Platinum-containing compounds are among organometallic compounds that are considered promising drug candidates for cancer therapy. They have an important role in the treatment of several solid tumors. The interactions of small molecule drugs with serum albumin affect the pharmacokinetics of these therapeutic agents. In this study, the binding characteristics of two novel platinum complexes, named C1 and C2, and bovine serum albumin (BSA) were investigated by FLUORESCENCE and UV-visible spectroscopies. The results showed that these compounds diminished the FLUORESCENCE emission intensity of BSA through ground state quenching mechanisms. The apparent binding constant (Kb) values for the association of these compounds and BSA were in the range of 103-104 M-1, showing their moderate affinity to BSA. The Kb and the numbers of binding sites of BSA-complexes were increased by the elevation of temperature indicating that binding pockets were more accessible to these compounds at higher temperatures. Thermodynamic PARAMETERs showed that hydrophobic interactions are involved in C1-BSA complex formation and the interaction of C2 with BSA is through van der Waals interactions and hydrogen bonds.

The use of chlorophyll FLUORESCENCE (ChlF) technique was evaluated on nondestructive measurement of sugar content during fruit development. Multivariate models, principal component analysis (PCA), and partial least-squares regression (PLSR), were developed for the classification and prediction of fructose, glucose, and sucrose in fig fruits. The results of this study showed a significant correlation between FLUORESCENCE PARAMETERs and sugar content during fruit development. The PCA-ChlF can be used as a fast screening method for discriminating the degree of maturity based on sugar content. In addition, the root mean squared error (RMSE) and coefficient of determination (R2) of PLSR-ChlF for predicting sugar content were 2.01 g 100 g-1 DW and 0.96 for fructose, 1.03 g 100 g-1 DW and 0.99 for glucose, and 0.17 g 100 g-1 DW and 1.00 for sucrose, respectively. Therefore, ChlF combined with chemometrics may be a potential tool to nondestructively evaluate sugar accumulation in not only fig fruits, but also any other chlorophyll-containing fruit during development.

Pavement functional and structural PARAMETERs are important for pavement evaluation. This research aims at developing a prediction model to predict the deflection of flexible pavements using International Roughness Index (IRI) values to optimize maintenance activities without conducting deflection testing. For this purpose, Multan-Lodhran south bound section, a part of N-5 is selected as study area. It starts from km 929+000 and ends at km 867+000, having a length of 62 km, dual carriageway having two lanes each side. The data used for this study had been collected from the database of Road Asset Management Division (RAMD), National Highway Authority, Islamabad, Pakistan on basis of data availability and reliability. Collected data includes International Roughness Index (IRI) values measured by profilometer and deflection data measured by Falling Weight Deflectometer (FWD). Existing pavement structure data was collected from NHA Regional office. Linear and Logarithmic Regression analysis methodology using SPSS Statistical Analysis Software was used to develop correlation between pavement functional and structural PARAMETERs for flexible pavement. Also, Microsoft Excel was used for data analysis and validation purpose. The developed regression model showed significant relationship between deflection and IRI. Additionally, AASHTO Back calculation method was also used for determining the effective structure number (SNeff) using deflection results from FWD to determine correlation of IRI with another structural PARAMETER. Considering this, a significant correlation was found between SNeff and IRI. However, it was found that other PARAMETERs i.e., subgrade resilient modulus (Mr) and pavement elastic modulous (Ep) did not show any significant relation. The result showed that if pavement IRI values are known, deflection and SNeff can be predicted providing feasibility for transportation agencies that do not have the capabilities as deflection tests are costly and require sophistication for its data handling.

The estimation of unknown PARAMETERs of two-PARAMETER Rayleigh distribution based on Type-II progressive censoring with binomial removals is studied. Maximum likelihood estimators of the PARAMETERs and their confidence intervals are derived. By applying Markov Chain Monte Carlo techniques, Bayes estimators, and corresponding highest posterior density confidence intervals of PARAMETERs are obtained. The expected time required to complete the life test under this censoring scheme is investigated. Monte Carlo simulations are performed to compare the performances of the different methods, and one data set is analyzed for illustrative purposes.