Abstract： Assessment of pharmaceutical product quality is important prerequisite to justify safe and effective release of the medicinal dosage form to the drug market. However, without rigorous implementation of good manufacturing practice (GMP), routine quality control testing may be not adequate to conclude compliance with reproducible procedures. Accordingly, the current study aimed to investigate manufacturing quality of pharmaceutical product batches through monitoring assay results and trends retrospectively for three components of the active ingredients using two types of control charts and to compare the value of each in-process monitoring. This product was manufactured in a pharmaceutical firm and subjected to the assay (expressed as relative potency to the claimed labeled dose per tablet) in quality control laboratory. The active components are Paracetamol (Acetaminophen) (Pa), Chlorpheniramine Maleate (CM) and Pseudoephedrine Hydrochloride (PH). General performance and trend of the studied batches were compared using Individual-Moving Range and Laney U΄ chart which were constructed using statistics software. Box-and-Whisker diagram that was constructed for the assay of the three active constituents showed that CM relative potency was significantly higher than Pa and PH using ANOVA (p<0.05). Capability analysis showed that Pa and PH assays have met the requirement of analysis. In contrast to CM potency which demonstrated a failure to be maintained within the specification window level as strong shift outside the upper border (right drift) could be observed. Both types of control charts variable (Individual-Moving Range) and attribute (Laney U΄) showed same control limits. But Individual-Moving Range was more sensitive in detection of out-of-control states.

Abstract： Fetal weight estimation before delivery is important in obstetrics, which assists doctors diagnose abnormal or diseased cases. Linear regression based on ultrasound measures such as bi-parietal diameter (bpd), head circumference (hc), abdominal circumference (ac), and fetal length (fl) is common statistical method for weight estimation but the regression model requires that time points of collecting such measures must not be too far from last ultrasound scans. Therefore this research proposes a method of early weight estimation based on expectation maximization (EM) algorithm so that ultrasound measures can be taken at any time points in gestational period. In other words, gestational sample can lack some or many fetus weights, which gives facilities to practitioners because practitioners need not concern fetus weights when taking ultrasound examinations. The proposed method is called dual regression expectation maximization (DREM) algorithm. Experimental results indicate that accuracy of DREM decreases insignificantly when completion of ultrasound sample decreases significantly. So it is proved that DREM withstands missing values in incomplete sample or sparse sample.

Abstract： The work presents the results of research on thermometry of cutaneous integument of biological objects, using the method of remote infrared thermography. The process of evaporation of drops of sweat during physical loading has been simulated in imitation of evaporation of drops of water. For the first time, research resulted in time dependence of the temperature of drops of water (sweat) with convective and diffuse mechanisms of a heat and mass exchange with the ambient air. Research results can be applied in experimental medicine for controlling process of athletes training.

Abstract： Fetal weight estimation before delivery is important in obstetrics, which assists doctors diagnose abnormal or diseased cases. Linear regression based on ultrasound measures such as bi-parietal diameter (bpd), head circumference (hc), abdominal circumference (ac), and fetal length (fl) is common statistical method for weight estimation. There is a demand to retrieve regression model in case of incomplete data because taking ultrasound examinations is a hard task and early weight estimation is necessary in some cases. In this research, we proposed so-called regression expectation maximization (REM) algorithm which is a combination of linear regression method and expectation maximization (EM) method to construct the regression model when both ultrasound measures and fetal weight are missing. The special technique in REM is to build parallelly an entire regression function and many partial inverse regression functions for solving the problem of highly sparse data, in which missing values are fulfilled by expectations relevant to both entire regression function and inverse regression functions. Experimental results proved resistance of REM to incomplete data, in which accuracy of REM decreases insignificantly when data sample is made sparse with loss ratios up to 80％.