Clinical psychologist responsible clinicians: exploring experiences and factors influencing uptake of the role

Section A presents a systematic literature review of the empirical research of the factors (barriers and facilitators) influencing uptake of the Multi-Professional Approved Clinician role by mental health professionals, other than psychiatrists. Seven studies were identified from the systematic search. Barriers and facilitators were categorised into internal and external factors. Internal factors included: attitudes, and knowledge and skills. External factors included: organisational structures, resources and peer support. A critical evaluation of the studies is discussed, and the practical and research implications are considered. Section B presents a qualitative study exploring the experiences of clinical psychologists in the role of Responsible Clinician. Eight clinical psychologists who had been working as responsible clinicians were interviewed, and interviews were analysed using interpretative phenomenological analysis. Five superordinate themes and accompanying subthemes capturing the experiences of the participants were identified. The superordinate themes are: “From psychologist to approved clinician psychologist”, “The psychological effects of responsibility”, “The system makes or breaks”, “Relationships shift in the face of power”, and “Making our mark: From paralysis to influence”. Findings are discussed in the context of existing literature. Clinical implications, as well as limitations and directions for future research, are also considered

Nutritional Assessment of Refugees at a Refugee Camp

Forced migration/refugee status is as old as man, ever increasing and literature is sparse when searching for references regarding nutritional assessment among this very important population. We aimed at making an assessment of the nutritional status among refugees at Oru refugee camp, Nigeria. Subjects were adult males and females, 100 in two groups: local residents and refugees that just moved into camp within the last 6months. A questionnaire was administered and venous blood was collected from each volunteer, centrifuged and stored at -200C until analysis at the chemical pathology departmental laboratory of Ambrose Alli University, Ekpoma. Mean values of albumin in refugees and residents are respectively; 34±3.5 Vs 36g/L. Refugees have lower albumin level. It is recommended that protein rich foods should be provided at refugee camps. Keywords: Refugees, Nutrition, Protein, Camp, Nigeria

Bayesian Geoadditive Seemingly Unrelated Regression

Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A weakness of parametric models is that they require strong assumptions on the functional form of possibly nonlinear effects of metrical covariates. In this paper, we develop a Bayesian semiparametric SUR model, where the usual linear predictors are replaced by more flexible additive predictors allowing for simultaneous nonparametric estimation of such covariate effects and of spatial effects. The approach is based on appropriate smoothness priors which allow different forms and degrees of smoothness in a general framework. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques

Stability of Stopped thyroid hormones in Enzyme Linked Immunosorbent Assay

Power outage is a common feature in the third world countries. Oftentimes after the preparation and completion/ and stopping of reaction in tube method of Immunometric assay there is power outage.  One wonders what should be done with the set up. How long can the stopped reaction wait before reading and the result will be useful? This aspect is not included in the method sheet. To answer this question, the  test is done as usual but the readings were taken at 0, 2hours, 1 day, 2 days, 3 days, 4 days and 5 days. The results were compared. It was evident that when read within two hours, the result remain unchanged but after 24hours the slope is depressed and all the readings wane. Effort should be made to take the readings within 24hours for reliable result. Keywords: Stability, thyroid hormones, hormonal assay

Effect of Hedging-Integrated Rule Curves on the Performance of the Pong Reservoir (India) During Scenario-Neutral Climate Change Perturbations

This study has evaluated the effects of improved, hedging-integrated reservoir rule curves on the current and climate-change-perturbed future performances of the Pong reservoir, India. The Pong reservoir was formed by impounding the snow- and glacial-dominated Beas River in Himachal Pradesh. Simulated historic and climate-change runoff series by the HYSIM rainfall-runoff model formed the basis of the analysis. The climate perturbations used delta changes in temperature (from 0° to +2 °C) and rainfall (from −10 to +10 % of annual rainfall). Reservoir simulations were then carried out, forced with the simulated runoff scenarios, guided by rule curves derived by a coupled sequent peak algorithm and genetic algorithms optimiser. Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. The results show that the historic vulnerability reduced from 61 % (no hedging) to 20 % (with hedging), i.e., better than the 25 % vulnerability often assumed tolerable for most water consumers. Climate change perturbations in the rainfall produced the expected outcomes for the runoff, with higher rainfall resulting in more runoff inflow and vice-versa. Reduced runoff caused the vulnerability to worsen to 66 % without hedging; this was improved to 26 % with hedging. The fact that improved operational practices involving hedging can effectively eliminate the impacts of water shortage caused by climate change is a significant outcome of this study

Physical and combustible properties of briquettes produced from a combination of groundnut shell, rice husk, sawdust and wastepaper using starch as a binder

This study investigated the use of agro-wastes for the production of briquettes. Briquettes were produced from a combination of groundnut shell, rice husk, sawdust and wastepaper using the 20:70:10, 30:60:10, 40:50:10, 50:40:10, 60:30:10 and 70:20:10 ratio. The feedstock of each blend was fed into a square mould [60mm] and screw-pressed at 20 MPa in a dwelling time of 60 seconds. Moisture content, density and combustion characteristics (ignition time and calorific value) of the briquettes were determined. Data obtained were analysed using appropriate statistical tools. The moisture content of all the briquettes ranged between 8 to 15%. The briquettes density was in the range of 800 to 900 kg.m−3, while the calorific value ranged from 0.03 to 0.19 and 0.02 to 0.27 MJkg−1 for Saw dust-rice husk- paper (SRP) and groundnut shell-saw dust-paper (GSP) briquettes. The quality of the briquettes in terms of density and burning time showed that 20% sawdust: 70% rice husk: 10% paper combination had a higher relaxed density of 387.4kg/m3, while on the basis of moisture content and ignition time, 70% sawdust: 20% rice husk: 10% paper combination had the least moisture content and ignition time of 16.7% and 18seconds, respectively. RSP had higher calorific value, lower ignition time, but less durability than GSP. However, the compressed and relaxed densities of SRP and GSP briquettes were significantly difference (p<0.05). The durability of the briquettes improved with increased starch proportion. It can be concluded that production of SRP and GSP briquettes is an effective and efficient agricultural waste disposal technique.Keywords: Agro-residues, briquettes, physical, mechanical, biomass, wast

Effect of reservoir zones and hedging factor dynamism on reservoir adaptive capacity for climate change impacts

When based on the zones of available water in storage, hedging has traditionally used a single hedged zone and a constant rationing ratio for constraining supply during droughts. Given the usual seasonality of reservoir inflows, it is also possible that hedging could feature multiple hedged zones and temporally varying rationing ratios but very few studies addressing this have been reported especially in relation to adaptation to projected climate change. This study developed and tested Genetic Algorithms (GA) optimised zone-based operating policies of various configurations using data for the Pong reservoir, Himachal Pradesh, India. The results show that hedging does lessen vulnerability, which dropped from  ≥  60 % without hedging to below 25 % with the single stage hedging. More complex hedging policies, e.g. two stage and/or temporally varying rationing ratios only produced marginal improvements in performance. All this shows that water hedging policies do not have to be overly complex to effectively offset reservoir vulnerability caused by water shortage resulting from e.g. projected climate change

Credit Rating Prediction Using Different Machine Learning Techniques. International

Credit rating prediction is a crucial task in the banking and financial industry. Financial firms want to identify the likelihood of customers repaying loans or credit. With the advent of machine learning algorithms and big data analytics, it is now possible to automate and improve the accuracy of credit rating prediction. In this research, we aim to develop a machine learning-based approach for customer credit rating prediction. Machine learning algorithms, including decision trees, random forests, support vector machines, and logistic regression, were evaluated and compared in terms of accuracy, precision, and AUC. Feature selection was also performed to analyze the importance of different features in predicting credit ratings. Findings suggested that status, duration, credit history, amount, savings, other debtors, property, and employment duration are the most important features in predicting credit ratings. Results showed that the support vector machine algorithm did best in predicting bad credits. This research demonstrates the potential of machine learning algorithms for customer credit rating prediction and could have significant implications for the banking and financial industry by enabling more accurate and efficient credit rating predictions and reducing the risk of defaults and financial losses

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling

Modelling Unconfined Groundwater Recharge Using Adaptive Neuro-Fuzzy Inference System

Estimating groundwater recharge using mathematical models such as water budget or soil water balance method has been proved to be very difficult due to the complex, uncertain multidimensional nature of the process, despite the simplicity of the concept. Artificial Intelligence (AI) techniques have been proposed to deal with this complexity and uncertainty in a similar way to human thinking and reasoning. This study proposed the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to model unconfined groundwater recharge using a set of data records from Kaharoa monitoring site in the North Island of New Zealand. Fifty-three data points, comprising a set of input parameters such as rainfall, temperature, sunshine hours, and radiation, for a period of approximately four and a half years, have been used to estimate ground water recharge. The results suggest that the ANFIS model is overall a reliable estimator for groundwater recharge, the correlation coefficient of the model reached 93% using independent data set. The method is easy, flexible and reliable; hence, it is recommended to be used for similar applications