Porosity Prediction of Granular Materials through Discrete element method and back propagation neural network algorithm

Granular materials are used directly or as the primary ingredients of the mixtures in industrial manufacturing, agricultural production and civil engineering. It has been a challenging task to compute the porosity of a granular material which contains a wide range of particle sizes or shapes. Against this background, this paper presents a newly developed method for the porosity prediction of granular materials through Discrete Element Modeling (DEM) and the Back Propagation Neural Network algorithm (BPNN). In DEM, ball elements were used to simulate particles in granular materials. According to the Chinese specifications, a total of 400 specimens in different gradations were built and compacted under the static pressure of 600 kPa. The porosity values of those specimens were recorded and applied to train the BPNN model. The primary parameters of the BPNN model were recommended for predicting the porosity of a granular material. Verification was performed by a self-designed experimental test and it was found that the prediction accuracy could reach 98%. Meanwhile, considering the influence of particle shape, a shape reduction factor was proposed to achieve the porosity reduction from sphere to real particle shape

Intratumor microbiota in cancer pathogenesis and immunity: from mechanisms of action to therapeutic opportunities

Microbial species that dwell human bodies have profound effects on overall health and multiple pathological conditions. The tumor microenvironment (TME) is characterized by disordered vasculature, hypoxia, excessive nutrition and immunosuppression. Thus, it is a favorable niche for microbial survival and growth. Multiple lines of evidence support the existence of microorganisms within diverse types of cancers. Like gut microbiota, intratumoral microbes have been tightly associated with cancer pathogenesis. Intratumoral microbiota can affect cancer development through various mechanisms, including induction of host genetic mutation, remodeling of the immune landscape and regulation of cancer metabolism and oncogenic pathways. Tumor-associated microbes modulate the efficacy of anticancer therapies, suggesting their potential utility as novel targets for future intervention. In addition, a growing body of evidence has manifested the diagnostic, prognostic, and therapeutic potential of intratumoral microorganisms in cancer. Nevertheless, our knowledge of the diversity and biological function of intratumoral microbiota is still incomplete. A deeper appreciation of tumor microbiome will be crucial to delineate the key pathological mechanisms underlying cancer progression and hasten the development of personalized treatment approaches. Herein, we summarize the most recent progress of the research into the emerging roles of intratumoral microbiota in cancer and towards clarifying the sophisticated mechanisms involved. Moreover, we discuss the effect of intratumoral microbiota on cancer treatment response and highlight its potential clinical implications in cancer

Roles of long noncoding RNAs and small extracellular vesicle-long noncoding RNAs in type 2 diabetes

Wenguang Chang, Man Wang, Yuan Zhang and Fei Yu collected all of the data, and Wenguang Chang, Bin Hu, Katarzyna Goljanek-Whysall and Peifeng Li wrote and revised the manuscript. All authors have read and approved the final version of the manuscript. This work was supported by National Natural Science Foundation of China (81700704).Peer reviewedPublisher PD

How to Achieve Efficiency and Accuracy in Discrete Element Simulation of Asphalt Mixture: A DRF-Based Equivalent Model for Asphalt Sand Mortar

The clump-based discrete element model is one of the asphalt mixture simulation methods, which has the potential to not only predict mixture performance but also simulate particle movement during compaction, transporting, and other situations. However, modelling of asphalt sand mortar in this method remains to be a problem due to computing capacity. Larger-sized balls (generally 2.0-2.36 mm) were usually used to model the smaller particles and asphalt binder, but this replacement may result in the mixture\u27s unrealistic volumetric features. More specifically, replacing original elements with equal volume but larger size particles will increase in buck volume and then different particle contacting states. The major objective of this research is to provide a solution to the dilemma situation through an improved equivalent model of the smaller particles and asphalt binders. The key parameter of the equivalent model is the diameter reduction factor (DRF), which was proposed in this research to minimize the effects of asphalt mortar\u27s particle replacement modelling. To determine DRF, the DEM-based analysis was conducted to evaluate several mixture features, including element overlap ratio, ball-wall contact number, and the average wall stress. Through this study, it was observed that when the original glued ball diameters are ranging from 2.00 mm and 2.36 mm, the diameter reduction factor changes from 0.82 to 0.86 for AC mixtures and 0.80 to 0.84 for SMA mixtures. The modelling method presented in this research is suitable not only for asphalt mixtures but also for the other particulate mix with multisize particles

Superconductivity in the cobalt-doped V3Si A15 intermetallic compound

The A15 structure of superconductors is a prototypical type-II superconductor that has generated considerable interest since the early history of superconducting materials. This paper discusses the superconducting properties of previously unreported V3-xCoxSi alloys. It is found that the lattice parameter decreases with increasing cobalt-doped content and leads to an increased residual resistivity ratio (RRR) value of the V3-xCoxSi system. Meanwhile, the superconducting transition temperature (Tc) cobalt-doped content. Furthermore, the fitted data show that the increase of cobalt-doped content also reduces the lower/upper critical fields of the V3-xCoxSi system. Type-II superconductivity is demonstrated on all V3-xCoxSi samples. With higher Co-doped content, V3-xCoxSi alloys may have superconducting and structural phase transitions at low-temperature regions. As the electron/atom (e/a) ratio increases, the Tc variation trend of V3Si is as pronounced as in crystalline alloys and monotonically follows the trend observed for amorphous superconductors.Comment: 20 pages, 7 figure

Superconductivity in the high-entropy ceramics Ti0.2Zr0.2Nb0.2Mo0.2Ta0.2Cx with possible nontrivial band topology

Topological superconductors have drawn significant interest from the scientific community due to the accompanying Majorana fermions. Here, we report the discovery of electronic structure and superconductivity in high-entropy ceramics Ti0.2Zr0.2Nb0.2Mo0.2Ta0.2Cx (x = 1 and 0.8) combined with experiments and first-principles calculations. The Ti0.2Zr0.2Nb0.2Mo0.2Ta0.2Cx high-entropy ceramics show bulk type-II superconductivity with Tc about 4.00 K (x = 1) and 2.65 K (x = 0.8), respectively. The specific heat jump is equal to 1.45 (x = 1) and 1.52 (x = 0.8), close to the expected value of 1.43 for the BCS superconductor in the weak coupling limit. The high-pressure resistance measurements show that a robust superconductivity against high physical pressure in Ti0.2Zr0.2Nb0.2Mo0.2Ta0.2C, with a slight Tc variation of 0.3 K within 82.5 GPa. Furthermore, the first-principles calculations indicate that the Dirac-like point exists in the electronic band structures of Ti0.2Zr0.2Nb0.2Mo0.2Ta0.2C, which is potentially a topological superconductor. The Dirac-like point is mainly contributed by the d orbitals of transition metals M and the p orbitals of C. The high-entropy ceramics provide an excellent platform for the fabrication of novel quantum devices, and our study may spark significant future physics investigations in this intriguing material.Comment: 28 pages, 7 figures,The manuscript with the same title will be published by Advanced Scienc

Antifungal effects and biocontrol potential of lipopeptide-producing Streptomyces against banana Fusarium wilt fungus Fusarium oxysporum f. sp. cubense

Fusarium wilt of banana (FWB), caused by Fusarium oxysporum f. sp. cubense (Foc), especially tropical race 4 (TR4), presents the foremost menace to the global banana production. Extensive efforts have been made to search for efficient biological control agents for disease management. Our previous study showed that Streptomyces sp. XY006 exhibited a strong inhibitory activity against several phytopathogenic fungi, including F. oxysporum. Here, the corresponding antifungal metabolites were purified and determined to be two cyclic lipopeptide homologs, lipopeptin A and lipopeptin B. Combined treatment with lipopeptin complex antagonized Foc TR4 by inhibiting mycelial growth and conidial sporulation, suppressing the synthesis of ergosterol and fatty acids and lowering the production of fusaric acid. Electron microscopy observation showed that lipopeptide treatment induced a severe disruption of the plasma membrane, leading to cell leakage. Lipopeptin A displayed a more pronounced antifungal activity against Foc TR4 than lipopeptin B. In pot experiments, strain XY006 successfully colonized banana plantlets and suppressed the incidence of FWB, with a biocontrol efficacy of up to 87.7%. Additionally, XY006 fermentation culture application improved plant growth parameters and induced peroxidase activity in treated plantlets, suggesting a possible role in induced resistance. Our findings highlight the potential of strain XY006 as a biological agent for FWB, and further research is needed to enhance its efficacy and mode of action in planta

Systemically Study the Role of Ubiquitin-26S Proteasome System (UPS) in Early Seed Development

We discovered that the pool of ubiquitylation substrates in immature siliques declined immediately at two days after pollinatio

Application-Aware Scheduling in Deep Learning Software Stacks

DL has pervaded many areas of computing due to the confluence of the explosive growth of large-scale computing capabilities, availability of datasets, and advances in learning techniques. However, the infrastructure that supports DL is still in its early stage, bearing mismatches among the hardware, the software stack, and DL applications. On the one hand, despite the emergence of new unique hardware and new use cases, the software stack that abstracts and schedules these hardware resources remains largely unchanged. On the other hand, user-defined performance metrics common in DL applications urge better schedulers tailored to the application's specific needs. Motivated by the mismatch, this dissertation revisits the system design across the stack, with a focus on the synergy between schedulers and application/system-specific information. At the bottom level, the ever-growing adoption of specialized hardware like GPUs poses challenges to efficient usage. Due to the lack of operating system arbitration, applications usually assume exclusive access, making the otherwise underutilized resources unusable for other jobs on the same host. We therefore design Salus to realize proper efficient GPU sharing. It leverages DL applications' specific usage patterns to schedule iterations and manage memory allocations, providing two missing primitives: fast job switching and memory sharing. However, even with an efficient execution platform, it is still not trivial to harvest the hardware's full potential for higher-level applications. We investigate two such cases sitting on opposite sides of a model's lifecycle: hyperparameter tuning and inference serving. Hyperparameter tuning -- which constitutes a great portion of DL cluster usage given the proliferation of distributed resources in clusters -- generates many small interdependent training trials. Existing tuning algorithms are oblivious of advanced execution strategies like intra-GPU sharing and inter-GPU execution, often causing poor resource utilization. Hence, we propose Fluid as a generalized hyperparameter tuning execution engine, that coordinates between tuning jobs and cluster resources. Fluid schedules training trials in such jobs using a water-filling approach to make the best use of resources at both intra- and inter-GPU granularity to speed up hyperparameter tuning. Moving on, inference serving also requires careful scheduling to achieve tight latency guarantees and maintain high utilization. Existing serving solutions assume inference execution times to be data-independent and thus highly predictable. However, with the rise of dynamic neural networks, data-dependent inferences see higher variance in execution times and become less predictable by a single, point estimation of the true running times. With Orloj, we show that treating and modeling inference execution times as probability distributions bring large gains for scheduling inference requests in the presence of SLO constraints. In this dissertation, we consider combining application/system-specific information with scheduling design as a means of efficiently supporting new hardware and new DL application use cases. Nevertheless, the pursuit of higher efficiency never ends. This dissertation tries to lay down the necessary mechanisms with the hope that our crude work may be a basis for further research to better scheduling algorithms and more efficient systems in the DL infrastructure.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/174199/1/peifeng_1.pd