Optimal Training Research Articles

Resistive versus active assisted robotic training for the upper limb after a stroke: A randomized controlled study

BackgroundSelection of a suitable training modality according to the status of upper limb function can maximize the effects of robotic rehabilitation; therefore, it is necessary to identify the optimal training modality. ObjectivesThis study aimed to compare robotic rehabilitation approaches incorporating either resistance training (RET) or active-assisted training (AAT) using the same rehabilitation robot in people with stroke and moderate impairment. MethodsIn this randomized controlled trial, we randomly allocated 34 people with stroke who had moderate impairment to either the experimental group (RET, n = 18) or the control group (AAT, n = 16). Both groups performed robot-assisted therapy for 30 min, 5 days per week, for 4 weeks. The same rehabilitation robot provided resistance to the RET group and assistance to the AAT group. Body function and structure, activity, and participation outcomes were evaluated before, during, and after the intervention. ResultsRET led to greater improvements than AAT in terms of smoothness (p = 0.006). The Fugl-Meyer Assessment (FMA)-upper extremity (p < 0.001), FMA-proximal (p < 0.001), Action Research Arm Test-gross movement (p = 0.011), and kinematic variables of joint independence (p = 0.017) and displacement (p = 0.011) also improved at the end of intervention more in the RET group. ConclusionsRobotic RET was more effective than AAT in improving upper limb function, structure, and activity among participants with stroke who had moderate impairment.

Pemanfaatan Alat Pengering Tenaga Surya Hybrid untuk Mengoptimalkan Produksi Simplisia Jahe di Desa Kayumas, Situbondo

The aimed of this KOSABANGSA service activity is to apply solar power technology as a dryer to optimize the production of ginger simplicia and empower village communities to add value to ginger products through the Kayumas Village BUMDes. The location and time for carrying out this service is in Kayumas Village, Situbondo Regency from August-December 2023. The method used in this activity is training on controlling ginger plant diseases, especially Fusarium sp., handing over drying equipment along with a demonstration plot on the use of the equipment, then providing training on product packaging and create an official Instagram account and optimize it. Based on the results of the implementation of this service, it is to provide solutions to partners' problems in terms of production by providing training on controlling diseases in ginger and providing drying tools and socializing the use of tools so that ginger simplicia can be produced. Apart from that, the solution to answer the problem of marketing derivative products from ginger is with product packaging training and Instagram optimization training where the output is an official Instagram account which is optimized as a means of promoting ginger products and Kayumas ginger simplicia.

Neuroplasicity and the developmental dyslexia intervention

A growing body of literature suggests that timing plays a critical role in neuroplasticity processes and the molecular mechanisms necessary for learning and memory [1, 2]. Of particular significance to remedial education is identifying the time parameters for primary stimulation that are necessary and sufficient, the time frame relevant for transitioning to long-term memory, and the appropriate periods for restimulation. The translation of short-term stimulation into long-term memory is regulated by diverse processes that are mechanistically distinct and activated by synaptic activity [1], while also relying on protein and glycoprotein synthesis [3] and myelination processes. The current research explores the potential benefits of incorporating neuroscience research on the timing of neuroplasticity mechanisms in designing intervention programs for individuals with developmental dyslexia, specifically focusing on enhancing cognitive functions and skills crucial for reading. Based on available evidence, we have determined optimal training and break time periods for a 10-year-old child with developmental dyslexia resulting from multiple deficits. During the 21-day intervention program, 12 training sessions were conducted each day, commencing at 8 or 9 am and held hourly thereafter. Each session comprised a brief, targeted training exercise ranging from 3 to 7 minutes, depending on the child’s aptitude, followed by a playing session or computer game lasting up to 15 minutes. A 40-minute break followed each session. Brief training sessions were required due to the swift exhaustion of the subject child. The sessions were selected based on the evidence that brief stimulation can still result in a high level of CERB phosphorylation, even if it lasts only a few minutes [4]. Playing sessions were necessary for supporting the child’s motivation throughout the lengthy and intensive intervention program, and the activities performed during these sessions were pertinent to developing specific skills. The duration of the breaks was determined by evidence indicating the time required for primary memory consolidation processes and protein synthesis necessary for long-lasting synaptic plasticity. We have made an effort to eliminate any potential sources of emotional engagement or significant new information during breaks, allowing the initial stage of memory consolidation to occur without any unnecessary disruption. During the training sessions, various tasks were used to target specific types of processing such as phonological, visual, speech, and multimodal processing (e.g., visual-motor, audio-visual, or reading). Each session exclusively focused on one type of exercise. In our prior study [5], we discussed the linguistic aspects of the program and the exercises employed. Significant progress was achieved as a result of the 21-day intervention, surpassing the progress achieved in three years of schooling and during traditional remediation programs with speech therapists that lasted 1–3 sessions per week for 40–120 minutes. Following the intensive intervention, supportive training was continued for one year while considering the crucial timing for neuroplasticity. Afterward, the child reached a normative level of reading, and the effect was maintained throughout their entire period of school education. Based on the timing of neuroplasticity processes, this is the first intensive intervention program experience for dyslexia that we are aware of. Intensive remediation programs, based on relevant findings regarding the mechanisms of memory consolidation, may enhance neural memory trace reinforcement. However, further research is necessary to optimize the timing and length of sessions and identify the most effective combination of linguistic and neurophysiological aspects for intervention.

THE OPINION OF YOUTH SOCCER COACHES ABOUT TALENT SELECTION FOR SOCCER

Society and clubs invest a lot of effort, time and money to discover young people who have the potential to develop into top athletes. But, there are still many questions about a universal theoretical approach, agreed terminology and possible characteristics of talent that would bring practical benefit.The purpose of this paper is to clarify the essence of talent concept in sports and to evaluate the degree of agreement between the opinions of Serbian youth soccer coaches on the important issues of detection, identification and selection of talented soccer players in comparison with other researches. 161 youth soccer coaches with a high level of competence participated in the survey.The word talent is often heard in everyday speech to emphasize the qualities or skills of a young person who achieves above-average results in a certain field. There is no universal definition of talent. Over time, talent concept expanded and dynamized, so that today talent is approached multidimensionally, assessed holistically, and a conclusion is reached with a certain degree of probability. Coaches traditionally select players based on personal taste, observing movement patterns in the game and using their intuition based on experience, and using test results to monitor changes in morphological characteristics and motor skills. In addition to the existence of a disposition for the development of appropriate abilities, strong motivation and a favorable environment with an optimal training process are needed to realize the talent according to the planned outcome in the form of a competitive result. A reliable judgment about the ultimate range of a talented athlete is hindered by intense developmental changes in childhood and youth, due to which talent changes its structure. Further research should be focused on discovering the main and secondary components of giftedness, their interconnections, influences and changes during childhood and youth. This topic raises many questions such as: How not to stifle the individuality of talent? Would the discovery of the undiscovered make the game of soccer lose its charm?

Cooperative optimal train operation algorithm for utilizing regenerative braking energy

The efficient use of regenerative braking energy between trains can significantly reduce the net traction energy consumption in urban rail transit systems, and has attracted considerable attention in recent years. An effective way of using regenerative braking energy is to adjust the train's coasting or cruising phase to an acceleration phase to match the braking phase of a nearby train within the same substation. This paper formulates the train driving phase adjustment problem as a cooperative optimal control problem, where a novel energy constraint is introduced to ensure efficient use of regenerative braking energy during the adjusted phase with optimal traction effort. By applying the control parameterisation method and elaborate handling of the energy constraint and the non-smooth control bound constraint, we transform the formulated problem into a nonlinear programming problem. We then use the sequential quadratic programming algorithm to solve the resulting problem, with the gradients of the cost and constraint functions computed using the sensitivity method. Numerical examples based on real-world data for a subway line show the effectiveness of the proposed method and its advantages over a genetic algorithm in terms of energy savings and computational efficiency.

Comprehensive study of the kinematics of double poling cross-country skiing: experiment, processing and modeling

In high performance sports, high results are usually the product of a whole set of achievements. Innovations in materials, training methods and techniques allow you to win and set records. One of the ways to improve results in cyclical sports, such as cross-country skiing, is to use information about the biomechanics of athletes movements. Analysis of the athlete's movement parameters allows us to characterize the effectiveness of these movements, as well as to develop tactics for conducting the race. In this paper, we present a method for obtaining data, develop an algorithm for digitizing them, and propose a mathematical model that describes a set of data on the movements of cross-country skiers. During the analysis of mathematical modeling data, a significant influence of both the class of skiers and their gender on the parameters of the mathematical model was revealed. The results of this study can be useful for a deeper understanding of the fundamental biomechanics of cross-country skier’s movements, as well as for building more optimal training techniques.

Air-to-Ground Path Loss Model at 3.6 GHz under Agricultural Scenarios Based on Measurements and Artificial Neural Networks

Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models.

Object detection in optical imaging of the Internet of Things based on deep learning.

This article endeavors to enhance image recognition technology within the context of the Internet of Things (IoT). A dynamic image target detection training model is established through the convolutional neural network (CNN) algorithm within the framework of deep learning (DL). Three distinct model configurations are proposed: a nine-layer convolution model, a seven-layer convolution model, and a residual module convolution model. Subsequently, the simulation model of CNN image target detection based on optical imaging is constructed, and the simulation experiments are conducted in scenarios of simple and salient environments, complex and salient environments, and intricate micro-environment. By determining the optimal training iterations, comparisons are drawn in terms of precision, accuracy, Intersection Over Union (IoU), and frames per second (FPS) among different model configurations. Finally, an attention mechanism is incorporated within the DL framework, leading to the construction of an attention mechanism CNN target detection model that operates at three difficulty levels: simple, intermediate, and challenging. Through comparative analysis against prevalent target detection algorithms, this article delves into the accuracy and detection efficiency of various models for IoT target detection. Key findings include: (1) The seven-layer CNN model exhibits commendable accuracy and confidence in simple and salient environments, although it encounters certain instances of undetected images, indicating scope for improvement. (2) The residual network model, when employing a loss function comprising both mean square error (MSE) and cross entropy, demonstrates superior performance in complex and salient environments, manifesting high precision, IoU, and accuracy metrics, thereby establishing itself as a robust detection model. (3) Within intricate micro-environments, the residual CNN model, utilizing loss functions of MSE and cross entropy, yields substantial results, with precision, IoU, and FPS values amounting to 0.99, 0.83, and 29.9, respectively. (4) The CNN model enriched with an attention mechanism outperforms other models in IoT target image detection, achieving the highest accuracy rates of 24.86%, 17.8%, and 14.77% in the simple, intermediate, and challenging levels, respectively. Although this model entails slightly longer detection times, its overall detection performance is excellent, augmenting the effectiveness of object detection within IoT. This article strives to enhance image target detection accuracy and speed, bolster the recognition capability of IoT systems, and refine dynamic image target detection within IoT settings. The implications encompass reduced manual recognition costs and the provision of a theoretical foundation for optimizing imaging and image target detection technologies in the IoT context.

Optimal resistance exercise training parameters for stroke recovery: A protocol for a systematic review

Background Stroke impacts nearly 14 million people annually. Muscle strength and physical function are often affected by stroke and important determinants of stroke recovery. Resistance exercise training (RT) has been shown to improve muscle strength, but RT prescriptions may be suboptimal for other aspects of stroke recovery. Parameters such as frequency, intensity, type, and duration may influence the effectiveness of RT interventions but have not been systematically evaluated. Objectives 1) To determine the effects of RT on stroke recovery, and 2) to examine the influence of RT parameters on intervention effects. Eligibility criteria Randomized controlled trials examining the effects of RT will be eligible for this systematic review if they: 1) included only adults with stroke or transient ischemic attack, 2) compared RT to no exercise or usual care, and 3) did not apply a co-intervention. Study selection Eight databases (MEDLINE, EMBASE, EMCARE, AMED, PsychINFO, CINAHL, SPORTDiscus, and Web of Science) and 2 clinical trials registries (ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform) will be searched from inception. Two independent pairs of authors will compare titles, abstracts, and full-text reports against the eligibility criteria. Conflicts will be resolved by consensus or third author. Main outcome measures The construct of interest is stroke recovery. An advisory group of clinicians, researchers, and partners with lived experience of stroke will be consulted to determine specific outcome measures of interest, and to rank their relative importance. We expect to include measures of physical function, strength, cognition, and quality of life. Random-effects meta-analyses will be used to pool results for each outcome across studies, and RT parameters (frequency, intensity, type, and duration) will be used as covariates in meta-regression analyses. Conclusion The results of this review will inform the optimal RT prescription parameters for promoting stroke recovery.

Comparing the Effectiveness of Hands-on vs. Observational Training of Residents in Interlaminar Epidural Steroid Injections (ILESI) Using a High-Fidelity Spine Simulator.

Introduction The Accreditation Council for Graduate Medical Education (ACGME) requires that residents in the Physical Medicine and Rehabilitation (PM&R) residency observe or perform certain interventional procedures, one of which is an interlaminar epidural steroid injection(ILESI). While the traditional learning model relying heavily on observation is commonplace, it leaves the practice phase of learning to happen on real patients. High-fidelity simulation may be a worthwhile alternative as a training approach to increase physician comfort with the procedure and improve patient safety. Methods Current PM&R residents from two programs between their second and fourth year, inclusively, who lacked prior training experience in ILESI attended one hour of either: (1) an experimental arm of supervised hands-on training on a simulation deviceor (2) a control arm observing the procedures performed by an attending on the same device. Assignments were made based on resident schedule availability. Pre-training knowledge, training, and post-training knowledge were assessed at the Multidisciplinary Pain Clinic at Montefiore Medical Center. Participants were assessed on their procedural competence using an adapted version of a previously published grading checklist before the session. Participants also evaluated their confidence in performing the procedure prior to and after training. Data was analyzed using the Wilcoxon signed-rank test and the Wilcoxon rank-sum test. SAS Version 9.4 was used for analysis. Results Fifteen residents initially participated, but three residents dropped out at the 15-week follow-up. There was a significant increase in test scores in both arms immediately after the intervention (p=0.008 in control, p=0.016 in the experiment), with greater improvement shown in the hands-on training group (p=0.063). At the 15-week follow-up, there was no significant change in test scores in the control arm (p=0.969) while there was a decrease in the experiment arm (p<0.001). Conclusion Hands-on learning with high-fidelity simulation demonstrated more improvement for short-term motor-skill acquisition, while observational learning with repetition showed more benefits for long-term retention. Optimal procedural training should employ both educational modalities for best short- and long-term results.

Sustaining and expanding telehealth activity: Training requirements for Australian residential aged care front-line staff.

To identify the training needs of front-line aged care staff as perceived by senior clinicians and managers at selected residential aged care facilities (RACFs). A qualitative explorative designed study using semi-structured interviews with a convenience sample of RACF senior managers and nurses. A hybrid analysis approach using a framework deductive analysis followed by inductive analysis for sub-themes. Four sub-themes emerged to sustain increased telehealth activity: technology knowledge and digital literacy skills, including understanding telehealth ecosystems and technical skills; evidence-based reviews and clinical frameworks for telehealth consultations to identify appropriate consultations and successful use cases; telehealth best practice guidelines and workflows including telehealth consultations protocols, communicating by videoconferencing, how to support families in attending telehealth consultations and optimal training models; and telehealth policy and legal guidance. Staff require comprehensive training to sustain and expand telehealth use in RACFs. Training should focus on knowledge, skills and competencies in using telehealth as well as the broad factors of policies and understanding ICT systems to support staffs' abilities and confidence. This study provides innovative findings that identify key components and associated activities and resources for training RACF staff to ensure they have sufficient knowledge, competency, skills and confidence to integrate telehealth into care provision.

Practical Evaluation of Federated Learning in Edge AI for IoT

AI running locally on IoT Edge devices is called Edge AI. Federated Learning (FL) is a Machine Learning (ML) technique that builds upon the concept of distributed computing and preserves data privacy while still supporting trainable AI models. This paper evaluates the FL regarding practical CPU usage and training time. Additionally, the paper presents how biased IoT Edge clients affect the performance of an AI model. Existing literature on the performance of FL indicates that it is sensitive to imbalanced data distributions and does not easily converge in the presence of heterogeneous data. Furthermore, model training uses significant on-device resources, and low-power IoT devices cannot train complex ML models. This paper investigates optimal training parameters to make FL more performant and researches the use of model compression to make FL more accessible to IoT Edge devices. First, a flexible test environment is created that can emulate clients with biased data samples. Each compressed version of the ML model is used for FL. Evaluation is done regarding resources used and the overall ML model performance. Our current study shows an accuracy improvement of 1.16% from modifying training parameters, but a balance is needed to prevent overfitting. Model compression can reduce resource usage by 5.42% but tends to accelerate overfitting and increase model loss by 9.35%.

Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy

Background: Addressing intraoperative bleeding remains a significant challenge in the field of robotic surgery. This research endeavors to pioneer a groundbreaking solution utilizing convolutional neural networks (CNNs). The objective is to establish a system capable of forecasting instances of intraoperative bleeding during robot-assisted radical prostatectomy (RARP) and promptly notify the surgeon about bleeding risks. Methods: To achieve this, a multi-task learning (MTL) CNN was introduced, leveraging a modified version of the U-Net architecture. The aim was to categorize video input as either “absence of blood accumulation” (0) or “presence of blood accumulation” (1). To facilitate seamless interaction with the neural networks, the Bleeding Artificial Intelligence-based Detector (BLAIR) software was created using the Python Keras API and built upon the PyQT framework. A subsequent clinical assessment of BLAIR’s efficacy was performed, comparing its bleeding identification performance against that of a urologist. Various perioperative variables were also gathered. For optimal MTL-CNN training parameterization, a multi-task loss function was adopted to enhance the accuracy of event detection by taking advantage of surgical tools’ semantic segmentation. Additionally, the Multiple Correspondence Analysis (MCA) approach was employed to assess software performance. Results: The MTL-CNN demonstrated a remarkable event recognition accuracy of 90.63%. When evaluating BLAIR’s predictive ability and its capacity to pre-warn surgeons of potential bleeding incidents, the density plot highlighted a striking similarity between BLAIR and human assessments. In fact, BLAIR exhibited a faster response. Notably, the MCA analysis revealed no discernible distinction between the software and human performance in accurately identifying instances of bleeding. Conclusion: The BLAIR software proved its competence by achieving over 90% accuracy in predicting bleeding events during RARP. This accomplishment underscores the potential of AI to assist surgeons during interventions. This study exemplifies the positive impact AI applications can have on surgical procedures.

The effect of dual-task training on cognitive ability, physical function, and dual-task performance in people with dementia or mild cognitive impairment: A systematic review and meta-analysis

Objective To summarize the effect of dual-task training on cognitive, physical function, and dual-task performance in people with mild cognitive impairment or dementia. Data sources Embase, PEDro, PsycINFO, PubMed, CINAHL, The Cochrane Library, and a forward search conducted via Web of Science have been searched from inception to July 2023. Review methods Good-quality randomized controlled trials compared dual-task training with no/placebo intervention or single-task training among people with a primary diagnosis of mild cognitive impairment or dementia were included. The PEDro scale was used to evaluate the methodological quality of individual studies. The Grading of Recommendations, Assessment, Development and Evaluations system was adopted to appraise the quality of evidence for each outcome. Results Eighteen trials (1325 participants) were included, and 17 provided data for meta-analysis. Comparing with no intervention, dual-task training led to significant improvements on attention (mean difference (MD) = −20.66, 95%CI [−39.42, −1.90]), functional mobility (MD = −2.73; 95%CI [−3.98, −1.49]). Compared with single-task training, dual-task training had greater effects on overall cognitive function (standardized mean difference (SMD) = 0.29, 95%CI [0.09, 0.49]), balance (SMD = 0.78, 95%CI [0.40, 1.15]) and functional mobility (MD = −1.17; 95%CI [−1.77, −0.58]). Its effect on dual-task performance remains inconclusive due to the inconsistent results reported. Conclusion Low- to moderate-quality evidence supports that dual-task training has beneficial effects on cognitive function and physical function in individuals with dementia or mild cognitive impairment. The optimal training protocol of dual-task training on cognitive and physical functions, and dual-task performance remains uncertain. Well-designed, randomized studies with large enough sample sizes are warranted.

Optimization of clothing style and color design mode and innovation of technique under symbiosis mode

Abstract In this paper, we combine the thesaurus matching through the clothing style feature mining method, get the conditional probability index through great likelihood estimation, use iterative scale training parameters, and calculate the similarity mean of the belonging feature values. To identify and classify clothing styles, texture features are extracted from clothing styles, and the optimal training sample set of popular clothing colors is selected. The highest data mining algorithm in symbiotic mode has an accuracy rate of 96.4% for short-skirt clothing styles, a statistical result of 82.61 for tie-dye clothing techniques, and a maximum color threshold of 0.14. Symbiotic mode achieves a visual effect of distinctive features, which leads to optimal clothing styles and color innovations.

A transfer learning-based machine learning approach to predict mechanical properties of different material types fabricated by selective laser melting process

The necessity for a massive dataset has limited the desirability of the machine learning approaches for industrial applications, especially in the metal additive manufacturing processes, where, collecting a large dataset is expensive and virtually ineffective. Concerning this restriction, an effective machine learning technique should be developed to bridge the gap between the academia and the industry. Hence, in this research, a transfer learning-based artificial neural network (TL-ANN) model was developed to predict the mechanical properties of different metallic specimens fabricated by selective laser melting (SLM) process. This model was integrated with a Bayesian hyperparameters optimization algorithm to select the optimum training parameters of the model. The proposed model consists of a target network and a source network. The source network was trained based on the mechanical properties that were obtained experimentally for various materials, including pure and alloyed copper, steel, titanium, nickel, etc. The overall regression correlation coefficient ( R) of the TL-ANN model was about 0.99, with the mean square error of testing, validation, and training of datasets of about 2.031, 1.423, and 1.068, respectively, representing the successful execution of the source network in prediction of the mechanical properties of the SLMed parts. Using the achieved knowledge of the source network, the target network was trained to predict the mechanical properties of the target material (here SLMed pure and alloyed aluminum specimens). The obtained results revealed that with the help of the transfer learning, the hybrid neural network could predict the mechanical properties of SLM-fabricated aluminum parts with a high accuracy level, even with the small number of training dataset of the target material. To demonstrate the influence of transfer learning in the accuracy of the model, a separate network was developed from scratch, i.e. with random initial weights of the neurons. The R-values of the test dataset of the individual model for the output parameters of ultimate tensile strength, relative density, and yield strength of the fabricated aluminum samples were 0.787, 0.742, and 0.817, respectively, as compared with that of TL-ANN model of 0.966, 0.903, and 0.971, respectively, representing an average of 21% enhancement in the accuracy of the predictivity of the model by application of transfer learning algorithm.

Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system

A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration. Additionally, we have introduced the bagging ensemble learning method to enhance the performance of the CANFIS model. As part of a comprehensive model evaluation process, the best-performing CANFIS model for each forecasting scenario has undergone uncertainty analysis using bootstrap sampling. Our results reveal that the CANFIS model performs satisfactorily for daily forecasting but leaves room for improvement in monthly forecasting, particularly for two-month and three-month ahead forecasts. Moreover, we have identified several optimal input combinations, highlighting the significance of the temperature variable in monthly forecasting. Furthermore, our findings indicate that additional training data does not necessarily lead to improved performance. The ensemble CANFIS model has demonstrated significant performance enhancement, particularly for monthly forecasting. Finally, the CANFIS model uncertainty analysis has shown satisfactory results for daily forecasting scenarios, while monthly forecasting models exhibit higher uncertainties, particularly during periods with distinctly different GWL fluctuation patterns.

Expectations related to home-based telemonitoring of high-risk pregnancies: A qualitative study addressing healthcare providers' and users' views in Norway.

A pregnancy can be evaluated as high-risk for the woman and/or the fetus based on medical history and on previous or ongoing pregnancy characteristics. Monitoring high-risk pregnancies is crucial for early detection of alarming features, enabling timely intervention to ensure optimal maternal and fetal health outcomes. Home-based telemonitoring (HBTM) is a marginally exploited opportunity in antenatal care. The aim of this study was to illuminate healthcare providers' and users' expectations and views about HBTM of maternal and fetal health in high-risk pregnancies before implementation. To address diverse perspectives regarding HBTM of high-risk pregnancies, four different groups of experienced healthcare providers or users were interviewed (n = 21). Focus group interviews were conducted separately with midwives, obstetricians, and women who had previously experienced stillbirth. Six individual interviews were conducted with hospitalized women with ongoing high-risk pregnancies, representing potential candidates for HBTM. None of the participants had any previous experience with HBTM of pregnancies. The study is embedded in a social constructivist research paradigm. Interviews were analyzed using a thematic approach. The participants acknowledged the benefits and potentials of more active roles for both care recipients and providers in HBTM. Concerns were clearly addressed and articulated in the following themes: eligibility and ability of women, availability of midwives and obstetricians, empowerment and patient safety, and shared responsibility. All groups problematized issues crucial to maintaining a sense of safety for care recipients, and healthcare providers also addressed issues related to maintaining a sense of safety also for the care providers. Conditions for HBTM were understood in terms of optimal personalized training, individual assessment of eligibility, and empowerment of an active patient role. These conditions were linked to the importance of competent and experienced midwives and obstetricians operating the monitoring, as well as the availability and continuity of care provision. Maintenance of safety in HBTM in high-risk pregnancies was crucial, particularly so insituations involving emerging acute health issues. HBTM requires new, proactive roles among midwives, obstetricians, and monitored women, introducing a fine-tuned balance between personalized and standardized care to provide safe, optimal monitoring of high-risk pregnancies.

TRAINING ON THE APPLICATION OF KINESIOTAPING COMBINED WITH CONTRACT RELAX AS A PREVENTIVE AND REHABILITATIVE MEASURE FOR SPORTS INJURIES

The problems experienced by PPLP/D Papua athletes is sports injuries which limit athletes in running an optimal training program. The dense and heavy training program causes a tendency for sports injuries to occur. The purpose of this community service is to upgrade knowledge and skills on how to install kinesiotaping and contract relax as an effort to preventive and rehabilitative sports injuries. The method used is training with the flow of delivering material and demonstration. Population in this community service are PPLP/D Papua athletes with a total of 98 athletes. The sample used was 60 athletes from 12 sports. The results obtained are an increase in PPLP/D athletes' knowledge about kinesiotaping and contract relax, the dominant pretest results of the very low category 53 athletes (88.3%) become the dominant category of very high 19 athletes (31.7%), high category 18 athletes (30%), and medium category 18 athletes (30%). The results of the t test analysis (paired sample test) found the sig value (2-tailed) = 0.001 &lt;0.05. Based on the results obtained, it can be concluded that the training provided has a positive impact on PPLP/D Papua athletes in terms of increasing knowledge about the application of kinesiotaping and contract relax as preventive and rehabilitative sports injuries.

Beyond educating students about business failure: The role of optimism training and hope

ABSTRACT Teaching entrepreneurship students about failure can help them better cope with business failure and learn from it. This is important given evidence that experienced entrepreneurs are more successful. Entrepreneurship educators raise the concern that teaching students about failure may make them apprehensive about pursuing entrepreneurship. We examine the impact of optimism training on individuals who are learning about business failure (distinguishing between large and small failure) to study the impact of failure on career choice. Using a randomized experiment, we study the underlying mechanism – the feeling of hope – in how failure magnitude impacts an individual’s subsequent career choice between wage employment and entrepreneurship. Our findings show that failure magnitude negatively impacts subsequent entrepreneurship career choice by reducing hope. We also find that having an optimistic mind-set moderates this relationship. The negative effect of failure on hope, and subsequent entrepreneurship career choice, is minimized for individuals with an optimistic mind-set.