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Novel complexes {[Dy(LN6en)(OAc)2](NO3)}·2H2O (1·2H2O) and {[Dy(LN6prop)(OAc)2](NO3)}·CHCl3 (2·CHCl3), containing partially flexible symmetric N6 macrocycles, are reported. We explore the influence of the spacer length between two symmetrical N3 rigid moieties of the ligand on their structural, magnetic, and luminescence properties. Crystallographic analysis reveals the presence of Dy3+ ions in distorted tetradecahedral (1·2H2O) or bicapped square antiprism (2·CHCl3) environments. This underscores the increased flexibility of the LN6prop ligand, resulting in greater distortion of the N6 macrocycle plane. Both complexes exhibit single-molecule magnet behaviour under an optimal field of 2000 Oe, with 2·CHCl3 displaying the highest Ueff value of 127 K, despite its less planar N6 macrocycle. Luminescence measurements indicate that the ratio between the integrated intensity of the ligands and that of the the Dy3+ 4F9/2 → 6H13/2 transition can define secondary luminescent thermometers. Maximum relative thermal sensitivity values of 2.3 (1·2H2O) and 5.1% K−1 (2·CHCl3) are achieved. Furthermore, deconvolution of the 4F9/2 → 6H15/2 transition in 2·CHCl3 supports the previous determination of the energy barrier for magnetic relaxation. This permits the demonstration of the first example of a Dy3+ primary luminescent thermometer based on two thermally coupled Kramer's doublets of the 4F9/2 level. Remarkably, 2·CHCl3 is also the first self-calibrated luminescent thermometer with magnetic relaxation operating within the 86–211 K range, showcasing its potential in precise temperature sensing applications

Water vapor sorption is a ubiquitous phenomenon in nature and plays an important role in various applications, including humidity regulation, energy storage, thermal management, and water harvesting. In particular, capturing moisture at elevated temperatures is highly desirable to prevent dehydration and to enlarge the tunability of water uptake. However, owing to the thermodynamic limit of conventional materials, sorbents inevitably tend to capture less water vapor at higher temperatures, impeding their broad applications. Here, an inverse temperature dependence of water sorption in poly(ethylene glycol) (PEG) hydrogels, where their water uptake can be doubled with increasing temperature from 25 to 50 °C, is reported. With mechanistic modeling of water–polymer interactions, this unusual water sorption is attributed to the first‐order phase transformation of PEG structures, and the key parameters for a more generalized strategy in materials development are identified. This work elucidates a new regime of water sorption with an unusual temperature dependence, enabling a promising engineering space for harnessing moisture and heat.

Hygroscopic hydrogels are emerging as scalable and low‐cost sorbents for atmospheric water harvesting, dehumidification, passive cooling, and thermal energy storage. However, devices using these materials still exhibit insufficient performance, partly due to the limited water vapor uptake of the hydrogels. Here, the swelling dynamics of hydrogels in aqueous lithiumchloride solutions, the implications on hydrogel salt loading, and the resulting vapor uptake of the synthesized hydrogel–salt composites are characterized. By tuning the salt concentration of the swelling solutions and the cross‐linking properties of the gels, hygroscopic hydrogels with extremely high salt loadings are synthesized, which enable unprecedented water uptakes of 1.79 and 3.86 gg−1 at relative humidity (RH) of 30% and 70%, respectively. At 30% RH, this exceeds previously reported water uptakes of metal–organic frameworks by over 100% and of hydrogels by 15%, bringing the uptake within 93% of the fundamental limit of hygroscopic salts while avoiding leakage problems common in salt solutions. By modeling the salt‐vapor equilibria, the maximum leakage‐free RH is elucidated as a function of hydrogel uptake and swelling ratio. These insights guide the design of hydrogels with exceptional hygroscopicity that enable sorption‐based devices to tackle water scarcity and the global energy crisis.

Europe imports large amounts of soybean that are predominantly used for livestock feed, mainly sourced from Brazil, USA and Argentina. In addition, the demand for GM-free soybean for human consumption is project to increase. Soybean has higher protein quality and digestibility than other legumes, along with high concentrations of isoflavones, phytosterols and minerals that enhance the nutritional value as a human food ingredient. Here, we examine the potential to increase soybean production across Europe for livestock feed and direct human consumption, and review possible effects on the environment and human health. Simulations and field data indicate rainfed soybean yields of 3.1 ± 1.2 t ha−1 from southern UK through to southern Europe (compared to a 3.5 t ha−1 average from North America). Drought-prone southern regions and cooler northern regions require breeding to incorporate stress-tolerance traits. Literature synthesized in this work evidenced soybean properties important to human nutrition, health, and traits related to food processing compared to alternative protein sources. While acknowledging the uncertainties inherent in any modelling exercise, our findings suggest that further integrating soybean into European agriculture could reduce GHG emissions by 37–291 Mt CO2e year−1 and fertiliser N use by 0.6–1.2 Mt year−1, concurrently improving human health and nutrition.

Thanks to the rise of deep learning and the availability of large-scale audio-visual databases, recent advances have been achieved in Visual Speech Recognition (VSR). Similar to other speech processing tasks, these end-to-end VSR systems are usually based on encoder-decoder architectures. While encoders are somewhat general, multiple decoding approaches have been explored, such as the conventional hybrid model based on Deep Neural Networks combined with Hidden Markov Models (DNN-HMM) or the Connectionist Temporal Classification (CTC) paradigm. However, there are languages and tasks in which data is scarce, and in this situation, there is not a clear comparison between different types of decoders. Therefore, we focused our study on how the conventional DNN-HMM decoder and its state-of-the-art CTC/Attention counterpart behave depending on the amount of data used for their estimation. We also analyzed to what extent our visual speech features were able to adapt to scenarios for which they were not explicitly trained, either considering a similar dataset or another collected for a different language. Results showed that the conventional paradigm reached recognition rates that improve the CTC/Attention model in data-scarcity scenarios along with a reduced training time and fewer parameters., Comment: Accepted at the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)

More than 7,000 known languages are spoken around the world. However, due to the lack of annotated resources, only a small fraction of them are currently covered by speech technologies. Albeit self-supervised speech representations, recent massive speech corpora collections, as well as the organization of challenges, have alleviated this inequality, most studies are mainly benchmarked on English. This situation is aggravated when tasks involving both acoustic and visual speech modalities are addressed. In order to promote research on low-resource languages for audio-visual speech technologies, we present AnnoTheia, a semi-automatic annotation toolkit that detects when a person speaks on the scene and the corresponding transcription. In addition, to show the complete process of preparing AnnoTheia for a language of interest, we also describe the adaptation of a pre-trained model for active speaker detection to Spanish, using a database not initially conceived for this type of task. The AnnoTheia toolkit, tutorials, and pre-trained models are available on GitHub., Comment: Accepted at the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)

Information Extraction processes in handwritten documents tend to rely on obtaining an automatic transcription and performing Named Entity Recognition (NER) over such transcription. For this reason, in publicly available datasets, the performance of the systems is usually evaluated with metrics particular to each dataset. Moreover, most of the metrics employed are sensitive to reading order errors. Therefore, they do not reflect the expected final application of the system and introduce biases in more complex documents. In this paper, we propose and publicly release a set of reading order independent metrics tailored to Information Extraction evaluation in handwritten documents. In our experimentation, we perform an in-depth analysis of the behavior of the metrics to recommend what we consider to be the minimal set of metrics to evaluate a task correctly.

Background and aimsCriminalization of drug use and punitive policing are key structural drivers of hepatitis C virus (HCV) risk among people who inject drugs (PWID). A police education program (Proyecto Escudo) delivering training on occupational safety together with drug law content was implemented between 2015 and 2016 in Tijuana, Mexico, to underpin drug law reform implementation. We used data from a longitudinal cohort of PWID in Tijuana to inform epidemic modeling and assess the long-term impact of Escudo on HCV transmission and burden among PWID in Tijuana.MethodsWe developed a dynamic, compartmental model of HCV transmission and incarceration among PWID and tracked liver disease progression among current and former PWID. The model was calibrated to data from Tijuana, Mexico, with 90% HCV seroprevalence. We used segmented regression analysis to estimate impact of Escudo on recent incarceration among an observational cohort of PWID. By simulating the observed incarceration trends, we estimated the potential impact of the implemented (2-year reduction in incarceration) and an extended (10-year reduction in incarceration) police education program over a 50-year follow-up (2016-2066) on HCV outcomes (incidence, cirrhosis, HCV-related deaths and disability adjusted life-years averted) compared with no intervention.ResultsOver the 2-year follow-up, Proyecto Escudo reduced HCV incidence among PWID from 21.5 per 100 person years (/100py) (95% uncertainty interval [UI] = 15.3-29.7/100py) in 2016 to 21.1/100py (UI = 15.0-29.1/100py) in 2018. If continued for 10 years, Escudo could reduce HCV incidence to 20.0/100py (14.0-27.8/100py) by 2026 and avert 186 (32-389) new infections, 76 (UI = 12-160) cases of cirrhosis and 32 (5-73) deaths per 10 000 PWID compared with no intervention over a 50-year time horizon.ConclusionsIn Tijuana, Mexico, implementation of a police education program delivering training on occupational safety and drug law content appears to have redu

BackgroundIncarceration is associated with increased risk of hepatitis C virus (HCV) among people who inject drugs (PWID). Mexico's previous attempt in implementing a public health-oriented drug law reform resulted in minimal impact on incarceration among PWID. However, implementation of reforms alongside Mexico's HCV elimination program has the potential to reshape the HCV epidemic among PWID in the next decade. We use data from a cohort of PWID in Tijuana, Mexico, to inform epidemic modeling to assess the contribution of incarceration and fully implemented drug reform on HCV transmission and elimination among PWID.MethodsWe developed a dynamic, deterministic model of incarceration, HCV transmission and disease progression among PWID. The model was calibrated to data from Tijuana, Mexico, with 90% HCV seroprevalence among 10,000 PWID. We estimated the 10-year population attributable fraction (PAF) of incarceration to HCV incidence among PWID and simulated, from 2022, the potential impact of the following scenarios: 1) decriminalization (80% reduction in incarceration rates); 2) fully implemented drug law reform (decriminalization and diversion to opiate agonist therapy [OAT]); 3) fully implemented drug law reform with HCV treatment (direct-acting antivirals [DAA]). We also assessed the number DAA needed to reach the 80% incidence reduction target by 2030 under these scenarios.ResultsProjections suggest a PAF of incarceration to HCV incidence of 5.4% (95% uncertainty interval [UI]:0.6-11.9%) among PWID in Tijuana between 2022-2032. Fully implemented drug reforms could reduce HCV incidence rate by 10.6% (95%UI:3.1-19.2%) across 10 years and reduce the number of DAA required to achieve Mexico's HCV incidence reduction goal by 14.3% (95%UI:5.3-17.1%).ConclusionsAmong PWID in Tijuana, Mexico, incarceration remains an important contributor to HCV transmission. Full implementation of public health-oriented drug law reform could play an important role in reducing HCV incid

BackgroundAmong people with HIV (PWH), sex differences in presentations of atherosclerotic cardiovascular disease (ASCVD) may be influenced by differences in coronary plaque parameters, immune/inflammatory biomarkers, or relationships therein.MethodsREPRIEVE, a primary ASCVD prevention trial, enrolled antiretroviral therapy (ART)-treated PWH. At entry, a subset of US participants underwent coronary computed tomography angiography (CTA) and immune phenotyping (n = 755 CTA; n = 725 CTA + immune). We characterized sex differences in coronary plaque and immune/inflammatory biomarkers and compared immune-plaque relationships by sex. Unless noted otherwise, analyses adjust for ASCVD risk score.ResultsThe primary analysis cohort included 631 males and 124 females. ASCVD risk was higher among males (median: 4.9% vs 2.1%), while obesity rates were higher among females (48% vs 21%). Prevalence of any plaque and of plaque with either ≥1 visible noncalcified portion or vulnerable features (NC/V-P) was lower among females overall and controlling for relevant risk factors (RR [95% CI] for any plaque: .67 [.50, .92]; RR for NC/V-P: .71 [.51, 1.00] [adjusted for ASCVD risk score and body mass index]). Females showed higher levels of IL-6, hs-CRP, and D-dimer and lower levels of Lp-PLA2 (P < .001 for all). Higher levels of Lp-PLA2, MCP-1, and oxLDL were associated with higher plaque (P < .02) and NC/V-P prevalence, with no differences by sex. Among females but not males, D-dimer was associated with higher prevalence of NC/V-P (interaction P = .055).ConclusionsAmong US PWH, females had a lower prevalence of plaque and NC/V-P, as well as differences in key immune/inflammatory biomarkers. Immune-plaque relationships differed by sex for D-dimer but not other tested parameters. Clinical Trial Registration. ClinicalTrials.gov; identifier: NCT0234429 (date of initial registration: 22 January 2015).

BackgroundMechanisms contributing to COVID-19 severity in people with HIV (PWH) are poorly understood. We evaluated temporal changes in plasma proteins following SARS-CoV-2 infection and identified pre-infection proteomic markers associated with future COVID-19.MethodsWe leveraged data from the global Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE). Antiretroviral therapy (ART)-treated PWH with clinical, antibody-confirmed COVID-19 as of September 2021 were matched on geographic region, age, and sample timing to antibody negative controls. For cases and controls, pre COVID-19 pandemic specimens were obtained prior to January 2020 to assess change over time and relationship to COVID-19 severity, using false-discovery adjusted mixed effects modeling.FindingsWe compared 257 unique plasma proteins in 94 COVID-19 antibody-confirmed clinical cases and 113 matched antibody-negative controls, excluding COVID-19 vaccinated participants (age 50 years, 73% male). 40% of cases were characterized as mild; 60% moderate to severe. Median time from COVID-19 infection to follow-up sampling was 4 months. Temporal patterns of protein changes differed based on COVID-19 disease severity. Among those experiencing moderate to severe disease vs. controls, NOS3 increased whereas ANG, CASP-8, CD5, GZMH, GZMB, ITGB2, and KLRD1 decreased. Higher pre-pandemic levels of granzymes A, B and H (GZMA, GZMB and GZMH) were associated with the future development of moderate-severe COVID-19 and were related to immune function.InterpretationWe identified temporal changes in proteins closely linked to inflammatory, immune, and fibrotic pathways which may relate to COVID-19-related morbidity among ART-treated PWH. Further we identified key granzyme proteins associated with future COVID-19 in PWH.FundingThis study is supported through NIH grants U01HL123336, U01HL123336-06 and 3U01HL12336-06S3, to the clinical coordinating center, and U01HL123339, to the data coordinating center as well as fun

This review is in support of the development of selective, precise, fast, and validated capillary electrophoresis (CE) methods. It follows up a similar article from 1998, Watzig H, Degenhardt M, Kunkel A. "Strategies for capillary electrophoresis: method development and validation for pharmaceutical and biological applications," pointing out which fundamentals are still valid and at the same time showing the enormous achievements in the last 25 years. The structures of both reviews are widely similar, in order to facilitate their simultaneous use. Focusing on pharmaceutical and biological applications, the successful use of CE is now demonstrated by more than 600 carefully selected references. Many of those are recent reviews; therefore, a significant overview about the field is provided. There are extra sections about sample pretreatment related to CE and microchip CE, and a completely revised section about method development for protein analytes and biomolecules in general. The general strategies for method development are summed up with regard to selectivity, efficiency, precision, analysis time, limit of detection, sample pretreatment requirements, and validation.

This study determined the level of acceptability of the e-learning module in Seamanship 2A as well as its strengths and weaknesses. The respondents of this study were the 286 randomly chosen Bachelor of Science in Marine Transportation students who took up Seamanship 2A during the first semester of the school year 2021-2022 and five purposively chosen experts in the field of maritime education, Information and Communication Technology, and production of learning materials. This study utilized a two-part research instrument where the first part was a 30-item Likert-type questionnaire, while the second part consisted of two open-ended questions. It utilized a survey research design. The results show that the students and the experts found the e-learning module in Seamanship 2A to have high acceptability in general and in terms of appearance, learning activities, evaluation procedure, ease of use, and usefulness. According to students, the strengths of the e-learning module in Seamanship 2A were: accessible, convenient, easy to understand, informative, relevant, sufficient content, user-friendly, several varied sources available, self-paced, well-organized, efficient, and systematic. Meanwhile, its weaknesses include the need for internet access, incomprehensive discussion of the contents, unreliable/unstable site, lack of human interaction, lack of hands-on experience, difficulty in using the site, misaligned lesson content and quizzes, excessive screen time, and availability of gadgets. It is recommended that the e-learning module in Seamanship 2A may be improved based on its strengths and weaknesses. Future researchers may conduct similar studies on other subject areas, look into longitudinal research or determine the effectiveness of the e-learning module.Received: 12 October 2023 Accepted: 02 December 2023 Published: 12 December 2023

Introduction: Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies. Methods: Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values. Results: We demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation. Di

Background: The European registry for minimally invasive pancreatic surgery (E-MIPS) collects data on laparoscopic and robotic MIPS in low-and high-volume centers across Europe.Methods: Analysis of the first year (2019) of the E-MIPS registry, including minimally invasive distal pancreatectomy (MIDP) and minimally invasive pancreatoduodenectomy (MIPD). Primary outcome was 90-day mortality.Results: Overall, 959 patients from 54 centers in 15 countries were included, 558 patients underwent MIDP and 401 patients MIPD. Median volume of MIDP was 10 (7-20) and 9 (2-20) for MIPD. Median use of MIDP was 56.0% (IQR 39.0-77.3%) and median use of MIPD 27.7% (IQR 9.7-45.3%). MIDP was mostly performed laparoscopic (401/558, 71.9%) and MIPD mostly robotic (234/401, 58.3%). MIPD was performed in 50/54 (89.3%) centers, of which 15/50 (30.0%) performed >= 20 MIPD annually. This was 30/ 54 (55.6%) centers and 13/30 (43%) centers for MIPD respectively. Conversion rate was 10.9% for MIDP and 8.4% for MIPD. Overall 90 day mortality was 1.1% (n = 6) for MIDP and 3.7% (n = 15) for MIPD.Conclusion: Within the E-MIPS registry, MIDP is performed in about half of all patients, mostly using laparoscopy. MIPD is performed in about a quarter of patients, slightly more often using the robotic approach. A minority of centers met the Miami guideline volume criteria for MIPD.

The indigenous population of the Canary Islands, which colonized the archipelago around the 3rd century CE, provides both a window into the past of North Africa and a unique model to explore the effects of insularity. We generate genome-wide data from 40 individuals from the seven islands, dated between the 3rd–16rd centuries CE. Along with components already present in Moroccan Neolithic populations, the Canarian natives show signatures related to Bronze Age expansions in Eurasia and trans-Saharan migrations. The lack of gene flow between islands and constant or decreasing effective population sizes suggest that populations were isolated. While some island populations maintained relatively high genetic diversity, with the only detected bottleneck coinciding with the colonization time, other islands with fewer natural resources show the effects of insularity and isolation. Finally, consistent genetic differentiation between eastern and western islands points to a more complex colonization process than previously thought.

We perform a blind search for particle signals in the XENON1T dark matter detector that occur close in time to gravitational wave signals in the LIGO and Virgo observatories. No particle signal is observed in the nuclear recoil, electronic recoil, CE$\nu$NS, and S2-only channels within $\pm$ 500 seconds of observations of the gravitational wave signals GW170104, GW170729, GW170817, GW170818, and GW170823. We use this null result to constrain mono-energetic neutrinos and Beyond Standard Model particles emitted in the closest coalescence GW170817, a binary neutron star merger. We set new upper limits on the fluence (time-integrated flux) of coincident neutrinos down to 17 keV at 90% confidence level. Furthermore, we constrain the product of coincident fluence and cross section of Beyond Standard Model particles to be less than $10^{-29}$ cm$^2$/cm$^2$ in the [5.5-210] keV energy range at 90% confidence level.

Based on the division of Spanish America into two republics – Spanish and Indian – which, in principle, corresponded to the urban and rural spaces, a theoretically static and rigid society was configured. This division implied that, as natives of a specific space, Indians didn’t have freedom of movement. If they moved, they would become outsiders, or even vagabonds, with no links to any community, without serving in the mita or paying tribute. This mobility, which occurred in places like Quito in the Prehispanic period, therefore posed a veritable problem for the Spanish Administration with regard to the control of the population, manual labor and, especially, fiscality. This article analyses the creation of a new Indian fiscal category for this mobility, as the Audiencia of Quito’s response to this problem; a response specifically based on the vagabonds that proliferated in the capital of the Audiencia in the 16th century. The study demonstrates the extent of this new fiscal category, its denomination, how it was conceived on the basis of preexisting elements in the indigenous tributary system and, finally, how said category was subject to the same difficulties and problems that affected the tribute of the native Indians., A partir de la división de la América Hispana en dos repúblicas –españoles e indígenas– a las que, en principio, correspondía el espacio urbano y el rural, se configuró una sociedad teóricamente estática y rígida. Así, esta división, implicaba que los indígenas, en tanto que naturales de un espacio no debían moverse. De hacerlo, pasaban a convertirse en forasteros e incluso en vagabundos, desvinculados de cualquier comunidad, sin servir la mita y sin pagar un tributo. Así, esta movilidad, que en lugares como Quito ya se producía en época prehispánica, suponían un verdadero problema para la Administración Hispana, tanto en lo relativo al control humano, como de la mano de obra y, especialmente, tributario. El presente artículo analiza la respuesta de la Audiencia de Quito a esta problemática mediante la creación de una nueva categoría fiscal indígena en torno a dicha movilidad, específicamente, a partir de los vagabundos que desde el siglo XVI proliferaban en la capital de la audiencia. Este estudio muestra la extensión de esta nueva categoría fiscal, su denominación, cómo se construyó a partir de los elementos preexistentes en el tributo indígena y, también, finalmente, cómo dicha categoría se vio afectada por las mismas dificultades y problemáticas que afectaban al tributo de los indígenas naturales.

Indígenas urbanos en la América Hispana (siglos XVII-XVIII)

This paper focuses on one orogenic belt that formed during the Rinconada phase on the final stage of the Famatinian orogeny, between 445 and 410 Ma, which is well exposed at Sierra de Ramaditas and neighbouring ranges in western Argentina. The Ramaditas Complex is formed by metasedimentary and meta-ultrabasic rocks and amphibolites. This complex forms the upper nappe of a thrust stack resulting from westward thrusting. Deformation consists of an early high-temperature S1 foliation (stromatic migmatites), coeval with thrusting and metamorphism. Metamorphism attained peak P–T conditions of 6.0–6.9 kbar and 795–810 °C, at c. 440 Ma, i.e. coincident with the Rinconada orogenic phase. The lower unit and intermediate nappes crop out in the nearby sierras of Maz and Espinal and underwent low- to medium-grade Silurian metamorphism, respectively, together with the upper nappe, defining an inverted Barrovian-type metamorphism with T decreasing and P increasing downwards across the thrust stack (i.e. westward). We argue that the Rinconada orogenic phase developed near the continental margin of SW Gondwana, during a magmatic lull following accretion of the Precordillera terrane to the continental margin at c. 470 Ma. The active margin jumped to the west after accretion, and flat-slab subduction resumed in the early Silurian, provoking thrusting and imbrication of nappe stack under the still hot root (800–900 °C) of the older Famatinian magmatic arc. This ‘hot-iron’ process explains both the inverted Barrovian-type metamorphism and the missing overburden of 21 to 24 km implied by the P estimate.

Common and important applications of person identification occur at distances and viewpoints in which the face is not visible or is not sufficiently resolved to be useful. We examine body shape as a biometric across distance and viewpoint variation. We propose an approach that combines standard object classification networks with representations based on linguistic (word-based) descriptions of bodies. Algorithms with and without linguistic training were compared on their ability to identify people from body shape in images captured across a large range of distances/views (close-range, 100m, 200m, 270m, 300m, 370m, 400m, 490m, 500m, 600m, and at elevated pitch in images taken by an unmanned aerial vehicle [UAV]). Accuracy, as measured by identity-match ranking and false accept errors in an open-set test, was surprisingly good. For identity-ranking, linguistic models were more accurate for close-range images, whereas non-linguistic models fared better at intermediary distances. Fusion of the linguistic and non-linguistic embeddings improved performance at all, but the farthest distance. Although the non-linguistic model yielded fewer false accepts at all distances, fusion of the linguistic and non-linguistic models decreased false accepts for all, but the UAV images. We conclude that linguistic and non-linguistic representations of body shape can offer complementary identity information for bodies that can improve identification in applications of interest., Comment: 9 pages, 5 figures, 4 tables

This paper focuses on one orogenic belt that formed during the Rinconada phase on the final stage of the Famatinian orogeny, between 445 and 410 Ma, which is well exposed at Sierra de Ramaditas and neighbouring ranges in western Argentina. The Ramaditas Complex is formed by metasedimentary and meta-ultrabasic rocks and amphibolites. This complex forms the upper nappe of a thrust stack resulting from westward thrusting. Deformation consists of an early high-temperature S1 foliation (stromatic migmatites), coeval with thrusting and metamorphism. Metamorphism attained peak P-T conditions of 6.0-6.9 kbar and 795-810 °C, at c. 440 Ma, i.e. coincident with the Rinconada orogenic phase. The lower unit and intermediate nappes crop out in the nearby sierras of Maz and Espinal and underwent low- to medium-grade Silurian metamorphism, respectively, together with the upper nappe, defining an inverted Barrovian-type metamorphism with T decreasing and P increasing downwards across the thrust stack (i.e. westward). We argue that the Rinconada orogenic phase developed near the continental margin of SW Gondwana, during a magmatic lull following accretion of the Precordillera terrane to the continental margin at c. 470 Ma. The active margin jumped to the west after accretion, and flat-slab subduction resumed in the early Silurian, provoking thrusting and imbrication of nappe stack under the still hot root (800-900 °C) of the older Famatinian magmatic arc. This 'hot-iron' process explains both the inverted Barrovian-type metamorphism and the missing overburden of 21 to 24 km implied by the P estimate.

In cross-domain retrieval, a model is required to identify images from the same semantic category across two visual domains. For instance, given a sketch of an object, a model needs to retrieve a real image of it from an online store's catalog. A standard approach for such a problem is learning a feature space of images where Euclidean distances reflect similarity. Even without human annotations, which may be expensive to acquire, prior methods function reasonably well using unlabeled images for training. Our problem constraint takes this further to scenarios where the two domains do not necessarily share any common categories in training data. This can occur when the two domains in question come from different versions of some biometric sensor recording identities of different people. We posit a simple solution, which is to generate synthetic data to fill in these missing category examples across domains. This, we do via category preserving translation of images from one visual domain to another. We compare approaches specifically trained for this translation for a pair of domains, as well as those that can use large-scale pre-trained text-to-image diffusion models via prompts, and find that the latter can generate better replacement synthetic data, leading to more accurate cross-domain retrieval models. Our best SynCDR model can outperform prior art by up to 15\%. Code for our work is available at https://github.com/samarth4149/SynCDR ., Comment: Pre-print

The future of work does not require a choice between human and robot. Aside from explicit human-robot collaboration, robotics can play an increasingly important role in helping train workers as well as the tools they may use, especially in complex tasks that may be difficult to automate or effectively roboticize. This paper introduces a form of smart tool for use by human workers and shows how training the tool for task recognition, one of the key requirements, can be accomplished. Machine learning (ML) with purely human-based data can be extremely laborious and time-consuming. First, we show how data synthetically-generated by a robot can be leveraged in the ML training process. Later, we demonstrate how fine-tuning ML models for individual physical tasks and workers can significantly scale up the benefits of using ML to provide this feedback. Experimental results show the effectiveness and scalability of our approach, as we test data size versus accuracy. Smart hand tools of the type introduced here can provide insights and real-time analytics on efficient and safe tool usage and operation, thereby enhancing human participation and skill in a wide range of work environments. Using robotic platforms to help train smart tools will be essential, particularly given the diverse types of applications for which smart hand tools are envisioned for human use., Comment: In submission at ICRA 2024; added acknowledgements

Different studies have shown the importance of visual cues throughout the speech perception process. In fact, the development of audiovisual approaches has led to advances in the field of speech technologies. However, although noticeable results have recently been achieved, visual speech recognition remains an open research problem. It is a task in which, by dispensing with the auditory sense, challenges such as visual ambiguities and the complexity of modeling silence must be faced. Nonetheless, some of these challenges can be alleviated when the problem is approached from a speaker-dependent perspective. Thus, this paper studies, using the Spanish LIP-RTVE database, how the estimation of specialized end-to-end systems for a specific person could affect the quality of speech recognition. First, different adaptation strategies based on the fine-tuning technique were proposed. Then, a pre-trained CTC/Attention architecture was used as a baseline throughout our experiments. Our findings showed that a two-step fine-tuning process, where the VSR system is first adapted to the task domain, provided significant improvements when the speaker adaptation was addressed. Furthermore, results comparable to the current state of the art were reached even when only a limited amount of data was available., Comment: Accepted in Proceedings of IberSpeech 2022 ( https://www.isca-speech.org/archive/iberspeech_2022/gimenogomez22_iberspeech.html )

During a conversation, our brain is responsible for combining information obtained from multiple senses in order to improve our ability to understand the message we are perceiving. Different studies have shown the importance of presenting visual information in these situations. Nevertheless, lipreading is a complex task whose objective is to interpret speech when audio is not available. By dispensing with a sense as crucial as hearing, it will be necessary to be aware of the challenge that this lack presents. In this paper, we propose an analysis of different speech visual features with the intention of identifying which of them is the best approach to capture the nature of lip movements for natural Spanish and, in this way, dealing with the automatic visual speech recognition task. In order to estimate our system, we present an audiovisual corpus compiled from a subset of the RTVE database, which has been used in the Albayz\'in evaluations. We employ a traditional system based on Hidden Markov Models with Gaussian Mixture Models. Results show that, although the task is difficult, in restricted conditions we obtain recognition results which determine that using eigenlips in combination with deep features is the best visual approach., Comment: Accepted in Proceedings of IberSpeech 2020 ( https://www.isca-speech.org/archive/iberspeech_2021/gimenogomez21_iberspeech.html )

Speech is considered as a multi-modal process where hearing and vision are two fundamentals pillars. In fact, several studies have demonstrated that the robustness of Automatic Speech Recognition systems can be improved when audio and visual cues are combined to represent the nature of speech. In addition, Visual Speech Recognition, an open research problem whose purpose is to interpret speech by reading the lips of the speaker, has been a focus of interest in the last decades. Nevertheless, in order to estimate these systems in the currently Deep Learning era, large-scale databases are required. On the other hand, while most of these databases are dedicated to English, other languages lack sufficient resources. Thus, this paper presents a semi-automatically annotated audiovisual database to deal with unconstrained natural Spanish, providing 13 hours of data extracted from Spanish television. Furthermore, baseline results for both speaker-dependent and speaker-independent scenarios are reported using Hidden Markov Models, a traditional paradigm that has been widely used in the field of Speech Technologies., Comment: Accepted in Proceedings of LREC 2022 ( https://aclanthology.org/2022.lrec-1.294 )

Linearization of the dynamics of recurrent neural networks (RNNs) is often used to study their properties. The same RNN dynamics can be written in terms of the ``activations" (the net inputs to each unit, before its pointwise nonlinearity) or in terms of the ``activities" (the output of each unit, after its pointwise nonlinearity); the two corresponding linearizations are different from each other. This brief and informal technical note describes the relationship between the two linearizations, between the left and right eigenvectors of their dynamics matrices, and shows that some context-dependent effects are readily apparent under linearization of activity dynamics but not linearization of activation dynamics., Comment: 10 pages

Background and aimsSyringe-sharing among people who inject drugs, which can occur during incarceration and post-release, has been linked with increased risk of blood-borne infections. We aimed to investigate the cumulative effect of repeated incarceration and the post-release period on receptive syringe-sharing.DesignOngoing community-based cohort, recruited through targeted sampling between 2011 and 2012 with 6-month follow-ups.SettingTijuana, Mexico.ParticipantsSample of 185 participants (median age 35 years; 67% female) with no history of incarceration at study entry, followed to 2017.MeasurementsCumulative incarceration and post-release period were constructed from incarceration events reported in the past 6 months for each study visit. Receptive syringe-sharing in the past 6 months was assessed as a binary variable. We used logistic regression with generalized estimating equations to examine the association between cumulative incarceration events and the post-release period with receptive syringe-sharing over time. Missing data were handled through multiple imputation.FindingsAt baseline, 65% of participants engaged in receptive syringe-sharing in the prior 6 months. At follow-up, 150 (81%) participants experienced a total of 358 incarceration events [median = 2, interquartile range (IQR) = 1-3]. The risk of receptive syringe-sharing increased with the number of repeated incarcerations. Compared with never incarcerated, those with one incarceration had 1.28 [95% confidence interval (CI) = 0.97-1.68] higher adjusted odds of syringe-sharing; two to three incarcerations, 1.42 (95% CI = 1.02-1.99) and more than three incarcerations, 2.10 (95% CI = 1.15-3.85). Participants released within the past 6 months had 1.53 (95% CI = 1.14-2.05) higher odds of sharing syringes compared with those never incarcerated. This post-release risk continued up to 1.5 years post-incarceration (adjusted

IntroductionAmong people who inject drugs (PWID), polysubstance use has been associated with fatal and non-fatal overdose (NFOD). However, the risk of overdose due to the cumulative number of various recently used drug types remains unexplored. We estimated the risk of NFOD for different polysubstance use categories among PWID in Tijuana, Mexico.MethodsData came from 661 participants followed for 2 years in Proyecto El Cuete-IV, an ongoing prospective cohort of PWID. A multivariable Cox model was used to assess the cumulative impact of polysubstance use on the time to NFOD. We used the Cochran-Armitage test to evaluate a dose-response relationship between number of polysubstance use categories and NFOD.ResultsWe observed 115 NFOD among 1029.2 person-years of follow-up (incidence rate: 11.2 per 100 person-years; 95% confidence interval [CI] 9.3-13.3). Relative to those who used one drug class, the adjusted hazard ratio of NFOD for individuals reporting using two drug classes was 1.11 (95% CI 0.69-1.79), three drug classes was 2.00 (95% CI 1.16-3.44) and for those reporting three compared to two was 1.79 (95% CI 1.09-2.97). A significant Cochran-Armitage trend test (P < 0.001) suggested a dose-response relationship.Discussion and conclusionsPolysubstance use was associated with increased risk of NFOD with a dose-response relationship over 2 years. We identified a subgroup of PWID at high risk of NFOD who reported concurrent use of opioids, stimulants and benzodiazepines. Prioritising tailored harm reduction and overdose prevention interventions for PWID who use multiple substances in Tijuana is needed.

This article aims to analyze the influence of top management team (TMT) characteristics and employees’ human capital on the adoption of open innovation practices in low-tech SMEs. The empirical study is based on the use of structural equations from the data derived from a questionnaire applied to 95 companies in the agro-industrial sector located in the northeastern region of Colombia. The results indicate that both the TMT’s diversity and coordination have a significant and positive influence on the adoption of open innovation practices. On the other hand, regarding human capital, the results indicate that the experience of the employees is a more decisive feature when implementing open innovation strategies than their level of education and training. This paper contributes to the literature by providing part of the missing microfoundations of open innovation and more strongly connecting open innovation, human capital, and TMT configuration.

Boiling is an effective energy-transfer process with substantial utility in energy applications. Boiling performance is described mainly by the heat-transfer coefficient (HTC) and critical heat flux (CHF). Recent efforts for the simultaneous enhancement of HTC and CHF have been limited by an intrinsic trade-off between them-HTC enhancement requires high nucleation-site density, which can increase bubble coalescence resulting in limited CHF enhancement. In this work, this trade-off is overcome by designing three-tier hierarchical structures. The bubble coalescence is minimized to enhance the CHF by defining nucleation sites with microcavities interspersed within hemi-wicking structures. Meanwhile, the reduced nucleation-site density is compensated for by incorporating nanostructures that promote evaporation for HTC enhancement. The hierarchical structures demonstrate the simultaneous enhancement of HTC and CHF up to 389% and 138%, respectively, compared to a smooth surface. This extreme boiling performance can lead to significant energy savings in a variety of boiling applications.

BackgroundAmong antiretroviral therapy (ART)-treated people with human immunodeficiency virus (PWH), persistent systemic immune activation contributes to atherogenesis atherosclerotic, cardiovascular disease (CVD) events, and mortality. Factors associated with key immune activation indices have not previously been characterized among a global primary CVD prevention cohort of PWH.MethodsLeveraging baseline Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) data, we evaluated factors associated with soluble CD14 (sCD14) and oxidized low-density lipoprotein (oxLDL).ResultsThe primary analysis cohort included 4907 participants from 5 global-burden-of-disease regions (38% female, 48% Black, median age 50 years). In fully adjusted models for sCD14, female sex and White race (among those in high-income regions) were associated with higher sCD14 levels, while higher body mass index (BMI) and current use of nucleoside reverse transcriptase inhibitor + integrase strand transfer inhibitor ART were associated with lower sCD14 levels. In fully adjusted models for oxLDL, male sex, residence in high-income regions, White race (among those in high-income regions), and higher BMI were associated with higher oxLDL levels. In a subanalysis cohort of 1396 women with HIV, increased reproductive age was associated with higher sCD14 levels but not with higher oxLDL levels.ConclusionsFactors associated with sCD14 and oxLDL, 2 key indices of immune-mediated CVD risk, differ. Future studies will elucidate ways in which medications (eg, statins) and behavioral modifications influence sCD14 and oxLDL and the extent to which dampening of these markers mediates CVD-protective effects.Clinical trials registrationNCT0234429.

The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California. Genome-wide association disaggregated by admixture mapping reveals novel COVID-19-severity-associated regions containing previously reported markers of neurologic, pulmonary and viral disease susceptibility. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. Summary data from multiomic investigation reveals metagenomic and HLA associations with severe COVID-19. The wealth of data available from residual nasopharyngeal swabs in combination with clinical data abstracted automatically at scale highlights a powerful strategy for pandemic tracking, and reveals distinct epidemiologic, genetic, and biological associations for those at the highest risk.

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras. Despite recent advances in deep convolutional neural networks (DCNNs), this remains challenging. Images or videos collected by long-range cameras often suffer from atmospheric turbulence, blur, low-resolution, unconstrained poses, and poor illumination. In this paper, we provide a brief survey of recent advances in person recognition at a distance. In particular, we review recent work in multi-spectral face verification, person re-identification, and gait-based analysis techniques. Furthermore, we discuss the merits and drawbacks of existing approaches and identify important, yet under explored challenges for deploying remote person recognition systems in-the-wild., Comment: This work has been accepted to the IEEE Asilomar Conference on Signals, Systems, and Computers (ACSSC) 2022

Conversational agents (CAs) embodied in speakers or chatbots are becoming very popular in some countries, and despite their adult-centred design, they have become part of children's lives, generating a need for children-centric trustworthy systems. This paper presents a literature review to identify the main opportunities, challenges and risks brought by CAs when used by children. We then consider relevant ethical guidelines for AI and adapt them to this particular system and population, using a Delphi methodology with a set of experts from different disciplines. From this analysis, we propose specific guidelines to help CAs developers improve their design towards trustworthiness and children., Comment: 19 pages

Deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participants (N=87) viewed pairs of face images of three types: same-identity, general imposter pairs (different identities from similar demographic groups), and twin imposter pairs (identical twin siblings). The task was to determine whether the pairs showed the same person or different people. Identity comparisons were tested in three viewpoint-disparity conditions: frontal to frontal, frontal to 45-degree profile, and frontal to 90-degree profile. Accuracy for discriminating matched-identity pairs from twin-imposters and general imposters was assessed in each viewpoint-disparity condition. Humans were more accurate for general-imposter pairs than twin-imposter pairs, and accuracy declined with increased viewpoint disparity between the images in a pair. A DCNN trained for face identification (Ranjan et al., 2018) was tested on the same image pairs presented to humans. Machine performance mirrored the pattern of human accuracy, but with performance at or above all humans in all but one condition. Human and machine similarity scores were compared across all image-pair types. This item-level analysis showed that human and machine similarity ratings correlated significantly in six of nine image-pair types [range r=0.38 to r=0.63], suggesting general accord between the perception of face similarity by humans and the DCNN. These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN.

The objectives of this study are: (1) to compare match load demands through load indicators between starters and substitutes, and (2) analyze the degree of correlation in the variables analyzed in this investigation. Twenty-two semi-professional soccer players were analyzed during a full season’s 38 official matches. Participants were assigned to two different groups according to their participation in the game: (a) starting-up players (≥90 min played) vs. substitute players (≥45 min played in the second half). Statistical analysis was performed by using Mann–Whitney U test to conduct pairwise comparison and Spearman correlation to demands correlation in each group. Significant differences in both absolute and relative variables in player load (P, p < 0.01; p < 0.01), metabolic power (MP, p < 0.01; p = 0.15), equivalent distance index (EDI, p = 0.87; p < 0.01), dynamic stress load (DSI, p < 0.01; p = 0.977), energy expenditure (EE, p < 0.01; p < 0.01), high metabolic load events (HMLE, p < 0.01; p < 0.01), and high metabolic load distance (HMLD, p < 0.01; p = 0.09). Overall, high direct correlations in the starting-up group in absolute and relative demands of PL, PM, HMLD, EE, and DSL were found, as well as high inverse correlation in the substitute group in all variables, excluding DSL and HMLD. In conclusion, the absolute differences found suggested a different training load management during training sessions.

Trial history biases in decision-making tasks are thought to reflect systematic updates of decision variables, therefore their precise nature informs conclusions about underlying heuristic strategies and learning processes. However, random drifts in decision variables can corrupt this inference by mimicking the signatures of systematic updates. Hence, identifying the trial-by-trial evolution of decision variables requires methods that can robustly account for such drifts. Recent studies (Lak'20, Mendon\c{c}a'20) have made important advances in this direction, by proposing a convenient method to correct for the influence of slow drifts in decision criterion, a key decision variable. Here we apply this correction to a variety of updating scenarios, and evaluate its performance. We show that the correction fails for a wide range of commonly assumed systematic updating strategies, distorting one's inference away from the veridical strategies towards a narrow subset. To address these limitations, we propose a model-based approach for disambiguating systematic updates from random drifts, and demonstrate its success on real and synthetic datasets. We show that this approach accurately recovers the latent trajectory of drifts in decision criterion as well as the generative systematic updates from simulated data. Our results offer recommendations for methods to account for the interactions between history biases and slow drifts, and highlight the advantages of incorporating assumptions about the generative process directly into models of decision-making., Comment: 18 pages, 4 figures

Predicting the geographic location (geo-localization) from a single ground-level RGB image taken anywhere in the world is a very challenging problem. The challenges include huge diversity of images due to different environmental scenarios, drastic variation in the appearance of the same location depending on the time of the day, weather, season, and more importantly, the prediction is made from a single image possibly having only a few geo-locating cues. For these reasons, most existing works are restricted to specific cities, imagery, or worldwide landmarks. In this work, we focus on developing an efficient solution to planet-scale single-image geo-localization. To this end, we propose TransLocator, a unified dual-branch transformer network that attends to tiny details over the entire image and produces robust feature representation under extreme appearance variations. TransLocator takes an RGB image and its semantic segmentation map as inputs, interacts between its two parallel branches after each transformer layer, and simultaneously performs geo-localization and scene recognition in a multi-task fashion. We evaluate TransLocator on four benchmark datasets - Im2GPS, Im2GPS3k, YFCC4k, YFCC26k and obtain 5.5%, 14.1%, 4.9%, 9.9% continent-level accuracy improvement over the state-of-the-art. TransLocator is also validated on real-world test images and found to be more effective than previous methods., Comment: Accepted in ECCV 2022

Facial morphs created between two identities resemble both of the faces used to create the morph. Consequently, humans and machines are prone to mistake morphs made from two identities for either of the faces used to create the morph. This vulnerability has been exploited in "morph attacks" in security scenarios. Here, we asked whether the "other-race effect" (ORE) -- the human advantage for identifying own- vs. other-race faces -- exacerbates morph attack susceptibility for humans. We also asked whether face-identification performance in a deep convolutional neural network (DCNN) is affected by the race of morphed faces. Caucasian (CA) and East-Asian (EA) participants performed a face-identity matching task on pairs of CA and EA face images in two conditions. In the morph condition, different-identity pairs consisted of an image of identity "A" and a 50/50 morph between images of identity "A" and "B". In the baseline condition, morphs of different identities never appeared. As expected, morphs were identified mistakenly more often than original face images. Moreover, CA participants showed an advantage for CA faces in comparison to EA faces (a partial ORE). Of primary interest, morph identification was substantially worse for cross-race faces than for own-race faces. Similar to humans, the DCNN performed more accurately for original face images than for morphed image pairs. Notably, the deep network proved substantially more accurate than humans in both cases. The results point to the possibility that DCNNs might be useful for improving face identification accuracy when morphed faces are presented. They also indicate the significance of the ORE in morph attack susceptibility in applied settings., Comment: 4 figures, 11 pages

Contemporary agricultural systems are poised to transition from linear to circular, adopting concepts of recycling, repurposing, and regeneration. This transition will require changing crop improvement objectives to consider the entire system, and thus provide solutions to improve complex systems for higher productivity, resource use efficiency, and environmental quality. The methods and approaches that underpinned the doubling of yields during the last century may no longer be fully adequate to target crop improvement for circular agricultural systems. Here we propose a multidimensional framework for prediction with outcomes useful to assess both crop performance traits and environmental sustainability of the designed agricultural systems. The study focuses on maize harvestable grain yield and total carbon production, water use, and use efficiency for yield and carbon. The framework builds on the crop growth model whole genome prediction system, which is enabled by advanced phenomics and the integration of symbolic and sub-symbolic artificial intelligence. We demonstrate the approach and prediction accuracy advantages over a standard statistical genomic prediction approach used to breed maize hybrids for yield, flowering time, and kernel set using a dataset comprised of 7004 hybrids, 103 breeding populations, and 62 environments resulting from six years of experimentation in maize drought breeding in the U.S. We propose this framework to motivate a dialogue for how to enable circularity in agriculture through prediction-based systems design.

© 2020 Author(s). Evaporation plays a critical role in a range of technologies that power and sustain our society. Wicks are widely used as passive, capillary-fed evaporators, attracting much interest since these devices are highly efficient, compact, and thermally stable. While wick-based evaporators can be further improved with advanced materials and fabrication techniques, modeling of heat and mass transport at the device level is vital for guiding these innovations. In this perspective, we present the design and optimization of capillary-fed, thin film evaporation devices through a heat and mass transfer lens. This modeling framework can guide future research into materials innovations, fabrication of novel architectures, and systems design/optimization for next generation, high-performance wick-based evaporators. Furthermore, we describe specific challenges and opportunities for the fundamental understanding of evaporation physics. Finally, we apply our modeling framework to the analysis of two important applications-solar vapor generation and electronics cooling devices.

Hygroscopic hydrogels hold significant promise for high-performance atmospheric water harvesting, passive cooling, and thermal management. However, a mechanistic understanding of the sorption kinetics of hygroscopic hydrogels remains elusive, impeding an optimized design and broad adoption. Here, we develop a generalized two-concentration model (TCM) to describe the sorption kinetics of hygroscopic hydrogels, where vapor transport in hydrogel micropores and liquid transport in polymer nanopores are coupled through the sorption at the interface. We show that the liquid transport due to the chemical potential gradient in the hydrogel plays an important role in the fast kinetics. The high water uptake is attributed to the expansion of hydrogel during liquid transport. Moreover, we identify key design parameters governing the kinetics, including the initial porosity, hydrogel thickness, and shear modulus. This work provides a generic framework of sorption kinetics, which bridges the knowledge gap between the fundamental transport and practical design of hygroscopic hydrogels.

The analysis of major and trace elements in zoned minerals is useful for deciphering parts of the tectonothermal evolution of polymetamorphic tarrain. We applied this approach to the Maz Metasedimentary Series in Western Sierras Pampeanas of Argentina, where polymetamorphism resulted in the overprinting of a Grenvillian basement (the Maz Complex) during the pervasive Rinconada tectonic phase of the Famatinian orogeny. The older metamorphism (M1) is assigned to the youngest Grenvillian metamorphic event recognized in this basement at c. 1035 Ma, whereas the Rinconada metamorphism (M2) was Silurian to early Devonian, essentially between 440 and 410 Ma. The latter resulted from oceanward migration of the orogenic front relative to earlier late Cambrian to Ordovician (490–470 Ma) tectonic phases of the Famatinian orogeny. The M1 and M2 metamorphic events have been recognized in a staurolite-garnet schist from the Maz Metasedimentary Series. Most metamorphic minerals from this rock were formed during the M2 event which was of the Barrovian type (±kyanite). Part of the metamorphic P–T evolution is recorded in the complex compositional zoning of garnet porphyroblasts. Three types of garnet were identified based on texture and chemistry, including trace elements (REEs). Phase equilibrium analysis, compositional isopleth, and multi-equilibrium thermobarometry were applied in order to establish the P–T history. M1 is represented by preservation of Grt1 ± Kfs ± Sil, with peak P–T condition of 790°C and 5.2 kbar, that is, granulite facies. This early metamorphic event was related to a deformational D1 episode represented by a relict S1 foliation. The latter is preserved as aligned inclusions in staurolite porphyroblasts and as relics of an older crenulated foliation in microlithons from the matrix. M2 followed a clockwise P–T path with three mineral growth stages. The earliest occurred at ~585°C and ~8.7 kbar and is represented by Grt2 ± St1 ± Bt1 + Qz. Grt2 was partially coeva

Boiling is an effective energy-transfer process with substantial utility in energy applications. Boiling performance is described mainly by the heat-transfer coefficient (HTC) and critical heat flux (CHF). Recent efforts for the simultaneous enhancement of HTC and CHF have been limited by an intrinsic trade-off between them-HTC enhancement requires high nucleation-site density, which can increase bubble coalescence resulting in limited CHF enhancement. In this work, this trade-off is overcome by designing three-tier hierarchical structures. The bubble coalescence is minimized to enhance the CHF by defining nucleation sites with microcavities interspersed within hemi-wicking structures. Meanwhile, the reduced nucleation-site density is compensated for by incorporating nanostructures that promote evaporation for HTC enhancement. The hierarchical structures demonstrate the simultaneous enhancement of HTC and CHF up to 389% and 138%, respectively, compared to a smooth surface. This extreme boiling performance can lead to significant energy savings in a variety of boiling applications.