This paper presents a theory and experiments on a reversible ultrasonic linear motor, consisting of a thin beam, two ultrasonic transducers, and a slider. The slider rides upon the crests of transverse traveling flexure waves propagating down the beam from one transducer to the other.

Three domain-swapped cytochrome cb 562 dimers formed a unique cage structure with a Zn–SO4 cluster inside the cavity.

Recently, an IEEE 802.11n access point (AP) prevailed over the wireless local area network (WLAN) due to the high-speed data transmission using the multiple input multiple output (MIMO) technology. Unfortunately, the signal propagation from the 802.11n AP is not uniform in the circumferential and height directions because of the multiple antennas for MIMO. As a result, the data transmission speed between the AP and a host could be significantly affected by their relative setup conditions. In this paper, we propose a minimax approach for optimizing the 802.11n AP setup condition in terms of the angles and the height in an indoor environment using throughput measurements. First, we detect a bottleneck host that receives the weakest signal from the AP in the field using the throughput estimation model. To explore optimal values of parameters for this model, we adopt the versatile parameter optimization tool. Then, we optimize the AP setup by changing the angles and the height while measuring throughput. For evaluations, we verify the accuracy of the model using measurement results and confirm the throughput improvements for hosts in the field by our approach.

The main goal of group testing with inhibitors (GTI) is to efficiently identify a small number of defective items and inhibitor items in a large set of items. A test on a subset of items is positive if the subset satisfies some specific properties. Inhibitor items cancel the effects of defective items, which often make the outcome of a test containing defective items negative. Different GTI models can be formulated by considering how specific properties have different cancellation effects. This work introduces generalized GTI (GGTI) in which a new type of items is added, i.e., hybrid items. A hybrid item plays the roles of both defectives items and inhibitor items. Since the number of instances of GGTI is large (more than 7 million), we introduce a framework for classifying all types of items non-adaptively, i.e., all tests are designed in advance. We then explain how GGTI can be used to classify neurons in neuroscience. Finally, we show how to realize our proposed scheme in practice.