A Dichotomy Result for Cyclic-Order Traversing Games

Traversing game is a two-person game played on a connected undirected simple graph with a source node and a destination node. A pebble is placed on the source node initially and then moves autonomously according to some rules. Alice is the player who wants to set up rules for each node to determine where to forward the pebble while the pebble reaches the node, so that the pebble can reach the destination node. Bob is the second player who tries to deter Alice\u27s effort by removing edges. Given access to Alice\u27s rules, Bob can remove as many edges as he likes, while retaining the source and destination nodes connected. Under the guide of Alice\u27s rules, if the pebble arrives at the destination node, then we say Alice wins the traversing game; otherwise the pebble enters an endless loop without passing through the destination node, then Bob wins. We assume that Alice and Bob both play optimally. We study the problem: When will Alice have a winning strategy? This actually models a routing recovery problem in Software Defined Networking in which some links may be broken. In this paper, we prove a dichotomy result for certain traversing games, called cyclic-order traversing games. We also give a linear-time algorithm to find the corresponding winning strategy, if one exists

Materials challenges of two-dimensional materials for flexible sensing applications

Sensors are playing an increasingly important role in our lives because they enable the detection of environmental changes and, therefore, initiate a response accordingly. Sensors convert detected physical or chemical changes, for example, motion, radiation, heat, acidity, chemicals, etc., to useful and readable signals. Field-effect transistors (FETs), a class of semiconductor device in which the electrical current is controlled through an applied gate voltage, are promising for many sensing applications. Even though FETs-based sensors have been well-developed, flexible version of such sensors remains a big challenge and requires new materials and new sensing designs. Two-dimensional (2D) materials such as graphene and transition metal dichalcogenides (TMDs) are promising candidates for FET-based sensors due to their flexibility, transparency and potential for high electrical performance. Because of the atomically thin nature of 2D materials, their electrical properties are extremely sensitive to their atomic-scale structure as well as to their surfaces and interfaces with other materials. Specifically, defects, dopants, attached molecules or change in the band structure due to strain can shift the Fermi level resulting in a measured change in current. The goal of this work is to meet the challenges faced by the 2D TMD-based electronics in sensor applications by developing a fundamental understanding of the impact of materials processing, structure, interfaces and surfaces on resultant electronic properties. Furthermore, a simplified strategy for chemical and biological electrical sensors is developed to bridge the current sensing technology to the use of next generation flexible 2D transducers. To improve 2D TMD-based electronics, this work demonstrates a solution-processed molecular doping technique to control the electronic band structure and the electrical performances of TMD-based devices. Charge transfer doping due to the electron transfer between TMD semiconductors and the redox-active molecular dopants is demonstrated to be a promising tool to control the carrier concentration as well as the Fermi level of MoS2 and WSe2. Understanding the impact of external strain on the flexible devices is crucial toward their practical application. This work investigates the electronic properties and stability of flexible TMD FETs under mechanical strain. The interesting mechano-electric properties of TMDs provide a new opportunity for transparent and flexible mechanical strain sensors. Furthermore, the fundamental physics and the controllability of this strain sensitivity are studied. FET-based potentiometric sensors provide a promising technique for the detection of chemical and biological species without the use of secondary bio-labels. This work first focuses on the comparison between two commonly used potentiometric sensing platforms – ion-sensitive field-effect transistor (ISFET) based on nano-materials, and a similar, but simplified, extended-gate FET (EGFET) in which the sensor surface is separated from the transducer. It is then demonstrated that the sensor sensitivity depends on the sensing surface instead of sensor platform. As a result, the following demonstration of biochemical sensing is based on EGFETs. In addition, EGFETs provide a more reliable operation and ready compatibility with any commercialized transistors currently as well as 2D TMD-based transistors in the future. Finally, EGFET is proved a promising candidate for practical sensing application by a direct comparison between EGFET potentiometric biosensors with impedimetric biosensors. The work presented in this thesis demonstrates initial first steps toward the sensing applications using 2D TMD semiconductors. Despite the current challenges faced by 2D TMD-based FETs in biochemical sensing applications, the proposed EGFET configuration provides a readily available biosensing technique for current technologies and the future compatibility to 2D TMD-based transducers.Ph.D

Plasticity of cerebellar Purkinje cells in behavioral training of body balance control

Neural responses to sensory inputs caused by self-generated movements (reafference) and external passive stimulation (exafference) differ in various brain regions. The ability to differentiate such sensory information can lead to movement execution with better accuracy. However, how sensory responses are adjusted in regard to this distinguishability during motor learning is still poorly understood. The cerebellum has been hypothesized to analyze the functional significance of sensory information during motor learning, and is thought to be a key region of reafference computation in the vestibular system. In this study, we investigated Purkinje cell (PC) spike trains as cerebellar cortical output when rats learned to balance on a suspended dowel. Rats progressively reduced the amplitude of body swing and made fewer foot slips during a 5-min balancing task. Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference. Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning. In response to unexpected perturbations and under anesthesia, SS coding capability of these PCs recovered. By plotting SS and CS firing frequencies over 15-s time windows in double-logarithmic plots, a negative correlation between SS and CS was found in awake, but not anesthetized, rats. PCs with prominent SS coding attenuation during motor learning showed weaker SS-CS correlation. Hence, we demonstrate that neural plasticity for filtering out sensory reafference from active motion occurs in the cerebellar cortex in rats during balance learning. SS-CS interaction may contribute to this rapid plasticity as a form of receptive field plasticity in the cerebellar cortex between two receptive maps of sensory inputs from the external world and of efference copies from the will center for volitional movements

Bis{1-[(E)-(2-methyl­phen­yl)diazen­yl]-2-naphtho­lato}palladium(II)

In the title compound, [Pd(C17H13N2O)2], the PdII atom is tetra­coordinated by two N atoms and two O atoms from two bidentate methylphenyl­diazenylnaphtolate ligands, forming a square-planar complex. The two N atoms and two O atoms around the PdII atom are trans to each other (as the PdII atom lies on a crystallographic inversion centre) with O—Pd—N bond angles of 89.60 (11) and 90.40 (11)°. The distances between the PdII atom and the coordinated O and N atoms are 1.966 (3) and 2.009 (3) Å, respectively

Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

[[abstract]]In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.[[incitationindex]]SCI[[booktype]]紙

Signatures of afterglows from light dark matter boosted by supernova neutrinos in current and future large underground detectors

Supernova neutrino boosted dark matter (SN$\nu$ BDM) and its afterglow effect have been shown to be a promising signature for beyond Standard Model (bSM) physics. The time-evolution feature of SN$\nu$ BDM allows for possibly direct inference of DM mass $m_\chi$, and results in significant background suppression with improving sensitivity. This paper extends the earlier study and provides a general framework for computing the SN$\nu$ BDM fluxes for a supernova that occurs at any location in our galaxy. A bSM $U(1)_{L_\mu-L_\tau}$ model with its gauge boson coupling to both DM and the second and third generation of leptons is considered, which allows for both DM-$\nu$ and DM-$e$ interactions. Detailed analysis of the temporal profile, angular distribution, and energy spectrum of the SN$\nu$ BDM are performed. Unique signatures in SN$\nu$ BDM allowing extraction of $m_\chi$ and detail features that contain information of the underlying interaction type are discussed. Expected sensitivities on the above new physics model from Super-Kamiokande, Hyper-Kamiokande, and DUNE detections of BDM events induced by the next galactic SN are derived and compared with the existing bounds.Comment: 17 pages, 15 figures, 1 table, 5 appendice

Involvement of the Cav3.2 T-type calcium channel in thalamic neuron discharge patterns

<p>Abstract</p> <p>Background</p> <p>Mice that have defects in their low-threshold T-type calcium channel (T-channel) genes show altered pain behaviors. The changes in the ratio of nociceptive neurons and the burst firing property of reticular thalamic (RT) and ventroposterior (VP) neurons in Cav3.2 knockout (KO) mice were studied to test the involvement of thalamic T-channel and burst firing activity in pain function.</p> <p>Results</p> <p>Under pentobarbital or urethane anesthesia, the patterns of tonic and burst firings were recorded in functionally characterized RT and VPL neurons of Cav3.2 KO mice. Many RT neurons were nociceptive (64% under pentobarbital anesthesia and 50% under urethane anesthesia). Compared to their wild-type (WT) controls, fewer nociceptive RT neurons were found in Cav3.2 KO mice. Both nociceptive and tactile RT neurons showed fewer bursts in Cav3.2 KO mice. Within a burst, RT neurons of Cav3.2 KO mice had a lower spike frequency and less-prominent accelerando-decelerando change. In contrast, VP neurons of Cav3.2 KO mice showed a higher ratio of bursts and a higher discharge rate within a burst than those of the WT control. In addition, the long-lasting tonic firing episodes in RT neurons of the Cav3.2 KO had less stereotypic regularity than their counterparts in WT mice.</p> <p>Conclusions</p> <p>RT might be important in nociception of the mouse. In addition, we showed an important role of Cav3.2 subtype of T-channel in RT burst firing pattern. The decreased occurrence and slowing of the bursts in RT neurons might cause the increased VP bursts. These changes would be factors contributing to alternation of pain behavior in the Cav3.2 KO mice.</p

The Source Detection of 28 September 2018 Sulawesi Tsunami by Using Ionospheric GNSS Total Electron Content Disturbance

The 28 September 2018 magnitude Mw7.8 Palu, Indonesia earthquake (0.178° S, 119.840° E, depth 13 km) occurred at 10:02 UTC. The major earthquake triggered catastrophic liquefaction, landslides, and a near-field tsunami. The ionospheric total electron content (TEC) derived from records of 5 ground-based global navigation satellite system (GNSS) receivers is employed to detect tsunami traveling ionospheric disturbances (TTIDs). In total, 15 TTIDs have been detected. The ray-tracing and beamforming techniques are then used to find the TTID source location. The bootstrap method is applied in order to further explore the possible location of the tsunami source based on results of the two techniques, which show the beamforming technique has a slightly better performance on finding possible locations of the tsunami source. Meanwhile, the circle method is employed to examine tsunami signatures of the sea-surface height and video records, and find possible tsunami origin locations. The coincidence of the TTID source location and the tsunami location shows that the ionospheric TEC recorded by local ground-based GNSS receivers can be used to confirm the tsunami occurrence, find the tsunami location, and support the tsunami early warning

Direct correlation between potentiometric and impedance biosensing of antibody-antigen interactions using an integrated system

A fully integrated system that combines extended gate field-effect transistor (EGFET)-based potentiometric biosensors and electrochemical impedance spectroscopy (EIS)-based biosensors has been demonstrated. This integrated configuration enables the sequential measurement of the same immunological binding event on the same sensing surface and consequently sheds light on the fundamental origins of sensing signals produced by FET and EIS biosensors, as well as the correlation between the two. Detection of both the bovine serum albumin (BSA)/anti-BSA model system in buffer solution and bovine parainfluenza antibodies in complex blood plasma samples was demonstrated using the integrated biosensors. Comparison of the EGFET and EIS sensor responses reveals similar dynamic ranges, while equivalent circuit modeling of the EIS response shows that the commonly reported total impedance change (DZtotal) is dominated by the change in charge transfer resistance (Rct) rather than surface capacitance (Csurface). Using electrochemical kinetics and the Butler-Volmer equation, we unveil that the surface potential and charge transfer resistance, measured by potentiometric and impedance biosensors, respectively, are, in fact, intrinsically linked. This observation suggests that there is no significant gain in using the FET/EIS integrated system and leads to the demonstration that low-cost EGFET biosensors are sufficient as a detection tool to resolve the charge information of biomolecules for practical sensing applications