Selecting Milk Spectra to Develop Equations to Predict Milk Technological Traits

Including all available data when developing equations to relate midinfrared spectra to a phenotype may be suboptimal for poorly represented spectra. Here, an alternative local changepoint approach was developed to predict six milk technological traits from midinfrared spectra. Neighbours were objectively identified for each predictand as those most similar to the predictand using the Mahalanobis distances between the spectral principal components, and subsequently used in partial least square regression (PLSR) analyses. The performance of the local changepoint approach was compared to that of PLSR using all spectra (global PLSR) and another LOCAL approach, whereby a fixed number of neighbours was used in the prediction according to the correlation between the predictand and the available spectra. Global PLSR had the lowest RMSEV for five traits. The local changepoint approach had the lowest RMSEV for one trait; however, it outperformed the LOCAL approach for four traits. When the 5% of the spectra with the greatest Mahalanobis distance from the centre of the global principal component space were analysed, the local changepoint approach outperformed the global PLSR and the LOCAL approach in two and five traits, respectively. The objective selection of neighbours improved the prediction performance compared to utilising a fixed number of neighbours; however, it generally did not outperform the global PLSR.

Development of Nano Soy Milk through Sensory Attributes and Consumer Acceptability

Nanotechnology is currently applied in food processing and packaging in the food industry. Nano encapsulation techniques could improve sensory perception and nutrient absorption. The purpose of this study was to identify the sensory characteristics and consumer acceptability of three types of commercial and two types of laboratory-developed soy milk. A total of 20 sensory attributes of the five different soy milk samples, including appearance, smell (odor), taste, flavor, and mouthfeel (texture), were developed. The soy milk samples were evaluated by 100 consumers based on their overall acceptance, appearance, color, smell (odor), taste, flavor, mouthfeel (texture), goso flavor (nuttiness), sweetness, repeated use, and recommendation. One-way analysis of variance (ANOVA), principal component analysis (PCA), and partial least square regression (PLSR) were used to perform the statistical analyses. The SM_D sample generally showed the highest scores for overall liking, flavor, taste, mouthfeel, sweetness, repeated consumption, and recommendation among all the consumer samples tested. Consumers preferred sweet, goso (nuttiness), roasted soybean, and cooked soybean (nuttiness) attributes but not grayness, raw soybean flavor, or mouthfeel. Sweetness was closely related to goso (nuttiness) odor and roasted soybean odor and flavor based on partial least square regression (PLSR) analysis. Determination of the sensory attributes and consumer acceptance of soymilk provides insight into consumer needs and desires along with basic data to facilitate the expansion of the consumer market.

Cash Holdings, Commercial Paper and Liquidity

Managing Liquidity has seldom been being as vital as it has during the Covid-19 era. The financial impact of Covid-19 has left many firms on the brink of liquidation. This paper explores the effect cash holdings has had on Profitability of the firm and how it compares with commercial paper between the pre-Covid-19 era and the Covid-19 era. This paper employs and compares the ordinary least square regression between these eras. I find that firms are less liquid during the Covid-19 period compared to the pre-covid period. More importantly, I find that Liquidity has been more critical to a firm's Profitability during Covid-19 era compared to pre-covid 19 periods. Furthermore, Cash holdings represent a significant chunk of Liquidity. However, these Cash holdings dropped by a little in the covid-19 era. Finally, I find that both commercial paper and cash holdings are used as complements. However, this result is weakly supported during the pre-covid period.

A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.

Intercomparison of Partial Least Square Regression-based Feature Selection Techniques for Snow Cover Area Models

Snow cover area on a river basin, affects so many meteorologic and environmental parameters. By growing remote sensing technology, nowadays snow cover area could be measured on a regular basis for scientific purposes. In this study, the monthly average of snow cover area of the Baranduz river basin from West Azerbaijan in Iran had been used for modelling by ANN and SVM. The snow cover area was extracted from MODIS 8-day maximum snow extent products from 2000 to 2019. Also, the 20 meteorologic parameters were collected from Bibakran and Babarud ground hydrometeorological stations and 20 parameters were collected from satellite base data powered by NASA LaRC projects. After BoxCox transformation analysis, the feature selection methods were used to select the modelling subsets. Partial least square regression base filter and wrapper feature selection methods were used to select modelling subsets. LW, RC, SR, VIP, SMC, MRMR, JT filter methods and GA, MCUVE and REP wrapper methods were used to select the best parameters for modelling. By increasing the thresholds of the feature selection methods, the number of the selected parameters in subsets was decreased, and after a certain amount of thresholding value, the number of parameters was fixed in 10 variables. Selected subsets were being evaluated by multicollinearity indexes and by performances of the ANN and the SVM models. 80% of the data used for training models and 20% of the data used for testing the models. The accuracy of all models was high and acceptable but, in some subsets, there was a serious multicollinearity issue. However, the correlation between parameters was so high despite this, the PLSR base feature selection methods have been very successful in reducing a great amount of multicollinearity in selected subsets. Also, the ANN and SVM models have shown very high performance in modelling the monthly snow cover area.

Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band

Propagated electromagnetic signal over the cellular radio communication channels and interfaces are usually highly stochastic and complex with unequal noise variation pattern. This is due to multipath nature of the propagation channels and diverse radio propagation mechanisms that impact the signal strength at the receiver en-route the transmitter, and verse versa. This also makes measurement, predictive modeling and estimation based analysis of such signal very challenging and complex as well. One important and popular parametric modelling and estimation technique in mathematics and engineering science, especially for signal processing applications is the least square regression (LSR). The dominance use and popularity of the LSR approach may be attributed to its simplified supporting theory, relatively fast application procedure and ubiquitous application packages. However, LSR is known to be very sensitive to outliers and unusual stochastic signal data. In this work, we propose the application of weighted least square regression method for enhanced propagation practical field strength estimation modelling over cellular radio communication networks interface. The signal data was collected from a commercial LTE networks service provider. Also, we provide statistical computational analyses to compare the resultant estimation outcome of the weighted least square and the standard least approach. From the result, it is found that the WLSR approach is reliably better the most popular standard least square method. The significance and academic of value of this paper is that our proposed and implemented WLSR method can used as replacement of the standard LSR approach for robust mobile signal processing of future communication system networks.