Miracle-dz/UniFAR-FTUs huggingface dataset:
Speed-Variable Gait Phase Estimation During Ambulation via Temporal Convolutional Network

Accurate estimation of gait phases during walking is a crucial prerequisite for both extracting clinically meaningful gait parameters and delivering gait-based feedback control information to rehabilitation devices. In addition, speed variation appears in our daily walking locomotion. However, most existing inertial measurement unit (IMU)-related methods based on heuristic algorithms were reported to be sensitive to walking speed changes. To address this problem, in this study, we presented a temporal convolutional network (TCN)-based approach for automatic and robust recognition of gait phases across multiscene ambulation and different walking speeds. We collected data on both real-world overground walking experiments and public treadmill datasets to validate the performance in terms of the accuracy and robustness of the proposed method. By comparing our method with six machine learning (ML) models and two neural network models, our method achieves 97% accuracy in gait phase estimation for both overground and treadmill walking, outperforming all compared benchmarks. It also excelled in both model generalizability evaluation and velocity robustness comparison tests over the other two neural networks. Notably, TCN can achieve 91% accuracy in velocity robustness tests and outperformed fully convolutional network (FCN) and long short-term memory (LSTM) in mean square error comparison ( ${p} < 0.05$ ). These results show that our method has outstanding estimation performance and high robustness on gait speed variations.
Learning to Lead: Challenges and Resources for The New Public Library Director

ABSTRACT Library directors are part of the infrastructure of information management, but how well do they manage their institutional knowledge for succession planning? Our study looked at strategies to ease managerial transitions between libraries, focusing on knowledge management, communities of practice, and mentorship. Our survey found that succession planning was dictated by local practice and size of library. The results of this paper inform knowledge of succession planning and reveal existing gaps between informal transfer of knowledge and structured knowledge management (KM), as well as librarians' resourcefulness. Library directors use KM strategies, mentoring, and involvement with communities of practice to foster innovation.
Enhancing Quality Control: Defect State Classification of Taralli Biscuits with MobileNet-v2 and DenseNet-201

Industrial production and packaging face significant challenges, such as product damage, color changes, and the presence of foreign bodies. These issues greatly impact product quality, profitability, and marketability, leading to increased consumer complaints. To address these concerns, this study presents a novel method for classifying Taralli biscuits using image processing techniques. The research encompasses a dataset of 4,900 images, featuring four types of defects: no defect, defect-shape, defect-object, and defect-color. Leveraging advanced deep learning architectures, including MobileNet-v2 and DenseNet-201, the classification process achieves impressive accuracy rates of 98.71% and 99.39% respectively. By automating the detection of biscuit damage, the proposed method enhances quality control and inspection processes within the food industry. The combination of state-of-the-art image processing and deep learning techniques in this research provides an effective solution for automatically detecting and categorizing biscuit defects.
A Review of Various Dimension Reduction Methods

Data analysis and prediction become an indispensable part of many fields. However, the data with high dimensionality may cause problems, such as memory waste, or data loss while processing. In this case, dimension reduction is necessary for data processing. In this paper, a number of different dimension reduction methods have been discussed, including the theoretical background and simple examples of how they perform.
Psychological Alterations in Youths with Type I Diabetes: Associations with Salivary Cortisol Concentration

Background and Objectives: Type 1 diabetes mellitus (T1DM) is one of the most common chronic diseases in children and adolescents, and is associated with stress and other psychological alterations. This study aims to assess psychological and sleep disorders and health-related quality of life in young people with T1DM and to determine the relationship between these parameters and levels of salivary cortisol, a hormone widely associated with stress and several psychological symptoms. Materials and Methods: In our cross-sectional study performed in 60 Moroccan children and adolescents with T1DM, detailed psychological evaluations were performed to assess symptoms of anxiety, attention-deficit hyperactivity disorder (ADHD), sleep quality and diabetes-specific quality of life (using the RCMAS-2, ADHD rating scale, Pittsburgh scale and the DQoL scale, respectively), and cortisol concentration was measured from saliva samples taken mid-morning. Results: A total of 60 children and adolescents with T1DM were recruited. The mean age was 11.05 ± 0.35 (6–17). The mean salivary cortisol level in ng/mL was 4.7 ± 0.49 (0.7–20.2) and was significantly associated with an anxiety RCMAS2 score for the Worry subdomain and DQoL subdomain “Anxiety”. Linear regression analysis showed that salivary cortisol was significantly higher in girls compared to boys (p = 0.004) (beta coefficient: 3.384 CI95%: 1.137–5.630) and with Hb1AC level as a continuous variable (p = 0.0001) (beta coefficient: 1.135 CI95%: 0.509–1.760). The other variables included in the model were not significant (p > 0.05). There was an association between salivary cortisol concentration with anxiety RCMAS2 score for Worry subdomain and QoL sub-domain “Anxiety”. Still, a significant (p = 0.018) association emerged for anxiety RCMAS2 score Worry subdomain and QoL anxiety subdomain (p = 0.044). Conclusions: Children and adolescents with T1DM experienced significantly elevated symptoms of anxiety and sleep disturbances, particularly in girls, and frequent symptoms of ADHD, particularly in boys. Salivary cortisol concentration collected in the morning is associated with anxiety burden but not with other psychological alterations. Further studies are needed to clarify the associations between salivary cortisol concentration and anxiety in type 1 diabetes in order to propose the hormone as a biomarker for interventions aimed to reduce anxiety levels in these patients.
The Identity Threat and Coping among the Followers of the Indigenous Javanese Religion

The present study was aimed at exploring how the adherents of an indigenous Javanese religion, known as Javanism or Kejawen, maintain their identity in the hegemonic Muslim identity. This study employed a qualitative case study. The participants were the leader and key members of a Javanese religious group in Mojokerto District, East Java, Indonesia. Data were collected using semi-structured interviews and analyzed using thematic analysis. The study found that most participants experience threats to their identity. They are perceived as being targeted and stigmatized since they are practicing mythic rituals and believing in superstition. They reported that they are labeled as obsolete, outdated, and even idolatrous. To cope with the identity threats, participants employ both intrapsychic and interpersonal strategies. The results indicate that the followers of the indigenous Javanese religion can efficaciously maintain their positive identity in response to the negative stigma imposed by the surrounding Muslim people.
Lived experience of Iranian pre-hospital medical staff during the COVID-19 pandemic: a descriptive phenomenological study

Background Pre-hospital medical staff faced numerous challenges during the COVID-19 pandemic. However, these challenges specific to pre-hospital services have not been thoroughly explored in Iran. This qualitative study aimed to examine the essence of pre-hospital care during the COVID-19 pandemic. Methods This phenomenological study was conducted from June to August 2021 in Tehran, Iran. Semi-structured interviews were conducted with pre-hospital medical staff. Data analysis was performed using Colaizzi’s approach, and rigor was ensured by adhering to the consolidated criteria for qualitative reporting research. Results A total of 17 pre-hospital medical staff were interviewed, and five themes were extracted from the data: workload and resilience, damage, lack of control, under preparedness, and post-traumatic growth. These themes highlight the resilience demonstrated by pre-hospital medical staff, who faced an unprecedented crisis with limited preparedness and significant damage. Conclusion The findings of this study indicate that pre-hospital medical staff in Iran encountered challenges during the COVID-19 pandemic due to a lack of preparedness and substantial damage. Despite these adversities, the participants exhibited resilience and experienced post-traumatic growth. The study emphasizes the importance of proper planning and preparedness to enhance the resilience of emergency medical services during pandemics. Furthermore, the results underscore the need to address the challenges faced by pre-hospital medical staff and improve the quality of care provided to patients during crises such as the COVID-19 pandemic.
Measurements and models of reactive transport in geological media

Reactive chemical transport plays a key role in geological media across scales, from pore scale to aquifer scale. Systems can be altered by changes in solution chemistry and a wide variety of chemical transformations, including precipitation/dissolution reactions that cause feedbacks that directly affect the flow and transport regime. The combination of these processes with advective‐dispersive‐diffusive transport in heterogeneous media leads to a rich spectrum of complex dynamics. The principal challenge in modeling reactive transport is to account for the subtle effects of fluctuations in the flow field and species concentrations; spatial or temporal averaging generally suppresses these effects. Moreover, it is critical to ground model conceptualizations and test model outputs against laboratory experiments and field measurements. This review emphasizes the integration of these aspects, considering carefully designed and controlled experiments at both laboratory and field scales, in the context of development and solution of reactive transport models based on continuum‐scale and particle tracking approaches. We first discuss laboratory experiments and field measurements that define the scope of the phenomena and provide data for model comparison. We continue by surveying models involving advection‐dispersion‐reaction equation and continuous time random walk formulations. The integration of measurements and models is then examined, considering a series of case studies in different frameworks. We delineate the underlying assumptions, and strengths and weaknesses, of these analyses, and the role of probabilistic effects. We also show the key importance of quantifying the spreading and mixing of reactive species, recognizing the role of small‐scale physical and chemical fluctuations that control the initiation of reactions.
Quantifying and mapping digital dermatitis-associated bacteria in lesion and non-lesion body sites and dairy farm environment.

The source of infection of digital dermatitis (DD), an infectious lameness condition, is still uncertain. In this cross-sectional study, we aimed to identify potential reservoirs of DD-bacteria in dairy cattle body sites with different stages of the disease and farm environments. We collected skin swabs from 85 dairy cows from 5 herds, 3 with and 2 without DD, from foot, hock, and udder cleft skin (with lesions or not), saliva, urine, and feces. We also obtained environmental samples. Real-time qPCR targeted Treponema phagedenis, Treponema medium, Treponema pedis, Porphyromonas levii, Bacteroides pyogenes, Fusobacterium necrophorum, and Fusobacterium mortiferum. DD-associated Treponema spp. were exclusively detected in DD-affected herds in DD-foot and other skin lesions, healthy skin, saliva, and environmental samples. In contrast, the non-Treponema spp. were found in samples from both DD-negative and affected herds. As expected, DD lesions had higher bacterial loads than healthy skin. Interestingly, similar counts were observed in udder cleft lesions, indicating a potential opportunistic behavior on compromised skin. None of the targeted species were detected in fecal samples, but P. levii, B. pyogenes, and F. necrophorum were detected in urine. All 7 species were detected in saliva, although in low quantities. No associations were observed between the presence of each bacterial species in DD lesions and urine; however, there was an association between the presence of DD-Treponema spp. in lesions and saliva, hock, and udder skin. Feces and urine do not seem to be a DD bacteria primary source, but saliva and other skin lesions may play a role. Longitudinal studies would improve our understanding of DD-associated bacteria's transient or persistent presence in these sites. Investigating the sources of DD-associated bacteria will guide future interventions to minimize bacterial shedding and transmission, ultimately more effectively reducing bacterial load, transmission, and sources of infection in dairy herds.
Cost-effectiveness of three echinocandins and fluconazole in the treatment of candidemia and/or invasive candidiasis in nonneutropenic adult patients

Objective: To estimate the cost-effectiveness of three echinocandins (anidulafungin, caspofungin, and micafungin) and generic fluconazole in the treatment of nonneutropenic adult patients with candidemia and/or invasive candidiasis in intensive care units in Spain. Materials and methods: A decision-tree model was applied. The success and safety (hepatic and renal adverse effects) of first-line treatments were obtained from meta-analyses and systematic reviews of clinical trials. In the case of failure, a second-line treatment (liposomal amphotericin B after the echinocandins, or one of the echinocandins after fluconazole) was administered. The duration of the treatments (14 days total) was established by a panel of clinical experts using the Delphi method and according to Infectious Diseases Society of America guidelines. The cost of the medications and renal toxicity were considered. Deterministic and probabilistic sensitivity analysis using Monte Carlo simulations were carried out. Results: The total cost of the treatment of candidemia and/or invasive candidiasis with anidulafungin, caspofungin, micafungin, and fluconazole was € 5,483, € 5,968, € 6,231, and € 2,088, respectively. Anidulafungin was the dominant treatment (more effective, less expensive) compared to micafungin and caspofungin. The cost of achieving one more patient successfully treated with anidulafungin, caspofungin, and micafungin compared to fluconazole was € 17,199, € 23,962, and € 27,339, respectively. The result remained stable, despite modification of the duration of the first-line and second-line treatments, as well as most of the dosing regimens. The probabilistic analysis also remained stable. Conclusion: In accordance with this economic study, anidulafungin would produce savings and would be the dominant treatment compared with micafungin and caspofungin in nonneutropenic adult patients with candidemia and/or invasive candidiasis in intensive care units in Spain.
Estrategias de aprendizaje autorregulado y el rendimiento no académico en el contexto de la pandemia COVID-19

La pandemia COVID-19 demandó la autorregulación del estudiante para aprovechar la enseñanza en línea. La evidencia de ese aprovechamiento a partir del uso de estrategias de aprendizaje, se estudió en base a los resultados no académicos que permitieron cuantificar la actitud y la satisfacción del estudiante hacia la enseñanza en ambientes virtuales en contextos contingentes. El objetivo de esta investigación fue analizar la influencia del uso de estrategias de aprendizaje autorregulado sobre el rendimiento no académico ante la mediación del contexto. La investigación es de corte cuantitativo, diseño no experimental, transversal y alcance explicativo. Se usó el método de regresión por mínimos cuadrados generalizados en una muestra de 611 universitarios. Los resultados confirmaron la moderación del contexto en la relación de uso de estrategias de aprendizaje autorregulado-rendimiento no académico, además se detectó influencia positiva significativa del contexto en el rendimiento no académico. Se dedujo la importancia de la inducción docente en la autorregulación del estudiante en respuesta a la nueva normalidad.
A novel technique for delineating the effect of variation in the learning rate on the neural correlates of reward prediction errors in model-based fMRI

Introduction Computational models play an increasingly important role in describing variation in neural activation in human neuroimaging experiments, including evaluating individual differences in the context of psychiatric neuroimaging. In particular, reinforcement learning (RL) techniques have been widely adopted to examine neural responses to reward prediction errors and stimulus or action values, and how these might vary as a function of clinical status. However, there is a lack of consensus around the importance of the precision of free parameter estimation for these methods, particularly with regard to the learning rate. In the present study, I introduce a novel technique which may be used within a general linear model (GLM) to model the effect of mis-estimation of the learning rate on reward prediction error (RPE)-related neural responses. Methods Simulations employed a simple RL algorithm, which was used to generate hypothetical neural activations that would be expected to be observed in functional magnetic resonance imaging (fMRI) studies of RL. Similar RL models were incorporated within a GLM-based analysis method including derivatives, with individual differences in the resulting GLM-derived beta parameters being evaluated with respect to the free parameters of the RL model or being submitted to other validation analyses. Results Initial simulations demonstrated that the conventional approach to fitting RL models to RPE responses is more likely to reflect individual differences in a reinforcement efficacy construct (lambda) rather than learning rate (alpha). The proposed method, adding a derivative regressor to the GLM, provides a second regressor which reflects the learning rate. Validation analyses were performed including examining another comparable method which yielded highly similar results, and a demonstration of sensitivity of the method in presence of fMRI-like noise. Conclusion Overall, the findings underscore the importance of the lambda parameter for interpreting individual differences in RPE-coupled neural activity, and validate a novel neural metric of the modulation of such activity by individual differences in the learning rate. The method is expected to find application in understanding aberrant reinforcement learning across different psychiatric patient groups including major depression and substance use disorder.
An Enhanced Vehicle Trajectory Prediction Model Leveraging LSTM and Social-Attention Mechanisms

Accurate trajectory prediction for multiple vehicles in complex social interaction environments is essential for ensuring the safety of autonomous vehicles and improving the quality of their planning and control. The social interactions between vehicles significantly influence their future trajectories. However, traditional trajectory prediction models based on Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN) often overlook or simplify these interactions. Although these models may exhibit high performance in short-term predictions, they fail to achieve high prediction accuracy in scenarios with long-term dynamic interactions. To address this limitation, we propose a Social-Attention Long Short-Term Memory (LSTM) model which predicts the future trajectories of neighboring vehicles and achieves increased accuracy. Our proposed model employs a Social-Pooling layer to effectively capture cooperative behaviors and mutual influences between vehicles. Additionally, we incorporate a self-attention mechanism to weight the inputs and outputs of the Social-Pooling layer, which is significant for assessing the influence between vehicles in different positions. This combination allows our model to take into consideration both the dependencies within the sequence and the social relationships between vehicles, providing a more comprehensive scene understanding. The efficacy of our model is tested on two real-world freeway trajectory datasets, namely NGSIM and HighD. Our model surpasses various baseline methods, exhibiting exceptional accuracy in both prediction and tracking.
Development of a mathematics module on circle material based on the small group discussion model

This research aims to produce a mathematics module on circle material based on the small group discussion model to improve learning outcomes. The fact is that there are 48% of students who get low mathematics learning outcomes. Another fact is that at the high school level, 62% of students in circle material scored below the minimum completeness criteria. Urgent development research was carried out to produce circular module products. The method in this research is research and development (RD). The subjects and objects of the research were high schools and there were 32 students. Data collection techniques, material expert instruments, teachers, and student data were also obtained from the results of student pre-tests and post-tests. The data was analyzed by adding up all the assessment scores on the instruments adding up the test results and averaging the individual and classical scores. The results, and assessment of all module components by material experts, mathematics teachers, small group trials, and large group trials were 88.29%, 90.45%, 93.50%, and 92.10% in the very good category. The average score of students' post-test results is 87.50. Conclusion, this circle module can improve student learning outcomes significantly.
Improved forest-cover mapping based on MODIS time series and landscape stratification

ABSTRACT Detailed forest-cover mapping at a regional scale by supervised classification is technically limited by various factors. This study evaluates the ability of a landscape stratification method to improve classification accuracy. An object-based segmentation technique (OBIA) was performed to delineate radiometrically homogeneous regions into the study area, used as strata for the classification of a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. As a reduction of the spatial variability of the signatures of the vegetation classes is expected, Maximum Likelihood Classifier (MLC) was used to analyse potential effects on classification accuracy. Accuracy assessment was based on the calculation of kappa coefficient (κ) and reject fraction (RF). The values obtained with and without stratification were compared, to assess their global and per-stratum influence on the quality of a detailed forest-cover map (20 different classes). To study the influence of topographical and landscape stratum characteristics on classification accuracy, eight indicators were calculated. Their correlation with κ and RF differences due to stratification was analysed. Our study showed that stratification improved global and per-stratum classification accuracy and in parallel caused an RF increase. Both these evolutions are not conditioned by the stratum topographical and landscape characteristics but strongly influenced by stratum and classified vegetation area.