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Background Basic Life Support (BLS) plays an important role in increasing the survival rate of hospitalized heart attack patients. There are no previous studies on the effect of BLS training among Palestinian nurses. This study aimed to evaluate the effect of simulation-based BLS training program on nurses’ knowledge Palestinian nurses at governmental hospitals. Methods A quasi-experimental, pre & post-test design was used. 700 nurses were recruited proportionally using a simple random sampling method among 2980 nurses from 13 public hospitals in the Gaza Strip. This study was conducted from June to August 2022. A practical BLS test consisting of 10 multiple-choice questions according to American Heart Association guidelines (2020) was collected and sociodemographic characteristics. SPSS software, version 24 was used for the statistical analysis. Descriptive statistics and weighted mean were used. T-Test and One-way analysis of variance (ANOVA) were applied to determine differences in means among groups. Results Most of the participating nurses (55.7%) were male, while (44.3%) were female. The majority of nurses (84.4%) are under 40 years of age. The weighted mean scores in the pre-test ranged from 52.2 to 75.1% and the mean scores was (6.16 ± 1.97). After applying conventional BLS training, the weighted mean scores ranged from 85.6 to 97.3% and the mean scores was (9.19 ± 1.04). The study revealed that the nurses’ knowledge increased after applying simulation-based training program. The mean of knowledge scores was statistically significant between the pre and post-test on the basis of the current work hospital (P-value < 0.001). Conclusion This study affords significant evidence of the positive effects of the BLS training program in improving nurses’ knowledge; we recommend advanced BLS training for all healthcare providers, doctors, and nurses working in hospitals and healthcare centers. Nursing managers can implement systematic strategies to enhance nurses’ knowledge and practice in BLS to target low-scoring Governorates.

Heart failure is a prevalent and serious cardiovascular condition characterized by the heart’s inability to pump blood to meet the body’s demands adequately. Current research on heart failure prediction primarily relies on conventional clinical assessment methods, traditional machine learning techniques, and traditional deep learning methods. Efficient and accurate heart failure prediction is a significant challenge due to its complex and multifactorial nature. In this study, we propose a heart failure prediction approach utilizing a broad learning system (BLS) that has the potential to capture intricate patterns in the data and enhance prediction accuracy. To evaluate our approach, we utilize an extensive dataset compiled from five previous independent datasets from the Cleveland, Hungary, Switzerland, Long Beach VA Hospital, and Stalog (heart) datasets. Experimental results demonstrate the effectiveness and efficiency of the BLS model, with a training time of 0.36 seconds and testing accuracy of 90%, precision of 88%, recall of 96%, and specificity of 82%, showcasing its potential performance for accurate heart failure prediction.

Fetal health problems are still a serious issue nowadays, affecting mothers and their children. The rate of mortality caused by fetal health problems is notably high in some areas of the earth, especially in lower-income countries. At present, besides the conventional clinical assessment, the methods of fetal health classification are mainly traditional machine learning and deep learning, such as KNN, SVM, Logistic Regression, and Naive Bayes, with the problems of low precision or long training time. In this study, we apply the broad learning system (BLS) to fetal health classification. The data set is sourced from the UCI ML repository database, which is made available by the Biomedical Engineering Institute and the University of Porto’s Faculty of Medicine. With the training accuracy of 92%, training time of 0.52s, and testing accuracy of 90%, the experimental results that highly satisfactory on BLS in the experiment.

Objective: To quantify the effectiveness of non-structured training versus a structured 4-step approach for basic life support (BLS) knowledge and skills using quantitative assessment tools. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Medicine, Combined Military Hospital, Peshawar Pakistan from Oct 2022 to Mar 2023. Methodology: Two hundred (n=200) healthcare workers from all Hospital Departments were included in the study through convenient sampling. They were divided into “Group-A” and “Group-B” of equal size. Group-A received BLS training through a four-step approach, whereas Group-B received non-structured teacher-based training. Pre and post-training MCQs judged the knowledge gained, and a checklist was used to assess the effectiveness of the BLS skills. Results: Both the groups had similar scores in the Pre-training test (p 0.692). Both groups improved their scores after their respective training (p<0.001 for both groups). However, Group-A got a better score (mean score =70.50±11.22) than Group- B (mean score =59.60±11.88) with a highly significant difference (p-value<0.001). There was also a significant improvement (p<0.001) in BLS skills performance as per the checklist in Group-A (mean 7.69±1.47) versus Group-B (mean 6.18±1.34) out of a maximum score of 10. Conclusion: The 4-step program is significantly better than non-standardised training in achieving BLS learning outcomes.

To investigate forest resources, it is necessary to identify the tree species. However, it is a challenge to identify tree species using 3D point clouds of trees collected by light detection and ranging (LiDAR). PointNet++, a point cloud deep learning network, can effectively classify 3D objects. It is important to establish high-quality individual tree point cloud datasets when applying PointNet++ to identifying tree species. However, there are different data processing methods to produce sample datasets, and the processes are tedious. In this study, we suggest how to select the appropriate method by designing comparative experiments. We used the backpack laser scanning (BLS) system to collect point cloud data for a total of eight tree species in three regions. We explored the effect of tree height on the classification accuracy of tree species by using different point cloud normalization methods and analyzed the effect of leaf point clouds on classification accuracy by separating the leaves and wood of individual tree point clouds. Five downsampling methods were used: farthest point sampling (FPS), K-means, random, grid average sampling, and nonuniform grid sampling (NGS). Data with different sampling points were designed for the experiments. The results show that the tree height feature is unimportant when using point cloud deep learning methods for tree species classification. For data collected in a single season, the leaf point cloud has little effect on the classification accuracy. The two suitable point cloud downsampling methods we screened were FPS and NGS, and the deep learning network could provide the most accurate tree species classification when the number of individual tree point clouds was in the range of 2048–5120. Our study further illustrates that point-based end-to-end deep learning methods can be used to classify tree species and identify individual tree point clouds. Combined with the low-cost and high-efficiency BLS system, it can effectively improve the efficiency of forest resource surveys.

Robotic belt grinding is a productive method frequently used to finish intricate parts. To address the challenges of ensuring grinding efficiency and quality due to the complexity of the robotic belt polishing process, a multi-algorithm fusion–based intelligent decision-making method is proposed in this study. The broad learning system (BLS) was utilized to construct prediction models for the process parameters of robotic belt grinding. This model then served as the objective function for subsequent process optimization models. Second, to achieve a more uniform Pareto front, several strategies were implemented to improve the multi-objective gray wolf optimizer (MOGWO). Then, the best process parameters were extracted from the Pareto front using the technique for order preference by similarity to the ideal solution (TOPSIS). The proposed approach achieved a 24.24% reduction in surface roughness (Ra) and a 3.82% increase in material removal rate (MRR) compared to those of existing processing methods. These results demonstrate that this intelligent approach significantly improves the determination of process parameters for robotic belt grinding, supporting the advancement of intelligent manufacturing.

Individual tree structural parameters are vital for precision silviculture in planted forests. This study used near-field LiDAR (light detection and ranging) data (i.e., unmanned aerial vehicle laser scanning (ULS) and ground backpack laser scanning (BLS)) to extract individual tree structural parameters and fit volume models in subtropical planted forests in southeastern China. To do this, firstly, the tree height was acquired from ULS data and the diameter at breast height (DBH) was acquired from BLS data by using individual tree segmentation algorithms. Secondly, point clouds of the complete forest canopy were obtained through the combination of ULS and BLS data. Finally, five tree taper models were fitted using the LiDAR-extracted structural parameters of each tree, and then the optimal taper model was selected. Moreover, standard volume models were used to calculate the stand volume; then, standing timber volume tables were created for dawn redwood and poplar. The extraction of individual tree structural parameters exhibited good performance. The volume model had a good performance in calculating the standing volume for dawn redwood and poplar. Our results demonstrate that near-field LiDAR has a strong capability of extracting tree structural parameters and creating volume tables for subtropical planted forests.

PurposeThe blackboard learning system is an online platform designed for e-learning employed by higher education institutes like universities that facilities students to continue learning and educational activities. This study explores the determinants that affect students' acceptance and use of Blackboard learning system (BLS) in Pakistan utilizing the modified UTAUT framework with social isolation as an additional variable.Design/methodology/approachA questionnaire survey was conducted, and the study gathered 494 university students' responses in Pakistan as participants. The collected data were interpreted applying PLS-SEM version 3.2.3 software.FindingsThe study's findings exhibited that PE, EE, FC and SI are the prime determinants influencing the intention and use of BLS in Pakistani universities. Hedonic motivation and social isolation interact differently between UTAUT variables and use intention. The results verify the suitability of the applied theory in the background of the study.Research limitations/implicationsHowever, the findings highlight the present understanding of BLS use from the learners' aspect, but the study's limitation cannot be evaded. The study respondents belonged to a specific region of Pakistan (Karachi) that might influence the usefulness of the outcomes. Other factors categorized as the environmental, system and organizational elements were not part of the study that may also differentiate the BLS acceptance. The model was extended by including the social isolation, but the effect is insignificant yet positive; therefore, it is required to evaluate the model differently, such as the organizational aspect, for future research. Moreover, the ethnic factors that vary in emerging and developed economies may provide different explanations; therefore, they can be incorporated in future studies. Likewise, the variables such as hedonic motivation need to be emphasized more by examining and evaluating its effect on students' education performance in the future.Practical implicationsThe outcome of the study suggests some implications. At first, being the modified framework UTAUT2 application makes the collaboration appropriate according to the current phenomena of the COVID-19 pandemic and its contribution to the higher education region to analyze the acceptance of e-learning systems. Educational institutions within Pakistan would emphasize enhancing students' accomplishment by improving the interface and the blackboard learning system workability. Hence, learners' effectiveness in learning would be escalated; they would be encouraged to accomplish study objectives using BLS, particularly when they consider it easy to use and a useful platform for e-learning during the pandemic. Furthermore, enhancing the e-learning system in the context of the effort demands to be required to utilize BLS should be the foremost objective as learners would be motivated to accept the technology if they consider it simple, convenient, and user-friendly to adopt. Thus, the benefits of using BLS during this situation when universities are not operational will make students adaptable to change in the prospect. Learners will accept the model of online education, even if the universities become operational. However, it can increase the rate of earnings and revenue for universities as they can enroll in online and regular classes. Therefore, it is suggested that higher education management should create a resilient online platform by which facilitators can communicate with learners without any obstacles.Social implicationsHence, it is recommended to introduce the online short course, qualification, certified courses and integrated coursework with international ventures and ongoing classes. Numerous learners continue their studies along with the job. Therefore, it is suggested to introduce online programs for those learners. Another benefit would be that it offers an integrated platform for sharing knowledge. BLS offers to maintain the complete information in one place, and learners can see them as per their convenience based on their availability. This reduces the burden on administration related to keeping the educational material and resource in various files. Thus, it also reduces the expense of universities. It is suggested to emphasize encouraging the use of BLS through an effective plan that can assist in execution and help learners identify the technology features rather than to face difficultly to accept the change. Moreover, the acceptance of BLS for educational purposes verifies that other learning events can occur on the online platform. Thus, it is recommended to promote the origin of the online atmosphere and the initiation of other events. Globally, dynamics are changing frequently and continuously and are moving towards artificial intelligence systems; the circumstances are suitable for promoting online educational platforms' acceptance by incorporating it with the current educational arrangement.Originality/valueThe study provides recommendations for the research to be conducted to explore the modified framework in different regions and boundaries to evaluate the effect of other factors on adopting e-learning platforms.

The aim of the current study was to examine whether self-control skills in childhood moderate the association between very preterm birth (<32 weeks of gestational age) and emotional problems and peer victimization in adolescence. We used data from four prospective cohort studies, which included 29,378 participants in total ( N = 645 very preterm; N = 28,733 full-term). Self-control was mother-reported in childhood at 5–11 years whereas emotional problems and peer victimization were both self- and mother-reported at 12–17 years of age. Findings of individual participant data meta-analysis showed that self-control skills in childhood do not moderate the association between very preterm birth and adolescence emotional problems and peer victimization. It was shown that higher self-control skills in childhood predict lower emotional problems and peer victimization in adolescence similarly in very preterm and full-term borns.