bluuebunny/crossref_metadata_2025_split huggingface dataset:
Potential of Alnus acuminata based agroforestry for carbon sequestration and other ecosystem services in Rwanda
Authors: Cyamweshi Athanase R. , Kuyah Shem , Mukuralinda Athanase , Muthuri Catherine W. 
Published: May 2021

AbstractAlnus acuminata Kunth. (alnus) is widely used in agroforestry systems across the globe and is believed to provide multiple ecosystem services; however, evidence is lacking in agroforestry literature to support the perceived benefits, particularly in Rwanda. To understand carbon sequestration potential and other benefits of alnus, a household survey, tree inventory and destructive sampling were conducted in north-western Rwanda. Over 75% of the respondents had alnus trees in their farms. The trees provide stakes for climbing beans, firewood and timber. They also improve soil fertility and control soil erosion. Farmers had between 130 and 161 alnus trees per hectare with an average height of 7.7 ± 0.59 m and diameter at breast height of 16.3 ± 1.39 cm. The largest biomass proportion was found in stems (70.5%) while branches and leaves stock about 16.5 and 13% of the total biomass, respectively. At farm level, aboveground biomass of alnus trees was estimated to be 27.2 ± 0.7 Mg ha−1 representing 13.6 Mg of carbon (C) per hectare. Biomass carbon increased with tree size, from 7.1 ± 0.2 Mg C ha−1 in 3 years old trees to 34.4 ± 2.2 Mg C ha−1 in 10 years old trees. The converse was observed with elevation; biomass carbon decreased with increasing elevation from 21.4 ± 1.29 Mg C ha−1 at low (2011–2110 m) to 9.6 ± 0.75 Mg C ha−1 in the high elevation (> 2510 m). In conclusion, alnus agroforestry significantly contributes to carbon sequestration, although the magnitude of these benefits varies with tree age and elevation. Planting alnus trees on farms can meet local needs for stakes for climbing beans, wood and soil fertility improvement, as well as the global need for regulation of climate change.
A society ill-equipped to deal with the effects of climate change on cultural heritage and landscape: a qualitative assessment of planning practices in transport infrastructure
Authors: Antonson Hans , Buckland Philip , Nyqvist Roger 
Published: May 2021

AbstractThis paper provides insights into the handling of climate change issues related to cultural heritage at different government decision levels dealing with physical planning, and in particular roads. Data are derived from a qualitative analysis of official reports and interviews with local and regional planners in three Swedish regions with contrasting climates. The theoretical lens of Institutional Interplay is applied to an analysis grouped into six themes: Climate threats to cultural heritage, Adaptation measures, Preparedness, Institutional preconditions, Institutional interplay, and Challenges. The results suggest that despite a strong environmental reputation internationally, Sweden is not particularly well prepared for dealing with future climate change impacts on cultural heritage and landscape. The lack of national standards and standardised methods risks regional and sectoral variation in the treatment of similar tasks, a problem which deficiencies in knowledge and continuing education are perpetuating. The degree to which discussions and cooperation occur between divisions within the same authority, between authorities, and in national networks varies considerably. Routines and criteria for prioritisation of cultural heritage mitigation, essential under conditions of limited resources, have yet to have been implemented. We conclude with five recommendations for improving the planning process with respect to climate change risks to cultural heritage.
Semantic and syntactic information for neural machine translation
Authors: Armengol-Estapé Jordi , Costa-jussà Marta R. 
Published: May 2021

AbstractIntroducing factors such as linguistic features has long been proposed in machine translation to improve the quality of translations. More recently, factored machine translation has proven to still be useful in the case of sequence-to-sequence systems. In this work, we investigate whether this gains hold in the case of the state-of-the-art architecture in neural machine translation, the Transformer, instead of recurrent architectures. We propose a new model, the Factored Transformer, to introduce an arbitrary number of word features in the source sequence in an attentional system. Specifically, we suggest two variants depending on the level at which the features are injected. Moreover, we suggest two combination mechanisms for the word features and words themselves. We experiment both with classical linguistic features and semantic features extracted from a linked data database, and with two low-resource datasets. With the best-found configuration, we show improvements of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task. Moreover, we experiment with the more challenging FLoRes English-to-Nepali benchmark, which includes both low-resource and very distant languages, and obtain an improvement of 1.2 BLEU. These improvements are achieved with linguistic and not with semantic information.
Contending with Spiritual Reductionism: Demons, Shame, and Dividualising Experiences Among Evangelical Christians with Mental Distress
Authors: Lloyd Christopher E. M. 
Published: May 2021

AbstractThe belief that mental distress is caused by demons, sin, or generational curses is commonplace among many evangelical Christian communities. These beliefs may have positive or negative effects for individuals and groups. Phenomenological descriptions of these experiences and the subjective meanings associated with them, however, remain somewhat neglected in the literature. The current study employed semi-structured interviews with eight evangelical Christians in order to idiographically explore their experiences of mental distress in relation to their faith and wider communities. Through an interpretative phenomenological analysis, two superordinate themes were constructed: negative spiritualisation and negotiating the dialectic between faith and the lived experience of mental distress. Participants variously experienced a climate of negative spiritualisation, whereby their mental distress was demonised and dismissed, and they were further discouraged from seeking help in secular institutions and environments. Participants often considered such dismissals of their mental distress as unhelpful and stigmatising and experienced heightened feelings of shame and suffering as a result. Such discouragement also contributed to the process of othering and relational disconnection. Alongside a rejection of church teachings, which exclusively spiritualised psychological distress, participants negotiated a nuanced personal synthesis of faith, theology, and distress, which assumed a localised and idiographic significance. This synthesis included advocating for the uptake of aetiological accounts, which contextualised mental distress in terms of the whole person and resisted de-politicised, dichotomised, and individualistic narratives. Results are discussed in relation to a broad range of literature in the field, while further research suggestions are provided.
Elucidating the Onset of Plasticity in Sliding Contacts Using Differential Computational Orientation Tomography
Authors: Eder S. J. , Grützmacher P. G. , Rodríguez Ripoll M. , Belak J. F. 
Published: May 2021

AbstractDepending on the mechanical and thermal energy introduced to a dry sliding interface, the near-surface regions of the mated bodies may undergo plastic deformation. In this work, we use large-scale molecular dynamics simulations to generate “differential computational orientation tomographs” (dCOT) and thus highlight changes to the microstructure near tribological FCC alloy surfaces, allowing us to detect subtle differences in lattice orientation and small distances in grain boundary migration. The analysis approach compares computationally generated orientation tomographs with their undeformed counterparts via a simple image analysis filter. We use our visualization method to discuss the acting microstructural mechanisms in a load- and time-resolved fashion, focusing on sliding conditions that lead to twinning, partial lattice rotation, and grain boundary-dominated processes. Extracting and laterally averaging the color saturation value of the generated tomographs allows us to produce quantitative time- and depth-resolved maps that give a good overview of the progress and severity of near-surface deformation. Corresponding maps of the lateral standard deviation in the color saturation show evidence of homogenization processes occurring in the tribologically loaded microstructure, frequently leading to the formation of a well-defined separation between deformed and undeformed regions. When integrated into a computational materials engineering framework, our approach could help optimize material design for tribological and other deformation problems.Graphic Abstract.
Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer
Authors: Georgopoulos Markos , Oldfield James , Nicolaou Mihalis A. , Panagakis Yannis , Pantic Maja 
Published: May 2021

AbstractDeep learning has catalysed progress in tasks such as face recognition and analysis, leading to a quick integration of technological solutions in multiple layers of our society. While such systems have proven to be accurate by standard evaluation metrics and benchmarks, a surge of work has recently exposed the demographic bias that such algorithms exhibit–highlighting that accuracy does not entail fairness. Clearly, deploying biased systems under real-world settings can have grave consequences for affected populations. Indeed, learning methods are prone to inheriting, or even amplifying the bias present in a training set, manifested by uneven representation across demographic groups. In facial datasets, this particularly relates to attributes such as skin tone, gender, and age. In this work, we address the problem of mitigating bias in facial datasets by data augmentation. We propose a multi-attribute framework that can successfully transfer complex, multi-scale facial patterns even if these belong to underrepresented groups in the training set. This is achieved by relaxing the rigid dependence on a single attribute label, and further introducing a tensor-based mixing structure that captures multiplicative interactions between attributes in a multilinear fashion. We evaluate our method with an extensive set of qualitative and quantitative experiments on several datasets, with rigorous comparisons to state-of-the-art methods. We find that the proposed framework can successfully mitigate dataset bias, as evinced by extensive evaluations on established diversity metrics, while significantly improving fairness metrics such as equality of opportunity.
Quantitative analysis of fine dust particles on moss surfaces under laboratory conditions using the example of Brachythecium rutabulum
Authors: Vanicela Bilitis Désirée , Nebel Martin , Stephan Marielle , Riethmüller Christoph , Gresser Götz Theo 
Published: May 2021

AbstractThe identification of a model organism for investigations of fine dust deposits on moss leaflets was presented. An optical method with SEM enabled the quantitative detection of fine dust particles in two orders of magnitude. Selection criteria were developed with which further moss species can be identified in order to quantify the number of fine dust particles on moss surfaces using the presented method. Among the five moss species examined, B. rutabulum had proven to be the most suitable model organism for the method presented here. The number of fine dust particles on the moss surface of B. rutabulum was documented during 4 weeks of cultivation in the laboratory using SEM images and a counting method. The fine dust particles were recorded in the order of 10 μm–0.3 μm, divided into two size classes and counted. Under laboratory conditions, the number of particles of the fine fraction 2.4 μm–0.3 μm decreased significantly.
Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing
Authors: Yuan Le , Popoola Olalekan A.M. , Hood Christina , Carruthers David , Jones Roderic L. , Liu Huan , Lv Zhaofeng , Zhang Qiang , Archibald Alexander T. 
Published: May 2021

AbstractThe reliability of air quality simulations has a strong dependence on the input emissions inventories, which are associated with various sources of uncertainties, particularly in regions undergoing rapid emission changes where inventories can be ‘out of date’ almost as soon as they are compiled. This work provides a new methodology for updating emissions inventories by source sector using air quality ensemble simulations and observations from a dense monitoring network. It is adopted to determine the short-term trends in carbon monoxide (CO) emissions, an important pollutant and precursor to tropospheric ozone, in a study area centred around Beijing following the implementation of clean air policies. We sample the uncertainties associated with using an a priori emissions inventory for the year 2013 in air quality simulations of 2016, using an atmospheric dispersion model combined with a perturbed emissions ensemble (PEE), which is constructed based on expert-elicited uncertainty ranges for individual source sectors in the inventory. By comparing the simulation outputs with observational constraints, we are able to constrain the emissions of key source sectors relative to those in the a priori emissions inventory. From 2013 to 2016, we find a 44–88% reduction in the transport sector emissions (0.92–4.4×105 Mg in 2016) and a minimum 61% decrease in residential sector emissions (<3.5×105 Mg in 2016) within the study area. We also provide evidence that the night-time fraction of traffic sources in 2016 was higher than that in the 2013 emissions inventory. This study shows the applicability of PEEs and high-resolution observations in providing timely updates of emission estimates by source sector.
Outcomes after stereotactic radiosurgery of brain metastases in patients with malignant melanoma and validation of the melanoma molGPA
Authors: Kessel K. A. , Deichl A. , Gempt J. , Meyer B. , Posch C. , Diehl C. , Zimmer C. , Combs S. E. 
Published: May 2021

Abstract
Introduction
Malignant melanoma is the third most common primary in the diagnosis of brain metastases. Stereotactic radiosurgery (SRS) is a well-established treatment option in limited brain disease. We analyzed outcomes of SRS with a particular focus on the graded prognostic assessment (GPA, melanoma molGPA), prognostic factors, and toxicity.

Methods
We evaluated 173 brain metastases in 83 patients with malignant melanoma. All were treated with SRS median dose of 20 Gy prescribed to the 80 or 100% isodose line between 2002 and 2019. All patients were followed-up regularly, including contrast‐enhanced brain imaging as well as clinical examination, initially 6 weeks after treatment, then in quarterly follow-up.

Results
The median age was 61 years (range 27–80); 36 female and 47 male patients were treated. After a median follow-up of 5.7 months, median OS (overall survival) was 9.7 months 95%-KI 4.7–14.7). LC (local control) at 6 months, 12, 24 months was 89%, 86%, and 72%, respectively (median was not reached). Median DBC (distant brain control) was 8.2 months (95%-KI 4.7–11.7). For OS, a KPS ≥ 80%, a positive BRAF mutation status, a small PTV (planning target volume), the absence of extracranial metastases, as well as a GPA and melanoma molGPA > 2 were prognostic factors. In the MVA, a small PTV and a melanoma molGPA > 2 remained significant.

Conclusion
The present survival outcomes support the use of the disease-specific melanoma molGPA as reliable prognostic score. Favorable outcomes for SRS compared to other studies were observed. In the treatment of brain metastases of malignant melanoma patients, a multidisciplinary approach consisting of surgery, SRS, chemotherapy, and immunotherapy should be considered.
Designing AI for Explainability and Verifiability: A Value Sensitive Design Approach to Avoid Artificial Stupidity in Autonomous Vehicles
Authors: Umbrello Steven , Yampolskiy Roman V. 
Published: May 2021

AbstractOne of the primary, if not most critical, difficulties in the design and implementation of autonomous systems is the black-boxed nature of the decision-making structures and logical pathways. How human values are embodied and actualised in situ may ultimately prove to be harmful if not outright recalcitrant. For this reason, the values of stakeholders become of particular significance given the risks posed by opaque structures of intelligent agents. This paper explores how decision matrix algorithms, via thebelief-desire-intentionmodel for autonomous vehicles, can be designed to minimize the risks of opaque architectures. Primarily through an explicit orientation towards designingforthe values of explainability and verifiability. In doing so, this research adopts the Value Sensitive Design (VSD) approach as a principled framework for the incorporation of such values within design. VSD is recognized as a potential starting point that offers a systematic way for engineering teams to formally incorporate existing technical solutions within ethical design, while simultaneously remaining pliable to emerging issues and needs. It is concluded that the VSD methodology offers at least a strong enough foundation from which designers can begin to anticipate design needs and formulate salient design flows that can be adapted to the changing ethical landscapes required for utilisation in autonomous vehicles.
Physiological response and transcriptome analysis of Prunus mume to early salt stress
Authors: Wang Xiujun , Song Zhenqi , Ti Yujing , Liu Yuxia , Li Qingwei 
Published: May 2021

AbstractSoil salinisation is an important factor limiting the cultivation and distribution of Prunus mume. Therefore, identifying the regulation mechanism of salt tolerance of P. mume will promote its cultivation and molecular breeding. In this study, the garden tree species P. mume ‘Meiren’ was investigated, with a soil NaCl content (w/w) of 0.3%. Photosynthetic gas exchange parameters, relative electrical conductivity, malondialdehyde, osmoregulation substances, and antioxidant enzyme activity were measured in the early (3d), middle (10d), and late (30d) stages of stress. Salt treatment was applied for 0 h, 6 h, 24 h, and 72 h; following this, the gene library was constructed from the leaves. The results showed that the CBL-CIPK, mitogen-activated protein kinase, calcium-dependent protein kinase, and abscisic acid signalling pathways were involved in salt stress signal transduction of P. mume. Furthermore, bHLH, WRPK, ERF, and MYB were identified as potential key transcription factor families of salt tolerance in P. mume. Combined with determined physiological responses, the results showed that early salt stress inhibited the activities of RuBisCO, chlorophyll synthase, glutamyl tRNA reductase, divinyl reductase, and magnesium chelatase; resultantly, this led to a reduction in the photosynthetic rate. Also, it was found that P5CS, SS, LEA, and dehydrin regulated the synthesis of proline, soluble sugar, and macromolecular protein to alleviate osmotic stress. POD gene plays an important role in scavenging reactive oxygen species due to its regulation of POD activity. The findings of this research reveal the internal mechanism of the physiological response and provide a foundation for the construction of a genetic regulatory network in response to salt stress in P. mume.
The Use of Effective Language and Communication in the Management of Obesity: the Challenge for Healthcare Professionals
Authors: Auckburally Sameera , Davies Elena , Logue Jennifer 
Published: May 2021

Abstract
Purpose of Review
Initial conversations about weight with patients are important to set the tone for future dialogue and management of obesity. There is often reluctance in raising the topic of overweight or obesity in consultations. We aimed to evaluate literature to discover the perceived barriers to optimal discussion about weight status and preferred weight-based terminology for adults, adolescents and parents of younger children.

Recent Findings
Fear of offending patients, insufficient training and lack of knowledge of referral pathways were identified as factors hindering healthcare professionals’ ability to discuss weight with patients. Neutral terms, such as ‘weight’, were preferred by patients, with ‘fat’ and ‘obese’ viewed as undesirable and stigmatising words.

Summary
There is a need for greater support and provision of specific training, including education on communicating weight status, for those involved in the management of obesity. More research is necessary to assess the impact of interventions to improve initial discussions with patients about weight.
Health Status of Adults with Autism Spectrum Disorder
Authors: Forde Jennifer , Bonilla Paola Molina , Mannion Arlene , Coyne Rory , Haverty Ross , Leader Geraldine 
Published: May 2021

AbstractIndividuals diagnosed with autism spectrum disorder (ASD) have a higher probability of developing co-occurring mental or physical health conditions. However, little is known about how these health conditions develop and impact the lives of the adult ASD population. This paper reviewed existing studies concerning factors affecting the health status of adults with ASD and described their outcomes and prevalence. A systematic search of electronic databases yielded 21 studies eligible to be included. The most common physical problems affecting health were epilepsy and immune, gastrointestinal, and sleep disorders. Mental health disorders, most prominently mood and anxiety disorders and OCD, were also strong factors for determining health in adults. Future research should focus on measuring the overall health status of the adult ASD population.
Colexification Networks Encode Affective Meaning
Authors: Di Natale Anna , Pellert Max , Garcia David 
Published: May 2021

AbstractColexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks.