Knowledge derivation from Likert scale using Z-numbers

Manual versus deep learning measurements to evaluate cumulus expansion of bovine oocytes and its relationship with embryo development in vitro

Cumulus expansion is an important indicator of oocyte maturation and has been suggested to be indicative of greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a gold standard. Additionally, these methods are subjective and time-consuming. In this manuscript, the reliability of three cumulus expansion measurement methods was assessed, and a deep learning model was created to automatically perform the measurement. Cumulus expansion of 232 cumulus-oocyte complexes was evaluated by three independent observers using three methods: (1) measurement of the cumulus area, (2) measurement of three distances between the zona pellucida and outer cumulus, and (3) scoring cumulus expansion on a 5-point Likert scale. The reliability of the methods was calculated in terms of intraclass-correlation coefficients (ICC) for both inter- and intra-observer agreements. The area method resulted in the best overall inter-observer agreement with an ICC of 0.89 versus 0.54 and 0.30 for the 3-distance and scoring methods, respectively. Therefore, the area method served as the base to create a deep learning model, AI-xpansion, which reaches a human-level performance in terms of average rank, bias and variance. To evaluate the accuracy of the methods, the results of cumulus expansion calculations were linked to embryonic development. Cumulus expansion had increased significantly in oocytes that achieved successful embryo development when measured by AI-xpansion, the area- or 3-distance method, while this was not the case for the scoring method. Measuring the area is the most reliable method to manually evaluate cumulus expansion, whilst deep learning automatically performs the calculation with human-level precision and high accuracy and could therefore be a valuable prospective tool for embryologists.

Knowledge, attitudes, and practices of caregivers on childhood immunization in Okaikoi sub-metro of Accra, Ghana

Background

Immunization remains one of the most cost-effective health interventions. However, there are still issues of vaccine hesitancy especially in caregivers who are required to protect their children from vaccine-preventable diseases. This thwarts the overall vaccine coverage in disease-endemic areas such as sub-Saharan Africa. Therefore, to determine the factors that promote vaccine hesitancy in caregivers, this study sought to assess the knowledge, attitude, and practices of caregivers on childhood immunization in Okaikoi, a sub-metro of Accra in Ghana.

Methods

A cross-sectional study on childhood immunization was conducted to determine the knowledge, attitudes, and practices of caregivers. A total of 120 caregivers with infants aged 12 months to 23 months were interviewed with a structured questionnaire containing open-ended and closed-ended queries.

Results

From the community, infants whose caregivers had adhered completely to immunization constituted 53.3% while the rest were partially immunized. The two main deterrents to complete immunization were time constraints (25.8%) and forgetfulness (17.5%). It was observed that vaccination uptake and maternal level of education, as well as vaccination adverse reaction, did not impact the completion of the EPI program by these caregivers. Unfortunately, it was noted that caregivers with higher education levels were unable to complete their vaccination schedules due to their busy work schedules. Nonetheless, the main deterrent to adhering to complete childhood immunization was poor maternal knowledge (58%).

Conclusion

The study revealed that, the caregivers in the community had poor knowledge on vaccination and its benefits, and therefore, with no strict adherence to vaccination schedules. This promoted the incomplete immunization of children in the community by their caregivers. Also, since the main source of information with regard to immunization in the sub-metro was through the antenatal and postnatal child welfare clinics and the media, we recommend that the health workers collaborate with media personnel to ensure that standardized information is disseminated.

Validity and reliability of the questionnaire of academic knowledge of teachers of basic general education

Background: The concern and analysis about the knowledge possessed by teachers of basic general education persists in academic debate and professional practice. It is noteworthy that in the studies consulted, there is no precise evidence that determines with accuracy the configurations of these knowledge factors that function as the basis of the teaching profession. Therefore, the objective of this study is to establish the construct validity and reliability of the questionnaire on the nature or origin of the academic knowledge of teachers of basic general education, adapted from the Pedagogical Content Knowledge (PCK) Competence Model. Methods: A methodological study was established that applies a test to the processes of reliability and internal consistency. The construct validity was performed through (n = 8) expert judges, using Cohen's Kappa. An exploratory factor analysis was performed following the criteria of the Kaiser-Meyer-Olkin Coefficient (KMO), the Bartlett sphericity test and the principal components extraction method in the factor analysis with varimax rotation. The sample consisted of (n = 27) teachers of basic general education of the Ibarra Canton. Results: The results show a reliability analysis for the instrument obtained a Cronbach's alpha (α = 0.901), estimated to be an excellent level. Conclusions: The questionnaire is relevant, valid and reliable, adapting to the needs of teachers of basic general education to determine the nature or origin of the academic knowledge in a fast and reliable manner.

A hybrid algorithm for adjusting the input parameters of the wirecut EDM machine in order to obtain maximum customer satisfaction

This study presents an algorithm to optimally adjust the input parameters of the wirecut to align its output with the customer’s expectations. For this, AHP and QFD are used to identify and prioritize customer needs in the form of a desirability function. Then, using the Taguchi method, variance analysis, and regression, a fitness function is prepared and optimized by the multi-objective genetic algorithm. Through a case study, the proposed method is validated in terms of flexibility, simplicity, speed, cost-effectiveness, and updateability. Also, customer satisfaction is calculated for two groups of 45 people, with and without using the proposed method. The growth of the customer satisfaction index (CSAT) from 57.6 to 70.3, and the customer satisfaction score from 30.2 to 54.2, show the positive performance of the method. This converts regular customers into loyal ones. It also makes them encourages others to use the mentioned services and widen the customer network. It is clearly seen in the growth of the net promoter score from 6.67 to 31.11. All in all, it can be said that this algorithm helps the survival, profitability, and expansion of an industrial organization.

Post-Occupancy Evaluation of Green Technologies for a High-Rise Building Based on User Experience

Post-occupancy evaluations of buildings help us understand the effectiveness of building designs. Most of such evaluations focus on the overall building performance and on the indoor air environment, and few focus on individual technologies. In this paper, we report a user experience-based evaluation on multiple green technologies applied in a high-rise building constructed 17 years ago. These technologies include a circular building design, innovative floor plans, a corridor-type, double-skin facade with cavity shading, and three-dimensional greening design. Data were obtained through a questionnaire for the building occupants and on-site measurements of environmental variables. The results show that the occupants were most satisfied with the three-dimensional greening, natural lighting and ventilation in the public spaces, and the sound and thermal environment of the rooms with the double facade. They were not satisfied with room ventilation, solar shading, or lighting adjustment provided by the double facade. In addition, the aesthetic appearance of the circular building was not recognized by the occupants, who appeared to pay more attention to its space efficiency than the external appearance. This was true especially for older occupants. The users expressed a strong demand for outdoor leisure spaces and green vegetation. However, the practicality and functionality of three-dimensional greening, along with the interference of the glass curtain wall on vegetation growth, calls for more attention to the design. The female occupants were found to be more sensitive to the thermal and acoustic environment. Based on these results, we provide recommendations on the maintenance of the building and the future design of these measures.

Risk Management of Island Petrochemical Park: Accident Early Warning Model Based on Artificial Neural Network

Island-type petrochemical parks have gradually become the ‘trend’ in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network to optimize the initial weights and thresholds of the neural network. A total of 30 groups of petrochemical park data were taken as samples—26 groups as training samples and 4 groups as test samples. Moreover, the safety status of the petrochemical park was set as the output parameter of the neural network. The comparative analysis shows that the optimized neural network is far superior to the unoptimized neural network in evaluation indicators. Finally, the Zhejiang Petrochemical Co., Ltd., park was used as a case to verify the accuracy of the proposed early warning model. Ultimately, the final output result was 0.8324, which indicates that the safety status of the case park was “safer”. The results show that the BP neural network introduced with PSO can effectively realize early warning, which is an effective model to realize the safety early warning of island-type petrochemical parks.