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"With each interaction, Alexa is training to hear better, to interpret more precisely, to trigger actions that map to the user's commands more accurately, and to build a more complete model of their preferences, habits and desires. What is required to make this possible? Put simply: each small moment of convenience - be it answering a question, turning on a light, or playing a song - requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data. The scale of resources required is many magnitudes greater than the energy and labor it would take a human to operate a household appliance or flick a switch. A full accounting for these costs is almost impossible, but it is increasingly important that we grasp the scale and scope if we are to understand and govern the technical infrastructures that thread through our lives. III The Salar, the world's largest flat surface, is located in southwest Bolivia at an altitude of 3,656 meters above sea level. It is a high plateau, covered by a few meters of salt crust which are exceptionally rich in lithium, containing 50% to 70% of the world's lithium reserves. 4 The Salar, alongside the neighboring Atacama regions in Chile and Argentina, are major sites for lithium extraction. This soft, silvery metal is currently used to power mobile connected devices, as a crucial material used for the production of lithium-Ion batteries. It is known as 'grey gold.' Smartphone batteries, for example, usually have less than eight grams of this material. 5 Each Tesla car needs approximately seven kilograms of lithium for its battery pack. 6 All these batteries have a limited lifespan, and once consumed they are thrown away as waste. Amazon reminds users that they cannot open up and repair their Echo, because this will void the warranty. The Amazon Echo is wall-powered, and also has a mobile battery base. This also has a limited lifespan and then must be thrown away as waste. According to the Ay

"My take on this issue is that the neoliberal use of identity politics continue and extends the cultural inculcation of individuals seeing themselves engaging with other in one-to-one transactions (commerce, struggles over power and status) and has the effect of diverting their focus and energy on seeing themselves as members of groups with common interests and operating that way, and in particular, of seeing the role of money and property, which are social constructs, in power dynamics."

Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters are the subjects of case studies, systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale are scarce. In this work, we use LinkedIn's employment history data from more than 500 million users over 25 years to construct a labor flow network of over 4 million firms across the world, from which we reveal hierarchical structure by applying network community detection. We show that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated workers and financial performance, compared to traditional aggregation units. Furthermore, our analysis of the skills of educated workers reveals richer insights into the relationship between the labor flow of educated workers and productivity growth. We argue that geo-industrial clusters defined by labor flow provide useful insights into the growth of the economy.

In the not-too-distant past, narratives were set more or less consensually by the New York-based media establishment (assisted by its Washington-based enablers). But as Martin Gurri and Bruno Maçães have shown, the narrative-setting days of elite media are now over, and we live in a world of fractured narratives proffered by Extremely Online factions that interpret reality-or jettison it entirely, in favor of constructing their own "unreality" (Maçães' phrase)-primarily through the lens of their own self-justifying and unfalsifiable narratives.

Following a series of studies on termite mound physiology and morphogenesis over the past decade, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences have now developed a mathematical model to help explain how termites construct their intricate mounds.

The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.

The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.

The Spanish Crown had a monopoly on the trade route between Manila and Mexico for more than 250 years. The ships that sailed this route were "the richest ships in all the oceans", but much of the wealth sank at sea and remain undiscovered. This column uses a newly constructed dataset of all of the ships that travelled the route to show how monopoly rents that allowed widespread bribe-taking would have led to overloading and late ship departure, thereby increasing the probability of shipwreck. Not only were late and overloaded ships more likely to experience shipwrecks or to return to port, but the effect is stronger for galleons carrying more valuable, higher-rent cargo. This sheds new light on the costs of rent-seeking in European colonial empires.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.

Historians don't just study history. We construct it. We puzzle pieces into meanings. Aided by our instincts and experiences, as well as by our research, we make sense of other times, other nations, other peoples. In that sense, the writing of history is always personal.