Description:
Nonsmooth nonconvex optimization problems arise in many applications including economics, business and data mining. In these applications objective functions are not necessarily differentiable or convex. Many algorithms have been proposed over the past three decades to solve such problems. In spite of the significant growth in this field, the development of efficient algorithms for solving this kind of problem is still a challenging task. The subgradient method is one of the simplest methods developed for solving these problems. Its convergence was proved only for convex objective functions. This method does not involve any subproblems, neither for finding search directions nor for computation of step lengths, which are fixed ahead of time. Bundle methods and their various modifications are among the most efficient methods for solving nonsmooth optimization problems. These methods involve a quadratic programming subproblem to find search directions. The size of the subproblem may increase significantly with the number of variables, which makes the bundle-type methods unsuitable for large scale nonsmooth optimization problems. The implementation of bundle-type methods, which require the use of the quadratic programming solvers, is not as easy as the implementation of the subgradient methods. Therefore it is beneficial to develop algorithms for nonsmooth nonconvex optimization which are easy to implement and more efficient than the subgradient methods. In this thesis, we develop two new algorithms for solving nonsmooth nonconvex optimization problems based on the use of the hyperbolic smoothing technique and apply them to solve the pumping cost minimization problem in water distribution. Both algorithms use smoothing techniques. The first algorithm is designed for solving finite minimax problems. In order to apply the hyperbolic smoothing we reformulate the objective function in the minimax problem and study the relationship between the original minimax and reformulated problems. We also study the main properties of the hyperbolic smoothing function. Based on these results an algorithm for solving the finite minimax problem is proposed and this algorithm is implemented in GAMS. We present preliminary results of numerical experiments with well-known nonsmooth optimization test problems. We also compare the proposed algorithm with the algorithm that uses the exponential smoothing function as well as with the algorithm based on nonlinear programming reformulation of the finite minimax problem. The second nonsmooth optimization algorithm we developed was used to demonstrate how smooth optimization methods can be applied to solve general nonsmooth (nonconvex) optimization problems. In order to do so we compute subgradients from some neighborhood of the current point and define a system of linear inequalities using these subgradients. Search directions are computed by solving this system. This system is solved by reducing it to the minimization of the convex piecewise linear function over the unit ball. Then the hyperbolic smoothing function is applied to approximate this minimization problem by a sequence of smooth problems which are solved by smooth optimization methods. Such an approach allows one to apply powerful smooth optimization algorithms for solving nonsmooth optimization problems and extend smoothing techniques for solving general nonsmooth nonconvex optimization problems. The convergence of the algorithm based on this approach is studied. The proposed algorithm was implemented in Fortran 95. Preliminary results of numerical experiments are reported and the proposed algorithm is compared with an other five nonsmooth optimization algorithms. We also implement the algorithm in GAMS and compare it with GAMS solvers using results of numerical experiments.
Description:
Nonsmooth nonconvex optimization problems arise in many applications including economics, business and data mining. In these applications objective functions are not necessarily differentiable or convex. Many algorithms have been proposed over the past three decades to solve such problems. In spite of the significant growth in this field, the development of efficient algorithms for solving this kind of problem is still a challenging task. The subgradient method is one of the simplest methods developed for solving these problems. Its convergence was proved only for convex objective functions. This method does not involve any subproblems, neither for finding search directions nor for computation of step lengths, which are fixed ahead of time. Bundle methods and their various modifications are among the most efficient methods for solving nonsmooth optimization problems. These methods involve a quadratic programming subproblem to find search directions. The size of the subproblem may increase significantly with the number of variables, which makes the bundle-type methods unsuitable for large scale nonsmooth optimization problems. The implementation of bundle-type methods, which require the use of the quadratic programming solvers, is not as easy as the implementation of the subgradient methods. Therefore it is beneficial to develop algorithms for nonsmooth nonconvex optimization which are easy to implement and more efficient than the subgradient methods. In this thesis, we develop two new algorithms for solving nonsmooth nonconvex optimization problems based on the use of the hyperbolic smoothing technique and apply them to solve the pumping cost minimization problem in water distribution. Both algorithms use smoothing techniques. The first algorithm is designed for solving finite minimax problems. In order to apply the hyperbolic smoothing we reformulate the objective function in the minimax problem and study the relationship between the original minimax and reformulated problems. We also study the main properties of the hyperbolic smoothing function. Based on these results an algorithm for solving the finite minimax problem is proposed and this algorithm is implemented in GAMS. We present preliminary results of numerical experiments with well-known nonsmooth optimization test problems. We also compare the proposed algorithm with the algorithm that uses the exponential smoothing function as well as with the algorithm based on nonlinear programming reformulation of the finite minimax problem. The second nonsmooth optimization algorithm we developed was used to demonstrate how smooth optimization methods can be applied to solve general nonsmooth (nonconvex) optimization problems. In order to do so we compute subgradients from some neighborhood of the current point and define a system of linear inequalities using these subgradients. Search directions are computed by solving this system. This system is solved by reducing it to the minimization of the convex piecewise linear function over the unit ball. Then the hyperbolic smoothing function is applied to approximate this minimization problem by a sequence of smooth problems which are solved by smooth optimization methods. Such an approach allows one to apply powerful smooth optimization algorithms for solving nonsmooth optimization problems and extend smoothing techniques for solving general nonsmooth nonconvex optimization problems. The convergence of the algorithm based on this approach is studied. The proposed algorithm was implemented in Fortran 95. Preliminary results of numerical experiments are reported and the proposed algorithm is compared with an other five nonsmooth optimization algorithms. We also implement the algorithm in GAMS and compare it with GAMS solvers using results of numerical experiments.
Description:
Summary Background It is generally accepted that nurses have insufficient knowledge about disaster preparedness due to a lack of acceptance of core competencies and the absence of disaster preparedness in nursing curricula.1 This study explored nurses’ knowledge and sources of knowledge, and skills as they relate to disaster management in Saudi Arabia, where more than 4660 people have died, 32,000 people have been affected, and US$4.65 billion in damage has been caused by disaster since 1980.2 Methods A quantitative, non-experimental, descriptive research design. Results Nurses in Saudi Arabia have moderate knowledge concerning disaster preparedness. However, nurses in military hospitals possess more knowledge than those who work in government hospitals. The majority of nurses gained their knowledge and skills from disaster drills. Conclusions Nurses need more education in all areas of disaster management, most importantly in their roles during response to disasters. Nurses perceive themselves as not well-prepared but they are willing to improve their skills in disaster preparedness if educational opportunities are provided.
Description:
Obstacles present in the line of sight transmission path of a wireless signal severely attenuates the received signal power. Extreme fluctuations of the received signal power caused by shadowing can create "blind spots". Blind spots are areas within the cellular coverage area from where no communication is possible to the base station (BS). For single path packet based services link failures results in total loss of communication. The problem of link failure and blind spots can almost be eliminated in the recently proposed multipath cellular architecture (MCA) which provisions up to three different communication links from the mobile node (MN) to three adjacent BSs through overlapped coverage. Voice and/or multimedia packet delivery can thus benefit by establishing communication from the best interface in terms of received signal power. In this paper, we evaluate the performance of the multi-path MCA model in reducing link failure and blind spot communication problems in presence of multiple obstacles and shadow fading. Obstacle mobility model is utilized as the preferred user mobility model. Video transmission performance improves significantly in terms of reduced packet loss and improved reproduced signal quality at the receiver for the multipath MCA model compared to the existing single path cellular network architecture
Multiple comorbidities of 21 psychological disorders and relationships with psychosocial variables: A study of the online assessment and diagnostic system within a web-based population
Description:
Background: Although relatively new, the field of e-mental health is becoming more popular with more attention given to researching its various aspects. However, there are many areas that still need further research, especially identifying attrition predictors at various phases of assessment and treatment delivery. Objective: The present study identified the predictors of posttreatment assessment completers based on 24 pre- and posttreatment demographic and personal variables and 1 treatment variable, their impact on attrition bias, and the efficacy of the 5 fully automated self-help anxiety treatment programs for generalized anxiety disorder (GAD), social anxiety disorder (SAD), panic disorder with or without agoraphobia (PD/A), obsessive-compulsive disorder (OCD), and posttraumatic stress disorder (PTSD). Methods: A complex algorithm was used to diagnose participants' mental disorders based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision; DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders were offered an online 12-week disorder-specific treatment program. A total of 3199 individuals did not formally drop out of the 12-week treatment cycle, whereas 142 individuals formally dropped out. However, only 347 participants who completed their treatment cycle also completed the posttreatment assessment measures. Based on these measures, predictors of attrition were identified and attrition bias was examined. The efficacy of the 5 treatment programs was assessed based on anxiety-specific severity scores and 5 additional treatment outcome measures. Results: On average, completers of posttreatment assessment measures were more likely to be seeking self-help online programs; have heard about the program from traditional media or from family and friends; were receiving mental health assistance; were more likely to learn best by reading, hearing and doing; had a lower pretreatment Kessler-6 total score; and were older in age. Predicted probabilities resulting from these attrition variables displayed no significant attrition bias using Heckman's method and thus allowing for the use of completer analysis. Six treatment outcome measures (Kessler-6 total score, number of diagnosed disorders, self-confidence in managing mental health issues, quality of life, and the corresponding pre- and posttreatment severity for each program-specific anxiety disorder and for major depressive episode) were used to assess the efficacy of the 5 anxiety treatment programs. Repeated measures MANOVA revealed a significant multivariate time effect for all treatment outcome measures for each treatment program. Follow-up repeated measures ANOVAs revealed significant improvements on all 6 treatment outcome measures for GAD and PTSD, 5 treatment outcome measures were significant for SAD and PD/A, and 4 treatment outcome measures were significant for OCD. Conclusions: Results identified predictors of posttreatment assessment completers and provided further support for the efficacy of self-help online treatment programs for the 5 anxiety disorders
Description:
Background: Although relatively new, the field of e-mental health is becoming more popular with more attention given to researching its various aspects. However, there are many areas that still need further research, especially identifying attrition predictors at various phases of assessment and treatment delivery. Objective: The present study identified the predictors of posttreatment assessment completers based on 24 pre- and posttreatment demographic and personal variables and 1 treatment variable, their impact on attrition bias, and the efficacy of the 5 fully automated self-help anxiety treatment programs for generalized anxiety disorder (GAD), social anxiety disorder (SAD), panic disorder with or without agoraphobia (PD/A), obsessive-compulsive disorder (OCD), and posttraumatic stress disorder (PTSD). Methods: A complex algorithm was used to diagnose participants' mental disorders based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision; DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders were offered an online 12-week disorder-specific treatment program. A total of 3199 individuals did not formally drop out of the 12-week treatment cycle, whereas 142 individuals formally dropped out. However, only 347 participants who completed their treatment cycle also completed the posttreatment assessment measures. Based on these measures, predictors of attrition were identified and attrition bias was examined. The efficacy of the 5 treatment programs was assessed based on anxiety-specific severity scores and 5 additional treatment outcome measures. Results: On average, completers of posttreatment assessment measures were more likely to be seeking self-help online programs; have heard about the program from traditional media or from family and friends; were receiving mental health assistance; were more likely to learn best by reading, hearing and doing; had a lower pretreatment Kessler-6 total score; and were older in age. Predicted probabilities resulting from these attrition variables displayed no significant attrition bias using Heckman's method and thus allowing for the use of completer analysis. Six treatment outcome measures (Kessler-6 total score, number of diagnosed disorders, self-confidence in managing mental health issues, quality of life, and the corresponding pre- and posttreatment severity for each program-specific anxiety disorder and for major depressive episode) were used to assess the efficacy of the 5 anxiety treatment programs. Repeated measures MANOVA revealed a significant multivariate time effect for all treatment outcome measures for each treatment program. Follow-up repeated measures ANOVAs revealed significant improvements on all 6 treatment outcome measures for GAD and PTSD, 5 treatment outcome measures were significant for SAD and PD/A, and 4 treatment outcome measures were significant for OCD. Conclusions: Results identified predictors of posttreatment assessment completers and provided further support for the efficacy of self-help online treatment programs for the 5 anxiety disorders
Description:
Although in its infancy, the field of e-mental health interventions has been gaining popularity and afforded considerable research attention. However, there are many gaps in the research. One such gap is in the area of attrition predictors at various stages of assessment and treatment delivery. Objective: This exploratory study applied univariate and multivariate analysis to a large dataset provided by the Anxiety Online (now called Mental Health Online) system to identify predictors of attrition in treatment commencers and in those who formally withdrew during treatment based on 24 pretreatment demographic and personal variables and one clinical measure. Methods: Participants were assessed using a complex online algorithm that resulted in primary and secondary diagnoses in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders (generalized anxiety disorder, social anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and panic disorder) were offered an online 12-week disorder-specific treatment program. Results: Of 9394 potential participants, a total of 3880 clients enrolled and 5514 did not enroll in one of the treatment programs following the completion of pretreatment assessment measures (pretreatment attrition rate: 58.70%). A total of 3199 individuals did not formally withdraw from the 12-week treatment cycle, whereas 142 individuals formally dropped out (formal withdrawal during treatment dropout rate of 4.25%). The treatment commencers differed significantly (P<.001-.03) from the noncommencers on several variables (reason for registering, mental health concerns, postsecondary education, where first heard about Anxiety Online, Kessler-6 score, stage of change, quality of life, relationship status, preferred method of learning, and smoking status). Those who formally withdrew during treatment differed significantly (P=.002-.03) from those who did not formally withdraw in that they were less likely to express concerns about anxiety, stress, and depression; to rate their quality of life as very poor, poor, or good; to report adequate level of social support; and to report readiness to make or were in the process of making changes. Conclusions: This exploratory study identified predictors of pretreatment attrition and formal withdrawal during treatment dropouts for the Anxiety Online program.
Description:
Although in its infancy, the field of e-mental health interventions has been gaining popularity and afforded considerable research attention. However, there are many gaps in the research. One such gap is in the area of attrition predictors at various stages of assessment and treatment delivery. Objective: This exploratory study applied univariate and multivariate analysis to a large dataset provided by the Anxiety Online (now called Mental Health Online) system to identify predictors of attrition in treatment commencers and in those who formally withdrew during treatment based on 24 pretreatment demographic and personal variables and one clinical measure. Methods: Participants were assessed using a complex online algorithm that resulted in primary and secondary diagnoses in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders (generalized anxiety disorder, social anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and panic disorder) were offered an online 12-week disorder-specific treatment program. Results: Of 9394 potential participants, a total of 3880 clients enrolled and 5514 did not enroll in one of the treatment programs following the completion of pretreatment assessment measures (pretreatment attrition rate: 58.70%). A total of 3199 individuals did not formally withdraw from the 12-week treatment cycle, whereas 142 individuals formally dropped out (formal withdrawal during treatment dropout rate of 4.25%). The treatment commencers differed significantly (P<.001-.03) from the noncommencers on several variables (reason for registering, mental health concerns, postsecondary education, where first heard about Anxiety Online, Kessler-6 score, stage of change, quality of life, relationship status, preferred method of learning, and smoking status). Those who formally withdrew during treatment differed significantly (P=.002-.03) from those who did not formally withdraw in that they were less likely to express concerns about anxiety, stress, and depression; to rate their quality of life as very poor, poor, or good; to report adequate level of social support; and to report readiness to make or were in the process of making changes. Conclusions: This exploratory study identified predictors of pretreatment attrition and formal withdrawal during treatment dropouts for the Anxiety Online program
Description:
Although in its infancy, the field of e-mental health interventions has been gaining popularity and afforded considerable research attention. However, there are many gaps in the research. One such gap is in the area of attrition predictors at various stages of assessment and treatment delivery. Objective: This exploratory study applied univariate and multivariate analysis to a large dataset provided by the Anxiety Online (now called Mental Health Online) system to identify predictors of attrition in treatment commencers and in those who formally withdrew during treatment based on 24 pretreatment demographic and personal variables and one clinical measure. Methods: Participants were assessed using a complex online algorithm that resulted in primary and secondary diagnoses in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders (generalized anxiety disorder, social anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and panic disorder) were offered an online 12-week disorder-specific treatment program. Results: Of 9394 potential participants, a total of 3880 clients enrolled and 5514 did not enroll in one of the treatment programs following the completion of pretreatment assessment measures (pretreatment attrition rate: 58.70%). A total of 3199 individuals did not formally withdraw from the 12-week treatment cycle, whereas 142 individuals formally dropped out (formal withdrawal during treatment dropout rate of 4.25%). The treatment commencers differed significantly (P<.001-.03) from the noncommencers on several variables (reason for registering, mental health concerns, postsecondary education, where first heard about Anxiety Online, Kessler-6 score, stage of change, quality of life, relationship status, preferred method of learning, and smoking status). Those who formally withdrew during treatment differed significantly (P=.002-.03) from those who did not formally withdraw in that they were less likely to express concerns about anxiety, stress, and depression; to rate their quality of life as very poor, poor, or good; to report adequate level of social support; and to report readiness to make or were in the process of making changes. Conclusions: This exploratory study identified predictors of pretreatment attrition and formal withdrawal during treatment dropouts for the Anxiety Online program.
Description:
Although in its infancy, the field of e-mental health interventions has been gaining popularity and afforded considerable research attention. However, there are many gaps in the research. One such gap is in the area of attrition predictors at various stages of assessment and treatment delivery. Objective: This exploratory study applied univariate and multivariate analysis to a large dataset provided by the Anxiety Online (now called Mental Health Online) system to identify predictors of attrition in treatment commencers and in those who formally withdrew during treatment based on 24 pretreatment demographic and personal variables and one clinical measure. Methods: Participants were assessed using a complex online algorithm that resulted in primary and secondary diagnoses in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders (generalized anxiety disorder, social anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and panic disorder) were offered an online 12-week disorder-specific treatment program. Results: Of 9394 potential participants, a total of 3880 clients enrolled and 5514 did not enroll in one of the treatment programs following the completion of pretreatment assessment measures (pretreatment attrition rate: 58.70%). A total of 3199 individuals did not formally withdraw from the 12-week treatment cycle, whereas 142 individuals formally dropped out (formal withdrawal during treatment dropout rate of 4.25%). The treatment commencers differed significantly (P<.001-.03) from the noncommencers on several variables (reason for registering, mental health concerns, postsecondary education, where first heard about Anxiety Online, Kessler-6 score, stage of change, quality of life, relationship status, preferred method of learning, and smoking status). Those who formally withdrew during treatment differed significantly (P=.002-.03) from those who did not formally withdraw in that they were less likely to express concerns about anxiety, stress, and depression; to rate their quality of life as very poor, poor, or good; to report adequate level of social support; and to report readiness to make or were in the process of making changes. Conclusions: This exploratory study identified predictors of pretreatment attrition and formal withdrawal during treatment dropouts for the Anxiety Online program
Comorbidity structure of psychological disorders in the online e-PASS data as predictors of psychosocial adjustment measures: psychological distress, adequate social support, self-confidence, quality of life, and suicidal ideation
Description:
BACKGROUND: A relative newcomer to the field of psychology, e-mental health has been gaining momentum and has been given considerable research attention. Although several aspects of e-mental health have been studied, 1 aspect has yet to receive attention: the structure of comorbidity of psychological disorders and their relationships with measures of psychosocial adjustment including suicidal ideation in online samples. OBJECTIVE: This exploratory study attempted to identify the structure of comorbidity of 21 psychological disorders assessed by an automated online electronic psychological assessment screening system (e-PASS). The resulting comorbidity factor scores were then used to assess the association between comorbidity factor scores and measures of psychosocial adjustments (ie, psychological distress, suicidal ideation, adequate social support, self-confidence in dealing with mental health issues, and quality of life). METHODS: A total of 13,414 participants were assessed using a complex online algorithm that resulted in primary and secondary Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision) diagnoses for 21 psychological disorders on dimensional severity scales. The scores on these severity scales were used in a principal component analysis (PCA) and the resulting comorbidity factor scores were related to 4 measures of psychosocial adjustments. RESULTS: A PCA based on 17 of the 21 psychological disorders resulted in a 4-factor model of comorbidity: anxiety-depression consisting of all anxiety disorders, major depressive episode (MDE), and insomnia; substance abuse consisting of alcohol and drug abuse and dependency; body image-eating consisting of eating disorders, body dysmorphic disorder, and obsessive-compulsive disorders; depression-sleep problems consisting of MDE, insomnia, and hypersomnia. All comorbidity factor scores were significantly associated with psychosocial measures of adjustment (P<.001). They were positively related to psychological distress and suicidal ideation, but negatively related to adequate social support, self-confidence, and quality of life. CONCLUSIONS: This exploratory study identified 4 comorbidity factors in the e-PASS data and these factor scores significantly predicted 5 psychosocial adjustment measures. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN121611000704998; http://www.anzctr.org.au/trial_view.aspx?ID=336143 (Archived by WebCite at http://www.webcitation.org/618r3wvOG).
Description:
BACKGROUND: A relative newcomer to the field of psychology, e-mental health has been gaining momentum and has been given considerable research attention. Although several aspects of e-mental health have been studied, 1 aspect has yet to receive attention: the structure of comorbidity of psychological disorders and their relationships with measures of psychosocial adjustment including suicidal ideation in online samples. OBJECTIVE: This exploratory study attempted to identify the structure of comorbidity of 21 psychological disorders assessed by an automated online electronic psychological assessment screening system (e-PASS). The resulting comorbidity factor scores were then used to assess the association between comorbidity factor scores and measures of psychosocial adjustments (ie, psychological distress, suicidal ideation, adequate social support, self-confidence in dealing with mental health issues, and quality of life). METHODS: A total of 13,414 participants were assessed using a complex online algorithm that resulted in primary and secondary Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision) diagnoses for 21 psychological disorders on dimensional severity scales. The scores on these severity scales were used in a principal component analysis (PCA) and the resulting comorbidity factor scores were related to 4 measures of psychosocial adjustments. RESULTS: A PCA based on 17 of the 21 psychological disorders resulted in a 4-factor model of comorbidity: anxiety-depression consisting of all anxiety disorders, major depressive episode (MDE), and insomnia; substance abuse consisting of alcohol and drug abuse and dependency; body image-eating consisting of eating disorders, body dysmorphic disorder, and obsessive-compulsive disorders; depression-sleep problems consisting of MDE, insomnia, and hypersomnia. All comorbidity factor scores were significantly associated with psychosocial measures of adjustment (P<.001). They were positively related to psychological distress and suicidal ideation, but negatively related to adequate social support, self-confidence, and quality of life. CONCLUSIONS: This exploratory study identified 4 comorbidity factors in the e-PASS data and these factor scores significantly predicted 5 psychosocial adjustment measures. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN121611000704998; http://www.anzctr.org.au/trial_view.aspx?ID=336143 (Archived by WebCite at http://www.webcitation.org/618r3wvOG).
Description:
Background: Anxiety has a powerful impact on learning due to activation of anxiety hormones, which target related receptors in the working memory. Experiential learning requires some degree of challenge and anxiety. Patient simulation, as a form of experiential learning, has been an integrated component of health professional education internationally over the last two decades, especially in undergraduate nursing education. Little information is available to determine if and how anxiety impacts nursing students’ clinical performance during simulation. Objectives: To investigate physiological and psychological anxiety during emergency scenarios in high-fidelity simulation and understand the effect of anxiety on clinical performance. Design: First2Act was the model for the simulation intervention. Second and third year undergraduate nursing students attended a two-hour simulation session and completed a demographic questionnaire plus pre-simulation self-reported psychological anxiety scale. A heart rate variability monitor was attached to each student's chest to measure heart rate variability (as a sign of anxiety) before engaging in two video-recorded simulated emergency scenarios (cardiac and respiratory) with a professional actor playing the patient. Performance was rated by a clinician followed by video-assisted debriefing. Finally, heart monitors were removed and students repeated self-reports of psychological anxiety. Results: Students’ psychological anxiety was high pre-simulation and remained high post-simulation. With regard to physiological anxiety, students were anxious at the start of the simulation but became more relaxed toward the end as they gained familiarly with the simulation environment (p < .007). Clinical performance increased significantly in the second scenario (p < .001). Factors found to positively affect clinical performance were length of enrolment in the nursing degree (p = .001), current employment in a nursing or allied healthcare field (p = .030), and previous emergency experience (p = .047). The relationship between physiological anxiety and clinical performance was statistically not significant, although there was an indication that low level anxiety led to optimal performance. Conclusion: High-fidelity patient simulation has the capacity to arouse novice nurses psychologically and physiologically while managing emergency situations. Indicative outcomes suggest that optimal performance was apparent when anxiety levels were low, indicating that they had received insufficient training to deal with situations that induced moderate to high anxiety levels.
Description:
The use of ultrasound technology in water treatment has gained great popularity in recent years owing to its benign environmental effects. The evaluation of this technology for water treatment purposes has mostly been conducted using synthetic water samples. This study however investigated the use of ultrasound for treating natural water with organic carbon predominantly derived from terrestrial sources. Ultrasound treatments were applied in continuous and pulsed modes with a range of On: Off ratios (R) and power intensity of 21.5 W/cm2 for 4 min. Physio-chemical and spectroscopic measurements were applied to determine the effect of ultrasound treatments on the concentration and structure of dissolved organic carbon (DOC) in the treated water. Post-hoc statistical analysis at a significance level of 0.05 showed that the performance of pulsed ultrasound treatments at least at one of pulse settings was better than that of continuous treatment. Overall, it was found that ultrasound treatments decreased DOC in the treated water and altered its nature to become more reactive towards oxidation and electrochemical reactions. Energy and cost analysis for DOC removal have also been conducted and results showed that applying the appropriate pulse settings can reduce the cost of the treatment by more than a half.
Description:
Microalgae is considered as an excellent potential renewable source of fuel in many forms including powder or slurry. A high percentage of emulsified water in the fuel is reported to reduce diesel engines’ emissions such as NOx, but that will compromise the engine output power. Using microalgae powder as an additive to enhance the emulsified water fuel heating value is the main objective of this work. Diesel engine combustion, vibration, performance and emissions were evaluated for pure cottonseed biodiesel (CS-B100), emulsified water 20% (vol.) in cottonseed biodiesel (CSB-E20) and emulsified water 20% (vol.) containing Fresh Water Microalgae Chlorella Vulgaris (FWM-CV) in cottonseed biodiesel (CSB-ME20). The emulsified water fuels showed a reduction in in-cylinder pressure, vibration, brake power, torque, exhaust gas temperature, CO2 and NOx, while BSFC and O2 were higher than the pure biodiesel (CS-B100). CSB-ME20 produced higher power and torque than CSB-E20 due to the presence of microalgae in the fuel that increased the energy content of the fuel.
Description:
Microalgae is considered as an excellent potential renewable source of fuel in many forms including powder or slurry. A high percentage of emulsified water in the fuel is reported to reduce diesel engines’ emissions such as NOx, but that will compromise the engine output power. Using microalgae powder as an additive to enhance the emulsified water fuel heating value is the main objective of this work. Diesel engine combustion, vibration, performance and emissions were evaluated for pure cottonseed biodiesel (CS-B100), emulsified water 20% (vol.) in cottonseed biodiesel (CSB-E20) and emulsified water 20% (vol.) containing Fresh Water Microalgae Chlorella Vulgaris (FWM-CV) in cottonseed biodiesel (CSB-ME20). The emulsified water fuels showed a reduction in in-cylinder pressure, vibration, brake power, torque, exhaust gas temperature, CO2 and NOx, while BSFC and O2 were higher than the pure biodiesel (CS-B100). CSB-ME20 produced higher power and torque than CSB-E20 due to the presence of microalgae in the fuel that increased the energy content of the fuel.