A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A 'before and after' study of nursing students' self-assessed competence in identifying the needs of older patients in hospital using an educational workbook.

Background: A core component of nurse education is clinical practice in order to support the development of clinical skills and competence. Assessment and measurement of the clinical competence of nursing students is important to gauge their professional development and educational needs.

Aim: To evaluate the impact of an Older Persons’ Assessment Educational Workbook (OPAEW) and explore second year nursing students’ competence and their opinions and use of the workbook.

Methods: A ‘before and after’ pre-experimental design was undertaken with n=6 second year nursing students. Outcome measures were the Nursing Competencies Questionnaire and the Self-efficacy in Clinical Performance Scale. Content analysis of workbooks and a survey (n=5) of opinions regarding the workbook was undertaken.

Findings: Pre and post test results for the study (n=5) were tested to determine if there was a relationship between changes in the NCQ and SECP repeated measures and use of an OPAEW. Testing identified evidence of a statistically significant difference for both SECP measures (SECP28 p=0.043; SECP7 p=0.042), with no clear statistical evidence of a difference for the NCQ (p=0.08). A weak negative association (NCQ ρ=-0.600 p=0.285; SECP28 ρ=-0.300 p=0.624; SECP7 ρ=-0.205 p=0.741), was found indicating that those participants who scored the lowest scores at the start of the study, benefited most from the workbook.

Content analysis of the OPAEW (n=5) found that 3 of the 5 participants completed all components of the workbook, with a mean of 1051 words used (SD 281.8). Through the survey (n=5) students reported the workbook as a useful guide when undertaking a patient assessment.

Conclusions: The OPAEW showed potential as an intervention to support the development of nursing students’ competence in older person assessment skills.

A 'before and after' study of nursing students' self-assessed competence in identifying the needs of older patients in hospital using an educational workbook.

Background: A core component of nurse education is clinical practice in order to support the development of clinical skills and competence. Assessment and measurement of the clinical competence of nursing students is important to gauge their professional development and educational needs.

Aim: To evaluate the impact of an Older Persons’ Assessment Educational Workbook (OPAEW) and explore second year nursing students’ competence and their opinions and use of the workbook.

Methods: A ‘before and after’ pre-experimental design was undertaken with n=6 second year nursing students. Outcome measures were the Nursing Competencies Questionnaire and the Self-efficacy in Clinical Performance Scale. Content analysis of workbooks and a survey (n=5) of opinions regarding the workbook was undertaken.

Findings: Pre and post test results for the study (n=5) were tested to determine if there was a relationship between changes in the NCQ and SECP repeated measures and use of an OPAEW. Testing identified evidence of a statistically significant difference for both SECP measures (SECP28 p=0.043; SECP7 p=0.042), with no clear statistical evidence of a difference for the NCQ (p=0.08). A weak negative association (NCQ ρ=-0.600 p=0.285; SECP28 ρ=-0.300 p=0.624; SECP7 ρ=-0.205 p=0.741), was found indicating that those participants who scored the lowest scores at the start of the study, benefited most from the workbook.

Content analysis of the OPAEW (n=5) found that 3 of the 5 participants completed all components of the workbook, with a mean of 1051 words used (SD 281.8). Through the survey (n=5) students reported the workbook as a useful guide when undertaking a patient assessment.

Conclusions: The OPAEW showed potential as an intervention to support the development of nursing students’ competence in older person assessment skills.

Testing the psychometric properties of two self-report clinical competence scales for nursing students.

Background: It is important to assess the clinical competence of nursing students to gauge their educational needs. Competence can be measured by self-assessment tools; however, Anema and McCoy (2010) contend that currently available measures should be further psychometrically tested.
Aim: To test the psychometric properties of Nursing Competencies Questionnaire (NCQ) and Self-Efficacy in Clinical Performance (SECP) clinical competence scales.
Method: A non-randomly selected sample of n=248 2nd year nursing students completed NCQ, SECP and demographic questionnaires (June and September 2013). Mokken Scaling Analysis (MSA) was used to investigate structural validity and scale properties; convergent and discriminant validity and reliability were also tested for each scale.
Results: MSA analysis identified that the NCQ is a unidimensional scale with strong scale scalability coefficients Hs =0.581; but limited item rankability HT =0.367. The SECP scale MSA suggested that the scale could be potentially split into two unidimensional scales (SECP28 and SECP7), each with good/reasonable scalablity psychometric properties as summed scales but negligible/very limited scale rankability (SECP28: Hs = 0.55, HT=0.211; SECP7: Hs = 0.61, HT=0.049). Analysis of between cohort differences and NCQ/SECP scores produced evidence of discriminant and convergent validity; good internal reliability was also found: NCQ α = 0.93, SECP28 α = 0.96 and SECP7 α=0.89.

Discussion: In line with previous research further evidence of the NCQ’s reliability and validity was demonstrated. However, as the SECP findings are new and the sample small with reference to Straat and colleagues (2014), the SECP results should be interpreted with caution and verified on a second sample.
Conclusions: Measurement of perceived self-competence could start early in a nursing programme to support students’ development of clinical competence. Further testing of the SECP scale with larger nursing student samples from different programme years is indicated.

References:
Anema, M., G and McCoy, JK. (2010) Competency-Based Nursing Education: Guide to Achieving Outstanding Learner Outcomes. New York: Springer.
Straat, JH., van der Ark, LA and Sijtsma, K. (2014) Minimum Sample Size Requirements for Mokken Scale Analysis Educational and Psychological Measurement 74 (5), 809-822.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.