New York Times Research Articles

How can data visualization support interdisciplinary research? LuxTIME: studying historical exposomics in Belval.

The Luxembourg Time Machine (LuxTIME) is an interdisciplinary project that studies the historical exposome during the industrialization of the Minett region, located in the south of Luxembourg. Exposome research encompasses all external and internal non-genetic factors influencing the health of the population, such as air pollution, green spaces, noise, work conditions, physical activity, and diet. Due to the wide scope of the interdisciplinary project, the historical study of the exposome in Belval involved the collection of quantitative and qualitative data from the National Archive of Luxembourg, various local archives (e.g., the communes of Esch-sur-Alzette and Sanem), the National Library, the Library of National Statistics STATEC, the National Geoportal of Luxembourg, scientific data from other research centers, and information from newspapers and journals digitized in eluxemburgensia. The data collection and the resulting inventory were performed to create a proof of concept to critically test the potential of a multi-layered research design for the study of the historical exposome in Belval. The guiding navigation tool throughout the project was data visualization. It has facilitated the exploration of the data collected (or just the data) and the metadata. It has also been a valuable tool for mapping knowledge and defining the scope of the project. Furthermore, different data visualization techniques have helped us to reflect on the process of knowledge sharing, to understand how the relevance of certain topics changed throughout the project and why, and to learn about the publication process in different journals and the experience of the participants. Data visualization is used not only as a means to an end but also to embrace the idea of sandcastles using a speculative and process-oriented approach to advance knowledge within all research fields involved. LuxTIME has proven to be an ideal case study to explore the possibilities offered by different data visualization concepts and techniques resulting in a data visualization toolbox that could be evaluated and extended in other interdisciplinary projects.

Analysis of Criticism of Newspaper by Jokowi & Joe Biden regarding the Development of Covid-19 in the IDN Times and NY Times Online Media: Cross-Cultural Pragmatic Studies

The research entitled "Analysis of Criticism of Newspaper by Jokowi & Joe Biden regarding the Development of Covid-19 in the IDN Times and NY Times Online Media: Cross-Cultural Pragmatic Studies" has two objectives, namely to analyze the functions and criticisms found in the online news articles IDN Times and NY Times and to explain the cultural differences and similarities between Indonesian and American society. This study analyzed ten IDN Times and NY Times news articles using a qualitative descriptive method. The data obtained in this study were through listening and note-taking techniques, because the data in this study were in the form of expressions that contain the function of speech and social criticism. Data validity uses theoretical triangulation. It was found 3 (three) assertive/representative functions in IDN Times and NY Times, 2 (two) commissive functions in IDN Times and 1 (one) commissive function in NY Times, 4 (four) expressive functions in IDN Times and 2 (two) expressive functions in NY Times, 2 (two) declarative functions in IDN Times and 1 (one) declarative function at the NY Times. There were also 3 (three) direct criticisms at the IDN Times and 5 (five) direct criticisms at the NY Times, as well as 2 (two) indirect criticisms at the IDN Times.

Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques

Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist several critical challenges and concerns to be resolved in order to reap the complete benefits of E-mobility. The impacts of unplanned EV charging are a major concern. Accurate EV load forecasting followed by an efficient charge scheduling system could, to a large extent, solve this problem. This work focuses on short-term EV demand forecasting using three learning frameworks, which were applied to real-time adaptive charging network (ACN) data, and performance was analyzed. Auto-regressive (AR) forecasting, support vector regression (SVR), and long short-term memory (LSTM) frameworks demonstrated good performance in EV charging demand forecasting. Among these, LSTM showed the best performance with a mean absolute error (MAE) of 4 kW and a root-mean-squared error (RMSE) of 5.9 kW.

A framework for understanding label leakage in machine learning for health care.

The pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying "no time machine rule." We provide a framework for contemplating whether and when model features pose leakage concerns by considering the cadence, perspective, and applicability of predictions. To ground these concepts, we use real-world clinical models to highlight examples of appropriate and inappropriate label leakage in practice. Finally, we provide recommendations to support clinical and technical stakeholders as they evaluate the leakage tradeoffs associated with model design, development, and implementation decisions. By providing common language and dimensions to consider when designing models, we hope the clinical prediction community will be better prepared to develop statistically valid and clinically useful machine learning models.

Trip Reduction in Turbo Machinery

Industrial plant uptime is top most importance for reliable, profitable & sustainable operation. Trip and failed start have major impact on plant reliability and all plant operators focussed on efforts required to minimise the trips & failed starts. The performance of these Critical to Quality (CTQs) are measured with 2 metrics, MTBT (Mean time between trips) and SR (Starting reliability). These metrics helps to identify top failure modes and identify units need more effort to improve plant reliability.Baker Hughes Trip reduction program structured to reduce these unwanted trip1. Real time machine operational parameters remotely available and capturing the signature of malfunction including related boundary condition.2. Real time alerting system based on analytics available remotely.3. Remote access to trip logs and alarms from control system to identify the cause of events.4. Continuous support to field engineers by remotely connecting with subject matter expert.5. Live tracking of key Critical to Quality (CTQs)6. Benchmark against fleet7. Break down to the cause of failure to component level8. Investigate top contributor, identify design and operational root cause9. Implement corrective and preventive action10. Assessing effectiveness of implemented solution.11. Develop analytics for predictive maintenanceWith this approach, Baker Hughes team is able to support customer in achieving their Reliability Key performance Indicators for monitored units, huge cost savings for plant operators. This paper explains this approach while providing successful case studies, in particular where 12nos. of Liquified Natural Gas (LNG) and Pipeline operators with about 140 gas compressing line-ups has adopted these techniques and significantly reduce the number of trips and improved MTBT.

China’s metaphorically “othered” image inThe New York Times(1949-2020)

This study investigates, within the framework of Critical Metaphor Analysis, how The New York Times ( NYT) has been metaphorically formulating China as an “other” from 1949 to 2020. Metaphors were identified with reference to Pragglejaz Group’s (2007) Metaphor Identification Procedure. WAR and GAME were detected as dominant concepts, and LIQUID, DISEASE, ANIMAL, MACHINE, and OBSTACLE secondary imagery. It is found that the newspaper’s journalistic values in foreign news reporting and judging criteria toward political leadership have remained radically unchanged. Having been influenced by changes in China-U.S. relations and America’s domestic socio-political ideals, the NYT has been othering China from the civilized world, which tends to be particularly prominent when China’s performance is short of the values and norms preached by the US, and became inconspicuous during China-U.S. alliance. Running counter to its commitment to “truth,” the newspaper employed discursive strategies to balance between America’s moral self-identity and its China policy, and vindicate behaviors identical to those it has been criticizing China for.

Robust Deep Learning Based Framework for Detecting Cyber Attacks from Abnormal Network Traffic

The internet's recent rapid growth and expansion have raised concerns about cyberattacks, which are constantly evolving and changing. As a result, a robust intrusion detection system was needed to safeguard data. One of the most effective ways to meet this problem was by creating the artificial intelligence subfields of machine learning and deep learning models. Network integration is frequently used to enable remote management, monitoring, and reporting for cyber-physical systems (CPS). This work addresses the primary assault categories such as Denial of Services(DoS), Probe, User to Root(U2R) and Root to Local(R2L) attacks. As a result, we provide a novel Recurrent Neural Networks (RNN) cyberattack detection framework that combines AI and ML techniques. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD), which covered all critical threats. We used normalisation to eliminate mistakes and duplicated data before pre-processing the data. Linear Discriminant Analysis(LDA) is used to extract the characteristics. The fundamental rationale for choosing RNN-LDA for this study is that it is particularly efficient at tackling sequence issues, time series prediction, text generation, machine translation, picture descriptions, handwriting recognition, and other tasks. The proposed model RNN-LDA is used to learn time-ordered sequences of network flow traffic and assess its performance in detecting abnormal behaviour. According to the results of the experiments, the framework is more effective than traditional tactics at ensuring high levels of privacy. Additionally, the framework beats current detection techniques in terms of detection rate, false positive rate, and processing time.

Trait‐based approaches as ecological time machines: Developing tools for reconstructing long‐term variation in ecosystems

Abstract Research over the past decade has shown that quantifying spatial variation in ecosystem properties is an effective approach to investigating the effects of environmental change on ecosystems. Yet, current consensus among scientists is that we need a better understanding of short‐ and long‐term (temporal) variation in ecosystem properties to plan effective ecosystem management and predict future ecologies. Trait‐based approaches can be used to reconstruct ecosystem properties from long‐term ecological records and contribute significantly to developing understandings of ecosystem change over decadal to millennial time‐scales. Here, we synthesise current trait‐based approaches and explore how organisms' functional traits (FTs) can be scaled across time and space. We propose a framework for reconstructing long‐term variation in ecosystems by means of analysing FTs derived from palaeoecological datasets. We then summarise challenges that must be overcome to reconcile trait‐based approaches with palaeo‐datasets. Finally, we discuss the benefits and limitations of trait‐based reconstructions of ecosystem temporal dynamics and suggest future directions for research. Reconstructing environmental properties through time vis‐à‐vis FTs can be separated into two parts. The first is to record trait data for organisms present in modern ecosystems, and the second is to reconstruct temporal variability in FTs from palaeoecological datasets, capturing changes in trait composition over time. Translating palaeoecological datasets into FTs is challenging due to taphonomic, taxonomic and chronological uncertainties, as well as uniformitarian assumptions. Explicitly identifying and addressing these challenges is important to effectively calculate changes in FT through time. Palaeo‐trait research offers insights into questions related to short‐ and long‐term ecosystem functioning, environmental change and extinction and community assembly rules across time. As work in this area matures, we expect that trait‐based approaches integrating palaeoecology and neo‐ecology will improve understanding of past ecologies and provide a deeper insight of their implications for present‐day and future ecosystem management and conservation. Read the free Plain Language Summary for this article on the Journal blog.

Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning

Accurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies.

Time Series Compression for IoT: A Systematic Literature Review

Time series are widely used to record information in applications developed with Internet of Things (IoT) devices, where sensors are used to collect large amounts of data. These devices, even with the exponential technological evolution of the last years, present limitations for the handling of this data given the enormous volume of data collected. Low storage or processing capacity and environments with restricted connection resources make it difficult to manipulate and transmit information. In this context, compression of information collected in the format of time series can be an alternative to reduce the amount of data handled by devices. However, given the peculiarities of IoT devices and applications, the compression process cannot be more costly than handling raw data. To identify the existing solutions to compress time series for IoT applications. In this paper, we present a descriptive systematic literature review on this topic. Based on a well-defined protocol, 40 papers were selected for review to analyze the strategies used, as well as performance and limitations. In this review, we aim to identify different approaches to solving the problem of data compression in the context addressed, as well as identifying directions for future research. The use of performance metrics in the reviewed papers was also reported in detail, to better understand how the authors compare their solutions to others. Additionally, the relationship between time series compression and machine learning (ML) techniques was highlighted. Being aware of the state-of-the-art in time series compression solutions for IoT can help us project future trends and challenges regarding this process and also to identify which algorithms, methods, and techniques can best be used in combination with ML models, the purpose inherent to the IoT context.

How to Construct a Time Machine: Dialogic Organizing and Cybernetic Ethics Within the Anthropocene

How to Construct a Time Machine: Dialogic Organizing and Cybernetic Ethics Within the Anthropocene

A systematic literature review on price forecasting models in construction industry

This paper summarizes a list of previously used forecasting models in the construction industry, using a three-stage review process. Specifically, articles published between 2012 to 2022 (inclusive) are investigated, and 34 documents are selected for further analysis. The results present a fluctuating number of publications from 2012 to 2016 and a significant increase since 2016. The distribution line between 2016 to 2020 indicates the growing interests in this topic. In addition, fluctuating material prices, construction costs and construction cost index were top concerns of researchers. Previous literatures frequently employed three forecasting models: Vector error correction model, Artificial Neural Network and Autoregressive Integrated Moving Average. Time series and machine learning techniques were widely used in prediction. Future studies could consider the hybrid model as a research method to improve the forecast accuracy. Additionally, future studies should take the effects of natural disasters into account. Findings of this study are expected to help researchers and industry practitioners select a suitable forecasting model and implement precise prediction in practice.

Rethinking the Public Space Design Process Using Extended Reality as a Game Changer for 3D Co-Design

Public space design processes are complex. Numerous preconditions and the involvement of stakeholders impede rapid decision making. Two-dimensional drawings remain the norm, although these are difficult for citizen stakeholders to understand. Public space designers rarely use 3D city models, infrastructure building information modeling, digital twins, or extended reality. Usually, 3D images (without animation) are only rendered after decision making for communication purposes. This study consists of an online questionnaire of 102 Flemish region (Belgium) stakeholders to show the appeal of and resistance to the use of 3D and extended reality in public space design processes. In a follow-up experiment, 37 participants evaluated various graphic techniques by their designs and observations. The questionnaire showed that all stakeholders lack experience with the use of virtual reality in design processes. We found that non-designer stakeholders and designers indicated that using virtual reality and interactive online 3D tools using game engines provided a better insight into communication and design. Reusing 3D designs in cycling simulators during the design process results in cost-effective quality optimization, and integration into digital twins or animated spatial time machines paves the way for hybrid, 4D cities. Extended reality supports 3D co-design that has simplicity and clarity from the outset of the design process, a trait that makes it a game changer.

Folk Tradition at the Creole Red River

Recognized by the National Park Service, the Cane River Creole National Historical Park area of Natchitoches, Louisiana serves as a main intercultural backdrop of history as American, French, Spanish, and Native American traditions once occupied its banks. Franklin D. Roosevelt’s Federal Writers’ Project, a byproduct of the New Deal documented new oral histories from the region. Nineteenth-century folklore from the Natchitoches Cane River area reveals that French, Cajun, and more importantly African influences cast allegories for the spiritual journey they interpreted. My paper uses African oral origin traditions in places like Natchitoches and elsewhere in colonial America to argue on behalf of a “Time Capsule Hypothesis” where forgetting history happens when the past is obscured and the future is apocalyptic. Preservation of landmark heritage sites through the Cane River’s origin folklore, architecture, and ecological history become a new esoteric medium. Reminiscent structures, such as the famous Magnolia and Melrose plantations on the Cane River have preserved a different history that focuses on conservation and cooperation. For us to understand the history of Natchitoches, Louisiana requires a new perspective on historical memory and technological sublime topics merging oral history and esotericism into an ecological time machine of Natchitoches. Creole Catholics emerged from Louisiana archdioceses and Black Christians became free by transforming mythic identities in their present moment to embrace creativity, literature, and technological acumen over their environment.

Framing responsibilities for climate change in Chinese and American newspapers: A corpus-assisted discourse study

This study conducts a corpus-assisted discourse study of framing responsibilities for climate change in China Daily (CD) and The New York Times (NYT). Based on the distinction between causal and treatment responsibilities, it focuses on the framing of human and non-human causal responsibilities as well as developed and developing countries’ causal and treatment responsibilities for climate change in the two newspapers. The findings suggest that CD tends to show consensus on the human causes of climate change while NYT is inclined to problematize human causes for climate change. While both newspapers favor treatment over causal responsibilities, CD prefers to underline developed countries’ historical causal responsibilities for climate change and urges developed countries to take more treatment responsibilities for climate change, whereas NYT prefers to underscore developing countries’ current causal responsibilities for climate change and their shared treatment responsibilities for climate change.

Examining the New York Times coverage of the crisis at the southern border in Biden’s America

With Biden’s election in 2020, the United States and the world anticipated a major shift in US immigration policy and a different approach to the US–Mexico border crisis. This study examines how media covered this through a textual analysis of 40 news stories published by the New York Times (NYT) between November 2020 and November 2021. It identifies four categories of content: ‘the Biden presidency – an era of hope and positive change’; ‘causes and complexities of the southern border crisis’; ‘critique of the Biden presidency’ and ‘migrant journeys, of confusion, suffering and hope’. Subcategories are also developed for these categories. The findings indicate that though violence in Central America is still a primary driver leading to a migrant surge at the US southern border, climate crises and the COVID-19 pandemic are two major events causing more migrations from Central America as well as bringing in new migrants to the United States from other countries around the world. The study shows that though the NYT does not portray the migrants negatively and offers some context about the causes of the crisis and a critique of the Biden administration’s handling of the crisis, the reporting lacks a long-term perspective about the deeper causes of the crisis.

Predictive modeling of marine fish production in Brunei Darussalam's aquaculture sector: A comparative analysis of machine learning and statistical techniques

The aquaculture industry has witnessed significant global growth, offering opportunities for sustainable fish production. This research delves into the application of data analytics to develop an appropriate predictive model, utilizing diverse machine learning and statistical techniques, to forecast marine fish production within Brunei Darussalam's aquaculture sector. Employing a machine learning-based algorithm, the study aims to achieve enhanced prediction accuracy, thereby providing novel insights into fish production dynamics. The primary objective of this research is to equip the industry with alternative decision-making tools, leveraging predictive modeling, to identify trends and bolster strategic planning in farm activities, ultimately optimizing marine fish aquaculture production in Brunei. The study employs various time series and machine learning techniques to generate a precise predictive model, effectively capturing the inherent seasonal and trend patterns within the time-series data. To construct the model, the research incorporates notable algorithms, including autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), linear regression, random forest, multilayer perceptron (MLP), and Prophet, in conjunction with correlation analysis. Evaluation of the model's performance and selection of the optimal forecasting model are based on mean absolute percentage error (MAPE) and root mean squared error (RMSE) metrics, ensuring a robust analysis of time series data. Notably, this pioneering research stands as the first-ever attempt to forecast marine fish production in Brunei Darussalam, setting a benchmark unmatched by any existing baseline studies conducted in other countries. The experiment's results reveal that straightforward machine learning and statistical techniques, such as ARIMA, linear regression, and random forest, outperform deep learning methods like MLP and LSTM when forecasting univariate time series datasets.

Flood Event Detection and Assessment using Sentinel-1 SAR-C Time Series and Machine Learning Classifiers Impacted on Agricultural Area, Northeastern, Thailand

Flood Event Detection and Assessment using Sentinel-1 SAR-C Time Series and Machine Learning Classifiers Impacted on Agricultural Area, Northeastern, Thailand

VTGAN: hybrid generative adversarial networks for cloud workload prediction

Efficient resource management approaches have become a fundamental challenge for distributed systems, especially dynamic environment systems such as cloud computing data centers. These approaches aim at load-balancing or minimizing power consumption. Due to the highly dynamic nature of cloud workloads, traditional time series and machine learning models fail to achieve accurate predictions. In this paper, we propose novel hybrid VTGAN models. Our proposed models not only aim at predicting future workloads but also predicting the workload trend (i.e., the upward or downward direction of the workload). Trend classification could be less complex during the decision-making process in resource management approaches. Also, we study the effect of changing the sliding window size and the number of prediction steps. In addition, we investigate the impact of enhancing the features used for training using the technical indicators, Fourier transforms, and wavelet transforms. We validate our models using a real cloud workload dataset. Our results show that VTGAN models outperform traditional deep learning and hybrid models, such as LSTM/GRU and CNN-LSTM/GRU, concerning cloud workload prediction and trend classification. Our proposed model records an upward prediction accuracy ranging from 95.4% to 96.6%.

A Discoursal Investigation on Accelerated Hostility between China Daily and the New York Times

Since COVID-19 outbreak hit China in December 2019, there has been a “narrative battle” between China and the US. Roughly 89% U.S. adults consider China a competitor or enemy, rather than a partner, according to a Pew Research Center survey in Otc. 2020. Faced with accusations internationally, there appears rising attacks and unfavourable feelings towards Asian people in the US during COVID-19, which leads to social exclusion and discrimination to Asian, especially the Chinese in the US. In the meantime, the COVID-19 pandemic has worsened a rising tide of nationalism in China, amplifying pre-existing nationalist and protectionist trends. According to a survey from Carter Center and RIWI (2021), 62% of Chinese internet-using population shows unfavorable feeling towards the US. As media plays a significant role in society in shaping public opinions, it is of significant to explore how the media reports accelerate the anti-sentiments towards each other. Although scholars have started to explore media reports during the pandemic, very less is known about how the COVID-19 pandemic is reported, discussed, and presented in the Chinese and American media from critical discouse analysis (CDA). This study will comparatively evaluate 36 editorials from China Daily (CD) and New York Times (NYT) with regards to ideological square framework. Based on data analysis, the study indicates that not only the headings, but also the media substance constructs a type of conflicting relations. Through positivizing “us” and negativizing “them”, both media accelerate the unfavorable feeling towards each other. This study suggests that both media should rationally report the pandemic, and people should work together to preserve our lives and live in peace.