Showing papers by "University of Houston published in 2018"

Clinical Practice Guidelines for Clostridium difficile Infection in Adults and Children: 2017 Update by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA)

Abstract: A panel of experts was convened by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA) to update the 2010 clinical practice guideline on Clostridium difficile infection (CDI) in adults. The update, which has incorporated recommendations for children (following the adult recommendations for epidemiology, diagnosis, and treatment), includes significant changes in the management of this infection and reflects the evolving controversy over best methods for diagnosis. Clostridium difficile remains the most important cause of healthcare-associated diarrhea and has become the most commonly identified cause of healthcare-associated infection in adults in the United States. Moreover, C. difficile has established itself as an important community pathogen. Although the prevalence of the epidemic and virulent ribotype 027 strain has declined markedly along with overall CDI rates in parts of Europe, it remains one of the most commonly identified strains in the United States where it causes a sizable minority of CDIs, especially healthcare-associated CDIs. This guideline updates recommendations regarding epidemiology, diagnosis, treatment, infection prevention, and environmental management.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

High Performance Bifunctional Porous Non-Noble Metal Phosphide Catalyst for Overall Water Splitting

Abstract: Water electrolysis is an advanced energy conversion technology to produce hydrogen as a clean and sustainable chemical fuel, which potentially stores the abundant but intermittent renewable energy sources scalably. Since the overall water splitting is an uphill reaction in low efficiency, innovative breakthroughs are desirable to greatly improve the efficiency by rationally designing non-precious metal-based robust bifunctional catalysts for promoting both the cathodic hydrogen evolution and anodic oxygen evolution reactions. We report a hybrid catalyst constructed by iron and dinickel phosphides on nickel foams that drives both the hydrogen and oxygen evolution reactions well in base, and thus substantially expedites overall water splitting at 10 mA cm−2 with 1.42 V, which outperforms the integrated iridium (IV) oxide and platinum couple (1.57 V), and are among the best activities currently. Especially, it delivers 500 mA cm−2 at 1.72 V without decay even after the durability test for 40 h, providing great potential for large-scale applications.

DarkSide-20k: A 20 tonne two-phase LAr TPC for direct dark matter detection at LNGS

Abstract: Building on the successful experience in operating the DarkSide-50 detector, the DarkSide Collaboration is going to construct DarkSide-20k, a direct WIMP search detector using a two-phase Liquid Argon Time Projection Chamber (LAr TPC) with an active (fiducial) mass of 23 t (20 t). This paper describes a preliminary design for the experiment, in which the DarkSide-20k LAr TPC is deployed within a shield/veto with a spherical Liquid Scintillator Veto (LSV) inside a cylindrical Water Cherenkov Veto (WCV). This preliminary design provides a baseline for the experiment to achieve its physics goals, while further development work will lead to the final optimization of the detector parameters and an eventual technical design. Operation of DarkSide-50 demonstrated a major reduction in the dominant 39Ar background when using argon extracted from an underground source, before applying pulse shape analysis. Data from DarkSide-50, in combination with MC simulation and analytical modeling, shows that a rejection factor for discrimination between electron and nuclear recoils of $>3 \times 10^{9}$ is achievable. This, along with the use of the veto system and utilizing silicon photomultipliers in the LAr TPC, are the keys to unlocking the path to large LAr TPC detector masses, while maintaining an experiment in which less than $< 0.1$ events (other than $ u$ -induced nuclear recoils) is expected to occur within the WIMP search region during the planned exposure. DarkSide-20k will have ultra-low backgrounds than can be measured in situ, giving sensitivity to WIMP-nucleon cross sections of $1.2 \times 10^{-47}$ cm2 ( $1.1 \times 10^{-46}$ cm2) for WIMPs of 1 TeV/c2 (10 TeV/c2) mass, to be achieved during a 5 yr run producing an exposure of 100 t yr free from any instrumental background.

When Mobile Blockchain Meets Edge Computing

Abstract: Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications (e.g., finance, healthcare, and logistics), its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data (i.e., a block) to the blockchain. Solving the proof of work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.

Low-Mass Dark Matter Search with the DarkSide-50 Experiment

Abstract: We present the results of a search for dark matter weakly interacting massive particles (WIMPs) in the mass range below 20 GeV/c2 using a target of low-radioactivity argon with a 6786.0 kg d exposure. The data were obtained using the DarkSide-50 apparatus at Laboratori Nazionali del Gran Sasso. The analysis is based on the ionization signal, for which the DarkSide-50 time projection chamber is fully efficient at 0.1 keVee. The observed rate in the detector at 0.5 keVee is about 1.5 event/keVee/kg/d and is almost entirely accounted for by known background sources. We obtain a 90% C.L. exclusion limit above 1.8 GeV/c2 for the spin-independent cross section of dark matter WIMPs on nucleons, extending the exclusion region for dark matter below previous limits in the range 1.8–6 GeV/c2.

Water splitting by electrolysis at high current densities under 1.6 volts

Abstract: Splitting water into hydrogen and oxygen by electrolysis using electricity from intermittent waste heat, wind, or solar energies is one of the easiest and cleanest methods for high-purity hydrogen production and an effective way to store the excess electrical power. The key dilemma for efficient large-scale production of hydrogen by splitting of water via the hydrogen and oxygen evolution reactions (HER and OER, respectively) is the high overpotential required, especially for the OER. We report an exceptionally active and durable OER catalyst yielding current densities of 500 and 1000 mA cm−2 at overpotentials of only 259 mV and 289 mV in alkaline electrolyte, respectively, fulfilling the commercial criteria of the OER process. Together with a good HER catalyst, we have achieved the commercially required current densities of 500 and 1000 mA cm−2 at 1.586 and 1.657 V, respectively, with very good stability, dramatically lower than any previously reported voltage. This discovery sets the stage for large-scale hydrogen production by water splitting using excess electrical power whenever and wherever available.

High thermal conductivity in cubic boron arsenide crystals.

Abstract: The high density of heat generated in power electronics and optoelectronic devices is a critical bottleneck in their application. New materials with high thermal conductivity are needed to effectively dissipate heat and thereby enable enhanced performance of power controls, solid-state lighting, communication, and security systems. We report the experimental discovery of high thermal conductivity at room temperature in cubic boron arsenide (BAs) grown through a modified chemical vapor transport technique. The thermal conductivity of BAs, 1000 ± 90 watts per meter per kelvin meter-kelvin, is higher than that of silicon carbide by a factor of 3 and is surpassed only by diamond and the basal-plane value of graphite. This work shows that BAs represents a class of ultrahigh-thermal conductivity materials predicted by a recent theory, and that it may constitute a useful thermal management material for high-power density electronic devices.

Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification

Abstract: Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for hyperspectral image classification tasks. Training deep neural networks, such as a convolutional neural network for classification requires a large number of labeled samples. However, in remote sensing applications, we usually only have a small amount of labeled data for training because they are expensive to collect, although we still have abundant unlabeled data. In this paper, we propose semi-supervised deep learning for hyperspectral image classification—our approach uses limited labeled data and abundant unlabeled data to train a deep neural network. More specifically, we use deep convolutional recurrent neural networks (CRNN) for hyperspectral image classification by treating each hyperspectral pixel as a spectral sequence. In the proposed semi-supervised learning framework, the abundant unlabeled data are utilized with their pseudo labels (cluster labels). We propose to use all the training data together with their pseudo labels to pre-train a deep CRNN, and then fine-tune using the limited available labeled data. Further, to utilize spatial information in the hyperspectral images, we propose a constrained Dirichlet process mixture model (C-DPMM), a non-parametric Bayesian clustering algorithm, for semi-supervised clustering which includes pairwise must-link and cannot-link constraints—this produces high-quality pseudo-labels, resulting in improved initialization of the deep neural network. We also derived a variational inference model for the C-DPMM for efficient inference. Experimental results with real hyperspectral image data sets demonstrate that the proposed semi-supervised method outperforms state-of-the-art supervised and semi-supervised learning methods for hyperspectral classification.

Recent advances in graphene-based biosensor technology with applications in life sciences

Abstract: Graphene’s unique physical structure, as well as its chemical and electrical properties, make it ideal for use in sensor technologies. In the past years, novel sensing platforms have been proposed with pristine and modified graphene with nanoparticles and polymers. Several of these platforms were used to immobilize biomolecules, such as antibodies, DNA, and enzymes to create highly sensitive and selective biosensors. Strategies to attach these biomolecules onto the surface of graphene have been employed based on its chemical composition. These methods include covalent bonding, such as the coupling of the biomolecules via the 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride and N-hydroxysuccinimide reactions, and physisorption. In the literature, several detection methods are employed; however, the most common is electrochemical. The main reason for researchers to use this detection approach is because this method is simple, rapid and presents good sensitivity. These biosensors can be particularly useful in life sciences and medicine since in clinical practice, biosensors with high sensitivity and specificity can significantly enhance patient care, early diagnosis of diseases and pathogen detection. In this review, we will present the research conducted with antibodies, DNA molecules and, enzymes to develop biosensors that use graphene and its derivatives as scaffolds to produce effective biosensors able to detect and identify a variety of diseases, pathogens, and biomolecules linked to diseases.

Nanoparticle orientation to control RNA loading and ligand display on extracellular vesicles for cancer regression

Abstract: Nanotechnology offers many benefits, and here we report an advantage of applying RNA nanotechnology for directional control. The orientation of arrow-shaped RNA was altered to control ligand display on extracellular vesicle membranes for specific cell targeting, or to regulate intracellular trafficking of small interfering RNA (siRNA) or microRNA (miRNA). Placing membrane-anchoring cholesterol at the tail of the arrow results in display of RNA aptamer or folate on the outer surface of the extracellular vesicle. In contrast, placing the cholesterol at the arrowhead results in partial loading of RNA nanoparticles into the extracellular vesicles. Taking advantage of the RNA ligand for specific targeting and extracellular vesicles for efficient membrane fusion, the resulting ligand-displaying extracellular vesicles were capable of specific delivery of siRNA to cells, and efficiently blocked tumour growth in three cancer models. Extracellular vesicles displaying an aptamer that binds to prostate-specific membrane antigen, and loaded with survivin siRNA, inhibited prostate cancer xenograft. The same extracellular vesicle instead displaying epidermal growth-factor receptor aptamer inhibited orthotopic breast cancer models. Likewise, survivin siRNA-loaded and folate-displaying extracellular vesicles inhibited patient-derived colorectal cancer xenograft.

Unusual high thermal conductivity in boron arsenide bulk crystals

Abstract: Conventional theory predicts that ultrahigh lattice thermal conductivity can only occur in crystals composed of strongly bonded light elements, and that it is limited by anharmonic three-phonon processes. We report experimental evidence that departs from these long-held criteria. We measured a local room-temperature thermal conductivity exceeding 1000 watts per meter-kelvin and an average bulk value reaching 900 watts per meter-kelvin in bulk boron arsenide (BAs) crystals, where boron and arsenic are light and heavy elements, respectively. The high values are consistent with a proposal for phonon-band engineering and can only be explained by higher-order phonon processes. These findings yield insight into the physics of heat conduction in solids and show BAs to be the only known semiconductor with ultrahigh thermal conductivity.

Hierarchical CoP/Ni5P4/CoP microsheet arrays as a robust pH-universal electrocatalyst for efficient hydrogen generation

Abstract: Highly active catalysts composed of earth-abundant materials, performing as efficiently as Pt catalysts, are crucial for sustainable hydrogen production through water splitting. However, most efficient catalysts consist of nanostructures made via complex synthetic methods, making scale-up quite challenging. Here we report an effective strategy for developing a very active and durable pH-universal electrocatalyst for the hydrogen evolution reaction (HER). This catalyst is constructed using a sandwich-like structure, where hierarchical cobalt phosphide (CoP) nanoparticles serve as thin skins covering both sides of nickel phosphide (Ni5P4) nanosheet arrays, forming self-supported sandwich-like CoP/Ni5P4/CoP microsheet arrays with lots of mesopores and macropores. The as-prepared electrocatalyst requires an overpotential of only 33 mV to achieve a benchmark of 10 mA cm−2, with a very large exchange current density and high turnover frequencies (TOFs) in acid media, superior to most electrocatalysts made of metal phosphides, well-known MoS2 and WS2 catalysts, and it performs comparably to state-of-the-art Pt catalysts. In particular, this electrocatalyst shows impressive operational stability at an extremely large current density of 1 A cm−2, indicating its possible application toward large-scale water electrolysis. Additionally, this electrocatalyst is very active in alkaline electrolyte (71 mV at 10 mA cm−2), which demonstrates its pH universality as a HER catalyst with outstanding catalytic activity. This simple strategy does not involve any solvothermal and hydrothermal processes, paving a new avenue toward the design of robust non-noble electrocatalysts for hydrogen production, aimed at commercial water electrolysis.

Soft Ultrathin Electronics Innervated Adaptive Fully Soft Robots.

Abstract: Soft robots outperform the conventional hard robots on significantly enhanced safety, adaptability, and complex motions. The development of fully soft robots, especially fully from smart soft materials to mimic soft animals, is still nascent. In addition, to date, existing soft robots cannot adapt themselves to the surrounding environment, i.e., sensing and adaptive motion or response, like animals. Here, compliant ultrathin sensing and actuating electronics innervated fully soft robots that can sense the environment and perform soft bodied crawling adaptively, mimicking an inchworm, are reported. The soft robots are constructed with actuators of open-mesh shaped ultrathin deformable heaters, sensors of single-crystal Si optoelectronic photodetectors, and thermally responsive artificial muscle of carbon-black-doped liquid-crystal elastomer (LCE-CB) nanocomposite. The results demonstrate that adaptive crawling locomotion can be realized through the conjugation of sensing and actuation, where the sensors sense the environment and actuators respond correspondingly to control the locomotion autonomously through regulating the deformation of LCE-CB bimorphs and the locomotion of the robots. The strategy of innervating soft sensing and actuating electronics with artificial muscles paves the way for the development of smart autonomous soft robots.

Exfoliation of a non-van der Waals material from iron ore hematite

Abstract: With the advent of graphene, the most studied of all two-dimensional materials, many inorganic analogues have been synthesized and are being exploited for novel applications. Several approaches have been used to obtain large-grain, high-quality materials. Naturally occurring ores, for example, are the best precursors for obtaining highly ordered and large-grain atomic layers by exfoliation. Here, we demonstrate a new two-dimensional material ‘hematene’ obtained from natural iron ore hematite (α-Fe2O3), which is isolated by means of liquid exfoliation. The two-dimensional morphology of hematene is confirmed by transmission electron microscopy. Magnetic measurements together with density functional theory calculations confirm the ferromagnetic order in hematene while its parent form exhibits antiferromagnetic order. When loaded on titania nanotube arrays, hematene exhibits enhanced visible light photocatalytic activity. Our study indicates that photogenerated electrons can be transferred from hematene to titania despite a band alignment unfavourable for charge transfer.

Predicting the Band Gaps of Inorganic Solids by Machine Learning.

Abstract: A machine-learning model is developed that can accurately predict the band gap of inorganic solids based only on composition. This method uses support vector classification to first separate metals from nonmetals, followed by quantitatively predicting the band gap of the nonmetals using support vector regression. The superb accuracy of the regression model is obtained by using a training set composed entirely of experimentally measured band gaps and utilizing only compositional descriptors. In fact, because of the unique training set of experimental data, the machine learning predicted band gaps are significantly closer to the experimentally reported values than DFT (PBE-level) calculated band gaps. Not only does this resulting tool provide the ability to accurately predict the band gap for any composition but also the versatility and speed of the prediction based only on composition will make this a great resource to screen inorganic phase space and direct the development of functional inorganic materials.

User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach

Abstract: Densely deployment of various small-cell base stations in cellular networks to increase capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the energy harvesting capabilities in base stations as an alternative energy supply is becoming a reality. How to make efficient utilization of radio resource and renewable energy is a brand-new challenge. This paper investigates the optimal policy for user scheduling and resource allocation in HetNets powered by hybrid energy with the purpose of maximizing energy efficiency of the overall network. Since wireless channel conditions and renewable energy arrival rates have stochastic properties and the environment’s dynamics are unknown, the model-free reinforcement learning approach is used to learn the optimal policy through interactions with the environment. To solve our problem with continuous-valued state and action variables, a policy-gradient-based actor-critic algorithm is proposed. The actor part uses the Gaussian distribution as the parameterized policy to generate continuous stochastic actions, and the policy parameters are updated with the gradient ascent method. The critic part uses compatible function approximation to estimate the performance of the policy and helps the actor learn the gradient of the policy. The advantage function is used to further reduce the variance of the policy gradient. Using the numerical simulations, we demonstrate the convergence property of the proposed algorithm and analyze network energy efficiency.

Constraints on sub-GeV dark matter-electron scattering from the DarkSide-50 experiment

Abstract: We present new constraints on sub-GeV dark-matter particles scattering off electrons based on 6780.0 kg d of data collected with the DarkSide-50 dual-phase argon time projection chamber. This analysis uses electroluminescence signals due to ionized electrons extracted from the liquid argon target. The detector has a very high trigger probability for these signals, allowing for an analysis threshold of three extracted electrons, or approximately 0.05 keVee. We calculate the expected recoil spectra for dark matter-electron scattering in argon and, under the assumption of momentum-independent scattering, improve upon existing limits from XENON10 for dark-matter particles with masses between 30 and 100 MeV/c^{2}.

Exploring Ethylene/Polar Vinyl Monomer Copolymerizations Using Ni and Pd α-Diimine Catalysts

Abstract: ConspectusThe most ubiquitous polymer, polyethylene (PE), is produced either through a radical-initiated process or, more commonly, through a coordination/insertion process employing early transition metal catalysts, particularly titanium- and chromium-based systems. These oxophilic early metal catalysts are not functional-group-tolerant and thus cannot be used to synthesize copolymers of ethylene and polar vinyl monomers such as alkyl acrylates and vinyl acetate. Such PE copolymers have enhanced properties relative to PE and are made through radical polymerization processes, requiring exceptionally high pressures and temperatures. Copolymerizations of polar vinyl monomers with ethylene using more functional group-tolerant late metal catalysts potentially offer an attractive alternative for generating such value-added copolymers since ligand variations may provide more control of polymer microstructures and milder reaction conditions would apply. This Account describes our efforts, particularly through de...

Measurement of the Electron Antineutrino Oscillation with 1958 Days of Operation at Daya Bay

Abstract: We report a measurement of electron antineutrino oscillation from the Daya Bay Reactor Neutrino Experiment with nearly 4 million reactor ν[over ¯]_{e} inverse β decay candidates observed over 1958 days of data collection. The installation of a flash analog-to-digital converter readout system and a special calibration campaign using different source enclosures reduce uncertainties in the absolute energy calibration to less than 0.5% for visible energies larger than 2 MeV. The uncertainty in the cosmogenic ^{9}Li and ^{8}He background is reduced from 45% to 30% in the near detectors. A detailed investigation of the spent nuclear fuel history improves its uncertainty from 100% to 30%. Analysis of the relative ν[over ¯]_{e} rates and energy spectra among detectors yields sin^{2}2θ_{13}=0.0856±0.0029 and Δm_{32}^{2}=(2.471_{-0.070}^{+0.068})×10^{-3} eV^{2} assuming the normal hierarchy, and Δm_{32}^{2}=-(2.575_{-0.070}^{+0.068})×10^{-3} eV^{2} assuming the inverted hierarchy.

The Shape of and Solutions to the MTurk Quality Crisis

Abstract: Amazon’s Mechanical Turk (MTurk) is widely used to collect affordable and high-quality survey responses. However, researchers recently noticed a substantial decline in data quality, sending shockwaves throughout the social sciences. The problem seems to stem from the use of Virtual Private Servers (VPSs) by respondents outside the U.S. to fool MTurk’s filtering system, but we know relatively little about the cause and consequence of this form of fraud. Analyzing 38 studies conducted on MTurk, we demonstrate that this problem is not new - we find a similar spike in VPS use in 2015. Utilizing two new studies, we show that data from these respondents is of substantially worse quality. Next, we provide two solutions for this problem using an API for an IP traceback application (IP Hub). We provide both a post-hoc method for identifying fraudulent respondents using an original R package (“rIP”) and an associated online application, and an a priori method using JavaScript and PHP code in Qualtrics to block fraudulent respondents from participating. We demonstrate the effectiveness of the screening procedure in a third study. Overall, our results suggest that fraudulent respondents pose a serious threat to data quality but can be easily identified and screened out.

Discovery of ZrCoBi based half Heuslers with high thermoelectric conversion efficiency.

Abstract: Thermoelectric materials are capable of converting waste heat into electricity. The dimensionless figure-of-merit (ZT), as the critical measure for the material’s thermoelectric performance, plays a decisive role in the energy conversion efficiency. Half-Heusler materials, as one of the most promising candidates for thermoelectric power generation, have relatively low ZTs compared to other material systems. Here we report the discovery of p-type ZrCoBi-based half-Heuslers with a record-high ZT of ∼1.42 at 973 K and a high thermoelectric conversion efficiency of ∼9% at the temperature difference of ∼500 K. Such an outstanding thermoelectric performance originates from its unique band structure offering a high band degeneracy (Nv) of 10 in conjunction with a low thermal conductivity benefiting from the low mean sound velocity (vm ∼2800 m s−1). Our work demonstrates that ZrCoBi-based half-Heuslers are promising candidates for high-temperature thermoelectric power generation. Identifying new compounds with intrinsically high conversion efficiency is the key to demonstrating next-generation thermoelectric modules. Here, Zhu et al. report the discovery of p-type ZrCoBi-based half Heuslers with thermoelectric conversion efficiency of 9% and large high-temperature stability.

Toward Distributed/Decentralized DC Optimal Power Flow Implementation in Future Electric Power Systems

Abstract: This paper reviews distributed/decentralized algorithms to solve the optimal power flow (OPF) problem in electric power systems. Six decomposition coordination algorithms are studied, including analytical target cascading, optimality condition decomposition, alternating direction method of multipliers, auxiliary problem principle, consensus+innovations, and proximal message passing. The basic concept, the general formulation, the application for dc-OPF, and the solution methodology for each algorithm are presented. We apply these six decomposition coordination algorithms on a test system, and discuss their key features and simulation results.

Ancient lowland Maya complexity as revealed by airborne laser scanning of northern Guatemala.

Abstract: INTRODUCTION Lowland Maya civilization flourished from 1000 BCE to 1500 CE in and around the Yucatan Peninsula. Known for its sophistication in writing, art, architecture, astronomy, and mathematics, this civilization is still obscured by inaccessible forest, and many questions remain about its makeup. In 2016, the Pacunam Lidar Initiative (PLI) undertook the largest lidar survey to date of the Maya region, mapping 2144 km 2 of the Maya Biosphere Reserve in Guatemala. The PLI data have made it possible to characterize ancient settlement and infrastructure over an extensive, varied, and representative swath of the central Maya Lowlands. RATIONALE Scholars first applied modern lidar technology to the lowland Maya area in 2009, focusing analysis on the immediate surroundings of individual sites. The PLI covers twice the area of any previous survey and involves a consortium of scholars conducting collaborative and complementary analyses of the entire survey region. This cooperation among scholars has provided a unique regional perspective revealing substantial ancient population as well as complex previously unrecognized landscape modifications at a grand scale throughout the central lowlands in the Yucatan peninsula. RESULTS Analysis identified 61,480 ancient structures in the survey region, resulting in a density of 29 structures/km 2 . Controlling for a number of complex variables, we estimate an average density of ~80 to 120 persons/km 2 at the height of the Late Classic period (650 to 800 CE). Extrapolation of this settlement density to the entire 95,000 km 2 of the central lowlands produces a population range of 7 million to 11 million. Settlement distribution is not homogeneous, however; we found evidence of (i) rural areas with low overall density, (ii) periurban zones with small urban centers and dispersed populations, and (iii) urban zones where a single, large city integrated a wider population. The PLI survey revealed a landscape heavily modified for intensive agriculture, necessary to sustain populations on this scale. Lidar shows field systems in the low-lying wetlands and terraces in the upland areas. The scale of wetland systems and their association with dense populations suggest centralized planning, whereas upland terraces cluster around residences, implying local management. Analysis identified 362 km 2 of deliberately modified agricultural terrain and another 952 km 2 of unmodified uplands for potential swidden use. Approximately 106 km of causeways within and between sites constitute evidence of inter- and intracommunity connectivity. In contrast, sizable defensive features point to societal disconnection and large-scale conflict. CONCLUSION The 2144 km 2 of lidar data acquired by the PLI alter interpretations of the ancient Maya at a regional scale. An ancient population in the millions was unevenly distributed across the central lowlands, with varying degrees of urbanization. Agricultural systems found in lidar indicate how these populations were supported, although an irregular distribution suggests the existence of a regional agricultural economy of great complexity. Substantial infrastructural investment in integrative features (causeways) and conflictive features (defensive systems) highlights the interconnectivity of the ancient lowland Maya landscape. These perspectives on the ancient Maya generate new questions, refine targets for fieldwork, elicit regional study across continuous landscapes, and advance Maya archaeology into a bold era of research and exploration.