Blind Beamforming for Intelligent Reflecting Surface in Fading Channels without CSI

This paper discusses how to optimize the phase shifts of intelligent reflecting surface (IRS) to combat channel fading without any channel state information (CSI), namely blind beamforming. Differing from most previous works based on a two-stage paradigm of first estimating channels and then optimizing phase shifts, our approach is completely data-driven, only requiring a dataset of the received signal power at the user terminal. Thus, our method does not incur extra overhead costs for channel estimation, and does not entail collaboration from service provider, either. The main idea is to choose phase shifts at random and use the corresponding conditional sample mean of the received signal power to extract the main features of the wireless environment. This blind beamforming approach guarantees an $N^2$ boost of signal-to-noise ratio (SNR), where $N$ is the number of reflective elements (REs) of IRS, regardless of whether the direct channel is line-of-sight (LoS) or not. Moreover, blind beamforming is extended to a double-IRS system with provable performance. Finally, prototype tests show that the proposed blind beamforming method can be readily incorporated into the existing communication systems in the real world; simulation tests further show that it works for a variety of fading channel models.Comment: 14 pages, 14 figure

Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us to predict the formation energy with sufficient precision of a large number of materials of which only the structural information is available. The enlarged training set is then used to train a convolutional neural network model (the screening model) with the generic Magpie elemental features with high prediction power. Extensive experiments demonstrate the superior performance of our transfer learning model and screening model compared to the baseline models. We then applied the screening model to filter out promising new perovskite materials out of 21,316 hypothetical perovskite structures with a large portion of them confirmed by existing literature

Chlamydia trachomatis Co-opts GBF1 and CERT to Acquire Host Sphingomyelin for Distinct Roles during Intracellular Development

The obligate intracellular pathogen Chlamydia trachomatis replicates within a membrane-bound inclusion that acquires host sphingomyelin (SM), a process that is essential for replication as well as inclusion biogenesis. Previous studies demonstrate that SM is acquired by a Brefeldin A (BFA)-sensitive vesicular trafficking pathway, although paradoxically, this pathway is dispensable for bacterial replication. This finding suggests that other lipid transport mechanisms are involved in the acquisition of host SM. In this work, we interrogated the role of specific components of BFA-sensitive and BFA-insensitive lipid trafficking pathways to define their contribution in SM acquisition during infection. We found that C. trachomatis hijacks components of both vesicular and non-vesicular lipid trafficking pathways for SM acquisition but that the SM obtained from these separate pathways is being utilized by the pathogen in different ways. We show that C. trachomatis selectively co-opts only one of the three known BFA targets, GBF1, a regulator of Arf1-dependent vesicular trafficking within the early secretory pathway for vesicle-mediated SM acquisition. The Arf1/GBF1-dependent pathway of SM acquisition is essential for inclusion membrane growth and stability but is not required for bacterial replication. In contrast, we show that C. trachomatis co-opts CERT, a lipid transfer protein that is a key component in non-vesicular ER to trans-Golgi trafficking of ceramide (the precursor for SM), for C. trachomatis replication. We demonstrate that C. trachomatis recruits CERT, its ER binding partner, VAP-A, and SM synthases, SMS1 and SMS2, to the inclusion and propose that these proteins establish an on-site SM biosynthetic factory at or near the inclusion. We hypothesize that SM acquired by CERT-dependent transport of ceramide and subsequent conversion to SM is necessary for C. trachomatis replication whereas SM acquired by the GBF1-dependent pathway is essential for inclusion growth and stability. Our results reveal a novel mechanism by which an intracellular pathogen redirects SM biosynthesis to its replicative niche

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

COMPOSITE SURFACE STRUCTURE GENERATION AND FORM-FINDING INSPIRED BY VASCULAR PLANT LEAVES’ GROWTH MECHANISMS

63 pagesThe leaves and petals of vascular plants have complex forms of high diversity. Their organic geometries showcase elegant surface designs which suggest great form and material integration. Because of this potential distinctive synthesis between geometry and materiality, plant leaves can be a good natural biomimetic model for the design and construction of high performance architectural surface structures. This research investigates leaf geometry and material composition with a focus on growth activities and explores the possibility of generating complex-shaped composite surface structures with high mechanical performance from an emergent process. This research has three stages of study: (1) the first study creates a simplified surface generation model by combining accurate mechanical simulations with abstracted cellular unit development patterns. The model is comprehensively parametrized to achieve a high capacity of generating and outputting shape diversity. (2) the second study explores methods to create different vein morphologies in 3D space, and creates a synchronized composite surface structure generation model by running the surface and vein generation models in parallel with each other. From the composite structure generation model, an emergent form-finding method is derived. (3) the third study explores how to translate the digital models of the composite system into physical structures while maintaining the advantages of a growth-based biomimetic system. It tests different options to design a fabrication process incorporating the advantageous aspects of leaf growth as a structure-building process. This research shows a framework of developmental thinking that can lead to the generation of structures with intrinsic component integration

Static and Discrete Berth Allocation for Large-Scale Marine-Loading Problem by Using Iterative Variable Grouping Genetic Algorithm

In this paper, we study the static discrete berth allocation problems (BAPs) for large-scale time-critical marine-loading scenarios. The objective is to allocate the vessels to different types of berths so that all the vessels can be loaded within the minimum time under the tidal condition. The BAP is formalized as a min–max problem. This problem is rather complex as the vessels and berths are quite numerous in the large-scale marine-loading problem. We analyze this problem from a novel perspective, and find out that this problem has the characteristic of partially separable. Therefore, the iterative variable grouping genetic algorithm (IVGGA) is designed to search the near-optimal berth allocation plans. The vessels and berths are divided into subgroups, and the genetic algorithm (GA) is applied to generate the near-optimal berth allocation plans in each subgroup. To achieve the balance of loading tasks among subgroups, we propose reallocating some vessels among subgroups according to the berth allocation plans in subgroups. To guarantee the convergency of the algorithm, an iterative vessel reallocation policy is devised considering the loading tasks of different types of berths. We demonstrate the proposed algorithm in dealing with large-scale BAPs through numerical experiments. According to the results, we find that the proposed algorithm would have good performance when the number of vessels in each subgroup are kept in medium scale. Compared with the original GA, our algorithm shows the effectiveness of the iterative variable grouping strategy. The performance of our algorithm is almost not changed as the number of vessels and berths increases. The proposed algorithm could obtain efficient berth allocation plans for the large-scale marine-loading problem

Delay-Aware Energy-Efficient Routing towards a Path-Fixed Mobile Sink in Industrial Wireless Sensor Networks

Wireless sensor networks (WSNs) involve more mobile elements with their widespread development in industries. Exploiting mobility present in WSNs for data collection can effectively improve the network performance. However, when the sink (i.e., data collector) path is fixed and the movement is uncontrollable, existing schemes fail to guarantee delay requirements while achieving high energy efficiency. This paper proposes a delay-aware energy-efficient routing algorithm for WSNs with a path-fixed mobile sink, named DERM, which can strike a desirable balance between the delivery latency and energy conservation. We characterize the object of DERM as realizing the energy-optimal anycast to time-varying destination regions, and introduce a location-based forwarding technique tailored for this problem. To reduce the control overhead, a lightweight sink location calibration method is devised, which cooperates with the rough estimation based on the mobility pattern to determine the sink location. We also design a fault-tolerant mechanism called track routing to tackle location errors for ensuring reliable and on-time data delivery. We comprehensively evaluate DERM by comparing it with two canonical routing schemes and a baseline solution presented in this work. Extensive evaluation results demonstrate that DERM can provide considerable energy savings while meeting the delay constraint and maintaining a high delivery ratio

Effect of intracoronary agents on the no-reflow phenomenon during primary percutaneous coronary intervention in patients with ST-elevation myocardial infarction: a network meta-analysis

Abstract Background Despite the restoration of epicardial flow after primary percutaneous coronary intervention (PPCI), myocardial reperfusion remains impaired in a significant proportion of patients. We performed a network meta-analysis to assess the effect of 7 intracoronary agents (adenosine, anisodamine, diltiazem, nicorandil, nitroprusside, urapidil, and verapamil) on the no-reflow phenomenon in patients with ST-elevation myocardial infarction (STEMI) undergoing PPCI. Methods Database searches were conducted to identify randomized controlled trials (RCTs) comparing the 7 agents with each other or with standard PPCI. Outcome measures included thrombolysis in myocardial infarction flow grade (TFG), ST-segment resolution (STR), left ventricular ejection fraction (LVEF), major adverse cardiovascular events (MACEs), and adverse events. Results Forty-one RCTs involving 4069 patients were analyzed. The addition of anisodamine to standard PPCI for STEMI was associated with improved post-procedural TFG, more occurrences of STR, and improvement of LVEF. The cardioprotective effect of anisodamine conferred a MACE-free survival benefit. Additionally, nitroprusside was regarded as efficient in improving coronary flow and clinical outcomes. Compared with standard care, adenosine, nicorandil, and verapamil improved coronary flow but had no corresponding benefits regarding cardiac function and clinical outcomes. The ranking probability for the 7 treatment drugs showed that anisodamine consistently ranked the highest in efficacy outcomes (TFG < 3, STR, LVEF, and MACEs). No severe adverse events, such as hypotension and malignant arrhythmia, were observed in patients treated with anisodamine. Network meta-regression analysis showed that age, the time to reperfusion, and study follow-up did not affect the treatment effects. Conclusions The intracoronary administration of anisodamine appears to improve myocardial reperfusion, cardiac function, and clinical outcomes in patients with STEMI undergoing PPCI. Given the limited quality and quantity of the included studies, more rigorous RCTs are needed to verify the role of this inexpensive and well-tolerated regimen