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PloS One 2024This study aims to enhance the post-training evaluation of the annual performance agreement (APA) training organized by the Bangladesh Public Administration Training...
This study aims to enhance the post-training evaluation of the annual performance agreement (APA) training organized by the Bangladesh Public Administration Training Centre (BPATC), the apex training institute for civil servants. Utilizing fuzzy-set qualitative comparative analysis (fsQCA) and artificial neural network (ANN) techniques within Kirkpatrick's four-stage model framework, data were collected from a self-administered questionnaire survey of 71 in-service civil servants who participated in the APA training program. This study employs an asymmetric, non-linear model analyzed through a configurational approach and ANN to explore interrelationships among the four Kirkpatrick levels namely, reaction, learning, behavior, and results. Findings indicate that trainees were satisfied across all levels, identifying a non-linear relationship among these levels in post-training evaluation process. The research highlights that "learning skills" are most significant in the APA post-training evaluation, followed by behavior, results, and reaction. Theoretically, this research advances Kirkpatrick's model and adds to the literature on public service post-training evaluation. Practically, it recommends prioritizing strategies that address cognitive barriers to enhance training effectiveness. This study's innovative approach lies in its concurrent use of fsQCA and ANN methods to analyze the success or failure of APA-related trainees, offering alternative pathways to desired outcomes and contrasting traditional quantitative methods that provide a single solution. The findings have practical implications for public service training institutions and bureaucratic policymakers involved in capacity development, guiding the creation of more effective in-service training courses for public officials. The methodology and analysis can be applied in other contexts, allowing bureaucratic policymakers to replicate these findings in their learning institutes to identify unique configurations that lead to successful or unsuccessful training outcomes, adopt effective strategies, and avoid detrimental ones.
Topics: Humans; Neural Networks, Computer; Bangladesh; Surveys and Questionnaires; Male; Female; Adult; Learning
PubMed: 38917194
DOI: 10.1371/journal.pone.0305916 -
PloS One 2024Little is known about the experience and the social and contextual factors influencing the acceptance of virtual reality (VR) physical activity games among long-term...
BACKGROUND
Little is known about the experience and the social and contextual factors influencing the acceptance of virtual reality (VR) physical activity games among long-term care (LTC) residents. Our study aims to address this research gap by investigating the unique experience of older adults with VR games. The findings will provide valuable insights into the factors influencing VR acceptance among LTC residents and help design inclusive VR technology that meets their needs and improves physical activity (PA) and well-being.
OBJECTIVE
We aimed to: (1) investigate how participants experience VR exergames and the meaning they associate with their participation; and (2) examine the factors that influence the participant's experience in VR exergames and explore how these factors affect the overall experience.
METHODS
We used a qualitative approach that follows the principles of the Interpretive Description methodology. Selective Optimization and Compensation (SOC) theory, Socioemotional Selectivity theory (SST) and technology acceptance models underpinned the theoretical foundations of this study. We conducted semi-structured interviews with participants. 19 Participants of a LTC were interviewed: five residents and ten tenants, aged 65 to 93 years (8 female and 7 male) and four staff members. Interviews ranged from 15 to 30 minutes and were transcribed verbatim and were analyzed using thematic analysis.
RESULTS
We identified four themes based on older adults' responses that reflected their unique VR gaming experience, including (1) enjoyment, excitement, and the novel environment; (2) PA and motivation to exercise; (3) social connection and support; and (4) individual preferences and challenges. Three themes were developed based on the staff members' data to capture their perspective on the factors that influence the acceptance of VR among LTC resident including (1) relevance and personalization of the games; (2) training and guidance; and (3) organizational and individual barriers.
CONCLUSIONS
VR gaming experiences are enjoyable exciting, and novel for LTC residents and tenants and can provide physical, cognitive, social, and motivational benefits for them. Proper guidance and personalized programs can increase understanding and familiarity with VR, leading to a higher level of acceptance and engagement. Our findings emphasize the significance of social connection and support in promoting acceptance and enjoyment of VR gaming among older adults. Incorporating social theories of aging helps to gain a better understanding of how aging-related changes influence technology acceptance among older adults. This approach can inform the development of technology that better meets their needs and preferences.
Topics: Humans; Female; Male; Aged; Long-Term Care; Exercise; Aged, 80 and over; Virtual Reality; Video Games; Qualitative Research
PubMed: 38917119
DOI: 10.1371/journal.pone.0305865 -
Journal of Vision Jun 2024A large body of literature has examined specificity and transfer of perceptual learning, suggesting a complex picture. Here, we distinguish between transfer over...
A large body of literature has examined specificity and transfer of perceptual learning, suggesting a complex picture. Here, we distinguish between transfer over variations in a "task-relevant" feature (e.g., transfer of a learned orientation task to a different reference orientation) and transfer over a "task-irrelevant" feature (e.g., transfer of a learned orientation task to a different retinal location or different spatial frequency), and we focus on the mechanism for the latter. Experimentally, we assessed whether learning a judgment of one feature (such as orientation) using one value of an irrelevant feature (e.g., spatial frequency) transfers to another value of the irrelevant feature. Experiment 1 examined whether learning in eight-alternative orientation identification with one or multiple spatial frequencies transfers to stimuli at five different spatial frequencies. Experiment 2 paralleled Experiment 1, examining whether learning in eight-alternative spatial-frequency identification at one or multiple orientations transfers to stimuli with five different orientations. Training the orientation task with a single spatial frequency transferred widely to all other spatial frequencies, with a tendency to specificity when training with the highest spatial frequency. Training the spatial frequency task fully transferred across all orientations. Computationally, we extended the identification integrated reweighting theory (I-IRT) to account for the transfer data (Dosher, Liu, & Lu, 2023; Liu, Dosher, & Lu, 2023). Just as location-invariant representations in the original IRT explain transfer over retinal locations, incorporating feature-invariant representations effectively accounted for the observed transfer. Taken together, we suggest that feature-invariant representations can account for transfer of learning over a "task-irrelevant" feature.
Topics: Humans; Photic Stimulation; Young Adult; Male; Visual Perception; Adult; Female; Transfer, Psychology; Learning; Orientation, Spatial; Computer Simulation; Orientation
PubMed: 38916886
DOI: 10.1167/jov.24.6.17 -
Frontiers in Physiology 2024This systematic review investigates the interplay between oxytocin and exercise; in terms of analgesic, anti-inflammatory, pro-regenerative, and cardioprotective...
INTRODUCTION
This systematic review investigates the interplay between oxytocin and exercise; in terms of analgesic, anti-inflammatory, pro-regenerative, and cardioprotective effects. Furthermore, by analyzing measurement methods, we aim to improve measurement validity and reliability.
METHODS
Utilizing PRISMA, GRADE, and MECIR protocols, we examined five databases with a modified SPIDER search. Including studies on healthy participants, published within the last 20 years, based on keywords "oxytocin," "exercise" and "measurement," 690 studies were retrieved initially (455 unique records). After excluding studies of clinically identifiable diseases, and unpublished and reproduction-focused studies, 175 studies qualified for the narrative cross-thematic and structural analysis.
RESULTS
The analysis resulted in five categories showing the reciprocal impact of oxytocin and exercise: Exercise (50), Physiology (63), Environment (27), Social Context (65), and Stress (49). Exercise-induced oxytocin could promote tissue regeneration, with 32 studies showing its analgesic and anti-inflammatory effects, while 14 studies discussed memory and cognition. Furthermore, empathy-associated rs53576 polymorphism might influence team sports performance. Since dietary habits and substance abuse can impact oxytocin secretion too, combining self-report tests and repeated salivary measurements may help achieve precision.
DISCUSSION
Oxytocin's effect on fear extinction and social cognition might generate strategies for mental training, and technical, and tactical development in sports. Exercise-induced oxytocin can affect the amount of stress experienced by athletes, and their response to it. However, oxytocin levels could depend on the type of sport in means of contact level, exercise intensity, and duration. The influence of oxytocin on athletes' performance and recovery could have been exploited due to its short half-life. Examining oxytocin's complex interactions with exercise paves the way for future research and application in sports science, psychology, and medical disciplines.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=512184, identifier CRD42024512184.
PubMed: 38915776
DOI: 10.3389/fphys.2024.1393497 -
Frontiers in Sports and Active Living 2024Coping with residual cognitive and gait impairments is a prominent unmet need in community-dwelling chronic stroke survivors. Motor-cognitive exergames may be promising...
BACKGROUND
Coping with residual cognitive and gait impairments is a prominent unmet need in community-dwelling chronic stroke survivors. Motor-cognitive exergames may be promising to address this unmet need. However, many studies have so far implemented motor-cognitive exergame interventions in an unstructured manner and suitable application protocols remain yet unclear. We, therefore, aimed to summarize existing literature on this topic, and developed a training concept for motor-cognitive exergame interventions in chronic stroke.
METHODS
The development of the training concept for personalized motor-cognitive exergame training for stroke (PEMOCS) followed Theory Derivation procedures. This comprised (1.1) a thorough (narrative) literature search on long-term stroke rehabilitation; (1.2) a wider literature search beyond the topic of interest to identify analogies, and to induce creativity; (2) the identification of parent theories; (3) the adoption of suitable content or structure of the main parent theory; and (4) the induction of modifications to adapt it to the new field of interest. We also considered several aspects of the "Framework for Developing and Evaluating Complex Interventions" by the Medical Research Council. Specifically, a feasibility study was conducted, and refining actions based on the findings were performed.
RESULTS
A training concept for improving cognitive functions and gait in community-dwelling chronic stroke survivors should consider the principles for neuroplasticity, (motor) skill learning, and training. We suggest using a step-based exergame training for at least 12 weeks, 2-3 times a week for approximately 45 min. Gentile's Taxonomy for Motor Learning was identified as suitable fundament for the personalized progression and variability rules, and extended by a third cognitive dimension. Concepts and models from related fields inspired further additions and modifications to the concept.
CONCLUSION
We propose the PEMOCS concept for improving cognitive functioning and gait in community-dwelling chronic stroke survivors, which serves as a guide for structuring and implementing motor-cognitive exergame interventions. Future research should focus on developing objective performance parameters that enable personalized progression independent of the chosen exergame type.
PubMed: 38915297
DOI: 10.3389/fspor.2024.1397949 -
Biomedical Engineering Online Jun 2024The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV)... (Comparative Study)
Comparative Study
BACKGROUND
The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms.
METHODS
Peak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise.
RESULTS
The quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data.
CONCLUSION
While machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting.
Topics: Intracranial Pressure; Humans; Signal Processing, Computer-Assisted; Monitoring, Physiologic; Machine Learning; Algorithms; Cerebrovascular Circulation; Signal-To-Noise Ratio
PubMed: 38915091
DOI: 10.1186/s12938-024-01245-9 -
Nature Communications Jun 2024Idling brain activity has been proposed to facilitate inference, insight, and innovative problem-solving. However, it remains unclear how and when the idling brain can...
Idling brain activity has been proposed to facilitate inference, insight, and innovative problem-solving. However, it remains unclear how and when the idling brain can create novel ideas. Here, we show that cortical offline activity is both necessary and sufficient for building unlearned inferential knowledge from previously acquired information. In a transitive inference paradigm, male C57BL/6J mice gained the inference 1 day after, but not shortly after, complete training. Inhibiting the neuronal computations in the anterior cingulate cortex (ACC) during post-learning either non-rapid eye movement (NREM) or rapid eye movement (REM) sleep, but not wakefulness, disrupted the inference without affecting the learned knowledge. In vivo Ca imaging suggests that NREM sleep organizes the scattered learned knowledge in a complete hierarchy, while REM sleep computes the inferential information from the organized hierarchy. Furthermore, after insufficient learning, artificial activation of medial entorhinal cortex-ACC dialog during only REM sleep created inferential knowledge. Collectively, our study provides a mechanistic insight on NREM and REM coordination in weaving inferential knowledge, thus highlighting the power of idling brain in cognitive flexibility.
Topics: Animals; Sleep, REM; Male; Mice, Inbred C57BL; Prefrontal Cortex; Learning; Mice; Gyrus Cinguli; Wakefulness; Sleep, Slow-Wave; Knowledge; Entorhinal Cortex; Neurons
PubMed: 38914541
DOI: 10.1038/s41467-024-48816-x -
PloS One 2024Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease...
BACKGROUND
Low-and-middle-income countries (LMICs) bear a disproportionate burden of communicable diseases. Social interaction data inform infectious disease models and disease prevention strategies. The variations in demographics and contact patterns across ages, cultures, and locations significantly impact infectious disease dynamics and pathogen transmission. LMICs lack sufficient social interaction data for infectious disease modeling.
METHODS
To address this gap, we will collect qualitative and quantitative data from eight study sites (encompassing both rural and urban settings) across Guatemala, India, Pakistan, and Mozambique. We will conduct focus group discussions and cognitive interviews to assess the feasibility and acceptability of our data collection tools at each site. Thematic and rapid analyses will help to identify key themes and categories through coding, guiding the design of quantitative data collection tools (enrollment survey, contact diaries, exit survey, and wearable proximity sensors) and the implementation of study procedures. We will create three age-specific contact matrices (physical, nonphysical, and both) at each study site using data from standardized contact diaries to characterize the patterns of social mixing. Regression analysis will be conducted to identify key drivers of contacts. We will comprehensively profile the frequency, duration, and intensity of infants' interactions with household members using high resolution data from the proximity sensors and calculating infants' proximity score (fraction of time spent by each household member in proximity with the infant, over the total infant contact time) for each household member.
DISCUSSION
Our qualitative data yielded insights into the perceptions and acceptability of contact diaries and wearable proximity sensors for collecting social mixing data in LMICs. The quantitative data will allow a more accurate representation of human interactions that lead to the transmission of pathogens through close contact in LMICs. Our findings will provide more appropriate social mixing data for parameterizing mathematical models of LMIC populations. Our study tools could be adapted for other studies.
Topics: Humans; Mozambique; Developing Countries; Guatemala; Pakistan; India; Focus Groups; Female; Infant; Social Interaction; Male; Communicable Diseases; Rural Population; Research Design
PubMed: 38913670
DOI: 10.1371/journal.pone.0301638 -
PloS One 2024Advanced Persistent Threat (APT) attacks are causing a lot of damage to critical organizations and institutions. Therefore, early detection and warning of APT attack...
Advanced Persistent Threat (APT) attacks are causing a lot of damage to critical organizations and institutions. Therefore, early detection and warning of APT attack campaigns are very necessary today. In this paper, we propose a new approach for APT attack detection based on the combination of Feature Intelligent Extraction (FIE) and Representation Learning (RL) techniques. In particular, the proposed FIE technique is a combination of the Bidirectional Long Short-Term Memory (BiLSTM) deep learning network and the Attention network. The FIE combined model has the function of aggregating and extracting unusual behaviors of APT IPs in network traffic. The RL method proposed in this study aims to optimize classifying APT IPs and normal IPs based on two main techniques: rebalancing data and contrastive learning. Specifically, the rebalancing data method supports the training process by rebalancing the experimental dataset. And the contrastive learning method learns APT IP's important features based on finding and pulling similar features together as well as pushing contrasting data points away. The combination of FIE and RL (abbreviated as the FIERL model) is a novel proposal and innovation and has not been proposed and published by any research. The experimental results in the paper have proved that the proposed method in the paper is correct and reasonable when it has shown superior efficiency compared to some other studies and approaches over 5% on all measurements.
Topics: Deep Learning; Humans; Computer Security; Neural Networks, Computer; Algorithms
PubMed: 38913651
DOI: 10.1371/journal.pone.0305618 -
Heliyon Jun 2024Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually....
Early cancer detection and treatment depend on the discovery of specific genes that cause cancer. The classification of genetic mutations was initially done manually. However, this process relies on pathologists and can be a time-consuming task. Therefore, to improve the precision of clinical interpretation, researchers have developed computational algorithms that leverage next-generation sequencing technologies for automated mutation analysis. This paper utilized four deep learning classification models with training collections of biomedical texts. These models comprise bidirectional encoder representations from transformers for Biomedical text mining (BioBERT), a specialized language model implemented for biological contexts. Impressive results in multiple tasks, including text classification, language inference, and question answering, can be obtained by simply adding an extra layer to the BioBERT model. Moreover, bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) have been leveraged to produce very good results in categorizing genetic mutations based on textual evidence. The dataset used in the work was created by Memorial Sloan Kettering Cancer Center (MSKCC), which contains several mutations. Furthermore, this dataset poses a major classification challenge in the Kaggle research prediction competitions. In carrying out the work, three challenges were identified: enormous text length, biased representation of the data, and repeated data instances. Based on the commonly used evaluation metrics, the experimental results show that the BioBERT model outperforms other models with an F1 score of 0.87 and 0.850 MCC, which can be considered as improved performance compared to similar results in the literature that have an F1 score of 0.70 achieved with the BERT model.
PubMed: 38912449
DOI: 10.1016/j.heliyon.2024.e32279