Modelling of River Catfish (Cephalocassis Jatia) Population in Malaysia

This research presents the mathematical modeling of the economic cycle of fish-population structures in Malaysia. This paper shows how to develop a model of river catfish based on system dynamics and simulates the model for policy planning and sustainable development. These experiences are essential if dynamic systems are to be modeled and simulated. The mathematical model predicts long-term trends for hatching, growth, and harvesting of the river catfish population. Simulated results suggest that the economic harvesting of adults entering the rivers has been discussed and effective strategies for sustainable fish production. Management strategies are put in place to harvest juvenile mortality and spawn adult harvesting, sustainable development of catfish could be maintained

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks

Hybrid models such as the Artificial Neural Network-Autoregressive Integrated Moving Average (ANN–ARIMA) model are widely used in forecasting. However, inaccuracies and inefficiency remain in evidence. To yield the ANN–ARIMA with a higher degree of accuracy, efficiency and precision, the bootstrap and the double bootstrap methods are commonly used as alternative methods through the reconstruction of an ANN–ARIMA standard error. Unfortunately, these methods have not been applied in time series-based forecasting models. The aims of this study are twofold. First, is to propose the hybridization of bootstrap model and that of double bootstrap mode called Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (B-ANN–ARIMA) and Double Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (DB-ANN–ARIMA), respectively. Second, is to investigate the performance of these proposed models by comparing them with ARIMA, ANN and ANN–ARIMA. Our investigation is based on three well-known real datasets, i.e., Wolf's sunspot data, Canadian lynx data and, Malaysia ringgit/United States dollar exchange rate data. Statistical analysis on SSE, MSE, RMSE, MAE, MAPE and VAF is then conducted to verify that the proposed models are better than previous ARIMA, ANN and ANN–ARIMA models. The empirical results show that, compared with ARIMA, ANNs and ANN–ARIMA models, the proposed models generate smaller values of SSE, MSE, RMSE, MAE, MAPE and VAF for both training and testing datasets. In other words, the proposed models are better than those that we compare with. Their forecasting values are closer to the actual values. Thus, we conclude that the proposed models can be used to generate better forecasting values with higher degree of accuracy, efficiency and, precision in forecasting time series results becomes a priority

Data on ectoparasites infestation on small mammals from different habitats in east-coast Peninsular Malaysia

This data article presents on the ectoparasites infestation on small mammals in Peninsular Malaysia. The dataset on ectoparasites infestation is important because it raises a major medical concern regarding the spread of potentially zoonotic disease from wildlife to human. Tick and chigger are the primary ectoparasites as reservoirs of vector-borne diseases found on small mammals in Malaysia. These small mammals that are infested with ectoparasites occupy various types of habitats, including human settlements, could be of community health risks as the carriers of potentially zoonotic diseases. Field samplings were conducted from February 2015 to February 2016 in three different ecological habitats of mixed dipterocarp forest, coastal forest and insular forest, in Terengganu, Malaysia. A total of 35 and 22 species of bats and rodents respectively were captured and examined for ectoparasites. Twenty-three species of bats and 16 species of small mammal were recorded as hosts for at least one species of ectoparasites. These findings show that the highest ectoparasite infestation occurred on bat community