New York Times Research Articles

Implementation Studies Documentation in Media and Culture Studies

Documentation is like a time machine as it facilitates the process of returning to history for future generations. ,The objective of this paper is to review the documentation in media and cultural studies. This paper discusses the importance of implementing documentation to create a culture that contributes to education. . Using digital literacy is one of the ways of documentation to strengthen media and culture as the community needs a culture that has rich literature and references to science. In media and cultural studies, documentation can be performed in various ways such as, archives, newspapers, magazines, books, videotapes, and audio. This can provide valuable information and data for understanding media and culture in the past, present, and future. Documentation also helps in understanding the changes in media and culture, influence the media on culture, and vice versa. Documentation helps researchers to identify various elements important in media and culture, such as messages conveyed, values carried, and patterns formed behavior. This study uses a method in the documentation that covers the election inclusion and exclusion criteria, in Indonesia. Keywords: study documentation, media, culture

A Comparative Study between Time Series and Machine Learning Technique to Predict Dengue Fever in Dhaka City

The dengue virus is the most dangerous one that mosquitoes may spread to people. Despite attempts by the government, dengue outbreaks are becoming increasingly common in Bangladesh. Interventions in public health rely heavily on KAP (knowledge, attitude, and practice) studies. The primary goal of this research is to forecast the occurrence of dengue disease in the city of Dhaka using methods from machine learning and time series analysis and then to compare the models in order to find the one with the lowest MAPE. From January 2016 through July 2021, monthly data were retrieved for this study from WHO and the Directorate General of Health and Services (DGHS). According to the findings of this research, neural networks outperform time series analysis when it comes to making predictions. The best-fitted neural network (NN) model was found in model 04 with 05 hidden layers which produced the minimum error model with the value of error 0.003032557, and the values of RMSE and MAPE are 7.588889e − 06 and 1.15273, respectively, for the prediction of the dengue fever in Dhaka city. In contrast, the original dengue data in the time series analysis is not stationary. Take the difference and run the unit root test by the augmented Dickey–Fuller (ADF) test to make it stationary. The dengue data series is stationary at the first-order difference, as evidenced by the ACF and PACF, which show no noticeable spike in the first-order difference. The ARIMA (6, 1, 1) model with the lowest AIC = −251.8, RMSE = 0.0310797, and MAPE = 15.2892 is the best choice model for predicting the dengue death rate. Therefore, from these two models, the NN model gives better prediction performance with the lowest value of MAPE. So, the neural network gives better prediction performance than time series analysis. The NN model forecasted 12-month death rates of dengue fever that suggest the death rate in dengue fever falling month by month. This study is more innovative than any other research because this research approach is different from any other research approach. The model selection criteria are based on the most effective performance metrics MAPE, indicating the lowest error and better prediction performance. Therefore, from this research, the author suggests machine learning gives better prediction performance than time series analysis for any other prediction performance.

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Abstract The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since these drivers vary in space and time, producing accurate spatially explicit maps with regular temporal updates is essential. Drivers can be recognized from satellite imagery but the scale of tropical deforestation makes it unfeasible to do so manually. Machine learning opens up possibilities for automating and scaling up this process. In this study, we have developed and trained a deep learning model to classify the drivers of any forest loss - including deforestation as well as temporary disturbances - from satellite image time series. The results show that time series bring a significant improvement over using single images. We have designed the model architecture to allow understanding of how the model uses the input time series to make a prediction. We analyzed these data and showed how the model learns different patterns for recognizing each driver. Finally, we used our model to classify over $588'000$ sites of recent forest loss to produce a map detailing the driving forces behind forest loss across the tropics.

Cis-regulatory control of transcriptional timing and noise in response to estrogen

Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single-cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We found that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.

Wave excitation force prediction for arrays of wave energy converters in directional waves

The implementation of advanced control strategies is an effective means of maximizing the energy production of wave energy converters (WECs), and most of them require future wave excitation forces to determine the optimal control sequence. The WECs deployed in arrays can enable a significant reduction in the cost of wave energy and improve prediction performance by incorporating excitation forces from the array. In this paper, a critical comparison of the excitation prediction methods, including the time series, machine learning, and neural network, for a two-body heaving point absorber WEC is presented. Considering the symmetry of the WEC and arrays, the directional spectrum is utilized to determine the reference measurement of wave excitation. The prediction methods are applied to isolated WEC and different array layouts. Moreover, additional sensitivity analyses are performed to evaluate the performance. The results show that the autoregressive with extra input (ARX) model has the best performance in short-term wave excitation force prediction. In most cases, the ARX model is weakly influenced by the sampling period and can adapt well to changes in the directional distribution and sea states of irregular waves, and the ARX model requires the least amount of computational time in the simulations.

(Re)framing online news through paratexts: a multisemiotic analysis of The New York Times in Chinese

ABSTRACT This study employs a multisemiotic approach to analyse various paratextual framing devices in online news texts, including news headlines, photographs and captions, as well as section names. Translated news articles about the 2014 Hong Kong protests were collected from the Chinese-language website of The New York Times (NYT) published between 28 September and 16 December 2014 and their original versions from its English website. The study finds that the Chinese edition mainly frames the events covered in the articles through paratextual rather than textual elements and, through those paratextual elements, a slightly different social reality to their English version for the events was (re)framed. This study underscores the influential role of paratextual devices in news translation, demonstrating how they can subtly shift the framing of events and potentially influence public perception.

Monitoring dynamics of urban expansion using time series Landsat imageries and machine learning in Delhi NCR

Monitoring dynamics of urban expansion using time series Landsat imageries and machine learning in Delhi NCR

Exploring Predictive Models for Airfare Forecasting: A Comparative Analysis of Time Series and Machine Learning Approaches

Exploring Predictive Models for Airfare Forecasting: A Comparative Analysis of Time Series and Machine Learning Approaches

Use of MTM, RAV and ZAK Methods in Determining Working Methods and Time Norms in Technological Operations of Sewing Clothes

The paper presents the application of MTM, RAV and ZAK methods in the determination of working methods and time norms for the technological process of sewing. The technological operation of sewing the back sleeve seam on a men’s jacket is used for the study. The paper presents the designed workplace in a real production process. In addition, the appropriate working method and the time norm are determined using the MTM (Method Time Measurement), RAV (determination of sewing time for straight seams) and ZAK (determination of sewing time for curved seams) methods. The aim of the study was to combine the methods for determining different types of time (manual, machine-hand and machine) for performing technological operations and precisely calculate the time norms. For the described methods of determining the production time, computer programs were developed that speed up the calculation of the time norm. If the working method is defined in advance and the characteristics of the sewing machine are known, the time norm for each technological operation can be determined in advance. Based on these calculations, it is possible to determine the total time for the production of clothing in advance and thus to plan and schedule the production process and plan the delivery dates to customers. The results show that the technical equipment of the sewing machine and the working method have an influence on the structure of the technological sewing process. By choosing an appropriate working method, the workload and fatigue of the workers is reduced, as an appropriate number of auxiliary hand sub-operations are used. In this way, the appropriate time required to perform the technological operation is achieved.

A stochastic programming approach to the antibiotics time machine problem

Antibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modelled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics are given. The transition probabilities of a ‘realization’ of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realizations. The aim is to maximize the expected probability of reaching to the genotype with all unmutated genes given the initial genotype in a predetermined number of transitions, considering the following two sources of uncertainties: (i) the randomness in growth rates, (ii) the randomness in transition probabilities, which are functions of growth rates. We develop stochastic mixed-integer linear programming and dynamic programming approaches to solve static and dynamic versions of the Antibiotics Time Machine Problem under the aforementioned uncertainties. We adapt a Sample Average Approximation approach that exploits the special structure of the problem and provide accurate solutions that perform very well in an out-of-sample analysis.

An enhanced machine learning approach for effective prediction of IgA nephropathy patients with severe proteinuria based on clinical data

IgA Nephropathy (IgAN) is a disease of the glomeruli that may eventually lead to chronic kidney disease or kidney failure. The signs and symptoms of IgAN nephropathy are usually not specific enough and are similar to those of other glomerular or inflammatory diseases. This makes a correct diagnosis more difficult. This study collected data from a sample of adult patients diagnosed with primary IgAN at the First Affiliated Hospital of Wenzhou Medical University, with proteinuria ≥1 g/d at the time of diagnosis. Based on these samples, we propose a machine learning framework based on weIghted meaN oF vectOrs (INFO). An enhanced COINFO algorithm is proposed by merging INFO, Cauchy Mutation (CM) and Oppositional-based Learning (OBL) strategies. At the same time, COINFO and Support Vector Machine (SVM) were integrated to construct the BCOINFO-SVM framework for IgAN diagnosis and prediction. Initially, the proposed enhanced COINFO is evaluated using the IEEE CEC2017 benchmark problems, with the outcomes demonstrating its efficient optimization capability and accuracy in convergence. Furthermore, the feature selection capability of the proposed method is verified on the public medical datasets. Finally, the auxiliary diagnostic experiment was carried out through IgAN real sample data. The results demonstrate that the proposed BCOINFO-SVM can screen out essential features such as High-Density Lipoprotein (HDL), Uric Acid (UA), Cardiovascular Disease (CVD), Hypertension and Diabetes. Simultaneously, the BCOINFO-SVM model achieves an accuracy of 98.56%, with sensitivity at 96.08% and specificity at 97.73%, making it a potential auxiliary diagnostic model for IgAN.

‶Don’t Say Neigh, Say Yay”

Introduction The television series The Great offers a sophisticated, satirical interpretation of Catherine the Great's rise to power in the Russian Empire. Set in the eighteenth-century aristocracy, it successfully blends historical facts with intentional anachronisms, showcasing Catherine’s evolution from a naïve outsider to a powerful, enlightened Empress. The series is notable for its sharp wit and deliberate anachronisms, transcending mere entertainment to provide a reflective commentary on societal and ethical themes. Crucially, The Great delves into the symbolic representation of animal treatment in the royal court, mirroring the era's hierarchical structures and ethical standards. It juxtaposes the archaic, often barbaric, practices against modern views on animal welfare, prompting audiences to reevaluate contemporary moral perspectives on human-animal relationships. This article aims to explore the complex interplay between ethical considerations and the human-animal relationship as depicted in The Great. It will examine the historical backdrop of animal treatment in the realm of eighteenth-century Russian nobility, contrasting with contemporary perspectives on animal treatment, and explore how the series utilises these contrasting viewpoints to stimulate viewers' introspection on our current societal ethics. “It is the way of things, a tradition for royalty”: The Great, and Eighteenth-Century Russia The Great, a satirical, pseudo-historical series, depicts Catherine the Great's ascent in Russia. It blends historical facts with fiction, showcasing her transformation within the eighteenth-century Russian court (see for instance Greenleaf). Known for its sharp humour and anachronisms, the series thoughtfully examines themes of power, gender dynamics, and societal evolution. A notable aspect is the portrayal of animal treatment as a symbol of the era's values and power structures, challenging viewers to contrast historical and contemporary societal norms. This article focusses on the ethics of human-animal relationships, as depicted in the series. The eighteenth-century Russian aristocracy's use of animals as power symbols reflected more than cultural peculiarities; it was an ingrained societal norm (Klemun, Loskutova, and Fedotova; Renner; Slezkine). Authors such as Cavender and Cartmill have noted that human-animal relationships, and in particular hunting, can symbolise dominance and social hierarchy, going beyond recreation or subsistence to signify human dominion over nature and societal status (Knoll). This historical backdrop is vividly brought to life in The Great, where scenes of aristocratic hunting and animal symbolism reflect these deeply ingrained societal norms.  “As we hunt, we shall discuss whether or not to kill the empress”: ‘Normal’ Royal-Animal Interactions The Great explores themes such as the abuse of power, the role of enlightenment in a traditional society, and the complexities of leadership and governance. The series uses character development to explore these themes, offering a satirical and insightful look at the societal and cultural dynamics of the time. The show often achieves this by playing with our expectations of how we believe monarchs should behave. For instance, in season one episode one, Peter is stalking through a forest followed by members of the court. Unlike typical royal hunts, Peter is hunting alone, not as part of a hunting party. Upon spotting a rabbit, he shoots, and misses (“The Great”, 1.1). We are used to royals hunting, and the hunt being analogous to the monarch's rule: a sign of strength, power, and dominion. But we are not used to them hunting poorly. A good monarch should be a good hunter. In this scene, we learn: one, Peter is alone with his alliances; and two, Peter is not a good emperor. While Peter and Catherine are walking through a forest hunting deer, Peter explains that “hunting is listening”, essentially saying that ruling is listening, to which Catherine retorts, “which is why you're so bad at it” (“The Bullet or the Bear”, 3.1). During the same scene, unbeknownst to both characters, Catherine’s longtime ally secretly hides in the bushes to assassinate Peter. Catherine, hearing a slight noise that Peter misses, fires into the bushes, inadvertently killing Orlo. Neither character realises what has occurred, and with Orlo later consumed by bears, the truth of the event is likely to remain unknown. As hunting is analogous with ruling, this scene is narratively important. For instance, Catherine is a better listener and therefore a better ruler, and merely listening does not always tell the full story. The connection between animals and ruling is extended to nature by Elizabeth, who explains to Peter on the cusp of a revolution: “I am going to the country for a week. I love to sit in nature and just watch it. It is beautiful, and sometimes harsh. But you realise, it is always right. That is what I know” ("The Beaver's Nose", 1.10). “If we had a philosophy, a criteria for action, I just wonder what a difference that would make”: Influences on Animal Perception in the Eighteenth Century In the eighteenth century, under the influence of thinkers like René Descartes, animals were perceived as mere automata, lacking consciousness or soul. This view, treating animals as emotionless machines, shaped the era's ethical attitudes towards animal welfare (Descartes). Contrastingly, modern perspectives show a significant shift, marked by an increased awareness of animal rights (Foster). Contemporary society has seen a rise in animal welfare advocacy, vegetarian and vegan lifestyles, and anti-cruelty legislation (Dawkins; Herzog et al.). The trend of recognising animals as sentient beings, especially pets as integral family members, reflects a major cultural and ethical transformation in human-animal relationships (Zuolo). This shift represents a departure from historical norms, highlighting a profound change in the way many people view animals. This change to recognising animals as sentient beings demonstrates an important difference between historical views and contemporary ethical perspectives. This transition in human-animal relationships sets the stage for exploring Allan G. Johnson's theories on societal systems and individual behaviours. Johnson's work delves into how societal structures shape personal actions and beliefs, offering a lens through which to examine the evolving societal attitudes towards animals and their treatment (Johnson Privilege, Power). “It's a barren wasteland of aloneness that you try to push us towards”: Johnson’s Societal Systems and Individual Behaviours This evolving attitude towards animals, characterised by increased empathy and ethical consideration, aligns with Allan G. Johnson's theories on the interplay between societal systems and individual behaviours (Johnson Privilege). Johnson's insights into how societal norms and structures influence personal actions and beliefs provide a framework for understanding the shift in how society views and treats animals. Allan G. Johnson's analogy of Monopoly and social systems offers an insight into the functioning of societal structures and individual behaviour (Johnson "People"). In his analogy, Johnson compares the societal system to a game of Monopoly, a board game where players accumulate wealth and property, often at the expense of others. In his analogy, Johnson demonstrates how greed is needed to win Monopoly, but this does not necessarily make one greedy. This analogy serves to illustrate how individuals, even without explicit malicious intent, can perpetuate inequality and injustice simply by playing within the rules of the prevailing system. Johnson's concept of the "path of least resistance" (Johnson Privilege, 86) refers to the natural human tendency to follow the course of action that requires the least effort or causes the least conflict. In societal terms, this path often aligns with existing norms, values, and structures, irrespective of their fairness or justice. By following this path, individuals, often unconsciously, reinforce and perpetuate the status quo. This analogy is powerful in demonstrating the social systems' influence on individual behaviour. It suggests that tackling societal issues like inequality and injustice requires more than individual goodwill; systemic change is necessary. Johnson's perspective encourages us to look beyond our actions and consider the systems that we are a part of. Johnson points out that the game itself might be flawed and by simply participating, we may be inadvertently supporting these flaws. “... have any of you read the latest Rousseau, The Social Contract?” Contractarianism in The Great, Ethical Frameworks, and Power Dynamics Viewing the historical fiction through contemporary moral sensibilities is tempting. Indeed, the blend of modern and historic elements invites the audience to do just this. However, it is crucial to understand that the characters in The Great are bound by the rules of their world. It prompts the question: under what ethical frameworks can we judge the ‘moral rightness’ of the characters' actions? One option is that The Great’s narrative situates Russian morality within a state of contractarianism, an ethical theory proposing that governmental authority derives from a hypothetical social contract among individuals. This concept is reflected in the depiction of eighteenth-century Russian society, particularly through its royal family, hierarchy and power dynamics (Rousseau; Hobbes). For contractarian philosophers like Thomas Hobbes and Jean-Jacques Rousseau, there are no intrinsic ethical imperatives. What is ethical and moral is decided by society and enforced through a social contract. The series illustrates a society bound by enforced rules and implicit agreements, aligning with contractarian principles. These 'contracts' shape the roles, behaviours, and expectations within the court, from the monarch to the commoners, reinforcing the existing ruling system (Hobbes). To the viewer, these rules appear absurd and often immoral. However, to the characters they are unquestionable norms. Catherine: “We cannot just burn fathers, mothers, and children alive.”Peter: “Yes, we can, they are serfs.” (“A Pox on Hope”, 1.7) Despite both representing contractarian views, Peter and Catherine represent two different approaches to contractarianism: firstly, as described by Hobbes, and secondly, by Rousseau. Peter leans towards Thomas Hobbes’s representation of the state of nature, a hypothetical scenario which illustrates the conditions of human existence without the constraints of social order or political authority. Hobbes, in his seminal work Leviathan, describes a state of nature as a state of war. “To this war of every man against every man, this also is consequent; that nothing can be unjust” (79). Within this state, “the life of man, solitary, poor, nasty, brutish, and short” (78). As Catherine says, in a moment, this man's blood will gush and my husband will have blackened his soul and ours with it ... . And you will all be reassured that we are all alone and living at the whim of each other's pain and madness, that any moment could be your last, that there is no rhyme or reason to our lives. This is the Russia I try to leave behind. (“You the People”, 3.3) Jean-Jacques Rousseau's version of social contract theory, outlined in his work The Social Contract, is referenced by Catherine in the first episode. Rousseau posits that the social contract arises from the collective will of the people, emphasising the general will as the foundation of a just society. Rousseau envisions a state of nature as harmonious, asserting that social inequalities and corruption emerge through private property and societal structures. Rousseau's contractarianism underscores the importance of preserving individual freedoms while promoting a collective agreement for the common good, advocating for a society that aligns with the general will of its citizens. Although the rights of animals are never openly discussed, Catherine is trying to remake Russia into a more civilised place. When attempting to pass her anti-murder bill, she explains: We should live free of the state deciding our lives.Or as Voltaire said, “don't be fucking animals anymore, is the point. Simple”. (“You the People”, 3.3) Catherine’s role in The Great is to fight against the path of least resistance. Recognising that the entrenched societal norms are part of a social contract and not intrinsic ethical imperatives, from the moment Catherine arrives in Russia she begins attempting to change the status quo. This portrayal in The Great underscores the anthropocentric contractarian nature of societal interactions and governance, suggesting that characters’ actions, including their interactions with animals and each other, are influenced by the social contract of their era, and that, importantly, these contracts are not immutable (Rousseau). The show thus critiques historical social contracts, encouraging viewers to consider how social contracts underpin current societal norms, behaviours, governance, and contemporary ethics. Now we understand the setting for The Great and its ethical framework, we can begin to see not only how the use of animals throughout the series highlights the actions of the aristocracy, but how these actions invite us to reflect on our own human-animal relationships. “Am I the Empress of Russia or a gamekeeper?” Truffle Dogs, Moose Lips and Trained Butterflies Domestic animals are animals that have in some way co-evolved with humans, generally through selective breeding and/or genetic modification (Serpell). This relationship is distinctly different from taming. Taming is an individual process, not a genetic one. A tamed animal is a wild animal that has been acclimated to human presence and may be trained to tolerate human interaction, perform tasks, or exhibit less fear of humans. The domestic animals in The Great are mostly dogs and horses. Horses remain a monarchic symbol of status. Monika Greenleaf wrote about the real Catherine the Great that “a ruler masters her steed as she does the nation, with finesse, not brutality” (417). In The Great, horses are regularly ridden and used to pull carriages. But they also serve to highlight the brutality of war: Catherine: what is that smell?Velementov: Bodies, mud, horse shit, smoke of cannons. It is not a place for women. (“War and Vomit”, 1.5) The most famous use of a horse is in retelling the famous real-world rumour that Catherine the Great had sex with a horse (Solly). The incorporation of this rumour was used throughout the series to ridicule, demean, and frustrate Catherine as a leader. It simultaneously sets a taboo within the aristocracy and is used to shock the audience with our current societal laws and perception of bestiality. As a ruler, this act of bestiality is one of brutality and not finesse. Dogs are often used as familiar dual-purpose pets. Some of Peter’s dogs are working dogs, such as his truffle dog from Italy, while others appear to be mere companions. In season 1 episode 2 (“The Beard”), we are introduced to a form of raccoon-baiting, whereby a dog and raccoon enter a hollow log at opposite ends, fight through the centre, and to the delight of the court, only one animal exits. Peter’s dog Zeus is regularly seen around the breakfast table until his death in episode three of season one (“And You, Sir, Are No Peter the Great”). The court's interactions with wild animals strike a stark contrast to modern, Western sensibilities. Unique remedies and theories were utilised to show the prominence of the court in what society now sees as more modern-day personal or private matters, such as draping a dead mouse around Peter's neck to draw out toxins or placing a frog on Catherine's pregnant belly to determine when she will give birth. The court takes the opportunity during late pregnancy of the monarch to make some money, with different bets including the gender, whether the baby will be part horse (again ridiculing the leader), and if Catherine will die in childbirth. In addition to unconventional medicinal practices, the aristocracy indulges in peculiar culinary preferences, like raw pheasant or black bread with moose lips. Lady Svenska: Empress, you look radiant. We would love for you to join us. We have cakes in the shape of woodland animals, and it is a lark to dunk their faces in vodka and eat them.Catherine: It sounds interminable, Lady Svenska, so I shall pass. (“The Beard”, 1.2) Wild and domestic animals also appear in folk tales and allegories. When a crocodile is loose in the palace, the members of the aristocracy believe it to be an omen. Spurred on by the crocodile, characters recant stories of other animal omens, such as donkeys that vomit blood and a dozen white doves before the birth of a child.  There is an exotic relationship with tamed wild animals which represents the monarch’s dominion over beasts. Upon arriving in Russia, Peter gifts Catherine a bear, a gift Catherine had dreamed of receiving. However, Peter thoughtlessly shoots the bear mere moments later. Aunt Elizabeth has a mutual relationship between her animal companions. For instance, in an early episode we see Elizabeth training butterflies, the implication being that this is one of her absurd and childish quirks. However, in season three, we see that she has indeed trained a butterfly, subverting the audience’s perspectives on the natural world. “I feel bad, watching like this”: The Modern Audience's Response to Historical Animal Treatment The response of modern audiences to the portrayal of animals in The Great may involve discomfort and ethical scrutiny, as we are forced to simultaneously observe how animals are treated in the show through the lens of history and reflect on what is done now. Contemporary viewers, influenced by current norms and the animal rights movement, may find historical practices depicted in The Great to be morally troubling or unacceptable. Conversely, viewers might experience dissonance or a sense of exonerative comparison (sensu Bandura) watching the treatment of animals in the historical drama, believing we have come so far since the archaic on-screen period. Reflecting on personal and societal values through historical narratives and contemporary media, such as The Great, facilitates a critical reassessment of our moral compass and societal norms, in this instance the treatment of animals, and its broader ethical considerations. Contemporary media that contrast historical practices with modern ethics prompt introspection among viewers, encouraging them to evaluate the progression of our ethical standards and how these historical practices diverge from contemporary values (Frentzel-Beyme and Krämer). This introspection is vital for understanding the evolution of societal norms and aligning personal values with current ethical expectations. Furthermore, such introspection could transcend animal treatment, encompassing broader social justice, human rights, and environmental stewardship issues, advocating for a comprehensive perspective on progress towards a more compassionate and ethical society (Francione; Tamborini). The series' depiction of animal-related activities encourages viewers to question modern practices. It prompts us to explore our current societal contracts, examining why certain actions are acceptable and others are not. As viewers, we can ask what are the modern equivalences of Russia’s past behaviours. What is different about the royal hunting in the show, compared to royal hunting today? What makes duck liver or ox tail any less strange than moose lips? And, what leads us to believe that Elizabeth’s attempts to train butterflies are ridiculous? “The journey ends in the inescapable conclusion: Death”: Conclusion The Great not only explores the relationship between animals and royalty in eighteenth-century Russia, but also reflects on broader themes of power, status, and emotional connections with animals in royal life. It juxtaposes the regal and animalistic, probing the complex roles animals played in asserting dominance and luxury, alongside being subjects of affection. Incorporating Allan G. Johnson's insights, the series encourages viewers to reflect on the historical treatment of animals within the framework of societal systems and social contracts. Catherine's story is one of struggle for progress against the path of least resistance. This narrative highlights the ongoing conflict between old and new ways of thinking, symbolising her efforts to redefine the social contract. The Great challenges modern audiences to consider how these historical perspectives and practices resonate with contemporary attitudes towards animal welfare, urging a deeper understanding of human-animal relationships and ethical standards across different eras and cultures. This reflection is crucial for understanding the dynamics of societal norms and the subsequent impact on our current ethical viewpoints. References "The Great." The Great. Dir. Colin Bucksey. 2020. "The Bullet or the Bear." The Great. Dir. Matthew Moore. 2023. "The Beaver's Nose." The Great. Dir. Geeta Patel. 2020. "A Pox on Hope." The Great. Dir. Colin Bucksey. 2020. "You the People." The Great. Dir. Sheree Folkson. 2023. "The Beard." The Great. Dir. Colin Bucksey. 2020. "And You, Sir, Are No Peter the Great." The Great. Dir. Bert & Bertie. 2020. Bandura, Albert. "Impeding Ecological Sustainability through Selective Moral Disengagement." International Journal of Innovation and Sustainable Development 2.1 (2007): 8-35. Cartmill, Matt. A View to a Death in the Morning: Hunting and Nature through History. Harvard UP, 1996. Cavender, Mary W. "Hunting in Imperial Russia: State Policy and Social Order in L.P. Sabaneev's Writing." The Russian Review 76.3 (2017): 484-501. Dawkins, Marian. Animal Suffering: The Science of Animal Welfare. Springer Science & Business, 2012. Descartes, René. Discourse on Method. Hackett, 2012. Francione, Gary L. "Animal Rights and Animal Welfare." Rutgers Law Review 48 (1995): 397. Frentzel-Beyme, Lea, and Nicole C Krämer. "Historical Time Machines: Experimentally Investigating Potentials and Impacts of Immersion in Historical VR on History Education and Morality." Technology, Mind, and Behavior 4.1: (2023). 34. Greenleaf, Monika. "Performing Autobiography: The Multiple Memoirs of Catherine the Great (1756-96)." The Russian Review 63.3 (2004): 407-26. Herzog, Harold, Stephanie Grayson, and David McCord. "Brief Measures of the Animal Attitude Scale." Anthrozoös 28.1 (2015): 145-52. Hobbes, Thomas. Leviathan or the Matter, Forme, & Power of a Common-Wealth Ecclesiasticall and Civill. The Green Dragon in St. Pauls Church-yard: McMaster University, 1651. Johnson, Allan G. "People, Systems, and the Game of Monopoly." 2013. 30 Jan. 2024 <https://www.youtube.com/watch?v=4rxL3ik9sJA>.   ———. Privilege, Power, and Difference. 2nd ed. Boston: McGraw-Hill, 2006. Klemun, Marianne, Marina Loskutova, and Anastasia Fedotova. "Skulls and Blossoms: Collecting and the Meaning of Scientific Objects as Resources from the 18th to the 20th Centuries." Centaurus 60.4 (2018): 231-37. Knoll, Martin. "Hunting in the Eighteenth Century: An Environmental History Perspective." Historical Social Research / Historische Sozialforschung 29.3 (109) (2004): 9-36. Renner, Andreas. "Progress through Power? Medical Practitioners in Eighteenth-Century Russia as an Imperial Elite." Acta Slavica Iaponica 27 (2009): 29-54. Rousseau, Jean-Jacques. Rousseau: The Social Contract and Other Later Political Writings. Cambridge UP, 2018. Serpell, James. "Pet-Keeping and Animal Domestication: A Reappraisal." The Walking Larder (2014): 10-21. Slezkine, Yuri. "Naturalists versus Nations: Eighteenth-Century Russian Scholars Confront Ethnic Diversity." Representations 47 (1994): 170-95. Solly, Meilan. "The Story of Catherine the Great." Smithsonian Magazine 2020. 30 Jan. 2024.  <https://www.smithsonianmag.com/history/true-story-catherine-great-180974863/>.    Tamborini, Ron. "A Model of Intuitive Morality and Exemplars." Media and the Moral Mind. Routledge, 2012. 43-74. Zuolo, Federico. "Dignity and Animals: Does It Make Sense to Apply the Concept of Dignity to All Sentient Beings?" Ethical Theory and Moral Practice 19 (2016): 1117-30. Acknowledgments We would like to thank three anonymous peer reviewers for their collegial and critical review of this article. Suggestions from reviewers have been incorporated into, and improved, the final version of this article. Huzzah!

Using Machine Learning Techniques to Forecast Mehram Company's Sales: A Case Study

Sales forecasting, situated at the intersection of art and science, is critical for inspiring managers toward achieving profitable outcomes. Its precision sustains production levels and capital and plays a pivotal role in the company's and its leaders' overall success and career progression. In the context of Mahram Food Industries, the challenge arises from diverse investor perspectives and the impactful nature of numerous variables. To address this, a new sales forecasting algorithm has been introduced to enhance accuracy. The aim is to predict future sales through a comprehensive approach, leveraging technical analysis, time series modeling, machine learning, neural networks, and random forest techniques. The research methodology integrates various advanced techniques to improve sales forecasting for Mahram Food Industries. Technical analysis, time series modeling, machine learning, neural networks, and random forest methods are combined to create a robust framework. The focus is on predicting sales for a future period within the artificial intelligence-based machine learning domain. The study employs metrics such as Mean Absolute Deviation (MAD), MAD Percentage (MADP), and Mean Squared Error (MSE) to evaluate and compare the performance of the proposed neural network against traditional methods like multiple variable regression and time series modeling. The study's results highlight the superior performance of the neural network in sales forecasting for Mahram Food Industries. The Mean Absolute Deviation (MAD) for the neural network is 28.33, outperforming multiple variable regression (28.54) and time series modeling (29.45). Additionally, the neural network demonstrates a better MAD Percentage (MADP) with a value of 10.2%, surpassing the values associated with multiple variable regression (10.35%) and time series modeling (10.30%). The Mean Squared Error (MSE) further confirms the neural network's superiority with a value of 6452 compared to 6472 and 7865 for multiple variable regression and time series modeling, respectively. In conclusion, the study showcases the effectiveness of integrating advanced techniques, particularly the neural network, in enhancing the accuracy of sales forecasting for Mahram Food Industries. The comprehensive approach, combining technical analysis, time series modeling, machine learning, neural networks, and random forest, is a valuable strategy for predicting future sales. The superior performance of the neural network, as evidenced by lower MAD, MADP, and MSE values, suggests its potential for guiding informed decision-making in goal setting, hiring, budgeting, and other critical aspects of business management.

Forecasting geomagnetic activity: Neural networks, moving windows and state transition models

Geomagnetic activity shows high degrees of nonlinear variability. The probability distribution has heavy tails, and there are intermittent outliers. This has led to increased interest in forecasting using neural networks and nonlinear regressions, which include time-varying coefficient techniques. Because geomagnetic storms pose the greatest threat to satellites and power grids, there is a particular interest in predicting outlying events. The model proposed here combines two techniques. Neural networks and regressions are trained over moving windows of observations, so that the weights or coefficients adjust to new data. Second, logistic regression is used to predict the periods of high activity, and the cumulative distribution function is used as a causal input in time series and machine learning models. The data set is the Aa index, corrected for secular drift. Forecasting experiments are run over horizons of 1–4 days. The other models include time-varying parameter regressions and a recurrent neural network with fixed weights. The model combining the neural net and logistic regression achieves the most accurate forecast, although the regression is a close second. The ability to predict outliers depends on the width of the moving window. With wider windows, the overall error is lower, but the forecasted values fall into a narrower range, missing the outliers. With narrower windows, the model predicts the outliers better but is vulnerable to calling them at the wrong times, so the average error is higher. Further, while the model achieves more accurate predictions at 1 day, at longer horizons the accuracy deteriorates quite rapidly.

Embodied Simulations and Neurodivergent Temporalities in To the Moon

ABSTRACT: We position games as ludic time machines; beyond simply representing neurodiversity, they can prompt players to consider neurodivergent temporalities. In the 2D psychological adventure game To the Moon (Gao 2011), players control two scientists who travel through the memories of a man on his deathbed. Digging ever deeper into their seemingly neurotypical client’s past, the scientists learn how his marriage was strained by the complexities of cross-neurotype communication. We show how To the Moon ’s storyworld and gameplay destabilize fixed understandings of neurotypicality, encourage deeper mutual understanding of temporal embodiment, and prompt players to reconsider their relationship to neurodiversity.

Estimating Winter Cover Crop Biomass in France Using Optical Sentinel-2 Dense Image Time Series and Machine Learning

Cover crops play a pivotal role in mitigating climate change by bolstering carbon sequestration through biomass production and soil integration. However, current methods for quantifying cover crop biomass lack spatial precision and objectivity. Thus, our research aimed to devise a remote-sensing-based approach to estimate cover crop biomass across various species and mixtures during fallow periods in France. Leveraging Sentinel-2 optical data and machine learning algorithms, we modeled biomass across 50 fields representative of France’s diverse cropping practices and climate types. Initial tests using traditional empirical relationships between vegetation indices/spectral bands and dry biomass revealed challenges in accurately estimating biomass for mixed cover crop categories due to spectral interference from grasses and weeds, underscoring the complexity of modeling diverse agricultural conditions. To address this challenge, we compared several machine learning algorithms (Support Vector Machine, Random Forest, and eXtreme Gradient Boosting) using spectral bands and vegetation indices from the latest available image before sampling as input. Additionally, we developed an approach that incorporates dense optical time series of Sentinel-2 data, generated using a Radial Basis Function for interpolation. Our findings demonstrated that a Random Forest model trained with dense time series data during the cover crop development period yielded promising results, with an average R-squared (r2) value of 0.75 and root mean square error (RMSE) of 0.73 t·ha−1, surpassing results obtained from methods using single-image snapshots (r2 of 0.55). Moreover, our approach exhibited robustness in accounting for factors such as crop species diversity, varied climatic conditions, and the presence of weed vegetation—essential for approximating real-world conditions. Importantly, its applicability extends beyond France, holding potential for global scalability. The availability of data for model calibration across diverse regions and timeframes could facilitate broader application.

Analysing the spatiotemporal variation and influencing factors of Lake Chaohu's CDOM over the past 40 years using machine learning

AbstractChromophoric dissolved organic matter (CDOM) in aquatic environments is an important component of the biogeochemical cycle and carbon cycle. The aim of this study is to investigate the long‐term changes in CDOM in shallow and eutrophic Chaohu Lake, as well as its relationship with climate, environment and social factors. Using long time series Landsat image data and machine learning technology, the spatiotemporal evolution of Chaohu CDOM since 1987 was reconstructed. A total of 180 samples were collected, which were divided into three parts based on regional and hydrological characteristics. The results show that the water quality in different regions were significantly different, and TN may be the key factor driving the change of CDOM in Chaohu Lake. Machine learning algorithms including random forest (RF), support vector regression (SVR), neural network (NN), multimodal deep learning (MDL) model and Extreme Gradient Boosting (XGBoost) were used, among which XGBoost model performed best (R2 = 0.955, mean absolute error [MAE] = 0.024 mg/L, root mean square error [RMSE] = 0.036 mg/L, bias = 1.005) and was used for CDOM spatiotemporal variation retrieval. The change of CDOM was seasonal, highest in August (0.67 m−1) and lowest in December (0.48 m−1), and the western lake is the main source of CDOM. Annual variability of the CDOM indicates that it began to decline after the completion of water pollution control in 2000. Temperature changes were closely related to CDOM (P < 0.01) and agricultural non‐point source pollution plays an important role in Chaohu Lake. This study will provide feasible methods and scientific basis for the long‐term remote sensing supervision of CDOM.

Falling into the Past: Geodesics in a Time Travel Metric

We investigate timelike and null geodesics within the rotating “time machine” spacetime proposed by Ralph, T.C.; et al. Phys. Rev. D 2020, 102, 124013. This is a rotating analogue of Alcubierre’s warp drive spacetime. We obtain geodesics that begin and end in the surrounding flat space region, yet achieve time travel relative to static observers there. This is a global property, as the geodesics remain locally future-pointing, as well as timelike or null.

Detection of abnormal operation in geothermal binary plant feed pumps using time-series analytics

In this study, we investigate potential pump cavitation events in circulating feed pumps deployed in a geothermal binary power plant using feature-based time series analytics and machine learning. The feed pumps have been in operation for over a decade but have been experiencing episodes wherein the pumps were drawing in lower power even while pump speeds were maintained at the same levels.A workflow combining time-series analytics and first-principle thermodynamics was applied to characterise potential drivers of the abnormal events in the feed pumps. An approach based on systematic time-series feature engineering and semi-supervised machine learning was used to fully annotate the input data and identify twenty (20) undocumented occurrences of the target event. Time-series features extracted from the fully labelled dataset were then used to train forecasting models to identify potential drivers of the target events. Forecasting models trained using pump data outperformed those trained on times-series data from other power plant components (Matthew’s correlation coefficient score of 0.490 vs 0.255). This result indicates that the abnormal events are primarily driven by operating conditions local to the feed pump system. This finding was confirmed by Bayesian A/B testing, showing that the target events were more likely to occur by 22% when four (4) feed pumps were running compared to when only three (3) pumps were in operation. Furthermore, thermodynamic analysis of the state of the working fluid across the feed pumps shows irregularities at the pump inlet, where indications of partial vaporisation of the working fluid have been identified.

Evaluating the Potential of Sentinel-2 Time Series Imagery and Machine Learning for Tree Species Classification in a Mountainous Forest

Accurate and reliable information on tree species composition and distribution is crucial in operational and sustainable forest management. Developing a high-precision tree species map based on time series satellite data is an effective and cost-efficient approach. However, we do not quantitatively know how the time scale of data acquisitions contributes to complex tree species mapping. This study aimed to produce a detailed tree species map in a typical forest zone of the Changbai Mountains by incorporating Sentinel-2 images, topography data, and machine learning algorithms. We focused on exploring the effects of the three-year time series of Sentinel-2 within monthly, seasonal, and yearly time scales on the classification of ten dominant tree species. A random forest (RF) and support vector machine (SVM) were compared and employed to map continuous tree species. The results showed that classification with monthly datasets (overall accuracy (OA): 83.38–87.45%) outperformed that with seasonal and yearly datasets (OA:72.38–85.91%), and the RF (OA: 81.70–87.45%) was better than the SVM (OA: 72.38–83.38%) at processing the same datasets. Short-wave infrared, the normalized vegetation index, and elevation were the most important variables for tree species classification. The highest classification accuracy of 87.45% was achieved by combining RF, monthly datasets, and topography information. In terms of single species’ accuracy, the F1 scores of the ten tree species ranged from 62.99% (Manchurian ash) to 97.04% (Mongolian Oak), and eight of them obtained high F1 scores greater than 87%. This study confirmed that monthly Sentinel-2 datasets, topography data, and machine learning algorithms have great potential for accurate tree species mapping in mountainous regions.