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Sifan Ji,1,2,* Hang Qi,1,2,* Li Yan,1,2,* Duo Zhang,1,2 Yang Wang,1,2 HaLiSai MuDanLiFu,1,2 Chuqing He,1,2 Wei Xia,1,2 Qian Zhu,1,2 Yan Liang,1,2 Jian Zhang1,2 1Department of Obstetrics and Gynecology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 2Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jian Zhang, Department of Obstetrics and Gynecology, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People’s Republic of China, Tel +86 18017316017, Email zhangjian_ipmch@sjtu.edu.cnIntroduction: Endometriosis (EM) is an estrogen-dependent benign gynecologic disease affecting approximately 10% of reproductive-age women with a high recurrence rate, but lacks reliable biomarkers. No previous studies have investigated the possible use of extracellular vesicle (EV)-associated micro RNAs (miRNAs) from menstrual blood (MB) as candidate diagnostic or prognostic markers of EM.Methods: Specimens were obtained from endometriosis and non-endometriosis patients at the International Peace Maternity and Child Health Hospital in Shanghai. Microarray was used to screen differentially expressed miRNAs among peritoneal fluid (PF), fallopian tube fluid (FF), and MB. Dual-luciferase reporter gene assay was carried out to verify the relationship between miR-4443 and ACSS2. Cell proliferation and Transwell invasion assays were performed in vitro after intervention on miR-4443 and ACSS2 in hEM15A human endometrial stromal cells and primary human endometrial stromal cells (hESCs). Spearman correlation analysis, receiver operating characteristic (ROC) curve analysis, and survival analysis were applied to clinical data, including severity of symptoms and relapse of EM among EM patie

Long-horizon task and motion planning (TAMP) is notoriously difficult to solve, let alone optimally, due to the tight coupling between the interleaved (discrete) task and (continuous) motion planning phases, where each phase on its own is frequently an NP-hard or even PSPACE-hard computational challenge. In this study, we tackle the even more challenging goal of jointly optimizing task and motion plans for a real dual-arm system in which the two arms operate in close vicinity to solve highly constrained tabletop multi-object rearrangement problems. Toward that, we construct a tightly integrated planning and control optimization pipeline, Makespan-Optimized Dual-Arm Planner (MODAP) that combines novel sampling techniques for task planning with state-of-the-art trajectory optimization techniques. Compared to previous state-of-the-art, MODAP produces task and motion plans that better coordinate a dual-arm system, delivering significantly improved execution time improvements while simultaneously ensuring that the resulting time-parameterized trajectory conforms to specified acceleration and jerk limits., Comment: First three authors made equal contributions to this study

Embedded 3D printing is widely adapted for fabricating architected soft and non-self-supporting hydrogels for applications ranging from tissue engineering to soft robotics. Although the matching between hydrogels and supportive baths sets the foundation in embedded 3D printing, the rule-of-thumb for supportive bath selection and creation is not yet established. Herein, the "shapeability" of distinct classes of hydrogel inks (i.e., pH-responsive, photo-crosslinkable, thermal-sensitive, chemically crosslinkable monomeric, and cationic and anionic inks) in diverse representative support baths (i.e., gelatin slurry, agarose fluid gel, Carbopol and oil-based baths) is evaluated. The results show that the dominate mechanisms for interfacial instabilities, including diffusion-driven or charge-driven, can be predicted by evaluating the composition pairing of the pre-crosslinked hydrogel ink and supportive bath. Based on this, a general and simplistic guideline for supportive bath selection to attain hydrogel shapeability is proposed. The approach can widen the spectrum of hydrogel materials that can be structured on-demand for a plethora of functionalities.

We present a semi-infinite program (SIP) solver for trajectory optimizations of general articulated robots. These problems are more challenging than standard Nonlinear Program (NLP) by involving an infinite number of non-convex, collision constraints. Prior SIP solvers based on constraint sampling cannot guarantee the satisfaction of all constraints. Instead, our method uses a conservative bound on articulated body motions to ensure the solution feasibility throughout the optimization procedure. We further use subdivision to adaptively reduce the error in conservative motion estimation. Combined, we prove that our SIP solver guarantees feasibility while approaches the critical point of SIP problems up to arbitrary user-provided precision. We have verified our method on a row of trajectory optimization problems involving industrial robot arms and UAVs, where our method can generate collision-free, locally optimal trajectories within a couple minutes.

Accurate reorientation and segmentation of the left ventricular (LV) is essential for the quantitative analysis of myocardial perfusion imaging (MPI), in which one critical step is to reorient the reconstructed transaxial nuclear cardiac images into standard short-axis slices for subsequent image processing. Small-scale LV myocardium (LV-MY) region detection and the diverse cardiac structures of individual patients pose challenges to LV segmentation operation. To mitigate these issues, we propose an end-to-end model, named as multi-scale spatial transformer UNet (MS-ST-UNet), that involves the multi-scale spatial transformer network (MSSTN) and multi-scale UNet (MSUNet) modules to perform simultaneous reorientation and segmentation of LV region from nuclear cardiac images. The proposed method is trained and tested using two different nuclear cardiac image modalities: 13N-ammonia PET and 99mTc-sestamibi SPECT. We use a multi-scale strategy to generate and extract image features with different scales. Our experimental results demonstrate that the proposed method significantly improves the reorientation and segmentation performance. This joint learning framework promotes mutual enhancement between reorientation and segmentation tasks, leading to cutting edge performance and an efficient image processing workflow. The proposed end-to-end deep network has the potential to reduce the burden of manual delineation for cardiac images, thereby providing multimodal quantitative analysis assistance for physicists., Comment: 17 pages, 7 figures

High temporal resolution data plays a vital role in effective short-term hydropower plant operations. In the majority of the Norwegian hydropower system, inflow data is predominantly collected at daily resolutions through measurement installations. However, for enhanced precision in managerial decision-making within hydropower plants, hydrological data with intraday resolutions, such as hourly data, are often indispensable. To address this gap, time series disaggregation utilizing deep learning emerges as a promising tool. In this study, we propose a deep learning-based time series disaggregation model to derive hourly inflow data from daily inflow data for short-term hydropower plant operations. Our preliminary results demonstrate the applicability of our method, with scope for further improvements.

The current framework for climate change negotiation models presents several limitations that warrant further research and development. In this track, we discuss mainly two key areas for improvement, focusing on the geographical impacts and utility framework. In the aspects of geographical impacts, We explore five critical aspects: (1) the shift from local to global impact, (2) variability in climate change effects across regions, (3) heterogeneity in geographical location and political structures, and (4) collaborations between adjacent nations, (5) the importance of including historical and cultural factors influencing climate negotiations. Furthermore, we emphasize the need to refine the utility and rewards framework to reduce the homogeneity and the level of overestimating the climate mitigation by integrating the positive effects of saving rates into the reward function and heterogeneity among all regions. By addressing these limitations, we hope to enhance the accuracy and effectiveness of climate change negotiation models, enabling policymakers and stakeholders to devise targeted and appropriate strategies to tackle climate change at both regional and global levels., Comment: Presented at AI For Global Climate Cooperation Competition, 2023 (arXiv:cs/2307.06951)

In this paper, we propose a dynamic grouping negotiation model for climate mitigation based on real-world business and political negotiation protocols. Within the AI4GCC competition framework, we develop a three-stage process: group formation and updates, intra-group negotiation, and inter-group negotiation. Our model promotes efficient and effective cooperation between various stakeholders to achieve global climate change objectives. By implementing a group-forming method and group updating strategy, we address the complexities and imbalances in multi-region climate negotiations. Intra-group negotiations ensure that all members contribute to mitigation efforts, while inter-group negotiations use the proposal-evaluation framework to set mitigation and savings rates. We demonstrate our negotiation model within the RICE-N framework, illustrating a promising approach for facilitating international cooperation on climate change mitigation., Comment: Presented at AI For Global Climate Cooperation Competition, 2023 (arXiv:cs/2307.06951)

Persistent memory (PM) technologies have inspired a wide range of PM-based system optimizations. However, building correct PM-based systems is difficult due to the unique characteristics of PM hardware. To better understand the challenges as well as the opportunities to address them, this paper presents a comprehensive study of PM-related issues in the Linux kernel. By analyzing 1,553 PM-related kernel patches in-depth and conducting experiments on reproducibility and tool extension, we derive multiple insights in terms of PM patch categories, PM bug patterns, consequences, fix strategies, triggering conditions, and remedy solutions. We hope our results could contribute to the development of robust PM-based storage systems, Comment: ACM TRANSACTIONS ON STORAGE(TOS'23)

Di Tang,1,* Duo Zhang,1,* Yu Heng,1 Xiao-Ke Zhu,1 Han-Qing Lin,1 Jian Zhou,1 Lei Tao,1 Li-Ming Lu2 1Department of Otorhinolaryngology and ENT Institute, Eye & ENT Hospital, Fudan University, Shanghai, People’s Republic of China; 2Shanghai Institute of Immunology, Shanghai Jiaotong University School of Medicine, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Li-Ming Lu, Shanghai Institute of Immunology, Shanghai Jiaotong University School of Medicine, 280 South Chongqing Road, Shanghai, 200025, People’s Republic of China, Tel +86-13916235624, Fax +86-021-63846383, Email lulunew2003@163.com Lei Tao, ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, People’s Republic of China, Tel +86-13916944810, Email doctortaolei@163.comPurpose: Chronic inflammation contributes to tumor initiation, progression, and immune escape. Neutrophils are the major component of inflammatory response and participate in the tumorigenesis process. However, compared to other immune cells in the tumor microenvironment of laryngeal squamous cell carcinoma (LSCC), neutrophils, especially the tumor-associated neutrophils (TANs), have not yet been comprehensively explored. The mechanism for regulating the crosstalk between TANs and tumor cells still remains unclear.Materials and Methods: The distribution profiles and phenotypic features of neutrophils and other inflammatory immune cell populations from a large LSCC patient cohort were systemically analyzed. Co-culturing of peripheral blood associated neutrophils (PANs) and TANs with PBMCs was performed, and the immunosuppression effect on T-cells was examined.Results: LSCC microenvironment is highly inflammatory with remarkable TANs infiltration, which is often associated with unfavorable prognosis and advanced clinical stage. We find that TANs in LSCC display morphologically immature a

Machine learning assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation. With the accumulation of high-quality electronic structure data, a model that can be pretrained on all available data and finetuned on downstream tasks with a small additional effort would bring the field to a new stage. Here we propose DPA-1, a Deep Potential model with a novel attention mechanism, which is highly effective for representing the conformation and chemical spaces of atomic systems and learning the PES. We tested DPA-1 on a number of systems and observed superior performance compared with existing benchmarks. When pretrained on large-scale datasets containing 56 elements, DPA-1 can be successfully applied to various downstream tasks with a great improvement of sample efficiency. Surprisingly, for different elements, the learned type embedding parameters form a $spiral$ in the latent space and have a natural correspondence with their positions on the periodic table, showing interesting interpretability of the pretrained DPA-1 model.

Carbonated MgO-based Engineered Cementitious Composite (ECC) has ultrahigh tensile ductility and tight crack width control behavior. However, the expectation of improved fire-resistant has not been confirmed. This study explored the alterations to mechanical and microstructure characteristics of this material after exposure to temperatures up to 500 °C. Material mass loss, compressive strength, tensile strength, strain capacity, and matrix fracture toughness were measured. Scanning electron microscopy, mercury intrusion porosimetry, thermogravimetric analysis, and X-ray diffraction were used to probe the degradation of the cement matrix and fibers. The effect of elevated temperature on carbonated MgO-based ECC was further assessed via examination of the micromechanical behavior of the fiber-matrix. The objective of this research was to assess the performance of carbonated MgO-based ECC exposed to fire hazards. The tensile ductility of carbonated MgO-based ECC was found to be enhanced when exposed to ∼100 °C as compared with those at room temperature ∼20 °C. Further increase in exposure temperature, however, posed a negative impact on the composite compressive strength, ultimate tensile strength, and ability to control crack width. The results provide a useful database for further investigations into carbonated MgO-based ECC for fire safety enhancement. © 2021

© 2019 The Authors The porous structure of the electrodes in redox flow batteries (RFBs) plays a critical role in their performance. We develop a framework for understanding the coupled transport and reaction processes in electrodes by combining lattice Boltzmann modelling (LBM) with experimental measurement of electrochemical performance and X-ray computed tomography (CT). 3D pore-scale LBM simulations of a non-aqueous RFB are conducted on the detailed 3D microstructure of three different electrodes (Freudenberg paper, SGL paper and carbon cloth) obtained using X-ray CT. The flow of electrolyte and species within the porous structure as well as electrochemical reactions at the interface between the carbon fibers of the electrode and the liquid electrolyte are solved by a lattice Boltzmann approach. The simulated electrochemical performances are compared against the experimental measurements with excellent agreement, indicating the validity of the LBM simulations for predicting the RFB performance. Electrodes featuring one single dominant peak (i.e., Freudenberg paper and carbon cloth) show better electrochemical performance than the electrode with multiple dominant peaks over a wide pore size distribution (i.e., SGL paper), whilst the presence of a small fraction of large pores is beneficial for pressure drop. This framework is useful to design electrodes with optimal microstructures for RFB applications.

We propose an optimization-based approach to plan power grasps. Central to our method is a reformulation of grasp planning as an infinite program under complementary constraints (IPCC), which allows contacts to happen between arbitrary pairs of points on the object and the robot gripper. We show that IPCC can be reduced to a conventional finite-dimensional nonlinear program (NLP) using a kernel-integral relaxation. Moreover, the values and Jacobian matrices of the kernel-integral can be evaluated efficiently using a modified Fast Multipole Method (FMM). We further guarantee that the planned grasps are collision-free using primal barrier penalties. We demonstrate the effectiveness, robustness, and efficiency of our grasp planner on a row of challenging 3D objects and high-DOF grippers, such as Barrett Hand and Shadow Hand, where our method achieves superior grasp qualities over competitors., Comment: 8 pages, 5 figures

Hefei Li,1 Shaoyong Dong,1 Duo Zhang,1 Zhimin Guo,1 Ce Li,1 Jianxing Xiang,2 Xiao Zou,2 Li Yan,2 Ying Sun,2 Wei Li1 1Department of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, 071000, People’s Republic of China; 2Department of Medicine, Burning Rock Biotech, Guangzhou, 510300, People’s Republic of ChinaCorrespondence: Wei LiDepartment of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, 071000, People’s Republic of ChinaTel +86 312-5981670Email liwei15930923618@163.comAbstract: Estimated to comprise approximately 10% of lung cancer cases, multiple pulmonary lesions pose a diagnostic and therapeutic challenge in thoracic oncology. Distinction between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) directly affects tumor staging and clinical management. In equivocal cases in which the lesions are histopathologically indistinguishable, targeted sequencing can provide key additional evidence for differential diagnosis. Herein, we describe an unusual patient who presented with seven lung lesions that consisted of primary tumors and metastatic lesions, each showing distinct clonality status based on histomolecular findings. Specifically, the 45-year-old female never-smoker underwent a surgery that removed one invasive lepidic predominant adenocarcinoma and five microinvasive adenocarcinomas. Next-generation sequencing revealed three of the lesions to carry a clonal driver mutation EGFR p.L858R, supporting an IMP diagnosis. EGFR p.L858R was not detected in two other surgical specimens, which instead harbored respective oncogenic BRAF p.G469A and an uncommon EGFR p.G779F. These results led to diagnosis of the two lesions as primary tumors of lineages different from that of the metastases. The patient had achieved a recurrence-free survival of 21 months as of the latest follow-up. In this rare case that presented with evidence of both MPLC and IPM, targeted sequencing proved valuab

Engineered Cementitious Composites (ECC) have demonstrated superior mechanical and durability performance than conventional concrete. In the micromechanical reinforcing system of ECC, fibers play a pivotal role in establishing the ultrahigh tensile ductility and autogenous crack width control. This article reviews the state-of-the-art of discontinuous micro-fibers as intrinsic reinforcement of ECC regarding technical performance as well as environmental and economic impacts. Mechanical properties of ECC made with different micro-fibers, man-made or natural, and their embodied energy, emissions and material cost, are comprehensively surveyed. Further, studies on fiber hybridization are discussed regarding the combination of different types of fibers to form synergetic reinforcements that mitigate total material cost, and potentially enhance the composite performance. Recommendations on fiber selections are highlighted and directions for future research are suggested.

The porous structure of the electrodes in redox flow batteries (RFBs) plays a critical role in their performance. We develop a framework for understanding the coupled transport and reaction processes in electrodes by combining lattice Boltzmann modelling (LBM) with experimental measurement of electrochemical performance and X-ray computed tomography (CT). 3D pore-scale LBM simulations of a non-aqueous RFB are conducted on the detailed 3D microstructure of three different electrodes (Freudenberg paper, SGL paper and carbon cloth) obtained using X-ray CT. The flow of electrolyte and species within the porous structure as well as electrochemical reactions at the interface between the carbon fibers of the electrode and the liquid electrolyte are solved by a lattice Boltzmann approach. The simulated electrochemical performances are compared against the experimental measurements with excellent agreement, indicating the validity of the LBM simulations for predicting the RFB performance. Electrodes featuring one single dominant peak (i.e., Freudenberg paper and carbon cloth) show better electrochemical performance than the electrode with multiple dominant peaks over a wide pore size distribution (i.e., SGL paper), whilst the presence of a small fraction of large pores is beneficial for pressure drop. This framework is useful to design electrodes with optimal microstructures for RFB applications.

Diagnosing storage system failures is challenging even for professionals. One example is the "When Solid State Drives Are Not That Solid" incident occurred at Algolia data center, where Samsung SSDs were mistakenly blamed for failures caused by a Linux kernel bug. With the system complexity keeps increasing, such obscure failures will likely occur more often. As one step to address the challenge, we present our on-going efforts called X-Ray. Different from traditional methods that focus on either the software or the hardware, X-Ray leverages virtualization to collects events across layers, and correlates them to generate a correlation tree. Moreover, by applying simple rules, X-Ray can highlight critical nodes automatically. Preliminary results based on 5 failure cases shows that X-Ray can effectively narrow down the search space for failures.

Objectives: The purpose of this in vitro study was by using quantitative real-time PCR and culturing to determine the effectiveness of two irrigation and cleaning systems in removing multispecies oral biofilms from root canals. Material and methods: Twenty extracted human molars were instrumented to size #15/.02 and then cleaned with the GentleWave (GW) System. The teeth were autoclaved to provide the same sterile baseline. The molars were filled with mixed plaque suspended in BHI and centrifuged to inoculate the biofilms. After 2 weeks of incubation, the teeth were randomly divided into two treatment groups. In GW group (26 canals), the teeth were further instrumented to size #15/04, and in PiezoFlow (PF) group (30 canals) to #35/.04. The teeth were then cleaned either with GW System or ProUltra PiezoFlow Active Ultrasonic System using 3% sodium hypochlorite NaOCl, 8% EDTA, and sterile water as irrigants. Samples (S1, S2, and S3) for bacterial cultures were taken from 13 canals before and after instrumentation and after final cleaning. Quantitative real-time PCR was performed from all 56 canals, and universal bacterial, one genus, and one species-specific primers were used to determine the presence of microorganisms in samples from root canals before and after instrumentation and after final cleaning. Statistical analyses were performed using the Mann-Whitney U test with the significance level set at P < 0.05. Results: Bacterial culturing from the canal samples revealed strong reduction of bacteria from S1 to S2 in both groups after instrumentation and irrigation with water only. No growth was detected in any of the S3 samples after cleaning in either group. A highly significant reduction in bacterial DNA was recorded by qPCR for both groups (P < 0.001). GW System showed more constant and a significantly higher reduction of total microbial DNA (P = 0.007), Enterococcus faecalis DNA (P = 0.011) and Streptococcus spp. DNA (P = 0.029) than the Ultrasonic System. The a

Yao Wang, Duo Zhang, Yao Li, Fang Fang Inspection Institute, Jilin Medical University, Jilin City, Jilin Province 132013, People’s Republic of ChinaCorrespondence: Fang Fang No. 5, Jilin Street, Jilin City, Jilin Province 132013, People’s Republic of ChinaEmail fangfangmed@126.comBackground: Oxaliplatin is one kind of platinum-based drug. It is effective and commonly used in the treatment of colorectal cancer (CRC). However, development of acquired drug resistance is still a big obstacle during the oxaliplatin therapy. It is urgent to take strategies to decrease the oxaliplatin resistance of CRC.Materials and Methods: Oxaliplatin-resistant HT29 and SW480 (HT29/R and SW480/R) cells were acquired through long-term exposure to oxaliplatin by using the routine HT29 and SW480 cells. Relative glucose consumption, lactate generation and LDH activity were tested to evaluate the glycolysis of CRC cell lines. MTT assays were conducted to evaluate the differences of oxaliplatin sensitivity between HT29/R (SW480/R) cells and their parental HT29 (SW480) cells. Regulation of miR-138 on PDK1 was confirmed through qRT-PCR, Western blot and dual-luciferase reporter assays. Reactive oxygen species (ROS) levels were measured by flow cytometry.Results: HT29/R and SW480/R cells exhibited higher glucose consumption, lactate production and LDH activity compared to their parental HT29 and SW480 cells. However, oxygen consumption rate (OCR) in HT29/R and SW480/R cells is lower than that in HT29 and SW480 cells, respectively. Results of MTT assays showed that treatment with miR-138 can increase the cytotoxicity of oxaliplatin to HT29/R and SW480/R cells. Research on mechanisms showed that PDK1 was the target of miR-138. Overexpression of miR-138 can inhibit the expression of PDK1, and thus increase the OCR of HT29/R and SW480/R cells. Under the treatment of oxaliplatin, the miR-138-overexpressed HT29/R and SW480/R cells generated more amount of ROS to ge

This is a special issue of ICJ guest-edited by Prof. Dr. Victor C. Li and his team. Prof Li led the research team that invented Engineering Cementitious Composites (ECC), popularly known as “bendable concrete.” Prof. Dr. Victor C. Li is the James R. Rice Distinguished University Professor of Engineering and the E.B. Wylie Collegiate Professor of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. His research interest is in multifunctional materials targeted at enhancing civil infrastructure sustainability and resilience. Prof. Li was awarded the International Grand Prize for Innovation by the Construction Industry Council and the Lifetime Achievement Award by RILEM in 2016. He has multiple patents to his credit and is a recipient of distinguished awards and recognition from the University of Michigan, Technical University of Denmark. We are pleased to bring to you the first-ever special issue of the ICJ exclusively focused on Engineered Cementitious Composites (ECC) with an objective to introduce and encourage more research and application in this field. This special issue has brought together the latest research in the field of ECC from around the world. We received an overwhelming response to the call for papers and have included the first set of papers that completed the double-blind peer-review process in this edition. A sequel edition in the second half of 2020 will include contribution of other authors. The editorial team is grateful to all the authors for sharing their contributions and to all the reviewers who helped us enhance these contributions with their valuable feedback and comments, overall enhancing the quality of this edition.

This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP), through collaborative operation of sewer system facilities. Using a hydraulic model, the effectiveness of ICWT is investigated in a sewer system in Drammen, Norway. Concerning the whole system performance, we found that the S{\o}ren Lemmich pump station plays a vital role in the ICWT framework. To enhance the operation of this pump station, it is imperative to construct a multi-step ahead water level prediction model. Hence, one of the most promising artificial intelligence techniques, Long Short Term Memory (LSTM), is employed to undertake this task. Experiments demonstrated that LSTM is superior to Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feed-forward Neural Network (FFNN) and Support Vector Regression (SVR).

Combined Sewer Overflow (CSO) is a major problem to be addressed by many cities. Understanding the behavior of sewer system through proper urban hydrological models is an effective method of enhancing sewer system management. Conventional deterministic methods, which heavily rely on physical principles, is inappropriate for real-time purpose due to their expensive computation. On the other hand, data-driven methods have gained huge interests, but most studies only focus on modeling a single component of the sewer system and supply information at a very abstract level. In this paper, we proposed the DeepCSO model, which aims at forecasting CSO events from multiple CSO structures simultaneously in near real time at a citywide level. The proposed model provided an intermediate methodology that combines the flexibility of data-driven methods and the rich information contained in deterministic methods while avoiding the drawbacks of these two methods. A comparison of the results demonstrated that the deep learning based multi-task model is superior to the traditional methods.

Exploiting capacity of sewer system using decentralized control is a cost effective mean of minimizing the overflow. Given the size of the real sewer system, exploiting all the installed control structures in the sewer pipes can be challenging. This paper presents a divide and conquer solution to implement decentralized control measures based on unsupervised learning algorithms. A sewer system is first divided into a number of subcatchments. A series of natural and built factors that have the impact on sewer system performance is then collected. Clustering algorithms are then applied to grouping subcatchments with similar hydraulic hydrologic characteristics. Following which, principal component analysis is performed to interpret the main features of sub-catchment groups and identify priority control locations. Overflows under different control scenarios are compared based on the hydraulic model. Simulation results indicate that priority control applied to the most suitable cluster could bring the most profitable result.

Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatio-temporal scales, crime distributions display dramatically different patterns. These distributions are of very low regularity in both space and time. In this work, we adapt the state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et al, AAAI, 2017], to collectively predict crime distribution over the Los Angeles area. Our models are two staged. First, we preprocess the raw crime data. This includes regularization in both space and time to enhance predictable signals. Second, we adapt hierarchical structures of residual convolutional units to train multi-factor crime prediction models. Experiments over a half year period in Los Angeles reveal highly accurate predictive power of our models.

The electronic structures of carbon nanocages (CNCs) and nitrogen/phosphorus doped carbon nanocages (N-CNCs/P-CNCs) have been studied by X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XAS), X-ray emission spectroscopy (XES) and resonant X-ray emission spectroscopy (RXES). The doping configurations for N/P dopants are identified from the experiments. The results have shown that there are three major doping configurations for nitrogen but only one doping configuration for phosphorus. The nitrogen doping reveals the complex coexistence of graphite-like, pyridine-like and pyrrole-like configurations that are proved by density functional theory (DFT) simulations, while the phosphorus doping presents only the "graphite-like" configuration. The different configuration profiles result in less atomic structure ordering of N-CNCs than that of P-CNCs. XAS spectra obtained from both surface and bulk sensitive detection suggest different chemical environments between the interior and shell for all types of nanocages. The electronic structure modifications show significant difference between nitrogen and phosphorus doping from the DOS calculations. (C) 2016 Elsevier Ltd. All rights reserved., QC 20160504

In this paper, we consider the transmit design for multi-input multi-output (MIMO) wiretap channel including a malicious jammer. We first transform the system model into the traditional three-node wiretap channel by whitening the interference at the legitimate user. Additionally, the eavesdropper channel state information (ECSI) may be fully or statistically known, even unknown to the transmitter. Hence, some strategies are proposed in terms of different levels of ECSI available to the transmitter in our paper. For the case of unknown ECSI, a target rate for the legitimate user is first specified. And then an inverse water-filling algorithm is put forward to find the optimal power allocation for each information symbol, with a stepwise search being used to adjust the spatial dimension allocated to artificial noise (AN) such that the target rate is achievable. As for the case of statistical ECSI, several simulated channels are randomly generated according to the distribution of ECSI. We show that the ergodic secrecy capacity can be approximated as the average secrecy capacity of these simulated channels. Through maximizing this average secrecy capacity, we can obtain a feasible power and spatial dimension allocation scheme by using one dimension search. Finally, numerical results reveal the effectiveness and computational efficiency of our algorithms., Comment: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

Aquaporin (AQP) water channels are expressed in the eye at sites of aqueous fluid production and outflow: AQP1 and AQP4 in nonpigmented ciliary epithelium, and AQP1 in trabecular meshwork endothelium. Novel methods were developed to compare aqueous fluid dynamics in wild-type mice versus mice lacking AQP1 and/or AQP4. Aqueous fluid production was measured by in vivo confocal microscopy after transcorneal iontophoretic introduction of fluorescein. Intraocular pressure (IOP), outflow, and anterior chamber compliance were determined from pressure measurements in response to fluid infusions using micropipettes. Aqueous fluid volume and [Cl(-)] were assayed in samples withdrawn by micropipettes. In wild-type mice (CD1 genetic background, age 4-6 wk), IOP was 16.0 +/- 0.4 mmHg (SE), aqueous fluid volume 7.2 +/- 0.3 microl, fluid production 3.6 +/- 0.2 microl/h, fluid outflow 0.36 +/- 0.06 microl/h/mmHg, and compliance 0.036 +/- 0.006 microl/mmHg. IOP was significantly decreased by up to 1.8 mmHg (P < 0.002) and fluid production by up to 0.9 microl/h in age/litter-matched mice lacking AQP1 and/or AQP4 (outbred CD1 and inbred C57/bl6 genetic backgrounds). However, AQP deletion did not significantly affect outflow, [Cl(-)], volume, or compliance. These results provide evidence for the involvement of AQPs in intraocular pressure regulation by facilitating aqueous fluid secretion across the ciliary epithelium. AQP inhibition may thus provide a novel approach for the treatment of elevated IOP.

Lithium/sulfur (Li/S) cells have attracted much attention due to their higher theoretical specific capacity and energy compared to those of current lithium-ion cells. However, the application of Li/S cells is still hampered by short cycle life. Sulfur-graphene oxide (S-GO) nanocomposites have shown promise as cathode materials for long-life Li/S cells because oxygen-containing functional groups on the surface of graphene oxide were successfully used as sulfur immobilizers by forming weak bonds with sulfur and polysulfides. While S-GO showed much improved cycling performance, the capacity decay still needs to be improved for commercially viable cells. In this study, we attempt to understand the capacity fading mechanism based on an ex situ study of the structural and chemical evolution of S-GO nanocomposite cathodes with various numbers of cycles using scanning electron microscopy (SEM), near edge X-ray absorption fine structure (NEXAFS) and X-ray photoelectron spectroscopy (XPS). It is found that both the surface morphologies and chemical structures of the cathode materials change considerably with increasing number of cycles. These changes are attributed to several unexpected chemical reactions of lithium with S-GO nanocomposites occurring during the discharge-charge processes with the formation of Li2CO3, Li2SO3, Li2SO4, and COSO2Li species. These reactions result in the loss of recyclable active sulfur on the surface of the electrode, and thus capacity fades while coulombic efficiency is near 100%. Moreover, the reaction products accumulate on the cathode surface, forming a compact blocking insulating layer which may make the diffusion of Li ions into/out of the cathode difficult during the discharge-charge process and thus lead to lower utilization of sulfur at higher rates. We think that these two observations are significant contributors to the capacity and rate capability degradation of the Li/S-GO cells. Therefore, for the rechargeable Li/S-GO cells, we sugg

Alzheimer's disease (AD) is the most common cause of dementia. One of the pathological hallmarks of AD is amyloid beta (A beta) deposition. MicroRNAs (miRNAs) are small non-coding RNAs whose expression levels change significantly during neuronal pathogenesis and may be used as diagnostic markers. Some miRNAs are important in AD development by targeting genes responsible for A beta metabolism. However, a systematic assessment of the miRNA expression profile induced by A beta-mediated neuronal pathogenesis is still lacking. In the present study, we examined miRNA expression profile by using the APPswe/PS1 Delta E9 mouse model of AD. Two sibling pairs of mice were examined, showing 30 and 24 miRNAs with significantly altered expression levels from each paired control, respectively. Nine known miRNAs were common in both groups. Prediction of putative target genes and functional annotation implied that these altered miRNAs affect many target genes mainly involved in PI3K/Akt signaling pathway. This study provides a general profile of miRNAs regulated by A beta-associated signal pathways, which is helpful to understand the mechanism of A beta-induced neuronal dysfunction in AD development.

This paper explores combinatorial optimization for problems of max-weight graph matching on multi-partite graphs, which arise in integrating multiple data sources. Entity resolution-the data integration problem of performing noisy joins on structured data-typically proceeds by first hashing each record into zero or more blocks, scoring pairs of records that are co-blocked for similarity, and then matching pairs of sufficient similarity. In the most common case of matching two sources, it is often desirable for the final matching to be one-to-one (a record may be matched with at most one other); members of the database and statistical record linkage communities accomplish such matchings in the final stage by weighted bipartite graph matching on similarity scores. Such matchings are intuitively appealing: they leverage a natural global property of many real-world entity stores-that of being nearly deduped-and are known to provide significant improvements to precision and recall. Unfortunately unlike the bipartite case, exact max-weight matching on multi-partite graphs is known to be NP-hard. Our two-fold algorithmic contributions approximate multi-partite max-weight matching: our first algorithm borrows optimization techniques common to Bayesian probabilistic inference; our second is a greedy approximation algorithm. In addition to a theoretical guarantee on the latter, we present comparisons on a real-world ER problem from Bing significantly larger than typically found in the literature, publication data, and on a series of synthetic problems. Our results quantify significant improvements due to exploiting multiple sources, which are made possible by global one-to-one constraints linking otherwise independent matching sub-problems. We also discover that our algorithms are complementary: one being much more robust under noise, and the other being simple to implement and very fast to run., Comment: 14 pages, 11 figures

Back-arc extension is the first stage in the formation of a back-arc basin (BAB). The driving mechanism of back-arc extension is still debated. To better understand the driving mechanism and factors controlling back-arc extension, a series of 2D thermally driven subduction models with composite rheology are simulated. Initially, models are investigated with fixed rheology parameters and a homogeneous overriding plate (OP). Varying the initial ages of the plates at the trench, these models show that the most probable driving mechanism of back-arc extension is poloidal flow induced by slab subduction (enhanced by trench retreat). I investigate varying activation energy (E) and prefactor (A) in diffusion, dislocation and Peierls creeps, as well as friction coefficient (fc) and maximum yielding strength (yldmax) in yielding deformation. Many modes of plate behaviours are recognised. The models show that a weaker OP does not necessarily facilitate back-arc extension. This is because these models also have a weaker mantle flow, which further verifies the importance of poloidal flow. I investigate extension of an OP with a triangular hot region to mimic a volcanic arc. The models produce back-arc extension at different locations: splitting the arc itself or extension away from the arc. Splitting the arc shows the importance of thermal weakening to OP extension, and extension away from the arc emphasises the role of poloidal flow. These models suggest that splitting the arc is more common (e.g. Mariana Trough and Lau Basin), than not extending at the arc (potentially Japan Sea). Overall, this study shows that the poloidal flow induced by subduction is the main driving mechanism of back-arc extension together with its thermal weakening of the OP. Extension occurs when the force from the sub-OP flow exceeds the OP strength, as evidenced in the models. Testing these findings will require 3D modelling.

Bioleaching of chalcopyrite with different crystal structures (alpha-phase, beta-phase and gamma-phase) by Acidianus manzaensis was comparatively studied by synchrotron radiation based X-ray diffraction (SR-XRD) and S K-edge X-ray absorption near edge structure (XANES) spectroscopy. The alpha-phase, beta-phase and gamma-phase chalcopyrite was prepared by heating original chalcopyrite at 583, 773 and 848 K, respectively. Bioleaching results showed that [Cu2+] in the leaching solution of alpha-phase, beta-phase, gamma-phase and original chalcopyrite after 10 days of bioleaching was 1.27, 1.86, 1.43 and 1.13 g/l, respectively, suggesting that beta-phase had a better leaching kinetics than others. SR-XRD and XANES results indicated that jarosite and chalcopyrite were the main components in the leaching residues in all cases, and elemental sulphur formed in the early stage of bioleaching. While for beta-phase and gamma-phase chalcopyrite during bioleaching, bornite was produced in the initial stage of leaching, and turned into chalcocite on day 6., Bioleaching of chalcopyrite with different crystal structures (alpha-phase, beta-phase and gamma-phase) by Acidianus manzaensis was comparatively studied by synchrotron radiation based X-ray diffraction (SR-XRD) and S K-edge X-ray absorption near edge structure (XANES) spectroscopy. The alpha-phase, beta-phase and gamma-phase chalcopyrite was prepared by heating original chalcopyrite at 583, 773 and 848 K, respectively. Bioleaching results showed that [Cu2+] in the leaching solution of alpha-phase, beta-phase, gamma-phase and original chalcopyrite after 10 days of bioleaching was 1.27, 1.86, 1.43 and 1.13 g/l, respectively, suggesting that beta-phase had a better leaching kinetics than others. SR-XRD and XANES results indicated that jarosite and chalcopyrite were the main components in the leaching residues in all cases, and elemental sulphur formed in the early stage of bioleaching. While for beta-phase and gamma-phase chalcopyrite during bioleaching, bornite was produced in the initial stage of leaching, and turned into chalcocite on day 6.

In order to find effective methods to decrease consumption of zinc dust during purification of zinc solutions with antimony trioxide, a study was made of the effects of temperature, amount of zinc dust, stirring velocity, dust particle size, content of cobalt in solution and pH. It was found that the major influences on the purification process were temperature and amount of zinc dust. Concentration of Co2+ can be reduced to less than 0.50 mg/l when there is more than 1.6 g/l of zinc dust, the temperature is 85 degrees C, the stirring velocity is greater than 300 rpm and the particle size of zinc dust is sufficiently small., In order to find effective methods to decrease consumption of zinc dust during purification of zinc solutions with antimony trioxide, a study was made of the effects of temperature, amount of zinc dust, stirring velocity, dust particle size, content of cobalt in solution and pH. It was found that the major influences on the purification process were temperature and amount of zinc dust. Concentration of Co2+ can be reduced to less than 0.50 mg/l when there is more than 1.6 g/l of zinc dust, the temperature is 85 degrees C, the stirring velocity is greater than 300 rpm and the particle size of zinc dust is sufficiently small.

Back-arc extension is the first stage in the formation of a back-arc basin (BAB). The driving mechanism of back-arc extension is still debated. To better understand the driving mechanism and factors controlling back-arc extension, a series of 2D thermally driven subduction models with composite rheology are simulated. Initially, models are investigated with fixed rheology parameters and a homogeneous overriding plate (OP). Varying the initial ages of the plates at the trench, these models show that the most probable driving mechanism of back-arc extension is poloidal flow induced by slab subduction (enhanced by trench retreat). I investigate varying activation energy (E) and prefactor (A) in diffusion, dislocation and Peierls creeps, as well as friction coefficient (fc) and maximum yielding strength (yldmax) in yielding deformation. Many modes of plate behaviours are recognised. The models show that a weaker OP does not necessarily facilitate back-arc extension. This is because these models also have a weaker mantle flow, which further verifies the importance of poloidal flow. I investigate extension of an OP with a triangular hot region to mimic a volcanic arc. The models produce back-arc extension at different locations: splitting the arc itself or extension away from the arc. Splitting the arc shows the importance of thermal weakening to OP extension, and extension away from the arc emphasises the role of poloidal flow. These models suggest that splitting the arc is more common (e.g. Mariana Trough and Lau Basin), than not extending at the arc (potentially Japan Sea). Overall, this study shows that the poloidal flow induced by subduction is the main driving mechanism of back-arc extension together with its thermal weakening of the OP. Extension occurs when the force from the sub-OP flow exceeds the OP strength, as evidenced in the models. Testing these findings will require 3D modelling.