Scaling Issues Research Articles

Creating order from the mess: web archive derivative datasets and notebooks

ABSTRACT For a quarter-century, memory institutions have been preserving web-based content. These web archives have been collected and stored in ARC and WARC (W/ARC) file formats and will form a basis for contemporary histories. Yet, these formats present significant challenges to researchers who wish to access and use web archival data. This is primarily due to the nature of collecting, storing, and providing access to these multifaceted digital objects. In other words, web archives are messy. Applying traditional archival methods of description to digital-born collections is complicated due to issues of provenance, original order, and scale. However, we believe that archival description offers a practical starting point for thinking about access. This paper argues a robust finding aid must extend beyond basic collection-level description to allow for more meaningful interactions with web archives. As such, we propose a reimagining of a traditional finding-aid model into a three-level mode of description to include computational methods, the generation of derivative datasets, and interactive code-rich notebooks. These three factors combine to ultimately contribute to the expanded access and use of web archives.

The environmental aspects of renewable energy consumption and structural change in Sweden: A new perspective from wavelet-based granger causality approach

The current paper assessed the time-frequency analysis interrelationship between CO2 emissions and financial development, economic growth, renewable energy use, structural change, and non-renewable energy use in Sweden. We utilized a quarterly dataset stretching from 1980-2019. In order to unlock these interrelationships, we leverage wavelet tools (wavelet-based Granger causality and wavelet coherence). The wavelet-based Granger causality (WGC) test accounts for the issue of multiple time scales in a time series analysis. Another uniqueness of the WGC lies in its resistance to distribution assumption and misspecification in a time series model. Additionally, the wavelet coherence estimator instantaneously evaluates correlation and causality among the interacting indicators in a model. The outcomes of the wavelet coherence exposed that renewable energy, financial development, economic growth, structural change, and trade openness enhance the environment's quality while non-renewable energy intensifies CO2. Moreover, the WGC shows that all the variables can predict each other. Based on these findings, policymakers in Sweden should focus more on improving public understanding of renewable energy and environmental preservation. We believe that Sweden's shift to service-sector-led growth will help to safeguard the environment.

Data-Driven Approach Develops Proactive Chemical Treatment Strategies

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3723425, “Delaware Basin Formational Solid Deposit Trends: A Data-Driven Look at Developing Proactive Chemical Treatment Strategies,” by Rachel W. Hudson, SPE, and Kevin J. Spicka, SPE, ChampionX, and Ryan W. Pagel, SPE, Cooper Gray Consulting, et al. The paper has not been peer reviewed. _ This paper highlights a chemical-treatment strategy developed specifically for solids mitigation for a Delaware Basin operator. More than 200 Delaware Basin solid samples from the New Mexico/Texas state line were collected over a 2-year period, spanning seven distinct producing intervals. A statistical approach was taken to determine which formations had the greatest potential for paraffin, carbonate, acid-soluble iron compounds, and sulfate scales. A similar macroscopic approach in other basins may be applied to identify what proactive treatment strategies could be developed based upon the specific challenges of those regions and may similarly improve field performance. Introduction A need exists for optimized cost-effective solids-mitigation and treatment strategies for oil and gas operators across multiple basins. The key is knowing what type of solids have the greatest potential to form before they precipitate and cause issues within a given system. There is a long-standing history of successful chemical and mechanical treatment strategies for solids in the Midland Basin, but less is known about recent unconventional developments in the Delaware Basin, particularly in the Wolfcamp and Bone Springs formations. The Delaware Basin, located within the Permian, is a series of stacked plays. The Avalon sits above the First, Second, and Third Bone Springs, all of which are Leonard-age strata. These formations sit stratigraphically above the Wolfcamp formations. Each producing interval has similar characteristics that directly affect the risk of solid precipitation. During the last decade, the most-common unconventional targets in the Delaware Basin have been the Avalon, Bone Springs, and Wolfcamp formations, which are the focus of the complete paper. Designing cost-effective proactive chemical or mechanical treatment strategies and optimizing those efforts is a challenging dynamic process. Historically, operators take one of two approaches: treat everything while changing chemical type on an as-needed basis or only treating locations where a problem has occurred in the past. Theory and Methods From experiences recorded across the Delaware Basin, it is clear that some formations have more scale or paraffin issues, but this has all been based on individual field experience in localized areas. For example, it is not uncommon to hear that the Second Bone Springs tends to have paraffin issues. If this is true on a larger scale, then widespread untreated solids-deposit data should be able to indicate which solids are the most probable from each formation. If a pattern can be found in the data, then a proactive risk-mitigation and treatment strategy potentially could be defined by formation. If such a data trend exists, it could provide great value and simplify upfront treatment methodology.

Temporal Context Modeling Network with Local-Global Complementary Architecture for Temporal Proposal Generation

Temporal Action Proposal Generation (TAPG) is a promising but challenging task with a wide range of practical applications. Although state-of-the-art methods have made significant progress in TAPG, most ignore the impact of the temporal scales of action and lack the exploitation of effective boundary contexts. In this paper, we propose a simple but effective unified framework named Temporal Context Modeling Network (TCMNet) that generates temporal action proposals. TCMNet innovatively uses convolutional filters with different dilation rates to address the temporal scale issue. Specifically, TCMNet contains a BaseNet with dilated convolutions (DBNet), an Action Completeness Module (ACM), and a Temporal Boundary Generator (TBG). The DBNet aims to model temporal information. It handles input video features through different dilated convolutional layers and outputs a feature sequence as the input of ACM and TBG. The ACM aims to evaluate the confidence scores of densely distributed proposals. The TBG is designed to enrich the boundary context of an action instance. The TBG can generate action boundaries with high precision and high recall through a local–global complementary structure. We conduct comprehensive evaluations on two challenging video benchmarks: ActivityNet-1.3 and THUMOS14. Extensive experiments demonstrate the effectiveness of the proposed TCMNet on tasks of temporal action proposal generation and temporal action detection.

Interactive Governance for the Sustainability of Marine and Coastal Resources in Thailand

Coastal zones are biodiverse, with complex and dynamic interconnectivity between terrestrial and marine areas, and with multiple interactions between ecological and social systems. Despite on-going efforts to conserve and protect these ecosystems, destructive extraction and unsustainable resource utilization are persistent, posing challenges for governance. Issues and concerns in coastal zones are cross-sectoral and cross-boundary, often with overlapping jurisdictions. They are considered ‘wicked’ governance problems, requiring nuanced approaches to address, rather than technical quick fixes. Interactive governance is one such approach that examines relationships within and between the ecological and social systems, as well as with the governing system. Theoretically, the governability of coastal zones depends on the inherent quality of these systems and their interactions, and improving governability needs to take place in all three orders of governance. At the ‘first order’, a better understanding of the diversity, complexity and dynamics of coastal zones, and related scale issues is required. Improving governability at the ‘second order’ involves evaluating and adjusting the existing legal and institutional frameworks to improve the performance and the correspondence with the systems they aim to govern. Finally, discussion about coastal governance needs to be elevated to ‘meta-order’ where principles are set and values derived so that hard choices can be made, for instance, between conservation and utilization of coastal resources. Guided by the interactive governance framework, the paper presents an overview of coastal governance in Thailand, summarizing key features of the natural, social and governing systems associated with coastal zones, and discussing what can be done to improve coastal governability.

Synthesis and evaluation of scale inhibitor with high-temperature resistance and low corrosion capability for geothermal exploitation

Scale and corrosion issues of well pipelines become more prominent in geothermal exploitation at temperatures exceeding 100 °C. Effective scale and corrosion inhibitors are the keys to addressing the concerns. In this study, maleic anhydride (MA), acrylic acid (AA), 2-acrylamide-2-methylpropane sulfonic acid (AMPS), sodium hypophosphite, and sodium dodecyl sulfate (SDS) were utilized to synthesize a multi-polymer scale inhibitor PZGS with high-temperature resistance and low corrosion capability for geothermal exploitation. The functional groups and molecular weight of PZGS were analyzed by Fourier transform infrared spectroscopy (FTIR) and gel permeation chromatography (GPC). The scale inhibitor contains carboxylic acid groups, amide groups, phosphonic acid groups, and sulfonic acid groups. These groups ensure the capability of PZGS to chelate Ca2+, thus promoting the lattice distortion of calcium carbonate scale and improving its scale inhibition performance and temperature resistance. The number average molecular weight and the weight average molecular weight of PZGS are 5446 g/mol and 7139 g/mol respectively. The scale inhibition rate of 480 mg/L PZGS could reach 95% at 150 °C which is better than common scale inhibitors such as nitrogen trimethyl triphosphonic acid (ATMPA) and diethylenetriamine pentamethylene phosphonic acid (DTPMPA), whose inhibition rates are just 62% and 60% at 150 °C respectively. With the increase of the concentration, the formation of aragonite and vaterite gradually increased while calcite decreased because of the chelation and lattice distortion of polycarboxylic acid groups, phosphonic acid, sulfonic acid, and other groups from PZGS over the calcium carbonate scale. The compound formula “240 mg/L PZGS plus 200 mg/L polyepoxysuccinic acid (PESA)" performed the best effectiveness in its anti-scale and low corrosion capability. Its scale inhibition rate on the calcium carbonate scale was 94.1% at 150 °C, and the corrosion rate of the KLF carbon steel corrosion test piece was only 0.35 g/(m2·h) due to the PESA film coverage. Compared with 0.94 g/(m2·h) from brine solution and 0.91 g/(m2·h) from 240 mg/L PZGS, the corrosion rate of the compound formula was reduced by 63.2% and 61.7% in simulated geothermal water respectively. The findings of this study can provide effective guidelines for the development of scale and corrosion inhibitors for geothermal exploitation.

Scale issues for geoheritage 3D mapping: The case of Lesvos Geopark, Greece

A geopark can be composed of many individual geosites of various geographical scales, thus, categorization according to cartographic scale is crucial for their 3D mapping. The UNESCO Global Geopark of the island of Lesvos in the north-east Aegean, Greece, is a distinctive example of this type of geopark as it contains many unique geosites that vary in geographical scale. The geographical scale is interconnected with the cartographic scale in which the geosite is visualized. The desired cartographic scale is an essential user requirement within an unmanned aerial vehicle's (UAVs) 3D mapping project as the basis for the data acquisition strategy. This research investigates the scale issues in 3D mapping of geosites. Furthermore, it contributes to the incorporation of the geographic and cartographical scales in association with UAV flight parameters such as Ground Sample Distance (GSD), altitude, gimbal pitch, orientation, and front and side overlapping. A total of 132 geosites located in Lesvos Geopark are being studied to determine the flight parameters of three different UAVs and their camera characteristics. The methodology followed to collect very high-resolution images suitable for 3D mapping consists of five main stages: i) determining the geographical scale of each geosite, ii) defining the cartographic scale of all geosites, iii) calculation of the GSD based on cartographic scale, iv) calculation of UAV flight altitude and flight characteristics, and v) classification of geosites based on the flight characteristics for their 3D mapping. Five geographic (G1: < 0.1 ha, G2: 0.1–1 ha, G3: 1–10 ha, G4: 10–100 ha, G5: >100 ha) and five cartographic (C1: > 1:50, C2: 1:50–1:100, C3: 1:100–1:250, C4: 1:250–1:500, C5: < 1:500) categories were defined based on the geosites' size and extent. The combination of the two scales determines the most efficient flight characteristics and optimally acquires very high-resolution images required for the 3D mapping of the selected geosites. Finally, the categorization and characteristics of flights for data collection for high-resolution 3D mapping are collected and presented in a web application. The web application is addressed to the management board of Lesvos Geopark and supports the decision-making processes on mapping geosites using UAVs.

Dimensionless impedance method for the transient response of pressurized pipeline system

Modeling an unsteady flow is an important problem in pressurized pipeline systems. Depending on the dimensions and properties of the fluid and pipeline material, the generation of hydraulic transient issues and their spatiotemporal variation patterns can be different. To address the water hammer problem without scale issues in pipeline systems, this study developed a dimensionless impedance method (DIM). The existing partial differential equations of mass, momentum, and energy conservation were converted into corresponding dimensionless equations. The solutions of proposed equations were analytically developed in terms of several operators in the dimensionless frequency domain. A dimensionless water hammer number was introduced to address the similarity in hydraulic transients between different systems. Both numerical and experimental comparisons showed that the DIM well matched the existing methods. Based on comprehensive simulations of water hammer phenomena for ranges of water hammer numbers, a vectorized chart was formulated for a reservoir pipeline valve system. Both simulations and calibrations of the experimental hydraulic transients achieved well-matched results between the DIM computations and chart interpolation results. The proposed DIM method can provide substantial efficiency for both simulation and calibration, as well as generality in transient solutions for pipeline systems.

Experimental and numerical analysis of wind field effects on the dynamic responses of the 10 MW SPIC floating wind turbine concept

Experimental methods are useful to study global responses of floating wind turbines under wind excitations. One experimental technique is based on the regeneration of wind fields in open space in a hydrodynamic laboratory and the use of the performance-matched rotor to produce the equivalent aerodynamic thrust force according to a target wind field. However, for wind generation systems, due to small variations in the output of each fan and differences among the fans, the obtained wind speed varies in space and time, making it difficult to generate the target ideal uniform, constant or time-varying wind fields. For investigating the quality of the wind field in the experiment and their effects on the global responses, numerical simulations under wind only conditions for the full-scale 10 MW SPIC wind turbine were compared with the upscaled responses from model tests. Because of the limitation in the generation and measurement of the target wind field, and the model scaling issue for wind turbine aerodynamics considering the TSR variation, there are uncertainties in interpreting the obtained responses. Numerical simulations were then performed to investigate the effects of spatial and temporal variations in wind fields. Wind shear influences 1P and 3P responses. Below the rated wind speed, when the turbulence intensity increases, the average responses decrease and the response spectra are dominated by low-frequency and 3P components. In Kaimal and Mann wind fields with coherent structures, the generated power and pitch motion show fewer fluctuations than in uniform time-varying wind field, while the nonuniform wind field excites greater 1P and 3P responses.

Design of a CMOS memristor emulator-based, self-adaptive spiking analog-to-digital data conversion as the lowest level of a self-x hierarchy

Abstract. The number of sensors used in modern devices is rapidly increasing, and the interaction with sensors demands analog-to-digital data conversion (ADC). A conventional ADC in leading-edge technologies faces many issues due to signal swings, manufacturing deviations, noise, etc. Designers of ADCs are moving to the time domain and digital designs techniques to deal with these issues. This work pursues a novel self-adaptive spiking neural ADC (SN-ADC) design with promising features, e.g., technology scaling issues, low-voltage operation, low power, and noise-robust conditioning. The SN-ADC uses spike time to carry the information. Therefore, it can be effectively translated to aggressive new technologies to implement reliable advanced sensory electronic systems. The SN-ADC supports self-x (self-calibration, self-optimization, and self-healing) and machine learning required for the internet of things (IoT) and Industry 4.0. We have designed the main part of SN-ADC, which is an adaptive spike-to-digital converter (ASDC). The ASDC is based on a self-adaptive complementary metal–oxide–semiconductor (CMOS) memristor. It mimics the functionality of biological synapses, long-term plasticity, and short-term plasticity. The key advantage of our design is the entirely local unsupervised adaptation scheme. The adaptation scheme consists of two hierarchical layers; the first layer is self-adapted, and the second layer is manually treated in this work. In our previous work, the adaptation process is based on 96 variables. Therefore, it requires considerable adaptation time to correct the synapses' weight. This paper proposes a novel self-adaptive scheme to reduce the number of variables to only four and has better adaptation capability with less delay time than our previous implementation. The maximum adaptation times of our previous work and this work are 15 h and 27 min vs. 1 min and 47.3 s. The current winner-take-all (WTA) circuits have issues, a high-cost design, and no identifying the close spikes. Therefore, a novel WTA circuit with memory is proposed. It used 352 transistors for 16 inputs and can process spikes with a minimum time difference of 3 ns. The ASDC has been tested under static and dynamic variations. The nominal values of the SN-ADC parameters' number of missing codes (NOMCs), integral non-linearity (INL), and differential non-linearity (DNL) are no missing code, 0.4 and 0.22 LSB, respectively, where LSB stands for the least significant bit. However, these values are degraded due to the dynamic and static deviation with maximum simulated change equal to 0.88 and 4 LSB and 6 codes for DNL, INL, and NOMC, respectively. The adaptation resets the SN-ADC parameters to the nominal values. The proposed ASDC is designed using X-FAB 0.35 µm CMOS technology and Cadence tools.

Molecular modelling of ionic liquids: General guidelines on fixed-charge force fields for balanced descriptions

It has been increasingly common to investigate dynamic and thermodynamic properties of green solvents at atomistic scales with molecular simulation. In this work, we present a detailed evaluation of the widely used fixed-charge regime, i.e., the combination of scaled RESP charges and GAFF derivatives. The benchmark set contains three ionic liquids formed by cations 1-butyl-3-methylimidazolium and 1-hexyl-3-methylimidazolium and anions bis(trifluoromethylsulfonyl)imide and PF6. For the charge scaling issue, two physical properties including the mass density and the solvation free energies of external agents in ionic liquids are selected as the criteria. Large-scale fast-growth solvation free energy simulations are performed to obtain the solvation thermodynamics, and the results are further combined with hydration data to form a water-ionic-liquids transfer dataset, which considers the partition of solutes between water and ionic liquids. It is observed that the density-derived charge scaling factor is always smaller than that derived from solvation/partition thermodynamics, which supports the use of a slightly larger scaling factor to obtain a balanced description of solute-solvent and solvent-solvent interactions. For the bonded-terms assessment, we refit the transferable GAFF2 derivatives with generalized force-matching in a molecule- and component-specific manner. The results suggest that the bond-stretching and angle-bending terms in GAFF derivatives are often problematic, while the torsional potential shows satisfactory reproduction of ab initio results. Finally, combining the extensive computational perspectives accumulated in our series works, general guidelines for molecular modelling of ionic liquids with fixed-charge force fields are summarized.

Correlation between the formation of new competing group and spatial scale for biodiversity in the evolutionary dynamics of cyclic competition.

Securing space for species breeding is important in the evolution and maintenance of life in ecological sciences, and an increase in the number of competing species may cause frequent competition and conflict among the population in securing such spaces in a given area. In particular, for cyclically competing species, which can be described by the metaphor of rock-paper-scissors game, most of the previous works in microscopic frameworks have been studied with the initially given three species without any formation of additional competing species, and the phase transition of biodiversity via mobility from coexistence to extinction has never been changed by a change of spatial scale. In this regard, we investigate the relationship between spatial scales and species coexistence in the spatial cyclic game by considering the emergence of a new competing group by mutation. For different spatial scales, our computations reveal that coexistence can be more sensitive to spatial scales and may require larger spaces for frequencies of interactions. By exploiting the calculation of the coexistence probability from Monte-Carlo simulations, we obtain that certain interaction ranges for coexistence can be affected by both spatial scales and mobility, and spatial patterns for coexistence can appear in different ways. Since the issue of spatial scale is important for species survival as competing populations increase, we expect our results to have broad applications in the fields of social and ecological sciences.

Extending the Continuum of Corporate Diplomacy: Insights for Effective Implementation of the SDGs

Denoting several of the “grand challenges” of our times, the Sustainable Development Goals (SDGs) provide a framework for addressing large scale issues through collaborative action. Yet to date, progress on the SDGs has been limited, with effective mechanisms for implementation lacking. In this paper, I argue that corporate diplomacy can act as a tool to support more effective multinational enterprise (MNE) participation and implementation of the SDGs, however, must be reconsidered from a holistic perspective in order to facilitate meaningful change. This paper builds off the continuum of corporate diplomacy proposed by Michelle Westermann-Behaylo, Kathleen Rehbein and Timothy Fort, by proposing three possible extensions of proactive corporate diplomacy. By extending existing conceptions of corporate diplomacy to incorporate theoretical perspectives from business and human rights as well as political corporate social responsibility scholarship, this paper aims to contribute to the literature on grand challenges by providing new approaches to improve collaboration.

A review of movement models in open population capture–recapture

AbstractUnderstanding rates of survival and recruitment is critical to population management, and capture–recapture methods of estimation are widely used. Spatial models allow for a spatial detection process and can include the movement of activity centres between sampling times. Movement is often treated as a random walk with the step length governed by a probability kernel. However, the movement component of open population spatially explicit capture–recapture models (open SECR) has evolved haphazardly and comparison among studies is difficult.We review published studies, document suitable probability kernels and address the issues of scale and buffer dependence in open SECR by a combination of simulation and case studies on ovenbirdsSeiurus aurocapillaand tigersPanthera tigris.Flexible 2‐parameter kernels, such as the bivariate ‐distribution, fit better than the popular bivariate normal and resulted in higher estimates of survival. We reconcile different parameterizations of the bivariate ‐distribution and identify a problem when the kernel is defined in terms of its margins.Movement models failed to separate mortality and emigration in simulated data when the data were a random mixture of long and short movements. Our estimates of ovenbird survival were buffer‐dependent, and we interpret this as a sign that the data are inadequate for joint modelling of survival and movement. Estimates of tiger survival were more nearly asymptotic on buffer width.We repeat the warning of earlier authors that movement models are effective for separating mortality and emigration only when the data span the range of movement. We appeal for more complete and consistent reporting of movement models and identify topics for future research.

HXDP

The network interface cards (NICs) of modern computers are changing to adapt to faster data rates and to help with the scaling issues of general-purpose CPU technologies. Among the ongoing innovations, the inclusion of programmable accelerators on the NIC's data path is particularly interesting, since it provides the opportunity to offload some of the CPU's network packet processing tasks to the accelerator. Given the strict latency constraints of packet processing tasks, accelerators are often implemented leveraging platforms such as Field-Programmable Gate Arrays (FPGAs). FPGAs can be re-programmed after deployment, to adapt to changing application requirements, and can achieve both high throughput and low latency when implementing packet processing tasks. However, they have limited resources that may need to be shared among diverse applications, and programming them is difficult and requires hardware design expertise. We present hXDP, a solution to run on FPGAs software packet processing tasks described with the eBPF technology and targeting the Linux's eXpress Data Path. hXDP uses only a fraction of the available FPGA resources, while matching the performance of high-end CPUs. The iterative execution model of eBPF is not a good fit for FPGA accelerators. Nonetheless, we show that many of the instructions of an eBPF program can be compressed, parallelized, or completely removed, when targeting a purpose-built FPGA design, thereby significantly improving performance. We implement hXDP on an FPGA NIC and evaluate it running real-world unmodified eBPF programs. Our implementation runs at 156.25MHz and uses about 15% of the FPGA resources. Despite these modest requirements, it can run dynamically loaded programs, achieves the packet processing throughput of a high-end CPU core, and provides a 10X lower packet forwarding latency.

Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis.

Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.

694 W sub-GHz polarization-maintained tapered fiber amplifier based on spectral and pump wavelength optimization.

The comprehensive suppression of the stimulated Brillouin scattering (SBS) and transverse mode instability (TMI) is a critical issue for the power scaling of fiber laser with sub-GHz spectral linewidth. In this manuscript, a narrow linewidth and polarization-maintained (PM) fiber amplifier based on tapered Yb-doped fiber (T-YDF) is established, and the effects of spectral linewidth, spectral shape and pump wavelength on the SBS and/or TMI thresholds are investigated. Up to 694 W polarization-maintained fiber laser with just ∼790 MHz linewidth is obtained by combining the advantages of tapered Yb-doped fiber, near-rectangular spectral injection and 915 nm pump manner. This work could provide a well reference solution for the realization of high-power ultra-narrow linewidth fiber lasers.

(Digital Presentation) Investigation of Top Electrodes Impact on Performance of Transparent Amorphous Indium Gallium Zinc Oxide (a-InGaZnO) Based Resistive Random Access Memory

The traditional von Neumann architecture limits the increase in computing efficiency and results in massive power consumption in modern computers due to the separation of storage and processing units. The novel neuromorphic computation system, an in-memory computing architecture with low power consumption, is aimed to break the bottleneck and meet the needs of the next generation of artificial intelligence (AI) systems. Thus, it is urgent to find a memory technology to implement the neuromorphic computing nanosystem. Nowadays, the silicon-based flash memory dominates non-volatile memory market, however, it is facing challenging issues to achieve the requirements of future data storage device development due to the drawbacks, such as scaling issue, relatively slow operation speed, and high voltage for program/erase operations. The emerging resistive random-access memory (RRAM) has prompted extensive research as its simple two-terminal structure, including top electrode (TE) layer, bottom electrode (BE) layer, and an intermediate resistive switching (RS) layer. It can utilize a temporary and reversible dielectric breakdown to cause the RS phenomenon between the high resistance state (HRS) and the low resistance state (LRS). RRAM is expected to outperform conventional memory device with the advantages, notably its low-voltage operation, short programming time, great cyclic stability, and good scalability. Among the materials for RS layer, indium gallium zinc oxide (IGZO) has shown attractive prospects in abundance and high atomic diffusion property of oxygen atoms, transparency. Additionally, its electrical properties can be easily modulated by controlling the stoichiometric ratio of indium and gallium as well as oxygen potential in the sputter gas. Moreover, since the IGZO can be applied to both the thin-film transistor (TFT) channel and RS layer, it has a great potential for fully integrated transparent electronics application.In this work, we proposed amorphous transparent IGZO-based RRAMs and investigated switching behaviors of the memory cells prepared with different top electrodes. First, ITO was choosing to serve as both TE and BE to achieve high transmittance. A multi-target magnetron sputtering system was employed to deposit all three layers (TE, RS, BE layers) on glass substrate. I-V characteristics were evaluated by a semiconductor parameter analyzer, and the bipolar RS feature of our RRAM devices was demonstrated by typical butterfly curves. The optical transmission analysis was carried out via a UV-Vis spectrometer and the average transmittance was around 80% out of entire devices in the visible-light wavelength range, implying high transparency. We adjusted the oxygen partial pressure during the sputtering of IGZO to optimize the property because the oxygen vacancy concentration governs the RS performance. Electrode selection is crucial and can impact the performance of the whole device. Thus, Cu TE was chosen for our second type of device because the diffusion of Cu ions can be beneficial for the formation of the conductive filament (CF). A ~5 nm SiO2 barrier layer was employed between TE and RS layers to confine the diffusion of Cu into the RS layer. At the same time, this SiO2 inserting layer can provide an additional interfacial series resistance in the device to lower the off current, consequently, improve the on/off ratio and whole performance. Finally, an oxygen affinity metal Ti was selected as the TE for our third type of device because the concentration of the oxygen atoms can be shifted towards the Ti electrode, which provides an oxygengettering activity near the Ti metal. This process may in turn lead to the formation of a sub-stoichiometric region in the neighboring oxide that is believed to be the origin of better performance.In conclusion, the transparent amorphous IGZO-based RRAMs were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of RRAMs, we integrated a set of TE materials, and a barrier layer on IGZO-based RRAM and compared the switch characteristics. Our encouraging results clearly demonstrate that IGZO is a promising material in RRAM applications and breaking the bottleneck of current memory technologies.

Investigation of scale inhibition effect and mechanism of S-HGMF in the clean recirculating cooling water system

The formation of scales in a recirculating water system is a common problem in industrial water treatment; it seriously affects the production in various industries and pollutes the environment. Although conventional scale inhibition methods are effective, they are expensive and harm the environment. Herein, an advanced method is proposed to solve the scaling issue in recirculating cooling water systems using the superconducting high-gradient magnetic field (S-HGMF) treatment. The scale inhibition performance could be improved by changing the magnetic flux density, operation time, and flow rate. The results showed that S-HGMF could increase the number of hydrogen bonds in the recirculating cooling water, enhance molecular interaction, increase the thickness of the ion hydration shell, reduce the nucleation rate, stabilize the water quality, improve the solubility of scale-forming ions, and inhibit scale formation. The scale inhibition performance reached 8.10%. Interestingly, S-HGMF had a memory effect in that it could maintain the scale inhibition effect for some period after treatment completion. Moreover, S-HGMF changed the crystal structure of the scale and promoted the transformation of the scale to a metastable phase. Ultimately, calcite was transformed to aragonite to reduce the precipitation of hard scale (calcite), achieving the purpose of scale inhibition. As a physical method, the application of S-HGMF to inhibit scaling has great potential for industrial applications.

After the Perfect Storm

Recent disruptions in waste management, including the COVID-19 pandemic and China’s decision to limit waste imports from the United States, have shocked materials management systems across the United States. In Maine, these disruptions have been exacerbated by significant disturbances in the state’s waste management infrastructure. These shocks, emerging on multiple scales, combine to strongly impact Maine’s communities. Drawing on interviews with stakeholders involved in waste hauling, processing, outreach and education, as well as state and municipal government. Our paper explores how participants are leveraging these experiences to envision a more resilient materials management system for the state. However, as this case study illustrates, the complexity of materials management systems means that there is no single solution for ongoing, emergent, and unforeseen disruptions. Our research identifies tensions related to how to define system boundaries, the respective roles of the government and markets, issues of scale, and the dual need for both centralized and distributed solutions. Our exploration of materials management disruptions in Maine demonstrates the complexity of building and managing systems that attempt to balance the social, economic and ecological dimensions of materials management systems.