomirsky, Rosalind W. Picard, Stephen M. Schueller, Claudia Trudel-Fitzgerald, Tyler J. VanderWeele, Katey Warran, David S. Yeager, Charlotte S. Yeh, Judith T. Moskowitz
Summary: Psychological well-being is associated with better physical health and can be improved through interventions. To improve population health, interventions need to be adapted and durable, and a shift to a public-health model is required. Interventions should be accessible and effective for diverse populations.
Article
Behavioral Sciences
Heather C. Abercrombie, Alexandra L. Barnes, Elizabeth C. Nord, Anna J. Finley, Estelle T. Higgins, Daniel W. Grupe, Melissa A. Rosenkranz, Richard J. Davidson, Stacey M. Schaefer
Summary: A greater cortisol response to acute stress is associated with smaller increases in negative affect, suggesting mood-protective effects of cortisol elevations.
STRESS-THE INTERNATIONAL JOURNAL ON THE BIOLOGY OF STRESS
(2023)
