Short-term Learning Research Articles

Early Detection of Potato Disease Using an Enhanced Convolutional Neural Network-Long Short-Term Memory Deep Learning Model

Early Detection of Potato Disease Using an Enhanced Convolutional Neural Network-Long Short-Term Memory Deep Learning Model

The paradox of growing technical capacities with low global governance: a review of Voluntary National Reviews’ SDG health-related indicators

BackgroundThis study delves into the States’ accountability for health-related Sustainable Development Goal (SDG) indicators from 2016 to 2020. An analysis of Voluntary National Reviews (VNR) is employed as an instrument to scrutinize the alignment of States’ indicators with the global indicator framework, shedding light on global health governance within the context of the 2030 Agenda and States’ strategic prioritization. A curation of 60 health-related indicators from 195 VNRs, produced during the aforementioned period, is organized into thematic groups.ResultsOur results highlight a concerning discrepancy in the reporting frequency of various health-related themes. The findings reveal a paradoxical coexistence characterized by the concurrent strengthening and diminution of the global health governance articulated in the Agenda’s global health governance. This manifests in the increased utilization and consistency of health-related indicators over the study years, coupled with an emphasis on infectious diseases and child and maternal health indicators. Conversely, a discernible governance decline is evidenced by the inadequate representation of health-related indicators in VNRs, notably within the domains of universal health coverage and health system indicators. Furthermore, High-Income States exhibit diminished accountability.ConclusionsThe VNRs unveil a paradox wherein burgeoning technical capacity coexists with governance deficits, a phenomenon attributable to both statistical capabilities and political preferences. The prevalent use of proxy indicators in VNRs oversimplifies the presentation of official indicators, thereby compromising the aspirational goal of pioneering statistical innovations for measuring intricate issues in the SDGs. In light of our conceptualization of the 2030 Agenda’s global health as a regime complex governance, we advocate for comprehensive investigations into each health regime cluster. This approach aims to unravel disputes, discern patterns, and elucidate States’ preferences concerning specific thematic areas. Functioning as an accountability mechanism for the Agenda’s governance, VNRs underscore States’ adaptability and short-term learning capabilities, offering valuable insights for identifying harmful goal prioritization. The discretionary nature of indicator selection by States in the VNRs, enabled by the Agenda’s proposition of a contextual adaptation of the SDGs and a blind eye to the guideline’s request to review all SDG indicators, highlights a critical flaw in the VNR as an accountability mechanism.

Mobile Technologies in Teaching Mathematics

Purpose. The article discusses the use of mobile technologies in teaching mathematics to engineering students. The relevance of the paper is due to the need to introduce new methods and means of teaching in connection with the increasing integration of mobile technologies into all areas of life, the interests and characteristics of the new, networked generation of students, the limited possibilities of using innovative methods and means of teaching mathematics due to the conduct of classes outside the computer lab.Materials and methods. The peculiarity of this study is that the integration of mobile technologies into the process of teaching mathematics is considered as a system of three components: technological, informational and social. The analysis of these components made it possible to obtain answers to research questions: where and how mobile devices should be used when studying mathematics; what mobile applications will provide the most effective learning of the discipline; how to organize the educational process using mobile technologies to increase interest and motivation in learning mathematics.Results. The main reasons for the lack of interest in studying mathematics and low motivation include the high level of abstraction of the discipline and the characteristics of the modern, networked generation of students. Analysis of scientific sources and our own observations made it possible to identify such characteristics as problems with understanding complex and long text, the ability to easily read from a small screen and process information in parallel, the choice of sound and images instead of text, the desire for short-term learning and instant results, and attitude towards mobile devices as vital items. Taking into account the interests and wishes of students, and relying on the didactic capabilities of mobile technologies, the paper proposes mobile applications for studying various topics of a mathematical course, assignment options that help increase motivation, develop group work skills and digital literacy. The importance and effectiveness of using social services for visualizing mathematical concepts and calculation schemes, as well as the WhatsApp messenger for instant messaging between students and receiving timely individual and group consultation from a lecturer, is shown. An important characteristic of the messenger is the presence of video communication.Conclusion. The analysis of scientific literature, the study of the requirements of employers and the interests of the modern, networked generation of students, indicated the need to integrate mobile technologies into the process of teaching mathematics. Working with different groups in terms of both the level of mathematical knowledge and the level of proficiency in mobile technologies made it possible to identify and outline ways to solve the main issues of using mobile technologies in teaching mathematics. With limited time to study mathematics, it is difficult for a lecturer to allocate additional time to present the capabilities and effectiveness of mobile applications. A solution to this issue may be to include a section “Mobile Technologies” in a course related to the study of information and communication technologies. It is noted that group work also contributes to a more effective study of the capabilities and techniques of working with mobile applications.

How Does Short-term Project-based Learning Improve Children's Creativity? Empirical Evidence from 3-6 Year Olds

How Does Short-term Project-based Learning Improve Children's Creativity? Empirical Evidence from 3-6 Year Olds

Virtual Nephron: Evaluation of a Novel Virtual Reality Educational Tool

Recent technological advancements allowed the development of engaging technological tools. Using ASN funding from the ASN, we developed a 3D Virtual Reality (VR) physiology course entitledDiAL-Neph (Diuretic Action and eLectrolyte transport in the Nephron). We hereby present its evaluation. The study consisted of 2 parts: evaluation of knowledge gain, and qualitative evaluation of platform reception. Internal medicine PGY1 residents were randomly assigned into 2 groups: a VR group and a conventional group. Knowledge acquisition was assessed with a post-test administered at the end of the course and repeated within 6 to 12 weeks. Independent t-tests were used to compare the number of correct answers between the groups. A survey and focus groups composed of medicine residents evaluated the platform. Sessions were recorded and transcribed verbatim. Data was analyzed through the content analysis approach by two independent reviewers. Of 117 PGY1 resident participants, 64 were randomized to the VR group and 53 were randomized to the traditional group. Initial test results showed higher scores among VR compared to the traditional group (76.5% correct vs. 68.8%). Seventy-eight PGY1s participated in the follow up testing (46 VR group vs. 32 traditional group) and results showed no significant difference in test results. Greater than 90% of the residents rated the platform positively and 77% preferred it as a teaching method. The DiAL-Neph VR platform appeared to improve short-term learning but not long-term retention. Further studies are needed to investigate the impact of such teaching platforms on overall interest in nephrology.

Pretraining alpha rhythm enhancement by neurofeedback facilitates short-term perceptual learning and improves visual acuity by facilitated consolidation.

Learning through perceptual training using the Gabor patch (GP) has attracted attention as a new vision restoration technique for myopia and age-related deterioration of visual acuity (VA). However, the task itself is monotonous and painful and requires numerous training sessions and some time before being effective, which has been a challenge for its widespread application. One effective means of facilitating perceptual learning is the empowerment of EEG alpha rhythm in the sensory cortex before neurofeedback (NF) training; however, there is a lack of evidence for VA. We investigated whether four 30-min sessions of GP training, conducted over 2 weeks with/without EEG NF to increase alpha power (NF and control group, respectively), can improve vision in myopic subjects. Contrast sensitivity (CS) and VA were measured before and after each GP training. The NF group showed an improvement in CS at the fourth training session, not observed in the control group. In addition, VA improved only in the NF group at the third and fourth training sessions, this appears as a consolidation effect (maintenance of the previous training effect). Participants who produced stronger alpha power during the third training session showed greater VA recovery during the fourth training session. These results indicate that enhanced pretraining alpha empowerment strengthens the subsequent consolidation of perceptual learning and that even a short period of GP training can have a positive effect on VA recovery. This simple protocol may facilitate use of a training method to easily recover vision.

The Influence of Separate and Combined Exercise and Foreign Language Acquisition on Learning and Cognition.

Aging contributes significantly to cognitive decline. Aerobic exercise (AE) has been shown to induce substantial neuroplasticity changes, enhancing cognitive and brain health. Likewise, recent research underscores the cognitive benefits of foreign language learning (FLL), indicating improvements in brain structure and function across age groups. However, the lack of a comprehensive paradigm integrating language learning with exercise limits research on combined effects in older adults. In order to address this gap, we devised a novel approach using a virtual world tourism scenario for auditory-based language learning combined with aerobic cycling. Our study examines the impact of simultaneous AE and FLL integration on cognitive and language learning outcomes compared to FLL alone. A total of 20 older adults were randomly assigned to AE + FLL and FLL-only groups. The results revealed significantly improved Spanish language learning outcomes in both combined and language learning-only groups. Additionally, significant cognitive function improvement was observed in the FLL group following short-term language learning.

A short-term equine assisted learning program improves family relationships. A Pilot study.

Young people with special educational needs and their families were particularly affected by the COVID-19 pandemic, with many parents reporting feeling overwhelmed. This study reports on the outcomes of an equine-assisted learning program piloted in summer 2021, which aimed to improve the wellbeing of families by providing them with a positive group experience. Families attended the Equine-Assisted Learning (EAL) program for either of two sessions, which lasted 2.5 hours each. The Family Relationships section of the Family Quality of Life Questionnaire was filled out before and after the program, as well as at a 2-month follow up. Outcomes were also measured using the Strengths and Difficulties Questionnaire, which was filled out by referrers for the young people who were the primary referees to the program. There were 14 families participating in the program, and analysis revealed a significant positive impact on family relationships scores at a total level which was maintained at 2 months follow up, as well as at item level for the majority of items in the short-term. There was also a positive change in Strengths and Difficulties Questionnaire scores for the young people, although this was non-significant. The results of this pilot study show that a short-term family-focussed Equine-Assisted Learning intervention may be effective for improving family relationships, which contribute to individual wellbeing. A larger scale randomised controlled trial would be beneficial to allow the results to be generalised in the future.

Enhanced Day-Ahead Electricity Price Forecasting Using a Convolutional Neural Network–Long Short-Term Memory Ensemble Learning Approach with Multimodal Data Integration

Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to instances wherein day-ahead electricity prices drop to nearly zero JPY/kWh during peak RES production periods, substantially affecting transactions between electricity retailers and consumers. This paper introduces an innovative DAEPF framework employing a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model designed to predict day-ahead electricity prices in the Kyushu area of Japan. To mitigate the inherent uncertainties associated with neural networks, a novel ensemble learning approach is implemented to bolster the DAEPF model’s robustness and prediction accuracy. The CNN–LSTM model is verified to outperform a standalone LSTM model in both prediction accuracy and computation time. Additionally, applying a natural logarithm transformation to the target day-ahead electricity price as a pre-processing technique has proven necessary for higher prediction accuracy. A novel “policy-versus-policy” strategy is proposed to address the prediction problem of the zero prices, halving the computation time of the traditional two-stage method. The efficacy of incorporating a suite of multimodal features: areal day-ahead electricity price, day-ahead system electricity price, areal actual power generation, areal meteorological forecasts, calendar forecasts, alongside the rolling features of areal day-ahead electricity price, as explanatory variables to significantly enhance DAEPF accuracy has been validated. With the full integration of the proposed features, the CNN–LSTM ensemble model achieves its highest accuracy, reaching performance metrics of R2, MAE, and RMSE of 0.787, 1.936 JPY/kWh, and 2.630 JPY/kWh, respectively, during the test range from 1 March 2023 to 31 March 2023, underscoring the advantages of a comprehensive, multi-dimensional approach to DAEPF.

The Effect of Long-Term Learning of BaduanJin on Emotion Regulation: Evidence from Resting-State Frontal EEG Asymmetry.

Baduanjin, as a Chinese traditional fitness exercise, can help people regulate emotions and promote their physical and psychological health. However, the underlying neural mechanisms have not been thoroughly explored. This study aimed to examine the effects of differences in the level of Baduanjin learning on individuals' brain and psychological response related to emotion regulation. Twenty-two participants with long-term Baduanjin learning (for more than one year), and 21 participants with short-term Baduanjin learning (for approximately three months) were recruited. All participants were asked to do a complete 12-minute set of Baduanjin. Before and after doing Baduanjin, their resting-state EEG signals were collected, besides, the Emotion Regulation Questionnaire (ERQ) and the Profile of Mood States-Short Form (POMS-SF) were used to assess participants' emotion regulation strategies and abilities. The results of psychological measurement indicated that participants in the long-term group were more likely to use cognitive reappraisal as an emotion regulation strategy compared to participants in the short-term group (p<0.05). Moreover, the analysis of the frontal alpha asymmetry (FAA) showed that participants in the long-term group rather than the short-term group exhibited significant left lateralization after doing Baduanjin (p<0.05). The findings provide preliminary evidence for the neural mechanism underlying how long-term Baduanjin learning promotes individuals' emotion regulation indexed by FAA. The study provides a new paradigm for research on how Baduanjin affects emotional regulation.

E-learning in transfusion medicine: An exploratory qualitative assessment.

E-learning programmes are increasingly offered in transfusion medicine (TM) education. The aim of this study was to explore facilitators and barriers to TM e-learning programmes, including assessment of learning outcomes and measures of effectiveness. Participants selected from a prior survey and representing a diverse number of international e-learning programmes were invited to participate. A mixed methodology was employed, combining a survey and individual semi-structured one-on-one interviews. Interview data were analysed inductively to explore programme development, evaluation, and facilitators and barriers to implementation. Fourteen participants representing 13 institutions participated in the survey and 10 were interviewed. The e-learning programmes have been in use for a variable duration between 5 and 16 years. Funding sources varied, including government and institutional support. Learner assessment methods varied and encompassed multiple-choice-questions (n = 12), direct observation (n = 4) and competency assessment (n = 4). Most regional and national blood collection agencies rely on user feedback and short-term learning assessments to evaluate their programmes. Only one respondent indicated an attempt to correlate e-learning with clinical practices. Factors that facilitated programme implementation included support from management and external audits to ensure compliance with regulatory educational and training requirements. Barriers to programme implementation included the allocation of staff time for in-house development, enforcing compliance, keeping educational content up-to-date and gaining access to outcome data for educational providers. There is evidence of considerable diversity in the evaluation of e-learning programmes. Further work is needed to understand the ultimate impact of TM e-learning on transfusion practices and patient outcomes.

Short-term number sense training recapitulates long-term neurodevelopmental changes from childhood to adolescence.

Number sense is fundamental to the development of numerical problem-solving skills. In early childhood, children establish associations between non-symbolic (e.g., a set of dots) and symbolic (e.g., Arabic numerals) representations of quantity. The developmental estrangement theory proposes that the relationship between non-symbolic and symbolic representations of quantity evolves with age, with increased dissociation across development. Consistent with this theory, recent research suggests that cross-format neural representational similarity (NRS) between non-symbolic and symbolic quantities is correlated with arithmetic fluency in children but not in adolescents. However, it is not known if short-term training (STT) can induce similar changes as long-term development. In this study, children aged 7-10 years underwent a theoretically motivated 4-week number sense training. Using multivariate neural pattern analysis, we investigated whether short-term learning could modify the relation between cross-format NRS and arithmetic skills. Our results revealed a significant correlation between cross-format NRS and arithmetic fluency in distributed brain regions, including the parietal and prefrontal cortices, prior to training. However, this association was no longer observed after training, and multivariate predictive models confirmed these findings. Our findings provide evidence that intensive STT during early childhood can promote behavioral improvements and neural plasticity that resemble and recapitulate long-term neurodevelopmental changes that occur from childhood to adolescence. More generally, our study contributes to our understanding of the malleability of number sense and highlights the potential for targeted interventions to shape neurodevelopmental trajectories in early childhood. RESEARCH HIGHLIGHTS: We tested the hypothesis that short-term number sense training induces the dissociation of symbolic numbers from non-symbolic representations of quantity in children. We leveraged a theoretically motivated intervention and multivariate pattern analysis to determine training-induced neurocognitive changes in the relation between number sense and arithmetic problem-solving skills. Neural representational similarity between non-symbolic and symbolic quantity representations was correlated with arithmetic skills before training but not after training. Short-term training recapitulates long-term neurodevelopmental changes associated with numerical problem-solving from childhood to adolescence.

Visual feedbacks influence short-term learning of torque versus motion profile with robotic guidance among young adults

Robotic assistance can improve the learning of complex motor skills. However, the assistance designed and used up to now mainly guides motor commands for trajectory learning, not dynamics learning. The present study explored how a complex motor skill involving the right arm can be learned without suppressing task dynamics, by means of an innovative device with robotic guidance that allows a torque versus motion profile to be learned with admittance control. In addition, we assessed how concurrent visual feedback on this profile can enhance learning without creating dependency, by means of a fading procedure (i.e., feedback reduction across trials). On Day 1, a Control group performed an acquisition session (6 blocks) featuring concurrent visual feedback, while a Fading group performed the session with a gradual reduction in feedback (from 100% to 0% over the 6 blocks). On Day 2, both groups performed a block first without feedback (i.e., Transfer test), then with feedback (i.e., Retention test). Results revealed that on Day 1, movement rehearsal induced a significant improvement in spatiotemporal parameters for the Control group, compared with the Fading group. On Day 2, the opposite was found when this visual feedback was removed, as the Fading group performed significantly better than the Control group on the Transfer test. Vision allows a relationship to be established between the required torque and the motion profile. Its suppression then forces the processing of more intrinsic information, leading to the development of a stable internal representation of the task.

Algorithm Design and Implementation of Automatic Assessment and Expansion of English Vocabulary

Automatic assessment and expansion of English vocabulary play a crucial role in language learning and proficiency evaluation. In this paper, we propose the use of an Optimized Bi-directional Long Short-Term Memory Deep Learning (Obi-LSTM-DL) model for these purposes. We conduct experiments using a comprehensive vocabulary dataset and evaluate the model's performance across various scenarios. The results demonstrate the effectiveness of the Obi-LSTM-DL model in accurately assessing vocabulary proficiency levels and expanding vocabulary knowledge. Through optimization experiments, we show that increasing the vocabulary size leads to improved model performance. Additionally, the model's proficiency level assessment capability allows for tailored instruction and support for language learners. Comparative analysis with baseline models further confirms the superiority of the Obi-LSTM-DL model. The results demonstrate the effectiveness of the Obi-LSTM-DL model in accurately assessing vocabulary proficiency levels and expanding vocabulary knowledge. Through optimization experiments, we show that increasing the vocabulary size leads to improved model performance, with an average increase in accuracy of 1.5% for every 1,000 words added to the vocabulary. Additionally, the model's proficiency level assessment capability allows for tailored instruction and support for language learners. Comparative analysis with baseline models further confirms the superiority of the Obi-LSTM-DL model, with an average increase in accuracy of 3.2% over traditional models.

Multi-hazard could exacerbate in coastal Bangladesh in the context of climate change

Floods and landslides have cascading effects on coastal areas of Bangladesh. This study aims to develop multi-hazard maps (e.g., floods and landslides) for the coastal region, by integrating a genetic algorithm optimizer with Long Short-Term Memory deep learning algorithm (GA-LSTM-DLA), global climate model, and geospatial data. It assesses vulnerabilities to multiple hazards and projects future risks considering the Australian Community Climate and Earth System Simulator Earth System Model 1.5 (ACCESS-ESM1-5) under three climate scenarios, i.e., Shared Socioeconomic Pathways1-2.6, 2–4.5, and 5–8.5 (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The Receiver Operating Characteristic (ROC) curve is used to assess the performance of GA-LSTM-DLA model. According to important feature obtained by random forest (RF) algorithm, most important features for flash, riverine, tidal floods, and landslides are drainage density, rainfall, and geology, respectively. The baseline susceptibility maps for flash, riverine, tidal floods, and landslides are initially recorded as 0.11%, 7.99%, 3.46%, and 0.03%. Projections for the year 2100, under different SSPs, show significant increase, i.e., SSP1-2.6—flash 1.31%, riverine 23.74%, tidal 26.97%, landslides 2.05%; SSP2-4.5—flash 0.12%, riverine 30.97%, tidal 10.23%, landslides 0.18%; SSP5-8.5—flash 0.02%, riverine 17.42%, tidal 6.19%, landslides 0.14%. These projections highlight urgent need for mitigation and adaptation measures against hazards susceptibility, particularly under more extreme socioeconomic scenarios. Overall, the findings of this work are critical for policymakers to develop informed strategies for climate resilience, sustainable development, and disaster risk reduction in the coastal region of Bangladesh.

Using Embedded Assessment to Evaluate Science Center Theatre

While some people claim museum theatre is a powerful tool that can affect visitors’ attitudes and inspire learning, others continue to question if its role is education, entertainment, or some combination of the two. In light of these mixed perceptions of museum theatre, there is ongoing demand for evidence that these shows promote meaningful learning. One method for measuring outcome achievement in these nontraditional theatre settings is embedded assessment (i.e., unobtrusive instruments integrated into the experience being evaluated). This article presents a case study of using embedded assessment to measure the stability of audience understanding of three key concepts from a science show. During Lunch in Space, the project team developed a strategy where the audience took on the role of food scientists and completed a paper and pencil “menu” by selecting good foods to send into space. Results suggest that this is a stable strategy for measuring short-term learning from this type of program. Three key insights learned for embedded assessment with museum theatre include: 1) creating a role for the audience; 2) refining the assessment through rapid iteration; 3) and designing assessments with a universal design approach, simple, easy-to-understand instructions, limited text, and clear visuals.

Can robot-supported learning enhance computational thinking?—A meta-analysis

Computational thinking (CT) is crucial for students. Robot-supported learning has emerged as a popular approach for CT cultivation. To understand the effects of robot-supported CT cultivation, the current study conducted a meta-analysis to analyze studies from Web of Science, Google Scholar, and Science Direct databases (2012–2022) which screened using keywords ``computational thinking'' and ``robot''. After further screening, 26 peer-reviewed articles were selected. We synthesized the 33 effect sizes to assess the overall effectiveness of robot-supported learning on CT and its sub-dimensions: concepts, practices, and perspectives. Our findings revealed a medium effect of robot-supported learning on students' CT (g = 0.643), a large effect on CT concepts (g = 0.650), a medium effect on CT practices (g = 0.587), and a huge effect on CT perspectives (g = 1.419). We also explored the moderators (i.e., grade level, study duration, culture, learning strategy, and assessment tool) that might influence the effects of robot-supported CT cultivation. Moderator analyses indicated that short-term (less than four weeks) robot-supported learning had a larger effect (g = 0.901) on CT practice than long-term (more than eleven weeks) learning (g = 0.309). This meta-analysis indicates that educators could apply robot-supported learning to cultivate students' CT and concludes with related implications.

Older and younger adults differ in time course of skill acquisition but not in overall improvement in a bimanual visuomotor tracking task.

Manual motor performance declines with age, but the extent to which age influences the acquisition of new skills remains a topic of debate. Here, we examined whether older healthy adults show less training-dependent performance improvements during a single session of a bimanual pinch task than younger adults. We also explored whether physical and cognitive factors, such as grip strength or motor-cognitive ability, are associated with performance improvements. Healthy younger (n = 16) and older (n = 20) adults performed three training blocks separated by short breaks. Participants were tasked with producing visually instructed changes in pinch force using their right and left thumb and index fingers. Task complexity was varied by shifting between bimanual mirror-symmetric and inverse-asymmetric changes in pinch force. Older adults generally displayed higher visuomotor force tracking errors during the more complex inverse-asymmetric task compared to younger adults. Both groups showed a comparable net decrease in visuomotor force tracking error over the entire session, but their improvement trajectories differed. Young adults showed enhanced visuomotor tracking error only in the first block, while older adults exhibited a more gradual improvement over the three training blocks. Furthermore, grip strength and performance on a motor-cognitive test battery scaled positively with individual performance improvements during the first block in both age groups. Together, the results show subtle age-dependent differences in the rate of bimanual visuomotor skill acquisition, while overall short-term learning ability is maintained.

Cardiac Dynamics in Auditory-Motor Synchronization: Roles of Short-Term Training and Rhythm Complexity

ABSTRACT Previous findings suggest that long-term musical training and rhythm complexity may affect performers’ auditory-motor synchrony and their cardiac rhythms. We investigated the effects of short-term training and rhythm complexity on auditory-motor synchronization and cardiac activity as individuals synchronized with auditory rhythms. Forty-two adult participants synchronized their taps with sounded rhythms to form a 2:3 duration ratio (stimulus duration:tap duration) or a 3:2 duration ratio (tap rate held constant across rhythms). Participants received short training with one rhythm and received longer training with the other rhythm. Then, participants completed five experimental trials in which they synchronized their sounded taps with the stimulus rhythm. Cardiac activity was recorded during synchronization. Tapping synchronization was less accurate and more variable for the 3:2 rhythm than the 2:3 rhythm. Linear measures of cardiac activity showed less high-frequency variability during the 3:2 rhythm than the 2:3 rhythm. Behavioral-cardiac correspondences emerged with training: Poorer synchronization was associated with more variable cardiac activity after long training, but not after short training. Finally, there were consistent individual differences in cardiac recurrence and predictability across training conditions. These findings demonstrate both short-term learning effects and rhythm complexity effects on cardiac dynamics during auditory-motor synchronization.

HypeAIR: A novel framework for real-time low-cost sensor calibration for air quality monitoring in smart cities

While less reliable than authorized air quality stations, low-cost sensors help monitor air quality in areas overlooked by traditional devices. A calibration process in the same environment as the sensor is crucial to enhance their accuracy. Furthermore, low-cost sensors deteriorate over time, necessitating repeated calibration for sustained performance. HypeAIR is a novel open-source framework for the management of sensor calibration in real-time. It incorporates two calibration methodologies: a combination of machine learning models (Voting Regressor and Support Vector Regression) and the Long Short-Term Memory deep learning model. To evaluate the framework, three extensive experiments were conducted over a 2-year period in the city of Modena, Italy, to monitor NO, NO2, and O3 gases. Both calibration methodologies outperform the manufacturer calibration and our baseline (i.e., a variation of the Random Forest algorithm) and maintain efficiency over time. The availability of the source code facilitates customization for monitoring additional pollutants, while shared air quality datasets ensure reproducibility.