The first phase selection and the phase formation sequence between metal and silicon (Si) couples are indispensably significant to microelectronics. With increasing scaling of device dimension to nano regime, established thermodynamic models in bulk and thin film fail to apply in one dimensional (1-D) nanostructures. Herein, we use a kinetic competition model to explain the phase formation sequence of 1-D nickel (Ni) silicides: multiple Ni silicides coexist at the initial stage and then the fastest one wins out as the first phase in a following growth competition. With kinetic parameters extracted from in-situ transmission electron microscope (TEM) observations, we quantitatively explain the unique size dependant first phase formation and the phase formation sequence changes in 1-D structures. We can further control the first phase by selectively enhancing or suppressing the growth rate of silicides through template structure modifications. Growth rate diffusion limited phases can be greatly enhanced in a porous Si nanowire (NW) template due to short diffusion paths. On the other hand, a thick aluminum oxide (Al₂O₃) shell around the NW is applied to impede the growth of large volume diffusion limited phases including Ni₃₁Si₁₂, theta-Ni₂Si and delta-Ni₂Si. Moreover, a thin platinum (Pt) interlayer between Si and Ni is used to suppress the nucleation of NiSi₂. Together, with the thick shell and Pt interlayer, we can suppress all competing silicides and render slow growing NiSi to form as the first phase. The resistivity of Pt doped NiSi (denoted as Ni(Pt)Si) NW are found compatible to pure NiSi from a two terminal and four terminal measurement. Controlled formation of Ni₃₁Si₁₂, theta-Ni₂Si, delta-Ni₂Si, NiSi or NiSi₂ as the first phase has also been achieved. To examine the kinetic competition model, 1-D cobalt (Co) and palladium (Pd) silicide formations are also studied and analyzed kinetically. A thick shell is found effective to suppress the Pd silicide NW broken at the interface.

A key contributor to urban ambient air pollutants is road traffic. Vehicle emissions are known to be associated with various adverse health effects. Particulate matter (PM), as one of the traffic-related air pollutants (TRAPs), is particularly crucial due to its various chemical composition, morphology, size and numerous adverse health impacts. In this study, we deployed twelve low-cost air sensors to explore the temporal characteristic and the spatial variability of PM in a community adjacent to an interstate highway. In addition, the long-term field performance of the low-cost air sensors and its potential for identifying the traffic-related PM were also examined. At every sampling site, ambient PM was continuously measured with a 120s resolution from December 1, 2017- November 30, 2018. These data were later converted into hourly data in order to match with the hourly PM2.5 and NO2 data acquired from the EPA stations. The highest PM2.5 was observed in the winter (15.7 � 16.7 g/m3) and the lowest was observed in spring (8.8 � 7.21 g/m3). Pairwise sensors were found to be temporally correlated (r > 0.98) to each other and spatially (CoD < 0.02) homogeneous within the community. A good correlation (r > 0.79) and small CoD (value = 0.17) were also found between the PM2.5 at the community and the PM2.5 at the EPA monitor site. This suggests that the PM2.5 across the community and the EPA monitor site are temporally correlated and spatially homogenous to each other. The temporal patterns of PM2.5, PN0.3 and NO2 clearly demonstrated that the traffic is one of the major contributors to the air pollutants at the University Village. In particular, by using pair-wise sensors (downwind sensor and upwind sensor), the highway adjacent to the community, was found to be the main cause of higher concentration of PM.

As the trend for wearable bioinformatic sensors continues to increase, researchers pursue integrating knowledge of the human body into the design of a simpler sensory system. By observing the consistency and discrepancy of human behavior, insight can be gained on how to design a monitoring architecture and what content to monitor. Because no two human individuals are identical, it is important to capture consistency among people based on an all-inclusive understanding. The first step of monitoring is to quantify the behavior. Because there is a limitation to a commercial motion capturing system, a durable sensory system, the Smartshoe, is designed to remotely record and measure ground contact force (GCF). The recorded data is locally processed by the hardware’s computing unit and a non-blocking software architecture regulates the data rate of the recording. The accuracy and repeatability of GCF measurements with the piezoelectric sensor are demonstrated. The detailed schematic design is also explained in the text. A graphical user interface (GUI) is designed to show the real-time applicability of the software.

An optimal sensor location consistently differentiates distinct human movements and requires a minimal usage of the computational resource. Drawing inspiration from L1 regularization in classification problems, the use of feature selection results is an optimal sensor layout for gait phase estimation. The method reveals the significant sensor location involving in the classification and filters out the redundant locations. As a consequence of this optimization, the input complexity is greatly reduced. The results resemble some of the previous choices but also eliminate some redundancy. The estimators' performance of the optimized layout and the anatomic layout are compared and there is no significant disagreement.

Finally, a two-level hierarchical decision framework is proposed for a comprehensive representation of human bipedal behavior. The method decouples behavior identification to two levels, discrete modes and mode-dependent special tasks. The double-peaked GCF pattern is used as a metric to distinguish walking mode from running mode. Support vector machine (SVM) with a moving window is implemented to identify the discrete modes in the first level. In the second level, the mode-dependent tasks are defined under single or multiple modes. In walking mode, a set of two-sided fuzzy logic rules is utilized for robustly recognizing the gait phases. The speed of walking is also estimable by observing the peak-to-valley ratio in the GCF pattern. The diagnosis of touch-based symptoms, on the other hand, are dual-mode specific tasks. Plantar Fasciitis causes a smaller Mid-Stance to Terminal Stance ratio in combination with asymmetric running GCF pattern in the affected side. And finally, the combined performance using the framework shows a robust and accurate analysis of human walking behavior.

Marketing theory and practice have become increasingly customer-centered in recent decades. To that end, marketers, consumer researchers, and the lay public alike have begun to take special interest in how understanding the human brain can help them better understand consumers. Despite advances in knowledge of how the brain represents simple goods such as those involving primary rewards, however, there is great difficulty in extending this understanding to more complex goods typical of modern human society, and in particular how the brain represents the set of intangible characteristics resulting from social and cultural influences, for example, the intangible characteristics captured by a good’s brand. Here we combine newly available machine learning techniques with functional neuroimaging data to characterize the set of processes that give rise to the intangible associations people have with brands. Our findings represent an important advance in the application of neuroscientific methods to consumer research, moving from work focused on cataloguing brain regions associated with marketing stimuli to testing and refining mental constructs central to theories of consumer behavior.