Capacity Scaling Research Articles

Capacity and Delay of Unmanned Aerial Vehicle Networks With Mobility

Unmanned aerial vehicles (UAVs) are widely exploited in environment monitoring, search-and-rescue, etc. However, the mobility and short flight duration of UAVs bring challenges for UAV networking. In this paper, we study the UAV networks with ${n}$ UAVs acting as aerial sensors. UAVs generally have short flight duration and need to frequently get energy replenishment from the control station. Hence, the returning UAVs bring the data of the UAVs along the returning paths to the control station with a store-carry-and-forward (SCF) mode. A critical range for the distance between the UAV and the control station is discovered. Within the critical range, the per-node capacity of the SCF mode is ${\Theta ({n}/{\log n})}$ times higher than that of the multihop mode. However, the per-node capacity of the SCF mode outside the critical range decreases with the distance between the UAV and the control station. To eliminate the critical range, a mobility control scheme is proposed such that the capacity scaling laws of the SCF mode are the same for all UAVs, which improves the capacity performance of UAV networks. Moreover, the delay of the SCF mode is derived. The impact of the size of the entire region, the velocity of UAVs, the number of UAVs and the flight duration of UAVs on the delay of SCF mode is analyzed. This paper reveals that the mobility and short flight duration of UAVs have beneficial effects on the performance of UAV networks, which may motivate the study of SCF schemes for UAV networks.

Ventral tegmental area astrocytes orchestrate avoidance and approach behavior

The ventral tegmental area (VTA) is a heterogeneous midbrain structure, containing neurons and astrocytes, that coordinates behaviors by integrating activity from numerous afferents. Within neuron-astrocyte networks, astrocytes control signals from distinct afferents in a circuit-specific manner, but whether this capacity scales up to drive motivated behavior has been undetermined. Using genetic and optical dissection strategies we report that VTA astrocytes tune glutamatergic signaling selectively on local inhibitory neurons to drive a functional circuit for learned avoidance. In this circuit, astrocytes facilitate excitation of VTA GABA neurons to increase inhibition of dopamine neurons, eliciting real-time and learned avoidance behavior that is sufficient to impede expression of preference for reward. Loss of one glutamate transporter (GLT-1) from VTA astrocytes selectively blocks these avoidance behaviors and spares preference for reward. Thus, VTA astrocytes selectively regulate excitation of local GABA neurons to drive a distinct avoidance circuit that opposes approach behavior.

Is cachexia associated with chemotherapy toxicities in gastrointestinal cancer patients? A prospective study.

BackgroundChemotherapy is an effective treatment with good clinical response in patients with cancer. However, it can cause exacerbated toxicities in patients and consequently change the course of treatment. Some factors may interfere with this toxicity such as body composition, especially in gastrointestinal cancer. The aim of this study was to evaluate the effects of body composition, nutritional status, and functional capacity scale in predicting the occurrence of toxicities in gastrointestinal cancer patients during chemotherapy treatment.MethodsThis is a prospective study with gastrointestinal cancer patients at the beginning of chemotherapy treatment. Sarcopenia and muscle attenuation were assessed using the skeletal muscle index from computerized tomography by measuring cross‐sectional areas of the L3 tissue (cm2/m2). Cachexia was graded according to involuntary weight loss associated with sarcopenia. Nutritional status was assessed by using anthropometric evaluation and Patient‐Generated Subjective Global Assessment. Functional capacity was evaluated by handgrip strength and Eastern Cooperative Oncology Group (ECOG) Performance Status scale. Haematological gastrointestinal and dose‐limiting toxicities (DLTs) were defined according to National Cancer Institute Common Toxicity Criteria. The associations among sarcopenia, cachexia, nutritional status, and functional capacity with DLT were assessed by univariate and multivariate Cox regression model.ResultsA total of 60 patients were evaluated (55% male, 60.9 ± 14.0 years) and followed up for a mean of 55 days. Most patients had normal weight (44.2%) and good ECOG Performance Status (≤1) at baseline (78%). During the chemotherapy period, the most prevalent toxicities were diarrhoea, nausea, and anorexia, but the presence of DLT was similar between cycles (P > 0.05). Cachexia was associated with a higher toxicity manifested by diarrhoea (P = 0.02), nausea (P = 0.02), and anorexia (P < 0.01 and P = 0.03 at Cycles 1 and 2, respectively). Sarcopenic and cachetic individuals experienced more toxicities and DLT during chemotherapy. The only factors associated with DLT in the multivariate Cox regression analyses including the presence of metastasis and the chemotherapy protocol were cachexia and the ECOG scale (P < 0.001 for both).ConclusionsCachexia and ECOG score may identify patients with an increased risk for developing severe toxicity events during chemotherapy treatment for gastrointestinal cancer.

Carbon sink calculation model of urban construction land based on building capacity.

The exploitation and utilization of mineral materials during urban construction causes a large amount of carbon emission, but could also contribute to carbon sequestration. In the related literature, carbon sequestration process of building mineral materials has received limited attention and scientific quantification. On the basis of extracting building capacity and identifying building types, we used the technology of remote sensing image shadow height inversion to quantify mineral material consumption and carbon content parameters. Carbonization rate was measured by thermogravimetric analysis (TGA). Finally, a calculation method for carbon sink in urban buildings was constructed. We investigated the uncertainty of this method with Puhe New Town in Shenyang as an example. The results showed that the order of carbon sink density of different types of buildings followed the order of residential buildings > public service buildings > other types of buildings > commercial and financial buildings > industrial buildings; the ratio of carbon sink volumetric in diffe-rent types of construction land followed the order of commercial and financial buildings > residential buildings > public service buildings > other types of buildings > industrial buildings. The carbon sink calculation method based on the urban scale of building capacity in this study could quickly and accurately estimate the magnitude of carbon sinks from the inorganic materials in various types of urban construction lands. Under the background of limited urban natural carbon sequestration, using building carbon sequestration to enhance the urban carbon sequestration could provide new ideas for the low-carbon development of cities in China.

An Iterative Interference Alignment Algorithm for the General MIMO X Channel

Interference alignment (IA) has been shown as an important technique to achieve a linear capacity scaling in wireless communications. However, the IA scheme over finite signaling dimensions for a general multiple-input-multiple-output (MIMO) X channel is still rarely studied. The main challenge of MIMO X channels is that the two sets of conditions for IA, namely the interference nulling conditions and the rank preservation conditions, get coupled. The usual IA methods for the interference channel and the broadcasting channel cannot be applied anymore. In this paper, we show that the rank preservation conditions can be replaced by a group of specific rank conditions, under which the IA problem is simplified. Then, based on this technique, an iterative algorithm of IA is designed for the MIMO X channel. The algorithm is designed with limited signaling dimensions. From the simulation results, we find that the algorithm has good performances even under limited signal-to-noise ratio.

A combined fast greedy heuristic for the capacitated multicommodity network design problem

The capacitated multicommodity network design problem represents a network design system and has a wide range of real-life applications, such as the construction of logistics networks, transportation networks, communication networks, and production networks. In this article, we introduce a fast greedy algorithm for solving the capacitated multicommodity network design problem. The greedy algorithm is based on link-rerouting and partial link-rerouting heuristics for the uncapacitated multicommodity network design problem. This algorithm involves a capacity scaling for reducing the number of candidate arcs and a restricted branch-and-bound for improving solutions. The algorithm succeeds in finding good solutions within a short computation time. The average computation time for solving benchmark problem instances is only several tens of seconds.

Amplitude Constrained MIMO Channels: Properties of Optimal Input Distributions and Bounds on the Capacity.

In this work, the capacity of multiple-input multiple-output channels that are subject to constraints on the support of the input is studied. The paper consists of two parts. The first part focuses on the general structure of capacity-achieving input distributions. Known results are surveyed and several new results are provided. With regard to the latter, it is shown that the support of a capacity-achieving input distribution is a small set in both a topological and a measure theoretical sense. Moreover, explicit conditions on the channel input space and the channel matrix are found such that the support of a capacity-achieving input distribution is concentrated on the boundary of the input space only. The second part of this paper surveys known bounds on the capacity and provides several novel upper and lower bounds for channels with arbitrary constraints on the support of the channel input symbols. As an immediate practical application, the special case of multiple-input multiple-output channels with amplitude constraints is considered. The bounds are shown to be within a constant gap to the capacity if the channel matrix is invertible and are tight in the high amplitude regime for arbitrary channel matrices. Moreover, in the regime of high amplitudes, it is shown that the capacity scales linearly with the minimum between the number of transmit and receive antennas, similar to the case of average power-constrained inputs.

(Invited) Progress Toward Large-Format Solid-State Cells Using Sulfide Solid Electrolytes

Solid-state batteries are widely considered the most promising candidate to begin displacing conventional lithium ion batteries for electric vehicles and consumer electronics in the next decade, but several challenges remain. High energy density and long cycle must still be demonstrated in large automotive-relevant cells. Low cost and scalable production also need to be proven. Solid Power is working to meet these challenges by combining a sulfide-based solid electrolyte, a nickel-rich NMC cathode, and a lithium metal anode in cells that surpass conventional lithium ion in energy density and safety. Solid Power and its collaborators have developed a suite of solid electrolytes, cathode materials, Nb-free surface treatments and production processes that deliver excellent energy density and cycle life in laboratory-scale cells. The attached figure shows early cycling performance for a recent 2 cm2 laboratory type lithium metal cell at 70°C and C/10 rate with a 4.3V charge voltage. This cell delivers 193 mAh/g based on surface-treated LiNi0.6Mn0.2Co0.2O2 (NMC622) mass and retains 191 mAh/g after 80 cycles at 100% depth-of-discharge. The excellent cycling stability of this composite cathode comes from improved electrochemical stability at the cathode-electrolyte interfaces as well as the ability to accommodate the volume changes of the NMC during cycling. Now focus is on scaling of both cell capacity and production throughput. Cells are currently produced up to 250 mAh, and larger cells up to 20 Ah in capacity with improved performance are planned for the next year as Solid Power’s pilot scale production line is completed in early 2019. This production line is built on low-cost processes and equipment that mirror lithium ion production and will be capable of producing up to 1 MWh per month when fully operational. Remaining challenges for Solid Power include demonstrating long life with lithium metal anodes in full-scale cells and working toward low-temperature operation. Figure 1

Stranded Coal Power Assets in China: A Case Study of Jilin Province

ABSTRACTThis article conducts the first provincial case study on the effect of environment-related risks on coal-fired power plants in Jilin, China, which creates “stranded assets”. Using power capacity expansion model and project evaluation model, the article first quantifies the rational coal power capacity during 2016–2020. Then, we calculate the value of stranded assets. The estimated scale of excess coal power capacity by 2020 ranges from 8,190 MW to 18,480 MW. The total value of stranded assets will decline over time under the different scenarios. Finally, policy implications for policymakers on power market reforms are proposed.

Resource Aware Chaining and Adaptive Capacity Scaling for Service Function Chains in Distributed Cloud Network

With the development of network technology such as software-defined network (SDN) and network function virtualization (NFV), Internet service providers (ISPs) are increasingly placing the virtual network function(VNF) instances at the network edge to provide network service. However, there are some issues to be tackled in the distributed SDN/NFV enabled cloud. Firstly, VNF instances require to be chained in predefined order to provide network services. It is a challenge to optimally select and chain VNF instances from the multi-instances. Moreover, due to the capacity limitation of the distributed edge nodes. The capacity of the Virtual Machines (VMs) that host VNFs should be proactively adjusted to cope with traffic demands. Since most existing works ignore the vertical capacity scaling problem in routing commodities with Service Function Chain (SFC) requests. In this paper, a fine-grained scheduling scheme at VM-level is proposed. Firstly, we formulate the SFC chaining problem as an Integer Linear Programming (ILP) model aiming to embed SFC requests with minimum estimated latency cost. Furthermore, we formulate the adaptive VNF resource allocation (VNF-AR) problem as a convex optimization. The theoretical optimal capacity for each VM can be derived from the Karush-Kuhn Tucker (KKT) conditions. At last, a novel joint optimization approach of VNF chaining and adaptive scaling (VNF-CAS) is proposed to efficiently embed the SFC requests. Performance evaluation shows that VNF-CAS can achieve better performance in SFC requests acceptance rate, average effective throughput, average load utilization and VM load balancing when it is compared with other algorithms in existing works.

With New Memories Come New Challenges

The performance of a modern computer system is increasingly getting dictated by the performance of the memory system. As we pack more cores, more threads per core, and domain specific accelerators on the chip, the memory system must scale in both capacity and bandwidth to supply the data to all these computing units. For the past four decades, we have had mainly two technologies for building the components of the memory system: SRAM for caches and DRAM for main memories, and systems have been limited by the constraints of these two technologies. We are at a juncture where several new memory technologies are emerging that can overcome the limitations of conventional technologies and offer new features such as nonvolatility. However, with new technologies come new challenges and system designers will need innovative solutions to address these challenges in order to fully realize the benefits offered by the emerging technologies.

Research on National Scientific Research Cooperation between China and the “One Belt and One Road”—Based on the Frontier Gravity Model

With the continuous development of science and technology and the deepening of international academic exchanges, international scientific research cooperation has become an important part of national strategy. Therefore, the research on the influencing factors of scientific research cooperation between China and “One Belt and One Road” countries plays an important guiding role in promoting the cultural, scientific and technological exchanges between them. This article is based on the United States patent and trademark office (USPTO) database in 2008-2017 patent cooperation data. On the basis of studying the current situation of scientific research cooperation between China and countries along the Belt and Road by means of intelligence mining, this paper innovate and quote the model of frontier gravity in economics creatively, and conducte an empirical study on the factors affecting scientific research cooperation between China and countries along the Belt and Road. The empirical results show that, except that the countries bordering on each other will inhibit China’s scientific research cooperation with countries along the “One Belt and One Road”, national scientific research strength, national innovation capacity, bilateral relations and economic scale will all promote the development of international scientific research cooperation. However, the geographical distance between the two countries has no significant impact on it. Besides, the limitation of this paper is that the number of patent cooperation between China and “One Belt and One Road” countries is generally small; many countries are not involved in patent cooperation, and data of some countries are missing and incomplete.

A General Method to Determine Asymptotic Capacity Upper Bounds for Wireless Networks

Capacity scaling laws offer fundamental understanding on the trend of user throughput behavior when the network size increases. Since the seminal work of Gupta and Kumar, there have been active research efforts in developing capacity scaling laws for ad hoc networks under various advanced physical (PHY) layer technologies. These efforts led to many custom-designed solutions, most of which were mathematically challenging and lacked general properties that can be extended to address scaling laws of ad hoc networks with other PHY layer technologies. So a question is: can we have a general methodology to obtain asymptotic capacity results for various PHY layer technologies? In this paper, we present a simple yet powerful method to determine capacity upper bounds under the protocol model. We prove the correctness of our proposed method and demonstrate its applications to various PHY layer technologies, including directional antenna, MIMO, multi-channel multi-radio, cognitive radio, multiple packet reception, and full-duplex radio. This new method offers a simple tool to researchers to quickly determine asymptotic capacity of wireless networks with a particular PHY layer technology without the need to resort to complex custom-designed analysis as done in the literature.

On the Capacity of Fading Channels With Peak and Average Power Constraints at Low SNR

The capacity of fading channels under peak and average power constraints in the low-SNR regime is investigated. We show that the capacity scales essentially as $C \approx A \text{SNR} \int _{1- \frac{1}{A}}^1 F^{-1}(t)dt$ , where $A$ is the peak-to-average power ratio (PAPR), and $F(\cdot)$ is the cumulative distribution function of the fading channel. We also prove that an On – Off power scheme is sufficient to asymptotically achieve the capacity. Furthermore, by considering the variable PAPR scenario, we generalize the scalability of the capacity and derive the asymptotic expression for the capacity in the low-SNR regime.

On the Achievable Rates of Virtual Full-Duplex Relay Channel

We study a multihop “virtual” full-duplex relay channel as a special case of a general multiple multicast relay network. For such a channel, quantize-map-and-forward (QMF) [and its generalization of noisy network coding (NNC) and short message NNC] achieves the cut-set upper bound within a constant additive gap, where the gap grows linearly with the number of relay stages K. This gap, however, may not be acceptable for practical communication systems with multihop transmissions (e.g., a wireless backhaul operating at high frequencies). Recently, we improved the capacity scaling by using a forward sliding-window (SW) decoding and by optimizing the quantization level at each relay, obtaining the gap that grows logarithmically as log K. Furthermore, the improved scheme has lower decoding complexity and delay than the general QMF and NNC approaches. In this paper, we further improve the performance by presenting a mixed scheme in which each relay can perform either decode-and-forward (DF) or the improved QMF (with SW decoding) and can choose to perform rate-splitting to enable partial interference cancellation. In general, the optimization of the relay DF/QMF configuration is combinatorial. Nevertheless, we provide that a simple greedy algorithm finds an optimal configuration under some practically reasonable assumptions. We derive an achievable rate that is easily computable and show that the proposed mixed scheme outperforms the QMF-only schemes. We demonstrate that the performance improvement increases with K, which indicates that the mixed scheme is indeed beneficial for multihop transmission.

A Primal-dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint

We consider the problem of a firm seeking to use personalized pricing to sell an exogenously given stock of a product over a finite selling horizon to different consumer types. We assume that the type of an arriving consumer can be observed but the demand function associated with each type is initially unknown. The firm sets personalized prices dynamically for each type and attempts to maximize the revenue over the season. We provide a learning algorithm that is near-optimal when the demand and capacity scale in proportion. The algorithm utilizes the primal-dual formulation of the problem and learns the dual optimal solution explicitly. It allows the algorithm to overcome the curse of dimensionality (the rate of regret is independent of the number of types) and sheds light on novel algorithmic designs for learning problems with resource constraints.

Effects of bundle care on colorectal cancer patients receiving peripherally inserted central catheter

Objective To explore the effects of bundle care on colorectal cancer patients receiving peripherally inserted central catheter (PICC) . Methods Colorectal patients who received PICC in Quzhou Third Hospital between January 2016 and December 2017 were selected by convenient sampling. Totally 90 patients receiving PICC between January and December 2016 were divided into the control group which received conventional nursing cate, while another 90 patients receiving PICC between January and December 2017 were divided into the observation group which received bundle care. The patients' clinical data were collected. The PICC Self-Management Capacity Scale, Connor-Davidson Resilience Scale and Quality of life Questionnaire-Core 30 were used to evaluate the patients' PICC self-management capacity, psychological elasticity and quality of life. Results There was no statistical difference in the total scores of PICC self-management capacity, psychological resilience and overall health/quality of life or in any single dimension of function or symptom between the two groups before the intervention (P>0.05) ; After intervention, the scores of PICC self-management capacity, psychological resilience, functional domain, overall health/quality of life of patients in the observation group were higher than those in the control group, while the symptom domain score and single domain score were lower than those in the control group, with statistical significances (P<0.05) . The incidence rate of phlebitis, catheter-related infection, oozing of puncture point, drug extravasation, catheter obstruction and venous thrombosis in the observation group were lower than that in the control group after the intervention (P<0.05) . Conclusions Bundle care show significant effects on colorectal cancer patients receiving PICC, which can improve patients' PICC self-management capacity, psychological elasticity and quality of life and reduce the incidence rate of catheter-related adverse effects, complications and venous thrombosis. Key words: Colorectal neoplasms; Bundle care; Peripherally inserted central catheter (PICC); Complications; Venous thrombosis

Enabling Effective Social Impact: Towards a Model for Impact Scaling Agreements

Scaling is a critical organizational phase for social organizations: their upfront financial needs increase dramatically. This paper responds—on the basis of initial research on capacity, up-, and deep scaling strategies—to the need for integrated knowledge on financing processes within the context of social organizations’ impact scaling phase. An exploration of a market leader’s processes uncovered both strengths and struggles, which in turn enabled new levels of understanding related to the research question, “How can impact scaling agreements enable effective social impact?” The fact that the financial provider examined in this empirical study lacked alignment in its scaling approach, goals, and reporting processes over time hampered its effectiveness and sustainability. The findings from this qualitative inter-temporal content analysis enable the development of a model for impact scaling agreements. This shows ongoing flows between the provider and recipient of financial and nonfinancial resources and impact information, as well as decision-making and reporting processes. The outcome of researching the question “how can impact scaling agreements enable effective social impact?” was the identification of three success enhancers for effective social impact scaling agreements to enable social impact: (1) Financial provider alignment pre- and per-engagement in terms of expectations with regard to scaling approach and goals; (2) Scaling approach coherence in terms of understanding and acting upon the inter-relatedness and, in fact, mutual dependency between capacity, up-, and deep scaling; (3) Impact reporting alignment of the target group with the financial recipient and the financial provider. This research makes a twofold contribution to the literature. First, the pivotal role of internal alignment between mission, strategy, reporting, and decision-making processes is explored; second, the three scaling strategies of capacity, up-, and deep scaling are established as interrelated dimensions of the same phenomenon.

Chemical Fuel Supply Chain Integration and Optimization under the Influence of Uncertain Factors

The petrochemical industry belongs to the dangerous chemical industry. The optimization of supply chain can significantly improve the overall profits of enterprises, reducing costs and risks. Based on the value-at-risk theory, this paper proposes an optimal dispatching model for chemical fuel supply chain, which is used to solve the uncertainty of market demand and capacity scale. The sample mean estimation method is used to calculate the optimal demand risk threshold and capacity risk threshold. The calculation results show that the higher the risk threshold is, the lower the supply chain cost will be. The impact of chemical fuel market demand risk threshold on the supply chain object function is greater than that of capacity risk threshold on the supply chain object function. Therefore, the supply chain optimal dispatching should take the market demand as a priority. According to calculation results, the optimal values of demand risk threshold and capacity risk threshold are set to 0.26 and 0.1, respectively. This research can provide theoretical reference for the large-scale supply chain optimal dispatching under the condition of uncertain factors.

Influence of cohesive stresses on shear capacity of reinforced SFRC beams without stirrups: A discrete crack approach

An experimental investigation on the fracture behavior and the dilatant crack opening in the shear response of concrete with discrete steel fiber reinforcement is presented. Hooked-ended steel fibers are used at 0.5% and 0.75% volume fractions. From the experimental fracture response of steel fiber reinforced concrete beams, the cohesive stress-crack opening relationship is derived using the cracked hinge model. Load tests are conducted on laboratory-sized reinforced concrete beams without steel stirrups, designed to fail in shear. The crack growth and propagation in the shear behavior of concrete with and without fibers are evaluated from displacements measured using digital image correlation (DIC). The in-situ dilatant behavior of the shear crack is established from the slip and the crack opening displacements measured across the critical shear crack. A discrete crack-based formulation with internal contact forces on the crack faces and cohesive stress from fibers is developed for predicting the shear capacity of a reinforced concrete beam. The discrete crack-based model is derived using the physical observation on cracks in the reinforced concrete beams. The prediction of the model includes an increase in the contact forces with an increase in the fiber force at peak shear resistance with an increasing volume fraction of fibers. The additional contact forces mobilized across the crack by the fibers maintains the shear transfer across the crack and hence the load carrying capacity is sustained for a larger crack opening. The model derived from laboratory-sized specimens accurately predicts the scaling of the shear capacity with the size of the beam.