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The non‐homogeneous gamma process can exhibit a convex average trend with a non‐linear shape parameter, providing significant flexibility in fitting an accelerated degradation process. However, few studies have been conducted on the maintenance modeling of systems undergoing a non‐homogeneous gamma degradation process despite its versatility. Meanwhile, most maintenance studies adopt a periodic inspection policy to reveal the degradation level. However, it is reasonable to inspect less frequently when the degradation level of the system is low and to perform inspections more frequently when the degradation level increases. Therefore, we conduct a study on the inspection/replacement policy of systems undergoing a non‐homogeneous gamma degradation process, where three non‐periodic inspection policies are investigated. We formulate the problem as a SMDP framework and present a value iteration algorithm to obtain the optimal action at each decision epoch that minimizes the long‐term average cost. A comprehensive numerical study is provided to illustrate the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

The interannual variation of the North Equatorial Current (NEC) across the North Pacific Ocean and the modulation of the Pacific Decadal Oscillation (PDO) are investigated based on both observations and reduced gravity model experiments. Results reveal that the interannual variation of the NEC is highly related to El Niño‐Southern Oscillation (ENSO) with unsynchronized variations both in zonal and meridional structure. The tropical and subtropical gyre branches of the NEC (NEC‐S and NEC‐N) behave antiphase variation during El Niño events with the transport of the former being enhanced and the latter being weakened across the Pacific. The NEC axis in the western/eastern Pacific shifts northward/southward during El Niño events, consistent with the meridional shift of the zero‐line of wind stress curl. Model experiments indicate that Rossby waves mainly modulate the NEC near the western boundary and the NEC‐N, while Ekman pumping modulates NEC‐S in the western basin. Meanwhile, the relationship between the interannual variation of the NEC and ENSO events is significantly modulated by different PDO phases. The PDO cold phase favors a much closer connection between the NEC and the Niño 3.4 index. The dynamics lie in that PDO‐related wind stress curl and sea level anomalies are consistent with that of El Niño, favoring the enhanced correlation between NEC and El Niño. The reverse is true for the warm phase. These results will contribute to a better understanding of the behavior of the tropical Pacific Ocean circulation and its role in climate change. Plain Language Summary: Serving as the boundary of tropical and subtropical gyres, the North Equatorial Current plays an important role in the exchanging of mass, heat, and salt between the mid‐ and low‐latitude oceans. It is a very broad current and behaves quite differently in both zonal and meridional directions across the Pacific due to the phase lag of propagated Rossby waves. This study reveals that the interannual variation of the North Equatorial Current is highly related to El Niño‐Southern Oscillation (ENSO) with unsynchronized variations in zonal and meridional structure. The tropical and subtropical gyre branches of the North Equatorial Current behave antiphase variation during El Niños with the transport of the former is enhanced and the latter weakened across the Pacific. Rossby waves excited west of the dateline modulate the North Equatorial Current near the western boundary, while those excited in the eastern Pacific modulate the subtropical branches. Local Ekman pumping plays a dominant role in tropical branches in the western Pacific. Further analysis indicates that the ENSO‐related air‐sea state in the tropical Pacific Ocean is modulated by Pacific Decadal Oscillation phases. The cold phase favors a much closer connection between the North Equatorial Current and ENSO events. Key Points: Interannual variation of the North Equatorial Current (NEC) is highly related to El Niño‐Southern Oscillation (ENSO) with unsynchronized variations both in zonal and meridional structureRossby waves mainly modulate the NEC near the western boundary and subtropical gyre branch, while Ekman pumping modulates the tropical gyre branchThe relationship between the NEC and ENSO is nonstationary and significantly modulated by different phases of the Pacific Decadal Oscillation [ABSTRACT FROM AUTHOR]

With the rapid development of industry, oily wastewater has caused serious environmental problems. Highly efficient oil–water separation based on superhydrophobic materials holds promise; however, superhydrophobic surfaces often lose their superhydrophobicity when exposed to hot water. Consequently, oil–water separation under high temperature is rarely explored. Herein, a heat resistant superhydrophobic porous glass membrane is fabricated by the vapor phase growth of a network layer of silicone nanofilaments using economical ethyltrichlorosilane as the only surface modifier. The superhydrophobic porous glass membrane possesses hot water resistance and can be used for highly efficient separation of water from oil under high temperatures, regardless of the water/oil ratio, the composition of the oil, and the temperature. Taking advantage of the superior performance in oil–water separation at high temperatures, we successfully adopted such a superhydrophobic membrane for removing water from condensation reactions such as amidation and esterification reactions. It was demonstrated that both the reaction rate and the conversion can be improved significantly. The idea to use superhydrophobic oil–water separation materials for condensation reactions paves the way for industrial synthesis of amides and esters. [ABSTRACT FROM AUTHOR]

In this study, self-organized criticality theory was applied to analyze the unscheduled downtime (USDT) characteristic of a complex manufacturing system. The long-range correlation of the USDT series was verified through the Hurst exponent value. Then, we developed a model to depict the relationship between the frequency and USDT duration with the extreme value theory. The parameters of the model were estimated with the maximum likelihood method and updated with Bayesian inference when new data was available. Finally, the effectiveness of the proposed method was validated by the USDT data of the bottleneck system from a semiconductor assembly and test factory. [ABSTRACT FROM AUTHOR]

Background: Obesity alters metabolic microenvironment and is thus associated with several tumours. The aim of the present study was to investigate the role, molecular mechanism of action, and potential clinical value of lipid metabolism–related long non‐coding RNA (lncRNA) SLC25A21‐AS1 in oesophageal squamous cell carcinoma (ESCC). Methods: A high‐fat diets (HFDs)‐induced obesity nude mouse model was established, and targeted metabolomics analysis was used to identify critical medium‐long chain fatty acids influencing the growth of ESCC cells. Transcriptomic analysis of public dataset GSE53625 confirmed that lncRNA SLC25A21‐AS1 was a lipid metabolism–related lncRNA. The biological function of lncRNA SLC25A21‐AS1 in ESCC was investigated both in vivo and in vitro. Chromatin immunoprecipitation(ChIP)assay, RNA‐pull down, mass spectrometry, co‐IP, and RNA IP(RIP) were performed to explore the molecular mechanism. Finally, an ESCC cDNA microarray was used to determine the clinical prognostic value of SLC25A21‐AS1 by RT‐qPCR. Results: Palmitic acid (PA) is an important fatty acid component of HFD and had an inhibitory effect on ESCC cell lines. LncRNA SLC25A21‐AS1 expression was downregulated by PA and associated with the proliferation and migration of ESCC cells in vitro and in vivo. Mechanistically, SLC25A21‐AS1 interacted with nucleophosmin‐1 (NPM1) protein to promote the downstream gene transcription of the c‐Myc in the nucleus. In the cytoplasm, SLC25A21‐AS1 maintained the stability of SLC25A21 mRNA and reduced the intracellular NAD+/NADH ratio by influencing tryptophan catabolism. Finally, we demonstrated that high expression of SLC25A21‐AS1 promoted resistance to cisplatin‐induced apoptosis and was correlated with poor tumour grade and overall survival. Conclusions: HFD/PA has an inhibitory effect on ESCC cells and SLC25A21‐AS1 expression. SLC25A21‐AS1 promotes the proliferation and migration of ESCC cells by regulating the NPM1/c‐Myc axis and SLC25A21 expression. In addition, lncRNA SLC25A21‐AS1 may serve as a favourable prognostic biomarker and a potential therapeutic target for ESCC. [ABSTRACT FROM AUTHOR]

Zero‐shot learning (ZSL) addresses the novel object recognition problem by leveraging semantic embedding to transfer knowledge from seen categories to unseen categories. Generative ZSL models synthesize the visual features of unseen classes and convert ZSL task into a classical supervised learning problem. These generative ZSL models are trained by using the seen classes. Although promising progress has been achieved in the ZSL and generalized zero‐shot learning (GZSL) tasks. The existing approaches still suffer from a strong bias problem between unseen and seen classes, where unseen objects in the target domain tend to be recognized as seen classes in the source domain. To deal with the problem, we propose a novel named semantic consistent Wasserstein generative adversarial network (scWGAN), which uses a semantic reconstructor to reconstruct semantic embeddings from generated visual features by incorporating a novel Semantic Consistent Loss noted Lrec. The Semantic Consistent Loss guides our proposed scWGAN to generate visual features that mirror the semantic relationships between seen and unseen classes. We also introduce a visual classifier to constrain visual feature generator. Extensive experiments show that the proposed approach is superior to previous state‐of‐the‐art works under both traditional ZSL and challenging GZSL settings on six popular data sets AWA1, AWA2, CUB, APY, and SUN. [ABSTRACT FROM AUTHOR]

The role and mechanism of transmembrane proteins (TMEMs) in tumorigenesis remain unclear. Based on 4 independent cohorts containing 1,208 cases, we identified 3 TMEMs (TMEM273, TMEM164, and TMEM125), which were used to construct a risk model to predict the prognosis of LUAD. The two patterns based on the risk score exhibited a high degree of consistency with the characteristics of immune cell infiltration and epigenetic distribution. Patients with a low-risk score, characterized by an increased activation of immunity, H3K4me3 modification, tumor cell apoptosis, chemokine secretion, and TMB, had better disease-free survival (DFS) and overall survival (OS). Obvious immunosuppression, increased epithelial–mesenchymal transition, a low H3K4me3 level, shortened cell cycle, and accelerated cell division manifested in high-risk patients, with poorer DFS and OS. The model showed a better prognostic value than the tumor immune dysfunction and exclusion score. Correlation analysis told us that patients with high scores were suitable for treatment with CD276 inhibitors for their higher levels of CD276 expression. The risk score had a strong negative correlation with HAVCR2 and ICOS among patients with EGFR-WT, KRAS-WT, STK11-WT, or TP53-MUT, and patients with these mutation types with low scores were suitable for treatment with HAVCR2 or ICOS inhibitors. This work comprehensively analyzed the role and mechanism of TMEMs in LUAD and revealed the characteristics of histone methylation modification. The TMEM-based signature gave us deep insight into immune cell infiltration profiles and provided an individualized immunotherapy strategy. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming is a hallmark of malignancy. Understanding the characteristics of metabolic reprogramming in esophageal squamous cell carcinoma (ESCC) helps uncover novel targets for cancer progression. In this study, 880 metabolism-related genes were identified from microarray data and then filtered to divide patients into two subgroups using consensus clustering, which exhibits significantly different overall survival. After a differential analysis between two subtypes, 3 genes were screened out to construct a two subtypes decision model on the training cohort (GSE53624), defined as high-risk and low-risk subtypes. These risk models were then verified in two public databases (GSE53622 and TCGA-ESCC), an independent cohort of 49 ESCC patients by RT-qPCR and an external cohort of 95 ESCC patients by immunohistochemistry analysis (IHC). Furthermore, the immune cell infiltration of regulatory T cells (Tregs) and plasma cells showed a significant difference between the high and low-risk subtypes in the IHC experiment with 119 ESCC patients. In conclusion, our study indicated that three metabolism-related prognostic genes could stratify patients into subgroups and were associated with immune infiltration, clinical features and clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: Colon cancer (CC) remains one of the most common malignancies with a poor prognosis. Pyroptosis, referred to as cellular inflammatory necrosis, is thought to influence tumor development. However, the potential effects of pyroptosis-related regulators (PRRs) on the CC immune microenvironment remain unknown. Methods: In this study, 27 PRRs reported in the previous study were used to cluster the 1,334 CC samples into three pyroptosis-related molecular patterns. Through subtype pattern differential analysis and structure network mining using Weighted Gene Co-expression Network Analysis (WGCNA), 854 signature genes associated with the PRRs were discovered. Further LASSO-penalized Cox regression of these genes established an eight-gene assessment model for predicting prognosis. Results: The CC patients were subtyped based on three distinct pyroptosis-related molecular patterns. These pyroptosis-related patterns were correlated with different clinical outcomes and immune cell infiltration characteristics in the tumor microenvironment. The pyroptosis-related eight-signature model was established and used to assess the prognosis of CC patients with medium-to-high accuracy by employing the risk scores, which was named "PRM-scores." Greater inflammatory cell infiltration was observed in tumors with low PRM-scores, indicating a potential benefit of immunotherapy in these patients. Conclusions: This study suggests that PRRs have a significant effect on the tumor immune microenvironment and tumor development. Evaluating the pyroptosis-related patterns and related models will promote our understanding of immune cell infiltration characteristics in the tumor microenvironment and provide a theoretical basis for future research targeting pyroptosis in cancer. [ABSTRACT FROM AUTHOR]

Background/Aims: Long non-coding ribonucleic acids (lncRNAs) are involved in the progression of cancers and affect the response to radiation therapy. This study was to investigate the mechanism of lncRNA EGOT in the radiosensitivity of rectal cancer. Methods: The mRNA expression of EGOT, miR-211-5p and ErbB4 in rectal cancer tissues and cells was detected by qRT-PCR. The protein expression of ErbB4 was detected by Western blot. Dual-luciferase reporter assay and ribonucleic acid immunoprecipitation (RIP) were used to confirm the interaction between EGOT and miR-211-5p or miR-211-5p and ErbB4. Transfection technology was used to down-regulate and up-regulate the expression of EGOT and miR-211-5p in rectal cancer cells, respectively. MTT, colony formation and flow cytometry were used to detect the effect of EGOT and miR-211-5p on proliferation, invasion, migration and apoptosis of rectal cancer cells. Results: The expression of EGOT was up-regulated in rectal cancer tissues and cells, and the expression of EGOT was related to the late stage of pathology. EGOT knockdown inhibited the proliferation and colony formation of rectal cancer cells and induced the apoptosis of rectal cancer cells. Moreover, EGOT knockdown was significantly enhanced the effects of radiotherapy on rectal cancer in vivo and in vitro. Furthermore, EGOT was found to serve as a sponge of miR-211-5p, and ErbB4 was a downstream target of miR-211-5p. EGOT enhanced the expression of ErbB4 by regulating miR-211-5p. MiR-211-5p inhibitor restored the effect of EGOT knockdown on the radiosensitivity of rectal cancer. Conclusion: Down-regulation of EGOT could inhibit the growth of rectal cancer cells by regulating the miR-211-5p/ErbB4 axis and improve the radiosensitivity of rectal cancer cells. EGOT may be a new therapeutic target for rectal cancer. [ABSTRACT FROM AUTHOR]

The present work aimed at the problems of less negative samples and more positive samples in rail fastener fault diagnosis and low detection accuracy of heavy manual patrol inspection tasks. Exploiting the capacity of a Convolution Neural Network (CNN) to process unbalanced data to solve tedious and inefficient manual processing, a fault diagnosis method based on a Generative Adversarial Network (GAN) and a Residual Network (ResNet) was developed. First, GAN was used to track the distribution of rail fastener failure data. To study the noise distribution, the mapping relationship between image data was established. Additional real fault samples were then generated to balance and extend the existing data sets, and these data sets were used as input to ResNet for recognition and detection training. Finally, the average accuracy of multiple experiments was used as the evaluation index. The experimental results revealed that the fault diagnosis of rail fastener based on GAN and ResNet could improve the fault detection accuracy in the case of a serious shortage of fault data. [ABSTRACT FROM AUTHOR]

It is known that rare-earth manganites LnMnO3 with Ln = La to Gd are typical Mott insulators favoring the A-type antiferromagnetic (A-AFM) state. Certainly no ferroelectricity can be possible although the alternatively stacked LnO layers are both polar. Nevertheless, under the inspiration that one plus one is more than two, it is appreciated that by combining two components of this manganite series into a superlattice functionality is added. In this work, we construct a (001)-oriented LaMnO3/RMnO3 (R = Pr, Pm, Sm and Gd) superlattice and investigate the possible emergent ferroelectricity by means of first-principles calculations. It is revealed that the lattice matching in these superlattices may generate lattice distortions to each component based on the scenario of hybrid improper ferroelectricity, resulting in spontaneous ferroelectric polarization, which is larger than the traditional type II Ln′MnO3 (Ln′ radius is smaller than that of Gd) polarization. In the meantime, the A-AFM state remains the magnetic ground state of these superlattices. Furthermore, it is predicted that the externally imposed in-plane compressive strain can trigger the semiconductor to half-metal transitions accompanying the A-AFM to ferromagnetic (FM) transitions. The present work sheds light on the possibility to design multiferroic materials and functionality by tailoring artificial superlattices/heterostructures from those non-ferroelectric systems, and to design electronic devices by utilizing the electronic transport properties under epitaxial strain. [ABSTRACT FROM AUTHOR]

Background: Metabolic syndrome (MetS) is associated with the development of esophageal squamous cell carcinoma (ESCC), and long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms of MetS and tumor. This study will explore the prognostic effect of MetS and the associated lncRNA signature on ESCC. Methods: Our previous RNA-chip data (GSE53624, GSE53622) for 179 ESCC patients were reanalyzed according to MetS. The recurrence-free survival (RFS) was collected for these patients. The status of the MetS-related tumor microenvironment was analyzed with the CIBERSORT and ESTIMATE algorithms. A lncRNA signature was established with univariate and multivariate Cox proportional hazards regression (PHR) analysis and verified using the Kaplan–Meier survival curve analysis and time-dependent receiver operating characteristic (ROC) curves. A clinical predictive model was constructed based on multiple risk factors, evaluated using C-indexes and calibration curves, and verified using data from the GEO and TCGA databases. Results: The results showed that MetS was an independent risk factor for ESCC patients conferring low OS and RFS. Tumor microenvironment analysis indicated that patients with MetS have high stromal scores and M2 macrophage infiltration. A six-lncRNA signature was established by 60 ESCC patients randomly selected from GSE53624 and identified with an effective predictive ability in validation cohorts (59 patients from GSE53624 and 60 patients from GSE53622), subgroup analysis, and ESCC patients from TCGA. MetS and the six-lncRNA signature could be regarded as independent risk factors and enhanced predictive ability in the clinical predictive model. Conclusions: Our results indicated that MetS was associated with poor prognosis in ESCC patients, and the possible mechanism was related to changes in the tumor microenvironment. MetS and the six-lncRNA signature could also serve as independent risk factors with available clinical application value. [ABSTRACT FROM AUTHOR]

Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial. [ABSTRACT FROM AUTHOR]

Abstract: Multicompartment micelles (MCMs), whose cores have at least two compartments, show potential applications in various areas, but the synthesis of polymers for preparing MCMs is usually tedious and time‐consuming. In this work, two well‐defined telechelic fluorocarbon‐terminated triblock copolymers, F8‐PEO100‐PPO65‐PEO100‐F8 (F8‐F127‐F8) and F8‐PEO132‐PPO50‐PEO132‐F8 (F8‐F108‐F8), are synthesized via a single‐step coupling reaction of Pluronics F127 or F108 with perfluoro‐1‐octanesulfonyl fluoride and characterized by Fourier‐transform infrared and NMR spectroscopies, as well as gel permeation chromatography and surface tensiometry. Both of these fluorocarbon‐terminated Pluronics can self‐assemble into spherical MCMs with Janus‐core in aqueous solution, as evidenced by transmission electron microscopy imaging. Since the lipophilic block (PPO) and fluorophilic segments (F8) are separated by the hydrophilic blocks (PEO), these fluorocarbon‐terminated Pluronics will loop to give rise to flower‐like MCMs, and the calculations based on thermodynamics and dynamics support the formation of such unique aggregates. A “pre‐self‐assembly” mechanism is proposed to explain the formation process of flower‐like MCMs with Janus‐core prepared by these telechelic fluorocarbon‐terminated triblock copolymers. [ABSTRACT FROM AUTHOR]

Despite the clear benefits of electric vehicles (EVs) in terms of reducing greenhouse gas emissions and traditional energy consumptions, the popularization of EVs remains a challenge in the short run. There are currently two major ways of refueling electric taxicabs (ETs): recharging and battery swapping. ETs usually choose battery swapping so as not to waste precious time during their daily work. While previous studies focused on planning battery swapping stations for private EVs, we investigate ways of supporting the upgrade of an entire urban taxi system, with demands differing both in scale and nature. Furthermore, most ETs are fully charged in the early morning and need to swap battery in approximately the same time period of the day, which results in a bottleneck for battery swapping and hurts the quality of service (QoS) of the taxicab fleet. With this insight, we model the ET fleet as a mobile sensor network, analyze the historical sensing data of taxi routes, and evaluate the battery swapping demand profile and the power consumption of individual taxis, as well as the driving time between positions in the road network. Based on these inputs, we propose a method to calculate an optimized battery swapping station scheme, then describe a real-time algorithm to schedule a subset of the unoccupied taxicabs to swap batteries early by giving them allowances to avoid congestion. Our strategies are then evaluated via a real-world 366-day, 3 976-taxi dataset. The results demonstrate that compared to uniform deployment, our planning scheme reduces the average time cost by 67.2%; and based on our deployment, our scheduling strategy decreases the in-station queuing time by 45.63%, 52.26%, and 29.57%, and the average driving time cost to the stations by 42.3%, 43.69%, and 28.77% compared to actual taxicab refueling, random scheduling, and a static heuristic strategy, respectively. Furthermore, our approach reduces the number of battery swapping that last more than 30 min by 40.8%, which is a significant improvement on the QoS of the whole system. [ABSTRACT FROM AUTHOR]

In this paper, we present GDA, a generalized decision aggregation framework that integrates information from distributed sensor nodes for decision making in a resource efficient manner. Different from traditional approaches, our proposed GDA framework is able to not only estimate the reliability of each sensor, but also take advantage of its confidence information, and thus achieves higher decision accuracy. Targeting generalized problem domains, our framework can naturally handle the scenarios where different sensor nodes observe different sets of events whose numbers of possible classes may also be different. GDA also makes no assumption about the availability level of ground truth label information, while being able to take advantage of any if present. For these reasons, our approach can be applied to a much broader spectrum of sensing scenarios. In this paper, we also propose two extensions of the GDA framework, i.e., incremental GDA (I-GDA) and parallel GDA (P-GDA) to deal with streaming and large-scale data. The advantages of our proposed methods are demonstrated through both theoretic analysis and extensive experiments. [ABSTRACT FROM PUBLISHER]

This paper explores efficient 3G/4G budget utilization in mobile sensing applications. Distinct from previous research work that either relied on limited WiFi access points or assumed the availability of unlimited 3G/4G communication capability, we offer a more practical mobile sensing system that leverages potential 3G/4G budgets that participants contribute at will, and uses it efficiently customized for the needs of multiple mobile sensing applications with heterogeneous sensitivity to environmental changes. We address the challenge that the information of data generation and WiFi encounters is not a priori knowledge, and propose an online decision making algorithm that takes advantage of participants’ historical data. Three typical mobile sensing applications, vehicular application, mobile health and video sharing application are explored. Experimental results demonstrate that our proposed algorithms lead to significantly better system performance compared to alternative solutions for both applications. [ABSTRACT FROM AUTHOR]

Several models have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). The AGR approach significantly accelerates graph-based learning by exploring a set of anchors. However, when a dataset becomes much larger, AGR still faces a big graph which brings dramatically increasing computational costs. To overcome this issue, we propose a novel Hierarchical Anchor Graph Regularization (HAGR) approach by exploring multiple-layer anchors with a pyramid-style structure. In HAGR, the labels of datapoints are inferred from the coarsest anchors layer by layer in a coarse-to-fine manner. The label smoothness regularization is performed on all datapoints, and we demonstrate that the optimization process only involves a small-size reduced Laplacian matrix. We also introduce a fast approach to construct our hierarchical anchor graph based on an approximate nearest neighbor search technique. Experiments on million-scale datasets demonstrate the effectiveness and efficiency of the proposed HAGR approach over existing methods. Results show that the HAGR approach is even able to achieve a good performance within 3 minutes in an 8-million-example classification task. [ABSTRACT FROM AUTHOR]

This paper explores direct phone-to-phone communication (via WiFi interface) among vehicles to support participatory sensing applications. Sensing data usually contains location, speed, and fuel consumption of the car, and has a long time delay between collected and transferred to the server. Direct communication among phones aboard is important in reducing data transfer delay time and sharing participatory sensing information in an inexpensive manner. We design a practical and optimized communication mechanism for direct phone-to-phone data transfer among phones aboard that strategically enables phone-to-phone and/or phone-to-WiFiAP communications by optimally toggling the phones between the normal client and the hotspot modes. We take advantage of the WiFi hotspot functionality on smartphones, and hence require neither involvement of participants nor changes to existing wireless infrastructure and protocols. An analytical model is established to optimize toggling between client and hotspot modes for optimal system efficiency. We fully implement this system on off-the-shelf Google Galaxy Nexus and Nexus S phones. Through a 35-vehicle two-month deployment study, as well as simulation experiments using the real-world T-drive 9,211-taxicab dataset, we show that our solution significantly reduces data transfer delay time and maintains over 80 percent system efficiency under varying system parameters. [ABSTRACT FROM PUBLISHER]

We introduce a three-parameter logistic model to analyze the dose limiting toxicity (DLT) as a time-to-event endpoint in oncology Phase I trials. In the proposed model, patients are allowed to stay on trial without the constraint of a maximum follow-up time. Our model accommodates late-onset DLT as well as early-onset DLT, by both of which the dose recommendation is informed. A Bayesian approach is used to incorporate prior knowledge of the test treatment into dose recommendation. Simulation examples show that our proposed model has good operating characteristics in assessing the maximum tolerated dose (MTD). [ABSTRACT FROM PUBLISHER]

Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context of GreenGPS; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in [1], however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5 over the fastest, 11.2 percent over the shortest, and 8.4 percent over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings. [ABSTRACT FROM PUBLISHER]