Characteristics of Particles Emitted from Waste Fires—A Construction Materials Case Study

This study aimed to determine the relative densities of populations of particles emitted in fire experiments of selected materials through direct measurement and parametrization of size distribution as number (NSD), volume (VSD), and mass (MSD). As objects of investigation, four typical materials used in construction and furniture were chosen: pinewood (PINE), laminated particle board (LPB), polyurethane (PUR), and poly(methyl methacrylate) (PMMA). The NSD and VSD were measured using an electric low-pressure impactor, while MSD was measured by weighing filters from the impactor using a microbalance. The parametrization of distributions was made assuming that each distribution can be expressed as the sum of an arbitrary number of log-normal distributions. In all materials, except PINE, the distributions of the particles emitted in fire experiments were the sum of two log-normal distributions; in PINE, the distribution was accounted for by only one log-normal distribution. The parametrization facilitated the determination of volume and mass abundances, and therefore, the relative density. The VSDs of particles generated in PINE, LPB, and PUR fires have similar location parameters, with a median volume diameter of 0.2–0.3 µm, whereas that of particles generated during PMMA burning is 0.7 µm. To validate the presented method, we burned samples made of the four materials in similar proportions and compared the measured VSD with the VSD predicted based on the weighted sum of VSD of raw materials. The measured VSD shifted toward smaller diameters than the predicted ones due to thermal decomposition at higher temperatures.

Equivariance and Invariance for Optimal Designs in Generalized Linear Models Exemplified by a Class of Gamma Models

The main intention of the present work is to outline the concept of equivariance and invariance in the design of experiments for generalized linear models and to demonstrate its usefulness. In contrast with linear models, pairs of transformations have to be employed for generalized linear models. These transformations act simultaneously on the experimental settings and on the location parameters in the linear component. Then, the concept of equivariance provides a tool to transfer locally optimal designs from one experimental region to another when the nominal values of the parameters are changed accordingly. The stronger concept of invariance requires a whole group of equivariant transformations. It can be used to characterize optimal designs which reflect the symmetries resulting from the group actions. The general concepts are illustrated by models with gamma distributed response and a canonical link. There, for a given transformation of the experimental settings, the transformation of the parameters is not unique and may be chosen to be nonlinear in order to fully exploit the model structure. In this case, we can derive invariant maximin efficient designs for the D- and the IMSE-criterion.

Estimation of Stress-Strength Reliability for Multicomponent System with Rayleigh Data

Inference is investigated for a multicomponent stress-strength reliability (MSR) under Type-II censoring when the latent failure times follow two-parameter Rayleigh distribution. With a context that the lifetimes of the strength and stress variables have common location parameters, maximum likelihood estimator of MSR along with the existence and uniqueness is established. The associated approximate confidence interval is provided via the asymptotic distribution theory and delta method. Meanwhile, alternative generalized pivotal quantities-based point and confidence interval estimators are also constructed for MSR. More generally, when the lifetimes of strength and stress variables follow Rayleigh distributions with unequal location parameters, likelihood and generalized pivotal-based estimators are provided for MSR as well. In addition, to compare the equivalence of different strength and stress parameters, a likelihood ratio test is provided. Finally, simulation studies and a real data example are presented for illustration.

Research on the SOH Prediction Based on the Feature Points of Incremental Capacity Curve

The accurate prediction of the state of health (SOH) is an important basis for ensuring the normal operation of the lithium-ion battery (LIB). The accurate SOH can extend the life-span, ensure safety, and improve the performance of LIBs. The charging voltage curve and incremental capacity (IC) curve of the LIB in different SOH are obtained through experiments. The location parameters of each feature point on IC curve are closely related to battery aging, to characterize the SOH of the LIB with the location of feature points. To solve the difficulty in identifying feature points due to the oscillation in solving IC curves with a traditional numerical analytic method, the piecewise polynomial fitting method is adopted to smooth IC. To discuss the law between the location change of all feature points on the IC curve and the capacity attenuation, a capacity prediction regression model is established after the dimensionality reduction of the coordinate data of feature points on the IC curve with the principal component analysis method. The proposed method can rapidly estimate the online SOH of LIBs during the charging process of electric vehicles and the results show the maximum error is 0.63AH (3.15%).

The Impact of Healthcare Traffic Over H2H and M2M Networks During the Spread Phase of Pandemic Diseases

Recently, the coronavirus pandemic has caused widespread panic around the world. Modern technologies can be used to monitor and control this highly contagious disease. A plausible solution is to equip each patient who is diagnosed with or suspected of having COVID-19 with sensors that can monitor various healthcare and location parameters and report them to the desired facility to control the spread of the disease. However, the simultaneous communication of numerous sensors installed in the majority of an area’s population results in a huge burden on existing Long-Term Evolution (LTE) networks. The existing network becomes oversaturated because it has to manage two kinds of traffic in addition to normal traffic (text, voice, and video): healthcare traffic generated by a large number of sensors deployed over a huge population, and extra traffic generated by people contacting their family members via video or voice calls. In pandemics, e-healthcare traffic is critical and should not suffer packet loss or latency due to network overload. In this research, we studied the performance of existing networks under various conditions and predicted the severity of network degradation in an emergency. We proposed and evaluated three schemes (doubling bandwidth, combining LTE-A and LTE-M networks, and request queuing) for ensuring quality of service (QoS) of healthcare sensor (HCS) network traffic without perturbation from routine human-to-human or machine-to-machine communications. Finally, we simulated all proposed schemes and compared them with existing network scenarios. The results have showed that when we have doubled the bandwidth the SCR of all traffic was 100% as same as the Queue strategy. However, when we prioritized the HCS traffic the SCR has recorded 100%, while H2H and M2M traffic has recorded 73%. When we used hybrid network LTE-A and LTE-M network, the HCS and H2H traffic has recorded 100% and M2M traffic has recorded 70%. After analyzing the results, we conclude that our proposed queuing schemes performed well in all conditions and provide the best QoS for HCS traffic.

Influence of Clinical and Tumor Factors on Interfraction Setup Errors With Rotation Correction for Vacuum Cushion in Lung Stereotactic Body Radiation Therapy

PurposeTo explore the influence of clinical and tumor factors over interfraction setup errors with rotation correction for non-small cell lung cancer (NSCLC) stereotactic body radiation therapy (SBRT) patients immobilized in vacuum cushion (VC) to better understand whether patient re-setup could further be optimized with these parameters.Materials and MethodsThis retrospective study was conducted on 142 NSCLC patients treated with SBRT between November 2017 to July 2019 in the local institute. Translation and rotation setup errors were analyzed in 732 cone-beam computed tomography (CBCT) scans before treatment. Differences between groups were analyzed using independent sample t-test. Logistic regression test was used to analyze possible correlations between patient re-setup and clinical and tumor factors.ResultsMean setup errors were the largest in anterior–posterior (AP) direction (3.2 ± 2.4 mm) compared with superior–inferior (SI) (2.8 ± 2.1 mm) and left–right (LR) (2.5 ± 2.0 mm) directions. The mean values were similar in pitch, roll, and rtn directions. Of the fractions, 83.7%, 90.3%, and 86.6% satisfied setup error tolerance limits in AP, SI, and LR directions, whereas 95% had rotation setup errors of <2° in the pitch, roll, or rtn directions. Setup errors were significantly different in the LR direction when age, body mass index (BMI), and “right vs. left” location parameters were divided into groups. Both univariate and multivariable model analyses showed that age (p = 0.006) and BMI (p = 0.002) were associated with patient re-setup.ConclusionsAge and BMI, as clinical factors, significantly influenced patient re-setup in the current study, whereas all other clinical and tumor factors were not correlated with patient re-setup. The current study recommends that more attention be paid to setup for elderly patients and patients with larger BMI when immobilized using VC, especially in the left–right direction.

Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data

Linear discriminant analysis (LDA) is a mathematically robust multivariate data analysis approach that is sometimes used for surface oil slick signature classification. Our goal is to rank the effectiveness of LDAs to differentiate oil spills from look-alike slicks. We explored multiple combinations of (i) variables (size information, Meteorological-Oceanographic (metoc), geo-location parameters) and (ii) data transformations (non-transformed, cube root, log10). Active and passive satellite-based measurements of RADARSAT, QuikSCAT, AVHRR, SeaWiFS, and MODIS were used. Results from two experiments are reported and discussed: (i) an investigation of 60 combinations of several attributes subjected to the same data transformation and (ii) a survey of 54 other data combinations of three selected variables subjected to different data transformations. In Experiment 1, the best discrimination was reached using ten cube-transformed attributes: ~85% overall accuracy using six pieces of size information, three metoc variables, and one geo-location parameter. In Experiment 2, two combinations of three variables tied as the most effective: ~81% of overall accuracy using area (log transformed), length-to-width ratio (log- or cube-transformed), and number of feature parts (non-transformed). After verifying the classification accuracy of 114 algorithms by comparing with expert interpretations, we concluded that applying different data transformations and accounting for metoc and geo-location attributes optimizes the accuracies of binary classifiers (oil spill vs. look-alike slicks) using the simple LDA technique.

Ordinal regression Assessment of orange Postharvest loss determinants among Orange Farmers in Konshisha Local Government Area of Benue State

Orange wastage through postharvest losses has contributed to food scarcity, economic loss and massive importation of food items in Nigeria. The research was mainly carried out to investigate the determinants of orange postharvest losses among orange farmers in Konshisha Local Government of Benue State, North Central geopolitical zone of Nigeria. Primary data was collected from the orange farmers using structured questionnaires and key informant interviews. Descriptive statistics and Ordinal Regression model were used to analyse the data collected. The quantity lost was perceived at six (6) categories. The results revealed that most (63.7%) of the farmers were above 34 years of age. Also the majority (95.1%) were male, while 55.3% of the respondents’ farm size was relatively large with 200 and above stands of orange. The farmers’ literacy level was 73.6%. Those that belonged to farmers groups were 39.5. Further results established the use of probit link function in the ordinal regression modelling and that the significant factors affecting orange postharvest losses in the area are farmer’s lack of education and farmers not belonging to any association or group. The only significant covariate with the postharvest loss quantity of orange is farm size. The test of parallel lines established that, the location parameters (slope coefficients) are the same across response categories.