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Today, in many areas of technology, we can come across applications of various artificial intelligence methods. They usually involve models trained on some specific pool of learning data. Sometimes, however, the data analyzed by these solutions can change its nature over time. This usually results in a decrease in classification efficiency. In such a case, the use of techniques to retrain the originally trained reference models should be considered. One of the industries where the nature of data changes quite dynamically over time is the broadly defined call/contact center systems. An example of a module that is often found in this type of system and that, due to frequently changing marketing campaigns, requires the use of learning techniques is the automatic classification of text data. The paper describes the process of retraining the original reference models used in a multi-label text message classification method dedicated directly to call/contact center systems applications. In order to carry out the retraining process, Polish-language data from the actual archives of a large commercial contact center system and English-language data extracted from a publicly available database were used. The study was conducted for models based on artificial neural networks and bidirectional encoder representations from transformer type models. In addition, two different retraining strategies were studied, the results of which were compared with data obtained from the operation of reference models. As a result of the research work, an improvement of up to 5% in classification efficiency, as described by the metric Emotica was obtained, which means that proper integration of the retraining process brings tangible benefits to the solution tested in the article. Thus, it can also benefit the solutions used in business.

The capacity of a robot to automatically adapt to new environments is crucial, especially in social robotics. Often, when these robots are deployed in home or office environments, they tend to fail because they lack the ability to adapt to new and continuously changing scenarios. In order to accomplish this task, robots must obtain new information from the environment, and then add it to their already learned knowledge. Deep learning techniques are often used to tackle this problem successfully. However, these approaches, complete retraining of the models, which is highly time-consuming. In this work, several strategies are tested to find the best way to include new knowledge in an already learned model in a deep learning pipeline, putting the spotlight on the time spent for this training. We tackle the localization problem in the long term with a deep learning approach and testing several retraining strategies. The results of the experiments are discussed and, finally, the best approach is deployed on a Pepper robot.

The movement ecology framework depicts animal movement as the result of the combined effects of internal and external constraints on animal navigation and motion capacities. Nevertheless, there are still fundamental problems to understand how these modulations take place and how they might be translated into observed statistical properties of animal trajectories. Of particular interest, here, is the general idea of intermittence in animal movement. Intermittent locomotion assumes that animal movement is, in essence, discrete. The existence of abrupt interruptions in an otherwise continuous flow of movement allows for the possibility of reorientations, that is, to break down previous directional memories of the trajectory. In this study, we explore the potential links between intermittent locomotion, reorientation behavior, and search efficiency. By means of simulations we show that the incorporation of Lévy intermittence in an otherwise nonintermittent search strongly modifies encounter rates. The result is robust to different types of landscapes (i.e., target density and spatial distribution), and spatial dimensions (i.e., 2D, 3D). We propose that Lévy intermittence may come from reorientation mechanisms capable of organizing directional persistence on time (i.e., fractal reorientation clocks), and we rationalize that the explicit distinction between scanning and reorientation mechanisms is essential to make accurate statistical inferences from animal search behavior. Finally, we provide a statistical tool to judge the existence of episodic and strong reorientation behaviors capable of modifying relevant properties of stochastic searches, ultimately controlling the chances of finding unknown located items.

PurposeThe purpose of this paper is to explore two identified knowledge gaps: first, the identification and analysis of online searching trends for Financial Technology (FinTech)-related jobs and education information in UK, and second to assess the current strength of the FinTech-related job distribution in terms of job titles and locations in UK, job market in UK and what is required to help it to grow.Design/methodology/approachTwo sets of data were used in this study in order to fill the two identified knowledge gaps. First, six years’ worth of data, for the period from September 2012 to August 2018 was collected from Google Trends. This was in the form of search term keyword text. The hypothesis was designed correspondingly, and the results were reviewed and evaluated using a relevant statistical tool. Second, relevant data were extracted from the “Indeed” website (www.indeed.co.uk) by means of a simple VBA programme written in Excel. In total, the textual data for 500 job advertisements, including the keyword “FinTech”, were downloaded from that website.FindingsThe authors found that there was a continuously increasing trend in the use of the keyword “fintech” under the category “Jobs and Education” in online searching from September 2012 to August 2018. The authors demonstrated that this trend was statistically significant. In contrast, the trends for searches using both “finance” and “accounting” were slightly decreased over the same period. Furthermore, the authors identified the geographic distribution of the fintech-related jobs in the UK. In regard to job titles, the authors discovered that “manager” was the most frequently searched term, followed by “developer” and “engineer”.Research limitations/implicationsEducators could use this research as a reference in the development of the portfolio of their courses. In addition, the findings from this study could also enable potential participators to reflect on their career development. It is worth noting that the motivations for carrying out an internet search are complex, and each of these needs to be understood. There are many factors that would affect how an information seeker would behave with the obtained information. More work is still needed in order to encourage more people to enter to the FinTech sector.Originality/valueIn the planning stage prior to launching a new course educators often need to justify the market need: this analysis could provide a supporting rationale and enable a new course to launch more quickly. Consequently, the pipeline of talent supply to the sector would also be benefitted. The authors believe this is the first time that a study like this had been conducted to explore specifically the availability and opportunities for FinTech education and retraining in UK. The authors anticipate that this study will become the primary reference for researchers, educators and policy makers engaged in future research or practical applications on related topics.

Severe asthma remains a major problem despite pharmacological advances. Pulmonary rehabilitation (PR) is established in chronic respiratory disease but its role in severe asthma is unclear. Summarise evidence on PR in severe and uncontrolled asthma, describe PR-modalities, and outline implementation and research priorities. Narrative review of systematic reviews and clinical studies of multidimensional PR programmes and isolated components [aerobic training, inspiratory muscle training (IMT), breathing retraining, neuromuscular electrical stimulation (NMES), telerehabilitation]. Outcomes included asthma control, HRQoL, exercise capacity and healthcare utilisation. Multicomponent PR improves exercise capacity and multiple QoL domains; pooled data show substantial increases in six-minute walk distance. Combined exercise, education and self-management produced clinically meaningful improvements in asthma control and symptoms, notably patients with uncontrolled disease and functional impairment. IMT, NMES and breathing retraining improved inspiratory strength, peripheral muscle function and hyperventilation symptoms. Telerehabilitation expands access but requires attention to digital literacy and adherence. Heterogeneity, small samples and attrition limit generalisability. PR is a promising personalised, multidisciplinary adjunct for severe asthma. Larger phenotype-stratified trials, harmonised outcome sets and implementation research are needed to define candidate selection, optimal dose and cost-effectiveness; embedding PR within severe asthma centres may optimise outcomes and reduce healthcare use.

Background Bystander intervention is known to be the most important factor in the survival chain for out-of-hospital cardiac arrest. However, bystander cardiopulmonary resuscitation (CPR) rates remain low—mainly due to a lack of training. Implementing basic life support (BLS) training in schools is a key aspect of increasing bystander intervention. We reviewed the literature on BLS training methods for schoolchildren and sought to identify the methodological elements that appeared to be the most effective in terms of the acquisition and retention of knowledge, practical skills, and attitudinal skills. Methods We searched the MEDLINE database (via PubMed) for relevant articles published between from January 2015 to January 2022. Articles on BLS training in children were selected if the primary objective was determine the effectiveness of the training method(s) used. Results Of the 1,098 publications identified, 28 were selected and reviewed. Half of the studies had a randomized, controlled design, the study sample size ranged from 57 to 1,917. Hands-on practice was included in 26 of the 28 studies, and the main training session lasted for 75 to 120 min. Hands-on practice gave better results than no practice. Learning tools promoted acquisition, and refresher training sessions appeared to be of value. Conclusions The results of our literature review showed that the conventional training pattern used with adults (i.e. theory, demonstration, and practice) is applicable to children if all the components are adapted for this population. Further studies of the development and evaluation of BLS training for young children are required.

Background: Simulation education is essential for the development of nurses' practical skills. This study evaluated the impact and duration of simulation-based neonatal resuscitation education on the knowledge, self-confidence, and performance ability of neonatal intensive care unit (NICU) nurses. Method: This quasi-experimental study was conducted in South Korea and included 35 NICU nurses working in tertiary hospitals between August and October 2021. Simulation-based neonatal resuscitation education (NRE) was provided for 80 minutes, and its effectiveness was measured in terms of nurses' knowledge, self-confidence, and performance ability. Data collection was conducted before, 1 week after, 3 weeks after, and 5 weeks after the training, and the collected data were calculated and analyzed using a t test and repeated measures analysis of variance. Results: Simulation-based NRE improved knowledge, self-confidence, and performance in neonatal resuscitation. Performance ability showed greater improvement than knowledge or self-confidence, and all three areas showed significant differences in score changes over time. Conclusion: The duration of the training effect should be considered an important factor. [J Contin Educ Nurs. 2024;55(2):79–86.]

Previous studies suggest that subjective distress in children with functional neurological disorder (FND) is associated with stress-system dysregulation and modulates aberrant changes in neural networks. The current study documents illness-promoting psychological processes in 76 children with FND (60 girls and 16 boys, aged 10.00−17.08 years) admitted to the Mind–Body Program. The children completed a comprehensive family assessment and self-report measures, and they worked with the clinical team to identify psychological processes during their inpatient admission. A total of 47 healthy controls (35 girls and 12 boys, aged 8.58–17.92 years) also completed self-report measures, but were not assessed for illness-promoting psychological processes. Children with FND (vs. controls) reported higher levels of subjective distress (total DASS score, t(104.24) = 12.18; p ˂ 0.001) and more adverse childhood experiences across their lifespans (total ELSQ score, t(88.57) = 9.38; p ˂ 0.001). Illness-promoting psychological processes were identified in all children with FND. Most common were the following: chronic worries about schoolwork, friendships, or parental wellbeing (n = 64; 84.2%); attention to symptoms (n = 61; 80.3%); feeling sad (n = 58; 76.3%); experiencing a low sense of control (helplessness) in relation to symptoms (n = 44; 57.9%); pushing difficult thoughts out of mind (n = 44; 57.9%); self-critical rumination (n = 42; 55.3%); negative/catastrophic-symptom expectations (n = 40; 52.6%); avoidance of activities (n = 38; 50%); intrusive thoughts/feelings/memories associated with adverse events (n = 38, 50%); and pushing difficult feelings out of mind (n = 37; 48.7%). In children with FND—disabled enough to be admitted for inpatient treatment—illness-promoting psychological processes are part of the clinical presentation. They contribute to the child’s ongoing sense of subjective distress, and if not addressed can maintain the illness process. A range of clinical interventions used to address illness-promoting psychological processes are discussed, along with illustrative vignettes.

While gait retraining paradigms that alter knee loads typically focus on modifying kinematics, the underlying muscle force modifications responsible for these kinematic changes remain largely unknown. As humans are generally thought to select uniform gait muscle patterns such as strategies based on fatigue cost functions or energy minimization, we hypothesized that a kinematic gait change known to reduce the knee adduction moment (i.e. toe-in gait) would be accompanied by a uniform muscle force modification strategy for individuals with symptomatic knee osteoarthritis. Ten subjects with self-reported knee pain and radiographic evidence of medial compartment knee osteoarthritis performed normal gait and toe-in gait modification walking trials. Two hundred muscle-actuated dynamic simulations (10 steps for normal gait and 10 steps from toe-in gait for each subject) were performed to determine muscle forces for each gait. Results showed that subjects internally rotated their feet during toe-in gait, which decreased the foot progression angle by 7° (p<0.01) and reduced the first peak knee adduction moment by 20% (p<0.01). While significant muscle force modifications were evidenced within individuals, there were no consistent muscle force modifications across all subjects. It may be that self-selected muscle pattern changes are not uniform for gait modification particularly for individuals with knee pain. Future studies focused on altering knee loads should not assume consistent muscle force modifications for a given kinematic gait change across subjects and should consider muscle forces in addition to kinematics in gait retraining paradigms.