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After decades of development, flow-based microfluidic biochips have become an increasingly attractive platform for biochemical experiments. The fluid transportation and the on-chip device operation are controlled by microvalves, which are driven by external pneumatic controllers. To meet the increasingly complex experimental demands, the number of microvalves has significantly increased, making it necessary to adopt multiplexers (MUXes) for the actuation of microvalves. However, existing MUX designs have limited coding capacities, resulting in area overhead and excessive chip-to-world interface. This paper proposes a novel gate structure for modifying the current MUX architecture, along with a mixed coding strategy that achieves the maximum coding capacity within the modified MUX architecture. Additionally, an efficient synthesis tool for the mixed-coding-based MUXes (LaMUXes) is presented. Experimental results demonstrate that the LaMUX is exceptionally efficient, substantially reducing the usage of pneumatic controllers and microvalves compared to existing MUX designs. 

Abstract Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particles (WIMPs). To mitigate this background, the XENONnT experiment is equipped with a novel gadolinium-doped water Cherenkov detector, which encloses the xenon dual-phase time projection chamber (TPC). The neutron veto (NV) can tag neutrons via their capture on gadolinium or hydrogen, which release$$\gamma $$ $\gamma$-rays that are subsequently detected as Cherenkov light. In this work, we present the first results of the XENONnT NV when operated with demineralized water only, before the insertion of gadolinium. Its efficiency for detecting neutrons is$$({82\pm 1}){\%}$$ $\left(82\pm 1\right)\%$, the highest neutron detection efficiency achieved in a water Cherenkov detector. This enables a high efficiency of$$({53\pm 3}){\%}$$ $\left(53\pm 3\right)\%$for the tagging of WIMP-like neutron signals, inside a tagging time window of$${250}~{\upmu }\hbox {s}$$ $250\mu \text{s}$between TPC and NV, leading to a livetime loss of$${1.6}{\%}$$ $1.6\%$during the first science run of XENONnT. 

Integral digitalization aims to liaise with Universal interface for human-computer interaction, assemble Brewing aggregation via online analytical processing, and engage Centered user experience (UBC), which enables wiseCIO to orchestrate “Anything-as-a-Service” (XaaS). This paper presents three important concepts such as iDATA, iDEA and ACTiVE that together orchestrate XaaS on wiseCIO. iDATA stands for “integral digitalization via archival transformation and analytics” in support of content management, iDEA denotes “intelligence-driven efficient automation” for UBC processing with little coding required via machine learning automata, and ACTiVE represents “accessible, contextual and traceable information for vast engagement” with content delivery. Where iDATA is central to XaaS through computational thinking applied to multidimensional online analytical processing (mOLAP). Case studies are through discussed on the massive basis through iDATA over broad fields, such as manageable ARM (archival repository for manageable accessibility), animated BUS (biological understanding from STEM), sensible DASH (deliveries assembled for fast search & hits), smart DIGIA (digital intelligence governing instruction and administering), informative HARP (historical archives & religious preachings), vivid MATH (mathematical apps in teaching and hands-on exercise), and engaging SHARE (studies via hands-on assignment, review/revision and evaluation). As a result, iDATA-orchestrated wiseCIO is in favor of archival content management (ACM) and massive content delivery (MCD). Most recently, the comprehensive online teaching and learning (COTL) has been prepared and published as ACTiVE courseware with various multimedia and the student online profiles for paperless homework, labs and submissions. The ACTiVE courseware is integrated with a capacity equivalent to 10,000 + traditional web pages and broadly used for advanced remote learning (ARL) in both synchronous model and asynchronous model with great ease.