No abstract

At the end of the final spin cycle of the laundry process, the residual moisture content (RMC) of fabric is directly related to the dynamic surface tension of the residual water in the fabric. The LaPlace equation for capillary rise predicts that the capillary rise of solutions in a capillary is proportional to the surface tension at the air-liquid interface. If fabric can be considered to be a large ensemble of capillaries due to interfiber spacing, then the RMC of fabrics will be directly related to the surface tension of residual solution in the fabric. The use of a tailored rinse additive has the potential to decrease the surface tension of solution significantly, thus leading to a decrease in the residual water content of the fabric. It is expected that as the surfactant concentration increases the surface tension decreases. Hence, the RMC of fabrics must decrease with increasing surfactant concentration. However, a peak is observed in the RMC of fabrics before the critical micelle concentration (CMC) is reached. Prior to the CMC, it is proposed that a sudden adsorption of surfactant is occurring on the fabric surface leading to a decrease in bulk monomer concentration. The decrease in free monomer concentration should result in an increase in the equilibrium surface tension of the residual solution leading to a concomitant increase in RMC. Because the dynamic surface tension is measured on a short time scale (on the order of milliseconds), there will be less adsorption of monomer onto the newly created air-liquid interface of the bubbles during the measurement process. This decrease in adsorption should lead to a pronounced increase in the dynamic surface tension. This indeed was observed. The RMC correlates very well with the dynamic surface tension of the residual solution.

Biological tissue consists of populations of cells exhibiting different responses to pharmacological stimuli. To probe the heterogeneity of cell function, we propose a multiplexed approach based on real‐time imaging of the secondary messenger levels within each cell of the tissue, followed by extraction of the changes of single‐cell fluorescence over time. By utilizing a piecewise baseline correction, we were able to quantify the effects of multiple pharmacological stimuli added and removed sequentially to pancreatic islets of Langerhans, thereby performing a deep functional profiling for each cell within the islet. Cluster analysis based on the functional profile demonstrated dose‐dependent changes in statistical inter‐relationships between islet cell populations. We therefore believe that the functional cytometric approach can be used for routine quantitative profiling of the tissue for drug screening or pathological testing.

Landmines are a major problem facing the world today; there are millions of these deadly weapons still buried in various countries around the world. Humanitarian organizations dedicate an immeasurable amount of time, effort, and money to find and remove as many of these mines as possible. Unfortunately, landmines can be made out of common materials which make the correct detection of them very difficult. This paper analyzes the effectiveness of combining certain statistical techniques with a neural network to improve detection. The detection method must not only detect the majority of landmines in the ground, it must also filter out as many of the false alarms as possible. This is the true challenge to developing landmine detection algorithms. Our approach combines a BackPropagation Neural Network (BPNN) with statistical techniques and compares the performance of mine detection against the performance of the energy detector and the δ-technique. Our results show that the combination of the δ-technique and the S-statistics with a neural network improves the performance.