University of Arkansas

About: University of Arkansas is a education organization based out in Fayetteville, Arkansas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 17225 authors who have published 33329 publications receiving 941102 citations. The organization is also known as: Arkansas & UA.

Parity–time symmetry and variable optical isolation in active–passive-coupled microresonators

Abstract: On-chip parity–time-symmetric optics is experimentally demonstrated at a wavelength of 1,550 nm in two directly coupled, high-Q silica microtoroid resonators with balanced effective gain and loss. Switchable optical isolation with a nonreciprocal isolation ratio between −8 dB and +8 dB is also shown. The findings will be useful for potential applications in optical isolators, on-chip light control and optical communications. Compound-photonic structures with gain and loss1 provide a powerful platform for testing various theoretical proposals on non-Hermitian parity–time-symmetric quantum mechanics2,3,4,5 and initiate new possibilities for shaping optical beams and pulses beyond conservative structures. Such structures can be designed as optical analogues of complex parity–time-symmetric potentials with real spectra. However, the beam dynamics can exhibit unique features distinct from conservative systems due to non-trivial wave interference and phase-transition effects. Here, we experimentally realize parity–time-symmetric optics on a chip at the 1,550 nm wavelength in two directly coupled high-Q silica-microtoroid resonators with balanced effective gain and loss. With this composite system, we further implement switchable optical isolation with a non-reciprocal isolation ratio from −8 dB to +8 dB, by breaking time-reversal symmetry with gain-saturated nonlinearity in a large parameter-tunable space. Of importance, our scheme opens a door towards synthesizing novel microscale photonic structures for potential applications in optical isolators, on-chip light control and optical communications.

Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management

Abstract: We illuminate the myriad of opportunities for research where supply chain management intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of a SCM data scientist. Such knowledge is crucial to developing future supply chain leaders. We propose definitions of data science and predictive analytics as applied to supply chain management. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of supply chain management and DPB.

Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management

Abstract: We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.

Bulk superconductivity at 120 K in the Tl-Ca/Ba-Cu-O system

Abstract: The discovery of 30-K superconductivity in the La–Ba–Cu–O system1 and 90-K superconductivity in the Y–Ba–Cu–O system2 stimulated a worldwide search for even higher-temperature superconductors. Unfortunately, most of the higher-temperature transitions reported in the past year have proved to be unstable, irreproducible, or not due to bulk superconductivity3–7. Recently, we and co-workers8,9 reported superconductivity above 90 K in a new Tl–Ba–Cu–O system, and pointed out that elemental substitutions in this system may lead to even higher-temperature superconductivity. Here we report stable and reproducible bulk superconductivity with an onset at 120 K and zero resistance above 100 K in the Tl–Ca/Ba–Cu–O system. This transition temperature is much higher than those observed for typical rare-earth-containing superconductors, and the onset temperatures are comparable to that in the Bi–Ca/Sr–Cu–O system, as reported in refs 10 and 11 (received after submission of this paper).