A sauna drying technique—the solar drier was designed and imposed, constructed and tested for drying of seaweed. The seaweed moisture content was decreased around 50% in 2-day sauna. Kinetic curves of drying of seawe...A sauna drying technique—the solar drier was designed and imposed, constructed and tested for drying of seaweed. The seaweed moisture content was decreased around 50% in 2-day sauna. Kinetic curves of drying of seaweed were known to be used in this system. The non-linear regression procedure was used to fit three different drying models. The models were compared with experimental data of red seaweed being dried on the daily average of air temperature about 40℃. The fit quality of the models was evaluated using the coefficient of determination (R2), Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The highest values of R2 (0.99027), the lowest MBE (0.00044) and RMSE (0.03039) indicated that the Page model was the best mathematical model to describe the drying behavior of sauna dried seaweed. The percentage of the saved time using this technique was calculated at 57.9% on the average solar radiation of about 500 W/m2 and air flow rate of 0.056 kg/s.展开更多
There has been much discussion of the sources of China's growth slowdown but little formal econometric analysis of this question.Chen and Groenewold(2019)show that the slowdown was primarily supply-driven,but they...There has been much discussion of the sources of China's growth slowdown but little formal econometric analysis of this question.Chen and Groenewold(2019)show that the slowdown was primarily supply-driven,but they stopped short of identifying specific supply variables.This paper extends their analysis and distinguishes several potential supply components:labor supply,productivity,and capital accumulation.Our results confirm their main conclusion that supply dominates the explanation of the slowdown.A model with two supply factors(labor supply and productivity)reveals that both components contribute to the slowdown,although productivity makes the greater contribution.However,when capital stock is added to the model,the decline in the capital accumulation rate becomes an important factor in the growth slowdown,to some extent replacing the effects of both labor supply and productivity.展开更多
Introduction:COVID-19 has affected almost every country in the world,which causing many negative implications in terms of education,economy and mental health.Worryingly,the trend of second or third wave of the pandemi...Introduction:COVID-19 has affected almost every country in the world,which causing many negative implications in terms of education,economy and mental health.Worryingly,the trend of second or third wave of the pandemic has been noted in multiple regions despite early success of flattening the curve,such as in the case of Malaysia,post Sabah state election in September 2020.Hence,it is imperative to predict ongoing trend of COVID-19 to assist crucial policymaking in curbing the transmission.Method:Generalized logistic growth modelling(GLM)approach was adopted to make prediction of growth of cases according to each state in Malaysia.The data was obtained from official Ministry of Health Malaysia daily report,starting from 26 September 2020 until 1 January 2021.Result:Sabah,Johor,Selangor and Kuala Lumpur are predicted to exceed 10,000 cumulative cases by 2 February 2021.Nationally,the growth factor has been shown to range between 0.25 to a peak of 3.1 throughout the current Movement Control Order(MCO).The growth factor range for Sabah ranged from 1.00 to 1.25,while Selangor,the state which has the highest case,has a mean growth factor ranging from 1.22 to 1.52.The highest growth rates reported were inWP Labuan for the time periods of 22 Nov-5 Dec 2020 with growth rates of 4.77.States with higher population densities were predicted to have higher cases of COVID-19.Conclusion:GLM is helpful to provide governments and policymakers with accurate and helpful forecasts on magnitude of epidemic and peak time.This forecast could assist government in devising short-and long-term plan to tackle the ongoing pandemic.展开更多
文摘A sauna drying technique—the solar drier was designed and imposed, constructed and tested for drying of seaweed. The seaweed moisture content was decreased around 50% in 2-day sauna. Kinetic curves of drying of seaweed were known to be used in this system. The non-linear regression procedure was used to fit three different drying models. The models were compared with experimental data of red seaweed being dried on the daily average of air temperature about 40℃. The fit quality of the models was evaluated using the coefficient of determination (R2), Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The highest values of R2 (0.99027), the lowest MBE (0.00044) and RMSE (0.03039) indicated that the Page model was the best mathematical model to describe the drying behavior of sauna dried seaweed. The percentage of the saved time using this technique was calculated at 57.9% on the average solar radiation of about 500 W/m2 and air flow rate of 0.056 kg/s.
文摘There has been much discussion of the sources of China's growth slowdown but little formal econometric analysis of this question.Chen and Groenewold(2019)show that the slowdown was primarily supply-driven,but they stopped short of identifying specific supply variables.This paper extends their analysis and distinguishes several potential supply components:labor supply,productivity,and capital accumulation.Our results confirm their main conclusion that supply dominates the explanation of the slowdown.A model with two supply factors(labor supply and productivity)reveals that both components contribute to the slowdown,although productivity makes the greater contribution.However,when capital stock is added to the model,the decline in the capital accumulation rate becomes an important factor in the growth slowdown,to some extent replacing the effects of both labor supply and productivity.
文摘Introduction:COVID-19 has affected almost every country in the world,which causing many negative implications in terms of education,economy and mental health.Worryingly,the trend of second or third wave of the pandemic has been noted in multiple regions despite early success of flattening the curve,such as in the case of Malaysia,post Sabah state election in September 2020.Hence,it is imperative to predict ongoing trend of COVID-19 to assist crucial policymaking in curbing the transmission.Method:Generalized logistic growth modelling(GLM)approach was adopted to make prediction of growth of cases according to each state in Malaysia.The data was obtained from official Ministry of Health Malaysia daily report,starting from 26 September 2020 until 1 January 2021.Result:Sabah,Johor,Selangor and Kuala Lumpur are predicted to exceed 10,000 cumulative cases by 2 February 2021.Nationally,the growth factor has been shown to range between 0.25 to a peak of 3.1 throughout the current Movement Control Order(MCO).The growth factor range for Sabah ranged from 1.00 to 1.25,while Selangor,the state which has the highest case,has a mean growth factor ranging from 1.22 to 1.52.The highest growth rates reported were inWP Labuan for the time periods of 22 Nov-5 Dec 2020 with growth rates of 4.77.States with higher population densities were predicted to have higher cases of COVID-19.Conclusion:GLM is helpful to provide governments and policymakers with accurate and helpful forecasts on magnitude of epidemic and peak time.This forecast could assist government in devising short-and long-term plan to tackle the ongoing pandemic.
