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Classification of cell types and isolation of targeted cells according to their imaging and spatial characteristics, beyond traditional fluorescently labeled biomarkers, enable the development of new biological insight and establishment of connections between phenotypical and morphological, and genomic cell information in normal and diseased states. Here, we demonstrate a microfluidic flow cytometer cell sorter that uses fast scanning laser excitation sources and photomultiplier tubes (PMTs), coupled with real-time image processing, to image and sort cells based on user-defined spatial features. Dozens of features, including label-free characteristics such as size, shape, scattering pattern, morphology, as well as the spatial distribution of fluorescently labeled biomarkers, greatly expand the available information space in cell analysis. The use of a disposable microfluidic cartridge that is compatible with the imaging system contributes to size reduction and lowered risk of cross contamination. The so-called cameraless design that uses PMTs to replace CCD makes the system more compatible with conventional fluorescence-activated cell sorters and user friendly, while enjoying the sensitivity and benefits of PMTs. The cell image features can be directly extracted from the temporal waveforms or from the reconstructed cell images via temporal–spatial transformation.

A low-power, wide-dynamic-range integrated humidity sensing chip is implemented using a printable polymer sensing material with an on-chip pulse-width-modulation interface circuit. By using the inkjet printing technique, poly(3,4-ethylene-dioxythiophene)/polystyrene sulfonate that has humidity sensing features can be printed onto the top metal layer of a 0.35 μm CMOS IC. The developed printing-on-chip humidity sensor achieves a heterogeneous three dimensional sensor system-on-chip architecture. The humidity sensing of the implemented printing-on-chip sensor system is experimentally tested. The sensor shows a sensitivity of 0.98% to humidity in the atmosphere. The maximum dynamic range of the readout circuit is 9.8 MΩ, which can be further tuned by the frequency of input signal to fit the requirement of the resistance of printed sensor. The power consumption keeps only 154 μW. This printing-on-chip sensor provides a practical solution to fulfill an ultra-small integrated sensor for the applications in miniaturized sensing systems.

Schedule delays commonly occur in construction projects and often result in delay claims. Several studies have proposed different schedule delay analysis methodologies. By using as-planned and as-built schedules, most previous studies have concentrated on calculating the impact of identified delay events on project duration but have neglected to connect impact calculation from detailed delay evidence in their analyses. Lost productivity, which is a primary cause of schedule delays, entails the completion of work at less than the planned rate of production; it is usually experienced by contractors. To solve analysis problem of schedule delays associated with lost productivity, this study proposes a delay analysis method (the LOPE method) that integrates a lost productivity calculation approach and a well-known schedule delay analysis method, the collapsed as-built method. As demonstrated through a simplified case study, the proposed method is beneficial for calculating the impacts of schedule delays caused by lost productivity.

An all-inkjet-printed organic thin film transistor (OTFT) with a double-layer dielectric structure is proposed and implemented in this study. By using the double-layer structure with different dielectric materials (i.e., polyvinylphenol with poly(vinylidene fluoride-co-hexafluoropropylene)), the threshold-voltage of OTFT can be adjusted. The threshold-voltage shift can be controlled by changing the composition of dielectric layers. That is, an enhancement-mode OTFT can be converted to a depletion-mode OTFT by selectively printing additional dielectric layers to form a high-k/low-k double-layer structure. The printed OTFT has a carrier mobility of 5.0 × 10 − 3 cm 2 /V-s. The threshold-voltages of the OTFTs ranged between − 13 V and 10 V. This study demonstrates an additional design parameter for organic electronics manufactured using inkjet printing technology.

With the fast increase in Web activities, Web data mining has recently become an important research topic and is receiving a significant amount of interest from both academic and industrial environments. While existing methods are efficient for the mining of frequent path traversal patterns from the access information contained in a log file, these approaches are likely to over evaluate associations. Explicitly, most previous studies of mining path traversal patterns are based on the model of a uniform support threshold, where a single support threshold is used to determine frequent traversal patterns without taking into consideration such important factors as the length of a pattern, the positions of Web pages, and the importance of a particular pattern, etc. As a result, a low support threshold will lead to lots of uninteresting patterns derived whereas a high support threshold may cause some interesting patterns with lower supports to be ignored. In view of this, this paper broadens the horizon of frequent path traversal pattern mining by introducing a flexible model of mining Web traversal patterns with dynamic thresholds. Specifically, we study and apply the Markov chain model to provide the determination of support threshold of Web documents; and further, by properly employing some effective techniques devised for joining reference sequences, the proposed algorithm dynamic threshold miner (DTM) not only possesses the capability of mining with dynamic thresholds, but also significantly improves the execution efficiency as well as contributes to the incremental mining of Web traversal patterns. Performance of algorithm DTM and the extension of existing methods is comparatively analyzed with synthetic and real Web logs. It is shown that the option of algorithm DTM is very advantageous in reducing the number of unnecessary rules produced and leads to prominent performance improvement.

A Solar-Powered Sensor Module (SPSM) that uses low-cost capacitors as energy buffers to power wireless sensor motes was investigated. Compared to batteries or super capacitors, SPSM is particularly suitable for light-weight tasks since it buffers limited energy and powers up periodically. With our method, the energy charging time can be shortened to about 100 ms for a maximum power point tracking component, and the current of the output regulator can reach 71% efficiency.

In this paper, a low cost and low power consumption humidity sensor was developed by blending SiO 2 nano-particles blending with PEDOT conductive polymers. The developed sensing material was fabricated by inkjet-printing technology on silicon substrate. Compared with pristine PEDOT:PSS materials, the adding of SiO 2 nano-particles improves the sensitivity of humidity with a factor of 2. The time response of the developed sensing material was also examined to evaluate its sensing stabilities. At the same time, the sensor selectivity to humidity, CO2, and NO was also experimentally tested. This work developed a promising inkjet-printable humidity sensing materials for various applications.

Schedule delays commonly appear in construction projects and result in delay claim progressively. Several studies have proposed various schedule delay analysis methodologies; however, most of the studies focus on the analysis of surface data (as-planned and as-built schedules), few of them on evaluating the effects of root causes, such as the problem of lost productivity. Lost productivity, one essential delay cause, is usually experienced by a contractor while accomplishing its works less than planned rate of production. For analyzing the situations of schedule impacts caused by lost productivity, this study discusses a case of delay analysis with lost productivity in Taiwan. Through the study case, this study has proposed an approach to calculate schedule impact from lost productivity. The study results will be a basis for developing a comprehensive delay analysis method considering lost productivity.

Schedule delays commonly appear in construction projects and result in delay claim progressively. Several studies have proposed various schedule delay analysis methodologies, however, most of the studies focused on the analysis of surface data (as-planned and as-built schedules), few of them on evaluating the effects of root causes, such as lost productivity. Loss of productivity is usually experienced by a contractor while accomplishing its works less than planned rate of production. Based on the literature review on lost productivity, this study further evaluated delay analysis methodologies, professional project management software and commercial delay analysis software to identify the capabilities of those methodologies and systems in dealing with the problem of lost productivity. Research results show that evaluated delay analysis methodologies, professional project management software and commercial delay analysis software cannot deal with the problem of lost productivity appropriately. That is, developing a comprehensive delay analysis method considering lost productivity is required.

In this paper, we broaden the horizon of traditional rule mining by introducing a new framework of causality rule mining in a distributed chain store database. Specifically, the causality rule explored in this paper consists of a sequence of triggering events and a set of consequential events, and is designed with the capability of mining non-sequential, inter-transaction information. Hence, the causality rule mining provides a very general framework for rule derivation. Note, however, that the procedure of causality rule mining is very costly particularly in the presence of a huge number of candidate sets and a distributed database, and in our opinion, cannot be dealt with by direct extensions from existing rule mining methods. Consequently, we devise in this paper a series of level matching algorithms, including Level Matching (abbreviatedly as LM), Level Matching with Selective Scan (abbreviatedly as LMS), and Distributed Level Matching (abbreviatedly as Distibuted LM), to minimize the computing cost needed for the distributed data mining of causality rules. In addition, the phenomena of time window constraints are also taken into consideration for the development of our algorithms. As a result of properly employing the technologies of level matching and selective scan, the proposed algorithms present good efficiency and scalability in the mining of local and global causality rules. Scale-up experiments show that the proposed algorithms scale well with the number of sites and the number of customer transactions.Index Terms: knowledge discovery, distributed data mining causality rules, triggering events, consequential events