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The steel-making process in a Basic Oxygen Furnace (BOF) must meet a combination of target values such as the final melt temperature and upper limits of the carbon and phosphorus content of the final melt with minimum material loss. An optimal blow end time (cut-off point), where these targets are met, often relies on the experience and skill of the operators who control the process, using both collected sensor readings and an implicit understanding of how the process develops. If the precision of hitting the optimal cut-off point can be improved, this immediately increases productivity as well as material and energy efficiency, thus decreasing environmental impact and cost. We examine the usage of standard machine learning models to predict the end-point targets using a full production dataset. Various causes of prediction uncertainty are explored and isolated using a combination of raw data and engineered features. In this study, we reach robust temperature, carbon, and phosphorus prediction hit rates of 88, 92, and 89 pct, respectively, using a large production dataset.

A very basic pathway from CO2 to ethyleneEthylene is an important commodity chemical for plastics. It is considered a tractable target for synthesizing renewable resources from carbon dioxide (CO2). The challenge is that the performance of the copper electrocatalysts used for this conversion under the required basic reaction conditions suffers from the competing reaction of CO2 with the base to form bicarbonate. Dinh et al. designed an electrode that tolerates the base by optimizing CO2 diffusion to the catalytic sites (see the Perspective by Ager and Lapkin). This catalyst design delivers 70% efficiency for 150 hours.Science, this issue p. 783; see also p. 707Carbon dioxide (CO2) electroreduction could provide a useful source of ethylene, but low conversion efficiency, low production rates, and low catalyst stability limit current systems. Here we report that a copper electrocatalyst at an abrupt reaction interface in an alkaline electrolyte reduces CO2 to ethylene with 70% faradaic efficiency at a potential of −0.55 volts versus a reversible hydrogen electrode (RHE). Hydroxide ions on or near the copper surface lower the CO2 reduction and carbon monoxide (CO)–CO coupling activation energy barriers; as a result, onset of ethylene evolution at −0.165 volts versus an RHE in 10 molar potassium hydroxide occurs almost simultaneously with CO production. Operational stability was enhanced via the introduction of a polymer-based gas diffusion layer that sandwiches the reaction interface between separate hydrophobic and conductive supports, providing constant ethylene selectivity for an initial 150 operating hours.

The LANSCE H− Ion Source delivers a 120 Hz, 15 mA, 10% duty factor beam, which is created via a filament driven hydrogen plasma and cesiated surface-conversion. To induce cesiated surface conversion, the converter is coated in cesium via a basic cesium transfer tube port that is connected to a heated cesium reservoir, such that the amount of cesium flux induced into the ion source is increased by increasing the cesium reservoir temperature. A COMSOL model of the cesium flux out of the existing transfer tube and into the LANSCE H− ion source will be utilized using data driven empirical variables: temperature measurements of the source walls and points along the cesium transfer tube, and the background H2 pressure inside the ion source. Using these same parameters a study will be done using COMSOL to propose an optimal cesium transfer tube for the LANSCE H− ion source.

Optical metasurfaces have opened an entirely new field in the quest to manipulate light. Optical metasurfaces can locally impart changes to the amplitude, phase, and polarization of propagating waves. To date, most of these metasurfaces have been passive, with the optical properties largely set in the fabrication process. Shaltout et al. review recent developments toward time-varying metasurfaces and explore the opportunities that adding dynamic control can offer in terms of actively controlling the flow of light. Science , this issue p. eaat3100 Optical metasurfaces have provided us with extraordinary ways to control light by spatially structuring materials. The space-time duality in Maxwell’s equations suggests that additional structuring of metasurfaces in the time domain can even further expand their impact on the field of optics. Advances toward this goal critically rely on the development of new materials and nanostructures that exhibit very large and fast changes in their optical properties in response to external stimuli. New physics is also emerging as ultrafast tuning of metasurfaces is becoming possible, including wavelength shifts that emulate the Doppler effect, Lorentz nonreciprocity, time-reversed optical behavior, and negative refraction. The large-scale manufacturing of dynamic flat optics has the potential to revolutionize many emerging technologies that require active wavefront shaping with lightweight, compact, and power-efficient components.

Nature extensively exploits high-energy transient self-assembly structures that are able to perform work through a dissipative process. Often, self-assembly relies on the use of molecules as fuel that is consumed to drive thermodynamically unfavourable reactions away from equilibrium. Implementing this kind of non-equilibrium self-assembly process in synthetic systems is bound to profoundly impact the fields of chemistry, materials science and synthetic biology, leading to innovative dissipative structures able to convert and store chemical energy. Yet, despite increasing efforts, the basic principles underlying chemical fuel-driven dissipative self-assembly are often overlooked, generating confusion around the meaning and definition of scientific terms, which does not favour progress in the field. The scope of this Perspective is to bring closer together current experimental approaches and conceptual frameworks. From our analysis it also emerges that chemically fuelled dissipative processes may have played a crucial role in evolutionary processes.

The technology for producing ultra-clean steel by the process of “KR-BOF-LF-VD-CC” was developed. Sulfur was efficiently reduced to less than 5 ppm by the LF-VD process. Phosphorus was stably removed to less than 20 ppm by optimizing the composition of dephosphorization slag in the converter. The [N] and [H] were reduced to less than 20ppm and 1.0ppm after the perfect combination of vacuum degree and vacuum treatment time during the VD process. T.[O] was controlled to less than 5ppm by the technology of precise controlling of inclusion composition and efficient removal. Industrial production results showed that the average content of [S]+[P]+[O]+[N])+[H] was 42.3 ppm and the lowest was 34.7 ppm.

We show how particle-vortex duality in d=2+1 dimensions arises as part of an intricate web of relationships between different field theories. The starting point is “bosonization,” a conjectured duality that uses flux attachment to transmute the statistics of relativistic particles. From this seed, we derive many old and new dualities. These include particle-vortex duality for bosons as well as the recently discovered counterpart for fermions.

In higher education, low teacher-student ratios can make it difficult for students to receive immediate and interactive help. Chatbots, increasingly used in various scenarios such as customer service, work productivity, and healthcare, might be one way of helping instructors better meet student needs. However, few empirical studies in the field of Information Systems (IS) have investigated pedagogical chatbot efficacy in higher education and fewer still discuss their potential challenges and drawbacks. In this research we address this gap in the IS literature by exploring the opportunities, challenges, efficacy, and ethical concerns of using chatbots as pedagogical tools in business education. In this two study project, we conducted a chatbot-guided interview with 215 undergraduate students to understand student attitudes regarding the potential benefits and challenges of using chatbots as intelligent student assistants. Our findings revealed the potential for chatbots to help students learn basic content in a responsive, interactive, and confidential way. Findings also provided insights into student learning needs which we then used to design and develop a new, experimental chatbot assistant to teach basic AI concepts to 195 students. Results of this second study suggest chatbots can be engaging and responsive conversational learning tools for teaching basic concepts and for providing educational resources. Herein, we provide the results of both studies and discuss possible promising opportunities and ethical implications of using chatbots to support inclusive learning.

Synthetic DNA is rapidly emerging as a durable, high-density information storage platform. A major challenge for DNA-based information encoding strategies is the high rate of errors that arise during DNA synthesis and sequencing. Here, we describe the HEDGES (Hash Encoded, Decoded by Greedy Exhaustive Search) error-correcting code that repairs all three basic types of DNA errors: insertions, deletions, and substitutions. HEDGES also converts unresolved or compound errors into substitutions, restoring synchronization for correction via a standard Reed–Solomon outer code that is interleaved across strands. Moreover, HEDGES can incorporate a broad class of user-defined sequence constraints, such as avoiding excess repeats, or too high or too low windowed guanine–cytosine (GC) content. We test our code both via in silico simulations and with synthesized DNA. From its measured performance, we develop a statistical model applicable to much larger datasets. Predicted performance indicates the possibility of error-free recovery of petabyte- and exabyte-scale data from DNA degraded with as much as 10% errors. As the cost of DNA synthesis and sequencing continues to drop, we anticipate that HEDGES will find applications in large-scale error-free information encoding.