bluuebunny/crossref_metadata_2025_split huggingface dataset:
Resource frontiers and agglomeration economies: The varied logics of transnational land-based investing in Southern and Eastern Africa
Authors: Abeygunawardane Dilini , Kronenburg García Angela , Sun Zhanli , Müller Daniel , Sitoe Almeida , Meyfroidt Patrick 
Published: January 2022

AbstractActor-level data on large-scale commercial agriculture in Sub-Saharan Africa are scarce. The peculiar choice of transnational investing in African land has, therefore, been subject to conjecture. Addressing this gap, we reconstructed the underlying logics of investment location choices in a Bayesian network, using firm- and actor-level interview and spatial data from 37 transnational agriculture and forestry investments across 121 sites in Mozambique, Zambia, Tanzania, and Ethiopia. We distinguish four investment locations across gradients of resource frontiers and agglomeration economies to derive the preferred locations of different investors with varied skillsets and market reach (i.e., track record). In contrast to newcomers, investors with extensive track records are more likely to expand the land use frontier, but they are also likely to survive the high transaction costs of the pre-commercial frontier. We highlight key comparative advantages of Southern and Eastern African frontiers and map the most probable categories of investment locations.
Evaluation of batch mesophilic anaerobic digestion of raw and trampled llama and dromedary dungs: methane potential and kinetic study
Authors: Fernández-Rodríguez M. J. , Mancilla-Leytón J. M. , de la Lama-Calvente D. , Borja R. 
Published: January 2022

AbstractThis research was carried out with the aim to evaluate the anaerobic digestion (AD) of llama and dromedary dungs (both untreated and trampled) in batch mode at mesophilic temperature (35 °C). The biochemical methane potential (BMP) tests with an inoculum to substrate ratio of 2:1 (as volatile solids (VS)) were carried out. The methane yield from trampled llama dung (333.0 mL CH4 g−1 VSadded) was considerably higher than for raw llama, raw and trampled dromedary dungs (185.9, 228.4, 222.9 mL CH4 g−1 VSadded, respectively). Therefore, trampled llama dung was found to be the best substrate for methane production due to its high content of volatile solids as well as its high nitrogen content (2.1%) and more appropriate C/N ratio (23.6) for AD. The experimental data was found to be in accordance with both first-order kinetic and transference function mathematical models, when evaluating the experimental methane production against time. By applying the first-order kinetic model, the hydrolysis rate constants, kh, were found to be 19% and 11% higher for trampled dungs in comparison with the raw dung of dromedary and llama, respectively. In addition, the maximum methane production rate (Rm) derived from the transference function model for trampled llama dung (22.0 mL CH4 g−1 VS d−1) was 83.3%, 24.4% and 22.9% higher than those obtained for raw llama manure and for raw and trampled dromedary dungs, respectively.
How the Exposure to Beauty Ideals on Social Networking Sites Influences Body Image: A Systematic Review of Experimental Studies
Authors: Fioravanti Giulia , Bocci Benucci Sara , Ceragioli Giulia , Casale Silvia 
Published: January 2022

AbstractSharing and viewing photos on social networking sites (SNSs) have been identified as particularly problematic for body image. Although correlational research to date has established that SNS use is associated with increased body dissatisfaction, only experimental studies can enhance confidence in the conclusions drawn. For this reason, this systematic review synthesizes data from 43 experimental studies (N = 8637; %F = 89.56; mean age = 21.58 ± 1.78) examining the effect of viewing idealized images (i.e., attractive, thin, and fit) and body positive content on SNSs on body image. Two studies were conducted on adolescents. Each study had slight variations in how the images were presented for each category (e.g., selfies and photos taken by others). The wide variability in experimental stimuli and psychological moderators used in the published research make a systematic review more feasible and meaningful than a meta-analysis. Findings indicate that viewing idealized images on SNSs lead to increased body dissatisfaction among young women and men. State appearance comparison (i.e., engaging in social comparison while viewing images) significantly mediated the effect, whereas trait appearance comparison (i.e., the relatively stable general tendency to engage in social comparison) was a significant moderator. Mixed results were found regarding the exposure to body positive images/captions. Viewing images on SNSs depicting unattainable beauty ideals leads young people to feel dissatisfied about their bodies, with appearance comparison processing playing an important role. More research is required to assess the long-term effects.
Are companies using Twitter to greenwash and hide bad environmental performance?
Authors: Johnson Thomas F. , Greenwell Matthew P. 
Published: January 2022

AbstractCompanies and related consumer behaviours contribute significantly to global carbon emissions. However, consumer behaviour is shifting, with the public now recognising the real and immediate impact of climate change. Many companies are aware and seemingly eager to align to consumer’s increasing environmental consciousness, yet there is a risk that some companies could be presenting themselves as environmentally friendly without implementing environmentally beneficial processes and products (i.e. greenwashing). Here, using longitudinal climate leadership, environmental messaging (Twitter) and stock price data, we explore how climate leadership (a relative climate change mitigation metric) and environmental messaging have changed for hundreds of UK companies. Using the environmental messaging, we also assess whether companies are simply greenwashing their true climate change performance. Finally, we explore how climate leadership and environmental messaging influence companies’ stock prices. We found that companies (on average) have increased their climate leadership (coef: 0.14, CI 0.12–0.16) and environmental messaging (coef: 0.35, CI 0.19–0.50) between 2010 and 2019. We also found an association where companies with more environmental messaging had a higher climate leadership (coef: 0.16, CI 0.07–0.26), suggesting messaging was proportionate to environmental performance, and so there was no clear pattern of using Twitter for greenwashing across UK companies. In fact, some companies may be under-advertising their pro-environmental performance. Finally, we found no evidence that climate leadership, environmental messaging or greenwashing impacts a company’s stock price.
Selected transgenic murine models of human autoimmune liver diseases
Authors: Trzos Katarzyna , Pydyn Natalia , Jura Jolanta , Kotlinowski Jerzy 
Published: January 2022

AbstractMurine models of human diseases are of outmost importance for both studying molecular mechanisms driving their development and testing new treatment strategies. In this review, we first discuss the etiology and risk factors for autoimmune liver disease, including primary biliary cholangitis, autoimmune hepatitis and primary sclerosing cholangitis. Second, we highlight important features of murine transgenic models that make them useful for basic scientists, drug developers and clinical researchers. Next, a brief description of each disease is followed by the characterization of selected animal models.
From Circular to Linear? Assessing the Life Cycle Environmental and Economic Sustainability of Steel and Plastic Beer Kegs
Authors: Martin Michael , Herlaar Sjoerd , Jönsson Aiden , Lazarevic David 
Published: January 2022

AbstractIn the craft brewing industry, kegging solutions have changed dramatically in recent years. While steel kegs once dominated the draught beer market, single-use plastic kegs have increased in popularity due to their convenience, especially in the craft brewing sector. With the increasing importance of the circular economy and the introduction of policies in Europe to move away from single-use plastic systems, this study aims to assess and compare the sustainability of conventional steel and single-use plastic kegs. The environmental and economic performance are assessed through life cycle assessment and life cycle costing approaches. The results suggest that steel kegs have better environmental performance and life cycle costs. However, these are limited to the local markets, and with larger distances, plastic kegs may become the better option due to their lower weight, suggesting that both kegs are useful in certain situations. This is especially important in countries that have long distances between breweries and their markets. The importance of extending the lifetime of the keg fleet is also highlighted to improve the environmental performance as the results are influenced by the assumption on the lifetime of the steel kegs. To improve the environmental performance of plastic kegs, efficient closed-loop recycling systems should be developed. Careful decision-making is needed to ensure that more sustainable packaging options are chosen for draught beer and that sustainability aspects be taken into account beyond convenience.
Sea‐Ice Detection From Near‐Nadir Ku‐Band Echoes From CFOSAT/SWIM Scatterometer
Authors: Peureux Charles , Longépé Nicolas , Mouche Alexis , Tison Céline , Tourain Cédric , Lachiver Jean‐Michel , Hauser Danièle 
Published: January 2022

AbstractSWIM on board CFOSAT is the first spaceborne, low incidence, rotating scatterometer, aiming at measuring in near‐real time ocean waves spectra. With five off‐nadir beams at incidences between 2° and 10° plus one nadir beam, it covers the Earth in 13 days, including polar regions thanks to its polar orbit. This work aims at exploiting SWIM data over ice regions with two objectives. An off‐nadir data‐based sea‐ice flag is here proposed that allows, first, to eliminate sea‐ice polluted echoes for improving the wave spectrum retrieval, and second, to open perspectives for application of sea‐ice monitoring with near‐nadir Ku‐band active sensors. To this end, the signature of both open water and sea‐ice radar backscatter is parameterized into Geophysical Model Functions. Then, comparisons with observed profiles through a Bayesian scheme provide a probability of sea‐ice presence. After comparison with both model (ECMWF‐IFS) and radiometer (SSMI) derived reference data sets, the proposed flag is found to be ready for operational use. At latitudes greater than 40° in absolute value, the proposed flagging algorithm exhibits accuracies of approximately 98% for all beams compared to SSMI data. Beam to beam performances are characterized and show potential for the characterization of sea‐ice at Ku‐band.
Secular Variations in Provenance of Sedimentary Components in the Western North Pacific Ocean Constrained by Sr Isotopic Features of Deep‐Sea Sediments
Authors: Tanaka Erika , Yasukawa Kazutaka , Nakamura Kentaro , Ohta Junichiro , Miyazaki Takashi , Vaglarov Bogdan Stefanov , Machida Shiki , Fujinaga Koichiro , Iwamori Hikaru , Kato Yasuhiro 
Published: January 2022

AbstractDeep‐sea sediments around Minamitorishima Island in the western North Pacific Ocean record the depositional environment in the central to western North Pacific since the late Cretaceous. Previous studies on the bulk chemical composition of deep‐sea sediments in this area have revealed that the sediment column can be divided into five chemostratigraphic units with at least three intercalated layers enriched in rare‐earth elements. However, the end‐member components and their changes in abundance, which indicate compositional variation, as well as the environmental factors affecting them, remain unclear. Here, we report the bulk chemistry, including newly analyzed 87Sr/86Sr ratios of the sediments, seafloor Mn nodules, and fish teeth collected from the deep‐sea basin around Minamitorishima Island, to identify the end‐member components of the sediments. Our results suggest that the pelagic sediments in the study area are mainly composed of terrigenous components, hydrogenous Mn oxides, biogenic calcium phosphate, and volcanic materials. The combination of Sr isotope modeling and bulk chemistry revealed that the input of volcanic materials has increased twice, which might be Izu‐Bonin‐Mariana tephras of the latest Eocene to Oligocene explosive volcanism at the later event but is unclear at the earlier event. The provenance of terrigenous materials has changed from North America to East Asia. The bulk sediment chemostratigraphy around Minamitorishima Island could have resulted not only from the local environmental changes in the pelagic realm, but also from volcanism around the Pacific Ocean and the northwestward movement of the area due to the Pacific plate motion.
Slow Geodynamics and Fast Morphotectonics in the Far East Tethys
Authors: Husson L. , Riel N. , Aribowo S. , Authemayou C. , de Gelder G. , Kaus B. J. P. , Mallard C. , Natawidjaja D. H. , Pedoja K. , Sarr A. C. 
Published: January 2022

AbstractHow can the sluggish, long‐wavelength mantle convection be expressed by so many time and space scales of morphotectonic activity? To investigate these relationships, we explore the Java‐Banda subduction zone, where geodynamic records cluster. In the far‐East Tethys, the exceptionally arcuate Banda subduction zone circumscribes the deepest oceanic basin on Earth, seismotectonic activity slices the upper plate more efficiently than anywhere else, and uncommonly vast expanses of continents are flooded in Sundaland and Northern Australia. By comparing numerical simulations of subduction dynamics to a set of independent observations, we reveal the many facets of tectonic and physiographic changes that the sole docking of the Australian continent onto the subduction zone triggered. While mantle flow remains slow and long‐wavelength at depth, intense tectonic activity, and dynamic uplift and subsidence profoundly rework the physiography at many spatial scales. These results demonstrate that a modest disruption in the slow geodynamic tempo may trigger manifold morphotectonic disturbances.
Measuring maladaptive avoidance: from animal models to clinical anxiety
Authors: Ball Tali M. , Gunaydin Lisa A. 
Published: January 2022

AbstractAvoiding stimuli that predict danger is required for survival. However, avoidance can become maladaptive in individuals who overestimate threat and thus avoid safe situations as well as dangerous ones. Excessive avoidance is a core feature of anxiety disorders, post-traumatic stress disorder (PTSD), and obsessive-compulsive disorder (OCD). This avoidance prevents patients from confronting maladaptive threat beliefs, thereby maintaining disordered anxiety. Avoidance is associated with high levels of psychosocial impairment yet is poorly understood at a mechanistic level. Many objective laboratory assessments of avoidance measure adaptive avoidance, in which an individual learns to successfully avoid a truly noxious stimulus. However, anxiety disorders are characterized by maladaptive avoidance, for which there are fewer objective laboratory measures. We posit that maladaptive avoidance behavior depends on a combination of three altered neurobehavioral processes: (1) threat appraisal, (2) habitual avoidance, and (3) trait avoidance tendency. This heterogeneity in underlying processes presents challenges to the objective measurement of maladaptive avoidance behavior. Here we first review existing paradigms for measuring avoidance behavior and its underlying neural mechanisms in both human and animal models, and identify how existing paradigms relate to these neurobehavioral processes. We then propose a new framework to improve the translational understanding of maladaptive avoidance behavior by adapting paradigms to better differentiate underlying processes and mechanisms and applying these paradigms in clinical populations across diagnoses with the goal of developing novel interventions to engage specific identified neurobehavioral targets.
White-matter free-water diffusion MRI in schizophrenia: a systematic review and meta-analysis
Authors: Carreira Figueiredo Inês , Borgan Faith , Pasternak Ofer , Turkheimer Federico E. , Howes Oliver D. 
Published: January 2022

AbstractWhite-matter abnormalities, including increases in extracellular free-water, are implicated in the pathophysiology of schizophrenia. Recent advances in diffusion magnetic resonance imaging (MRI) enable free-water levels to be indexed. However, the brain levels in patients with schizophrenia have not yet been systematically investigated. We aimed to meta-analyse white-matter free-water levels in patients with schizophrenia compared to healthy volunteers. We performed a literature search in EMBASE, MEDLINE, and PsycINFO databases. Diffusion MRI studies reporting free-water in patients with schizophrenia compared to healthy controls were included. We investigated the effect of demographic variables, illness duration, chlorpromazine equivalents of antipsychotic medication, type of scanner, and clinical symptoms severity on free-water measures. Ten studies, including five of first episode of psychosis have investigated free-water levels in schizophrenia, with significantly higher levels reported in whole-brain and specific brain regions (including corona radiata, internal capsule, superior and inferior longitudinal fasciculus, cingulum bundle, and corpus callosum). Six studies, including a total of 614 participants met the inclusion criteria for quantitative analysis. Whole-brain free-water levels were significantly higher in patients relative to healthy volunteers (Hedge’s g = 0.38, 95% confidence interval (CI) 0.07–0.69, p = 0.02). Sex moderated this effect, such that smaller effects were seen in samples with more females (z = −2.54, p < 0.05), but antipsychotic dose, illness duration and symptom severity did not. Patients with schizophrenia have increased free-water compared to healthy volunteers. Future studies are necessary to determine the pathological sources of increased free-water, and its relationship with illness duration and severity.
Bandwidth constrained client selection and scheduling for federated learning over SD‐WAN
Authors: Du Chunhui , Xiao Jianhang , Guo Wei 
Published: January 2022

AbstractCross‐silo federated learning (FL) has been proposed and applied in many domains such as financial risk prediction, pharmaceutical discovery, electronic health records mining. In this paradigm, multiple clients can collaboratively train machine learning models with higher accuracy while protecting their privacy under the coordination of a central server. To interconnect these geo‐distributed clients and the central server, the architecture of FL is proposed over software‐defined wide area network (SD‐WAN). The total time to achieve the required accuracy for FL depends on both the time of each iteration and the number of iterations. Because the bandwidth for each path from client to central server is constrained, different client selection schemes and different path routing mechanisms may lead to different times of iteration. The authors propose FedMT algorithm to minimize the total time through well‐designed client selection and routing, which greatly reduces the upload time of each iteration while slightly affecting the number of iterations. Extensive experiments are conducted and demonstrated the time to achieve the same accuracy for FedMT is less than half of that for FedAvg and FedProx.
Optimal control input for discrete‐time networked control systems with data dropout
Authors: Zanma Tadanao , Yamamoto Naohiro , Koiwa Kenta , Liu Kang‐Zhi 
Published: January 2022

AbstractThese days, networked control systems (NCSs) in which data is transmitted via communication have been actively studied for many potential applications. In an NCS, data dropout degrades control performance depending on network conditions. For an NCS with data dropout, the authors propose a model‐predictive‐control‐based input optimisation, representing data dropout as both a Bernoulli model and a finite‐order Markov chain. Using the proposed NCS data dropout model, the authors derive an optimal input that provides the estimated error between the expected state of the plant and a given reference. The proposed control problem is formulated as its equivalent quadratic programming, as executed at each online sampling. The authors also demonstrate simulations and experiments to show the effectiveness of the proposed method.
A multi‐scale learning method with dilated convolutional network for concrete surface cracks detection
Authors: Zhou Qiang , Qu Zhong , Ju Fang‐rong 
Published: January 2022

AbstractConcrete surface cracks detection is an important task to ensure the safety of infrastructure. Because of the complexity of background and low contrast of concrete surface, it is difficult to detect the cracks on the concrete surface accurately. To tackle this problem, a multi‐scale dilated convolutional method for concrete surface crack detection is proposed to improve the accuracy of detection. The proposed network is based on the encoder‐decoder structure of U‐Net. Cascade multi‐scale dilated convolutions in the centre of the network is used to get larger receptive field without additional parameters. In the decoder stage, the feature fusion module is used to integrated the multi‐scale and multi‐level side network feature for the final prediction. A large crack dataset is collected as a training set and other three smaller datasets are used for evaluation. Extensive experiments have been conducted on these three crack datasets, which achieves optimal dataset scale (ODS) F‐score over 0.84, optimal image scale (OIS) F‐score over 0.85 and average precision (AP) over 0.86. This algorithm performs better than the current crack detection, edge detection and semantic segmentation methods.
A verification framework for behavioral safety of self‐driving cars
Authors: Wu Huihui , Lyu Deyun , Zhang Yanan , Hou Gang , Watanabe Masahiko , Wang Jie , Kong Weiqiang 
Published: January 2022

AbstractWhile self‐driving cars have already been widely investigated and achieved spectacular progress, a major obstacle in applications is the great difficulty in providing formal guarantees about their behaviors. Since the environment of the self‐driving is usually not known beforehand and highly uncertain, classical verification approaches cannot be applied to guarantee safety. To cope with any traffic situation, a novel online verification framework is presented for verifying behavioral safety of self‐driving cars. The framework is based on the proposed five safety considerations: new longitudinal and lateral safe distances, lane changes, overtaking and how to face new traffic participants. Different from the previous verification considerations, this verification framework allows actual behaviors of self‐driving cars to be temporarily inconsistent with the popular strict safe distance. As long as the self‐driving car respects the minimum safe distance calculated by our technique and executes improvement behaviors to restore the safe distance, it is still believed that the predictive behavior is safe. The framework can easily be integrated to existing self‐driving systems and evaluate different indicators involving the steering angle, acceleration and braking. The benefits of the framework in different urban scenarios of the CARLA simulator and real traffic data provided by the NGSIM project are demonstrated. Results show that the technology can successfully detect unsafe behaviors and provide effective measures to avoid potential collisions.