Massive Scale Research Articles

Exploring Potential Non-CCUS Pathways for Emissions-Free Energy

To be effective in addressing the emissions challenge on the global scale, the focus has to be on the processes that are inexpensive to implement and can be applied at a massive scale. CCS satisfies the ‘massive scale’ condition but at present falls short of satisfying the ‘inexpensive’ part. The CCUS approaches (other than CO<sub>2</sub> based EOR) where useful products from CO<sub>2</sub> are hoped to be monetized to offset costs, take the focus even farther away from the desired objective. This is because (a) costs increase due to required energy input, and (b) market value of the products, and hence the desired monetization cannot survive the massive oversupply thus created. A more direct and efficient strategy involves either converting CO<sub>2</sub> into a (non-monetizable) solid or liquid with minimal energy input, or restricting the waste by-product of the fuel oxidation process to be in a liquid or solid state that does not interact with the atmosphere to add to the greenhouse gas effect. The paper presents novel lower REDOX approaches as more energy-efficient and affordable alternatives to provide emissions-free energy. A strong case is made with available information for the techno-economic viability of these methods, and gaps identified for further development.

The consequences and barriers of power system voltage stability in the integration of solar and wind energy: An in-depth analysis

Wind and solar energy production is intermittent and variable, causing voltage fluctuations in the power system. These fluctuations can affect supply and demand balance, leading to voltage instability. In some cases, integrating solar and wind energy can result in overvoltage or undervoltage conditions, which can negatively impact the power grid's operation. The research examines the effects and challenges of integrating wind and solar energy into power grids, concentrating in particular on issues with voltage stability. The investigation explores fundamental issues like voltage stability-based distributed power generation (DG) unit sizing and optimal locations, stability of voltage assessment and methods for enhancement. Experiments are conducted to examine the effects of energy storage devices, adaptive alternate current (AC) transmission lines, static caps and other electric components of the system on the stability of the voltage of the distribution and transmission networks. It provides a comprehensive examination of the technical difficulties, such as the stability issues associated with incorporating massive solar systems into the electrical grid. The research obtained results on technical methods to address the power instability challenges with the integration of solar power on a massive scale into the transmission system and its subtransmissions, known as the medium voltage distribution system.

How to Worship in the Metaverse: Religious Experience and the Affordances of a New Medium

The extent to which the metaverse will become a site for religious and spiritual experience depends on how the aims of users align with the new medium’s potentialities for action, or “affordances”. Affordances are formed as the social, technological, and contextual capacities of a medium are recognized and then enacted by users. This exploratory essay argues that the metaverse’s affordances, which overlap with those of already existing virtual reality (VR) environments, can deepen a sense of belonging for users of online religious spaces and mediate new ways of being present in those spaces. The following affordances of the metaverse (and of VR) are discussed analytically in the essay: immersion, presence, embodiment, usability, empathy, and contemplation. The phases of the continuing “buildout” of the metaverse are also assessed to uncover their likely effects on the metaverse’s affordances. These phases are massive scale, system interoperability, robust rendering, and persistent continuity.

Named entity recognition in the perovskite field based on convolutional neural networks and MatBERT

Due to the significant increase in publications in the field of materials science, there has been a bottleneck in organizing material science knowledge and discovering new materials. The number of literature in the emerging field of perovskite materials has grown to a massive scale. It is necessary to compile information on the structure, properties, synthesis methods, characterization techniques, and applications of perovskite materials. To address this issue, we employed named entity recognition, a natural language processing technique, to extract important entities from perovskite material texts. In this paper, we propose a method based on convolutional neural networks (CNN) and MatBERT. Firstly, we utilized MatBERT, which has been pre-trained on a large amount of material science text, to generate contextualized word embeddings. Next, we extracted feature information using a CNN model. Finally, a conditional random field (CRF) layer was used for decoding sequences in addition to calculating the training and validation loss. Experimental results demonstrated that the performance of our model on perovskite material dataset was improved by 1 %∼6% compared with BERT, SciBERT and MatBERT models. Through this model, we extracted the entities of 2389 abstracts to obtain knowledge of perovskite materials.

Potential scale of industrial outputs of the bronze bell casting industry in 500 BCE China

The potential scale of industrial outputs in the ancient world is often underestimated in current archaeological studies due to the lack of surviving artifacts for validation of the production scale. However, production traces left on extant artifacts can help us reconstruct production methods, and thus reveal the potential scale of production outputs of certain industries, even although there may not be a sufficient number of existing artifacts to demonstrate such volumes. The bronze bell casting industry operating in around 500 BCE in Xinzheng in Henan province, China, can be used as an example to demonstrate the then use by bell casters of the “pattern-block method” to efficiently create multiple bells sharing identical components. With their strong focus on efficiency, production speed, and low production costs, these casters intelligently designed assembly lines and assembled identical components replicated from models to prepare molds for casting. Knowledge of their production methods and currently preserved bells can provide evidence that the bell casting industry produced industrial outputs on a massive scale that was rare in the ancient world. This article also shows how innovative methods such as 3D model superimposition can be used to validate these hypotheses.

Instability of excitatory synapses in experimental autoimmune encephalomyelitis and the outcome for excitatory circuit inputs to individual cortical neurons

Synapses are lost on a massive scale in the brain and spinal cord of people living with multiple sclerosis (PwMS), and this synaptic loss extends far beyond demyelinating lesions. Post-mortem studies show the long-term consequences of multiple sclerosis (MS) on synapses but do not inform on the early impacts of neuroinflammation on synapses that subsequently lead to synapse loss. How excitatory circuit inputs are altered across the dendritic tree of individual neurons under neuroinflammatory stress is not well understood. Here, we directly assessed the structural dynamics of labeled excitatory synapses in experimental autoimmune encephalomyelitis (EAE) as a model of immune-mediated cortical neuronal damage. We used in vivo two-photon imaging and a synthetic tissue-hydrogel super-resolution imaging technique to reveal the dynamics of excitatory synapses, map their location across the dendritic tree of individual neurons, and examine neurons at super-resolution for synaptic loss. We found that excitatory synapses are destabilized but not lost from dendritic spines in EAE, starting with the earliest imaging session before symptom onset. This led to changes in excitatory circuit inputs to individual cells. In EAE, stable synapses are replaced by synapses that appear or disappear across the imaging sessions or repeatedly change at the same location. These unstable excitatory inputs occur closer to one another in EAE than in healthy controls and are distributed across the dendritic tree. When imaged at super-resolution, we found that a small proportion of dendritic protrusions lost their presynapse and/or postsynapse. Our finding of diffuse destabilizing effects of neuroinflammation on excitatory synapses across cortical neurons may have significant functional consequences since normal dendritic spine dynamics and clustering are essential for learning and memory.

Manipulation Strategy to Increase Expression Level of Soluble Recombinant Protein Penicillin G Acylase (PGA) in Bacterial Host Escherichia coli: A Review Article

The strategy of producing PGA on a massive scale with high levels of soluble protein can be through recombinant genetic techniques and expressed in a certain host. E. coli is still a popular bacterial host to produce a recombinant protein which has advantages such as fast growth, low production cost, and high expression rate. Apart from its advantages, E. coli as a production host also has disadvantages including the expression of recombinant proteins often failing to form the proper folding conformation which makes the protein biologically inactive. Many strategies can be developed to overcome these problems, such as the selection of the host strain (E. coli HB101 & JM109), fusion protein to enhance the recovery of soluble protein (MBP & NusA), optimization of fermentation (low-temperature incubation), and optimization of the protein isolation process for the recovery of active PGA (Freeze-thawing method).

Abstract 7420: Optimizing proteomic data access and analysis in the cloud: Leveraging Terra's integration with the Proteomic Data Commons

Abstract The Proteomic Data Commons (PDC) hosts cancer proteomics data with the goal of making this data available to the public to support development of cancer diagnostics, treatment, and progression tracking. As a part of the Cancer Research Data Commons (CRDC), the Terra platform provides a cloud workbench for the PDC data. FireCloud is a Broad Institute project funded by NCI to empower cancer researchers to access data, run analysis tools and collaborate securely in the cloud. It is powered by Terra, a secure, scalable cloud-native platform developed by the Broad Institute, Microsoft, and Verily, an Alphabet company. It provides batch workflow execution, interactive analysis including data visualization, and ~2,900 publicly available tools with the ability to import more tools from Dockstore. The integration of PDC and Terra enables researchers to leverage the data navigation and file-level search capabilities on the PDC web browser (pdc.cancer.gov) and export selected data manifests to a Terra workspace. This integrated hand-off specifically allows metadata and cloud links to PDC data in Portable Format for Bioinformatics (PFB) to be transferred to Terra and used in analysis with cloud resources. A featured workspace for Terra-PDC integration includes tools for (i) downloading data files and relevant additional metadata; (ii) organizing the data and metadata for running the FragPipe workflow, a comprehensive collection of tools for reading and processing raw Mass Spec (MS) data; and (iii) implementing a pre-configured FragPipe workflow to process isobarically labeled MS data from Tandem Mass Tag (TMT) or Isobaric tags for relative and absolute quantitation (iTRAQ) experiments using information automatically imported from PDC via the PFB import. The pipelines can also be customized as needed to process any type of raw MS data the PDC supports. In addition, Terra connects to the NCI Genomic Data Commons (GDC) and hosts The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) datasets allowing users to pull in other data types for co-analysis. As a whole, using the CRDC cloud resources, in conjunction with Terra, allows analysis of data on a massive scale, enabling multi-omic integration spanning the genomic and proteomic commons data, with easy sharing of data and tools. Citation Format: Emily LaPlante, Bingxing Huo, DR Mani, Ratna R. Thangudu. Optimizing proteomic data access and analysis in the cloud: Leveraging Terra's integration with the Proteomic Data Commons [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7420.

Organic Rankine Cycle (ORC) Systems: A fundamental Overview of Small-scale Applications Fuelled by Low-grade Heat Sources

Environmental issues shift energy production from conventional methods to new and more efficient alternatives. One of these alternatives is the use of organic Rankine cycles (ORC) in low-grade heat sources to generate both heat and power at small scales. Among different technologies available for this purpose, ORC-based systems seem to be the most suitable and promising option due to their simplicity and versatility. Thus, such systems have been investigated intensively. However, current studies often focus on only one aspect of these systems due to the massive research scale in this field. Therefore, this study aims to provide a fundamental and holistic overview to evaluate ORC-based low-heat sourced and small-scale applications from multiple perspectives. As a result, the basic operating principles and application areas of ORCs, selection and design criteria of their working fluids and all other system components, methods of improving their performance, and other thermodynamic cycles that can be ORC alternatives are examined in detail. The results of this study show that ORC applications can enable small-scale combined heat and power generation, while geothermal and solar energy sources have the potential to scale the size of such applications up to kW capacities. The results also showed that dry & isentropic fluids and vane & scroll expanders are the most suitable refrigerant and expander types, respectively, for small-scale ORC applications. Furthermore, the implications of all findings are critically discussed.

Rhesus infant nervous temperament predicts peri-adolescent central amygdala metabolism & behavioral inhibition measured by a machine-learning approach.

Anxiety disorders affect millions of people worldwide and impair health, happiness, and productivity on a massive scale. Developmental research points to a connection between early-life behavioral inhibition and the eventual development of these disorders. Our group has previously shown that measures of behavioral inhibition in young rhesus monkeys (Macaca mulatta) predict anxiety-like behavior later in life. In recent years, clinical and basic researchers have implicated the central extended amygdala (EAc)-a neuroanatomical concept that includes the central nucleus of the amygdala (Ce) and the bed nucleus of the stria terminalis (BST)-as a key neural substrate for the expression of anxious and inhibited behavior. An improved understanding of how early-life behavioral inhibition relates to an increased lifetime risk of anxiety disorders-and how this relationship is mediated by alterations in the EAc-could lead to improved treatments and preventive strategies. In this study, we explored the relationships between infant behavioral inhibition and peri-adolescent defensive behavior and brain metabolism in 18 female rhesus monkeys. We coupled a mildly threatening behavioral assay with concurrent multimodal neuroimaging, and related those findings to various measures of infant temperament. To score the behavioral assay, we developed and validated UC-Freeze, a semi-automated machine-learning (ML) tool that uses unsupervised clustering to quantify freezing. Consistent with previous work, we found that heightened Ce metabolism predicted elevated defensive behavior (i.e., more freezing) in the presence of an unfamiliar human intruder. Although we found no link between infant-inhibited temperament and peri-adolescent EAc metabolism or defensive behavior, we did identify infant nervous temperament as a significant predictor of peri-adolescent defensive behavior. Our findings suggest a connection between infant nervous temperament and the eventual development of anxiety and depressive disorders. Moreover, our approach highlights the potential for ML tools to augment existing behavioral neuroscience methods.

Excavation response and reinforcement practice of large underground caverns within high-stress hard rock masses: The case of Shuangjiangkou hydropower Station, China

The construction of large-scale underground caverns poses numerous challenges due to their massive scale and high complexity. Among them, stability has always been a major concern during the construction process of large underground caverns within high-stress hard rock masses. It is not only influenced by the geological background of the project but also by the engineering activities during construction. This study takes the construction of the Shuangjiangkou (SJK) hydropower station as a case study and combines field investigations to summarize engineering issues and solutions under high geostress in three aspects. Firstly, it focuses on estimating and modifying the in-situ stress field of the underground caverns in the deep-cut valley area using limited geostress testing data and the observed failure phenomena in the pilot tunnel. Secondly, it discusses the typical failure phenomena of caverns under high geostress, including global stress-induced failures and local vein-controlled failures, as well as the corresponding failure mechanisms and treatment measures. Finally, it addresses the special failure phenomena occurring in the protective layer of the rock-anchored beam and optimizes the excavation scheme based on the observed failure patterns. The research results and solutions provided in this study, based on the case of the SJK hydropower station, are expected to serve as a reference for subsequent construction of large underground caverns within high-stress hard rock masses.

On the impact of mass screening for SARS-CoV-2 through self-testing in Greece.

Screening programs that pre-emptively and routinely test population groups for disease at a massive scale were first implemented during the COVID-19 pandemic in a handful of countries. One of these countries was Greece, which implemented a mass self-testing program during 2021. In contrast to most other non-pharmaceutical interventions (NPIs), mass self-testing programs are particularly attractive for their relatively small financial and social burden, and it is therefore important to understand their effectiveness to inform policy makers and public health officials responding to future pandemics. This study aimed to estimate the number of deaths and hospitalizations averted by the program implemented in Greece and evaluate the impact of several operational decisions. Granular data from the mass self-testing program deployed by the Greek government between April and December 2021 were obtained. The data were used to fit a novel compartmental model that was developed to describe the dynamics of the COVID-19 pandemic in Greece in the presence of self-testing. The fitted model provided estimates on the effectiveness of the program in averting deaths and hospitalizations. Sensitivity analyses were used to evaluate the impact of operational decisions, including the scale of the program, targeting of sub-populations, and sensitivity (i.e., true positive rate) of tests. Conservative estimates show that the program reduced the reproduction number by 4%, hospitalizations by 25%, and deaths by 20%, translating into approximately 20,000 averted hospitalizations and 2,000 averted deaths in Greece between April and December 2021. Mass self-testing programs are efficient NPIs with minimal social and financial burden; therefore, they are invaluable tools to be considered in pandemic preparedness and response.

One-step generation of hollow graphitic carbon nanospheres with suitable oxygenated structures as conductive additives for water-soluble binder in highly stable supercapacitor electrodes

Although the weight ratio of electron conductive additives and the binder to porous carbons in the electrode component of electric double-layer capacitors (EDLC) is minimal, however, without them the electrochemical performances of EDLC would decay quickly. In this work, we present a one-step approach for manufacturing a unique conductive carbon material that shows highly connecting hollow structures as well as approximate oxygenated surface groups to create a synergy effect with aqueous binder and the main component, porous carbon. The as-prepared carbon nanospheres (HCNs) were generated from a typical carbon black-PC20 in air (denoted as PC20HT) at mild temperatures ranging from 400 to 700 °C for 2–4 h at a massive scale of approximate 1 kg h−1. The as-prepared PC20HT displays a high specific surface area of 657 m2 g−1 (the pristine PC20 has 112 m2 g−1) with a pore volume of 0.71 cm3 g−1, and an oxygen content of 14.10 wt%. Interestingly, a premixing of PC20HT and porous carbon is required before making the electrode pastes. The well-premixed carbon materials and a water-soluble binder-polyacrylic acid are composed into highly-stable components as EDLC electrodes in an organic electrolyte. As consequently, the PC20HT-assisted electrodes demonstrate highly stable life cycles (charge-discharge behaviors), with a retention rate 82.13 % over 50,000 cycles at a narrow voltage window of 2.5–2.7 V at 0.5 A g−1 when compared to two conventional conductive carbons, BP2000 (68.45 %) and Super P (67.46 %). Thermal annealing at 900 °C in Ar reduces the oxygen content of PC20HT to 4.25 wt% (referred to as TPC20HT), increasing the retention rate of life cycle to 88.89 % after the same cycles. TPC20HT-assisted EDLC electrodes exhibit a superior energy density of 34.76 Wh kg−1, which is higher than CB-BP2000 and Super P-assisted EDLC electrodes. The transforming of structural morphologies as well as the surface oxygenated and graphitic structures of the as-prepared HCS, PC20HT, and TPC20HT were also described before and after long-term cycling. This simple route for producing conductive carbons is applicable in an aqueous binder system for preparing EDLC electrodes with a cost-effective and environmentally friendly approach.

Traditional and Modern Disaster Resistant House Construction Techniques

A large portion of the wealth of any nation is invested in its built-up environment viz, housing, infrastructure, industrial and commercial facilities. The quality of this built-up environment, expressed in terms of durability, safety and functionality, is a determining factor for the quality of life and economic development of the society, and the competitiveness of its industry and services. Extensive loss of this wealth is caused by natural disasters that strike periodically. A natural disaster is an event of nature that causes sudden destruction, damaging the economy and social structure on a massive scale. The calamity results in a huge financial burden in terms of relief and rescue operations. All-round decline in production, unemployment, indebtedness as well as increased cost of goods and services are the other debilitating results of natural disasters. This research paper deals with the traditional as well as modern disaster resistant construction techniques to meet the aftermath of disasters. It examines the issue of damage to Reinforced Concrete Cement (RCC) buildings by underlying the necessity of adherence to building codes and standards. Some recent advances in housing technology are also highlighted.

Blockchain for Enhancing IoT Privacy and Security

The technological advancements in the network technologies that deal with interconnected computing devices has led to the dramatic increase in the number of Internet of Things (IoT) devices. It promises to improve the convenience of our lives by leveraging to modernize the way day to day items are connected to various elements of existence by converting it to smart object. However, with the unprecedented convenience, accessibility, and efficiency, IoT has caused acute security and privacy threats in recent years, and it must be addressed properly. Due to the massive scale and distributed nature of IoT networks, overlooking these privacy and security issues will negatively impact many elements of our existence that involves our surroundings, and these effects will influence our own masses too. The national or jurisdictional boundaries are not restricted to the centralized cloud infrastructure that provides interconnectivity of IoT items, hence maintaining robustness, authentication and security becomes crucial. Recently, the development of blockchain technology is a plausible solution to offer such features. Security, authentication, and maintenance issues can be solved by the secure decentralization of blockchain and overcome the current IoT ecosystem's drawbacks. This paper highlights the IoT features and presents a IoT security and privacy problems are examined and classified in accordance with the IoT layered architecture. Further, we explore the IoT security and privacy challenges and examine blockchain technology towards a solution that help solve many of the IoT privacy and security issues

MODEL KEPEMIMPINAN KONTIGENSI FIEDLER DALAM PENERAPAN E-GOVERNMENT

The utilisation of information and communication technology in government processes to improve the efficiency, effectiveness, transparency and accountability of governance. The application of E-government is important to help the government provide public services. The demand for transparency and accountability has finally become E-government as a key requirement so that the quality of good governance is not just a theory on paper. So that government agencies from the central to the regional level are obliged to implement the use of information systems and technology on a massive scale. The implementation of E-government in the regions is also in line with the demands of central government regulations that are concerned with the development of digital-based services. This research focuses on the role or model of government leadership. This research uses descriptive qualitative research methods. The results of the study found that the first Fiedler Contingency Model Leader Member Relationship is the most needed leadership model in the Public Service mechanism with data 60% strongly agree and 40% agree regarding this model which prioritises democracy as a pillar of service. The leadership model is carried out through democratic values. Second, the Degree Of Task Structure leadership model found that 80% strongly agree and 20% disagree. This means that the leadership model based on command lines is still relevant in the government bureaucracy. So that all duties and responsibilities are carried out based on TUPOKSI. So that there are no mistakes in providing policies or implementing policies. Third, namely Leader Position Power, the results showed that 40% strongly disagreed, 40% agreed and 20% strongly agreed. This means that this leadership model is less favoured by government officials because this model tends to be authoritarian and prioritise the selfishness of a leader.

The Depiction of Narcissistic Personality Disorder of Elizabeth Halsey as Found in "Bad Teacher" Movie

Narcis Narcisism gained attention and discussion in recent years. This was caused by the rise of social media facilitating self-promotion and self-expression on a massive scale so that narcissistic behaviours can be seen more and more obvious. It could become a problem when they were excessive, inflexible, and maladaptive, leading to harm or distress to individuals or those around them. These extreme traits can be classified as Narcissistic Personality Disorder (NPD). This study aimed to describe the narcissistic's trait of Elizabeth Halsey in Bad Teacher Movie. Elizabeth Halsey worked as a teacher who should demonstrate empathy by understanding her students' perspectives, needs, and emotions, how she can creates a safe and supportive learning environment where students feel valued and heard but her character as a teacher showed narcissistic traits that are the opposite of what a teacher should have. This research used a psychoanalysis approach. The method of this research was qualitative method. In collecting the data, the researchers watched the movie, marking and classifying in what kinds of types of traits that the Elizabeth Halsey had. In analyzing the data, the researchers used descriptive method firstly by identifying all the statements that describe the types of the traits faced by Elizabeth Halsey, and then analyzed the data based on the finding. The results of this study found that there were four of nine traits of narcissistic in Elizabeth Halsey's Character. The first is entitlement trait, she only thought about herself, she did not have any shame on herself, she still asked money from her ex fiance. The second was exploitative traits, in this case, Elizabeth Halsey took advantage on many people, she would do everything without thinking's other feelings and opinion. The third was lack of empathy, she did not showed any empathy for her students who got bullied in front of her, and the last was envy trait, she did not accept the reality that the wealthy man fell in love with her enemy at school, she arranged everything to make her enemy kicked out from school.

Investigating the Impact of the of Technological Developments, Environmental Quality, and Economy on Tourism Receipts in Indonesia

This paper uses a panel technique to investigate the relationship between Technological Developments, Environmental, and GDP Quality on Tourism Receipts in Indonesia (including 33 provinces) for the 2011-2022 period. As a country with rich natural, cultural and ancestral heritage, Indonesia has the potential to attract foreign tourists. Especially with technological advancements that have made tourism promotion possible on a massive scale. On the other hand, an increase in the number of tourists has an impact on the environment as explained in the theory of the Environmental Kuznet Curve (EKC). The research method used is Vector Error Correction Model (VECM) model and Granger causality test. Environmental quality is measured through the Environmental Quality Index indicator. Meanwhile, technology development is measured through the Information and Communication Technology Development Index. The research results explain that Economy, Environmental Quality, and Technological Developments have a positive impact on Tourism Receipts in Indonesia. Therefore, it is important for the government to utilize digital technology to introduce Indonesia's tourism wealth. The increase in tourists must also be accompanied by efforts to protect the environment so that it does not have a negative impact on environmental damage.

Managing the remediation strategy of contaminated megasites using field-scale calibration of geo-electrical imaging with chemical monitoring

Groundwater contamination is a threat to drinking water resources and ecosystems. Remediation by injection of chemical reagents into the aquifer may be preferred to excavation to reduce cost and environmental footprint. Yet, successful remediation requires complete contact between contamination and reagents. Subsurface heterogeneities are often responsible for diffusion into low-permeable zones, which may inhibit this contact. Monitoring the spatial distribution of injected reagents over time is crucial to achieve complete interaction. Source zone contamination at megasites is particularly challenging to remediate and monitor due to the massive scale and mixture of contaminants. Source zone remediation at Kærgård Plantation megasite (Denmark) is monitored here, with a new methodology, using high-resolution cross-borehole electrical resistivity tomography (XB-ERT) imaging calibrated by chemical analyses on groundwater samples. At this site, high levels of toxic non-aqueous phase liquids (NAPL) are targeted by in-situ chemical oxidation using activated persulfate. It may take numerous injection points with extensive injection campaigns to distribute reagents, which requires an understanding of how reagent may transport within the aquifer. A geophysical (XB-ERT) monitoring network of unprecedented size was installed to identify untreated zones and help manage the remediation strategy. The combination of spatially continuous geophysical information with discrete but precise chemical information, allowed detailed monitoring of sulfate distribution, produced during persulfate activation. Untreated zones identified in the first remediation campaign were resolved in the second campaign. The monitoring allowed adjusting the number of injection screens and the injection strategy from one campaign to the next, which resulted in better persulfate distribution and contaminant degradation in the second campaign. Furthermore, geophysical transects repeated over the timespan of a remediation campaign allowed high-resolution time-lapse imaging of reagent transport, which could in the future improve the predictability of transport models, compared to only using on a-priori assumptions of the hydraulic conductivity field.

The convergence of total-factor energy efficiency across Chinese cities: A distribution dynamics approach

Improving energy efficiency is considered the most direct route to reducing carbon emissions on a massive scale. To examine the long-run trend of China's energy efficiency, this paper employs a distribution dynamics approach to analyze the convergence of total-factor energy efficiency (TFEE) across 286 Chinese cities between 2002 and 2014. The result suggests the existence of convergence, which comes more from the efficiency declines in the high TFEE cities rather than the efficiency gains in the low TFEE cities. Despite the fact that a few cities have performed well in energy efficiency, the majority of cities have converged to a low-efficiency point. Further analyses show that the inefficiency was attributed to inefficient energy use in small- and medium-sized cities. Our analysis provides strong scientific support for China's ongoing energy conservation and environmental protection policies.