Mathematical modelling for the drying method and smoothing drying rate using Cubic Spline for seaweed Kappaphycus Striatum variety durian in a solar dryer

The solar drying experiment of seaweed using Green V-Roof Hybrid Solar Drier (GVRHSD) was conducted in Semporna, Sabah under the metrological condition in Malaysia. Drying of sample seaweed in GVRHSD reduced the moisture content from about 93.4% to 8.2% in 4 days at average solar radiation of about 600W/m2 and mass flow rate about 0.5 kg/s. Generally the plots of drying rate need more smoothing compared moisture content data. Special cares is needed at low drying rates and moisture contents. It is shown the cubic spline (CS) have been found to be effective for moisture-time curves. The idea of this method consists of an approximation of data by a CS regression having first and second derivatives. The analytical differentiation of the spline regression permits the determination of instantaneous rate. The method of minimization of the functional of average risk was used successfully to solve the problem. This method permits to obtain the instantaneous rate to be obtained directly from the experimental data. The drying kinetics was fitted with six published exponential thin layer drying models. The models were fitted using the coefficient of determination (R2), and root mean square error (RMSE). The modeling of models using raw data tested with the possible of exponential drying method. The result showed that the model from Two Term was found to be the best models describe the drying behavior. Besides that, the drying rate smoothed using CS shows to be effective method for moisture-time curves good estimators as well as for the missing moisture content data of seaweed Kappaphycus Striatum Variety Durian in Solar Dryer under the condition tested

Efficient Model Selection for Moisture Ratio Removal of Seaweed Using Hybrid Of Sparse And Robust Regression Analysis: Efficient Model Selection for Moisture Ratio Removal of Seaweed

The Internet of things ((IoT) consisted of physical devices networks such as sensors, home appliances, electronics, and software’s. It enables us to collect and exchange data in several fields. After data collection from IoT, variable selection is considered a major problem because many variables are involved in real life datasets. The current study focused on large data analysis of the problem of model selection, including interaction terms. The dataset used in this study is taken from solar drier with moisture ratio removal (%) as dependent variable while ambient temperature, chamber temperature, collector temperature, chamber relative humidity, ambient relative humidity, and solar radiation as independent variables. LASSO with Huber M, LASSO with Hampel M and LASSO with Bisquare M are proposed in this study. Comparison of proposed techniques are made with ridge regression and OLS (ordinary least square) after multicollinearity test and coefficient test. MAPE (mean absolute percentage error) is calculated for the efficient selected model to forecast. As a result, the model using LASSO with Bisquare-M provides a minimum MAPE value for the best efficient model. Thus, the resulting model with the selected variables can be used to predict Moisture Ratio Removal (%) to determine seaweed drying behavior

Plasmonic Biosensors for the Detection of Lung Cancer Biomarkers: A Review

International audienceLung cancer is the most common and deadliest cancer type globally. Its early diagnosis can guarantee a five-year survival rate. Unfortunately, application of the available diagnosis methods such as computed tomography, chest radiograph, magnetic resonance imaging (MRI), ultrasound, low-dose CT scan, bone scans, positron emission tomography (PET), and biopsy is hindered due to one or more problems, such as phenotypic properties of tumours that prevent early detection, invasiveness, expensiveness, and time consumption. Detection of lung cancer biomarkers using a biosensor is reported to solve the problems. Among biosensors, optical biosensors attract greater attention due to being ultra-sensitive, free from electromagnetic interference, capable of wide dynamic range detection, free from the requirement of a reference electrode, free from electrical hazards, highly stable, capable of multiplexing detection, and having the potential for more information content than electrical transducers. Inspired by promising features of plasmonic sensors, including surface plasmon resonance (SPR), localised surface plasmon resonance (LSPR), and surface enhanced Raman scattering (SERS) such as ultra-sensitivity, single particle/molecular level detection capability, multiplexing capability, photostability, real-time measurement, label-free measurement, room temperature operation, naked-eye readability, and the ease of miniaturisation without sophisticated sensor chip fabrication and instrumentation, numerous plasmonic sensors for the detection of lung cancer biomarkers have been investigated. In this review, the principle plasmonic sensor is explained. In addition, novel strategies and modifications adopted for the detection of lung cancer biomarkers such as miRNA, carcinoembryonic antigen (CEA), cytokeratins, and volatile organic compounds (VOCs) using plasmonic sensors are also reported. Furthermore, the challenges and prospects of the plasmonic biosensors for the detection of lung cancer biomarkers are highlighted

Physicians' guideline adherence is associated with long-term heart failure mortality in outpatients with heart failure with reduced ejection fraction: the QUALIFY international registry

Background: Physicians' adherence to guideline-recommended therapy is associated with short-term clinical outcomes in heart failure (HF) with reduced ejection fraction (HFrEF). However, its impact on longer-term outcomes is poorly documented. Here, we present results from the 18-month follow-up of the QUALIFY registry. Methods and results: Data at 18 months were available for 6118 ambulatory HFrEF patients from this international prospective observational survey. Adherence was measured as a continuous variable, ranging from 0 to 1, and was assessed for five classes of recommended HF medications and dosages. Most deaths were cardiovascular (CV) (228/394) and HF-related (191/394) and the same was true for unplanned hospitalizations (1175 CV and 861 HF-related hospitalizations, out of a total of 1541). According to univariable analysis, CV and HF deaths were significantly associated with physician adherence to guidelines. In multivariable analysis, HF death was associated with adherence level [subdistribution hazard ratio (SHR) 0.93, 95% confidence interval (CI) 0.87–0.99 per 0.1 unit adherence level increase; P = 0.034] as was composite of HF hospitalization or CV death (SHR 0.97, 95% CI 0.94–0.99 per 0.1 unit adherence level increase; P = 0.043), whereas unplanned all-cause, CV or HF hospitalizations were not (all-cause: SHR 0.99, 95% CI 0.9–1.02; CV: SHR 0.98, 95% CI 0.96–1.01; and HF: SHR 0.99, 95% CI 0.96–1.02 per 0.1 unit change in adherence score; P = 0.52, P = 0.2, and P = 0.4, respectively). Conclusion: These results suggest that physicians' adherence to guideline-recommended HF therapies is associated with improved outcomes in HFrEF. Practical strategies should be established to improve physicians' adherence to guidelines. © 2019 The Authors. European Journal of Heart Failure © 2019 European Society of Cardiolog