bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rotational pasture management to increase the sustainability of mountain livestock farms in the Alpine region
Authors: Baronti Silvia , Ungaro Fabrizio , Maienza Anita , Ugolini Francesca , Lagomarsino Alessandra , Agnelli Alessandro Elio , Calzolari Costanza , Pisseri Francesca , Robbiati Giorgia , Vaccari Francesco Primo 
Published: March 2022

AbstractOver the past 30 years, farming in the Alpine region has undergone important changes: the average number of animals per farm and the use of external inputs have increased while the diversity of farming practices has decreased, becoming similar to intensive farming. This change has led to a reduction in the supply of agroecosystem services and the sustainability of the mountain livestock sector. In this study, we investigated rotational grazing as alternative to continuous grazing to improve the sustainability of mountain farming practices. Greenhouse gas (GHG) emissions such as carbon dioxide, methane and nitrous oxide were measured together with soil properties (bulk density, saturated hydraulic conductivity, organic carbon content and plant biomass) for two grazing seasons using static chambers. The results showed that rotational grazing had a positive impact on plant biomass: minimize soil disturbance, reduce compaction and GHG emissions of the soil and increase water infiltration. Therefore, this practice has revealed clear benefits in terms of soil protection and climate change mitigation and adaptation.
Too expensive to keep — bidding farewell to an iconic mountain glacier?
Authors: Abermann Jakob , Theurl Manuel , Frei Elisabeth , Hynek Bernhard , Schöner Wolfgang , Steininger Karl W. 
Published: March 2022

AbstractHumans currently perceive glaciers as icons of a pristine high mountain landscape. Glaciers attract millions of visitors every year and add to building Alpine identity, but also provide ecosystem services. Using Austria’s largest glacier as an example, we show that abundant water availability would allow artificial management to conserve the glacier under ongoing climate change, although costs in the order of €108 per year exceed threefold related total visitor tourism revenues. While we present a theoretical experiment that due to economic reasons most likely never will be realized, we quantify (un-)feasibility and discuss potential environmental constraints.
The nucleolus and inheritance of properties in communication situations
Authors: Schouten J. , Dietzenbacher B. , Borm P. 
Published: March 2022

AbstractThis paper studies the nucleolus of graph-restricted games as an alternative for the Shapley value to evaluate communication situations. We focus on the inheritance of properties of cooperative games related to the nucleolus: strong compromise admissibility and compromise stability. These two properties allow for a direct, closed formula for the nucleolus. We characterize the families of graphs for which the graph-restricted games inherit these properties from the underlying games. Moreover, for each of these two properties, we characterize the family of graphs for which the nucleolus is invariant
How can we make born-digital and digitised archives more accessible? Identifying obstacles and solutions
Authors: Jaillant Lise 
Published: March 2022

AbstractAccess to data is seen as a key priority today. Yet, the vast majority of digital cultural data preserved in archives is inaccessible due to privacy, copyright or technical issues. Emails and other born-digital collections are often uncatalogued, unfindable and unusable. In the case of documents that originated in paper format before being digitised, copyright can be a major obstacle to access. To solve the problem of access to digital archives, cross-disciplinary collaborations are absolutely essential. The big challenges of our time—from global warming to social inequalities—cannot be solved within a single discipline. The same applies to the challenge of “dark” archives closed to users. We cannot expect archivists or digital humanists to find a magical solution that will instantly make digital records more accessible. Instead, we need to set up collaborations across disciplines that seldom talk to each other. Based on 21 interviews with 26 archivists, librarians and other professionals in cultural institutions, we identify key obstacles to making digitised and born-digital collections more accessible to users. We outline current levels of access to a wide range of collections in various cultural organisations, including no access at all and limited access (for example, when users are required to travel on-site to consult documents). We suggest possible solutions to the problems of access—including the ethical use of Artificial Intelligence to unlock “dark” archives inaccessible to users. Finally, we propose the creation of a global user community who would participate in decisions on access to digital collections.
Distinguishing liquid ammonia from sodium hydroxide mercerization in cotton textiles
Authors: Manian Avinash P. , Braun Doris E. , Široká Barbora , Bechtold Thomas 
Published: March 2022

AbstractThe question addressed in this paper is whether cotton textiles mercerized with liquid ammonia (L-NH3) can be distinguished from NaOH mercerized cotton textiles with FTIR-ATR spectroscopy. In collaboration with a process-house, cotton textiles were treated with L-NH3, NaOH, neither or both. The treatment conditions were set to mimic commercial-scale operations. The samples were then analyzed with FTIR-ATR spectroscopy, and for comparison, also with powder X-ray diffraction. The analyses were aimed at detecting changes in the cellulose crystal lattice structure that result from the two mercerization processes. The measured spectra and diffractograms were evaluated both visually and with Principal Component Analysis (PCA), and the results show that with FTIR-ATR spectroscopy it was possible to differentiate between the untreated, L-NH3 mercerized and NaOH mercerized textiles, whereas with X-ray diffraction it was difficult to detect differences between the untreated and treated samples. The better performance of FTIR-ATR spectroscopy is attributed to the method being more sensitive to changes on sample surfaces, whereas X-ray diffraction results are more representative of the sample bulk. The samples were treated under conditions mimicking commercial-scale operations, which are optimized to maximize treatment speed. Hence, the mercerization induced changes are likely to be limited to sample surfaces and hence detectable with FTIR-ATR spectroscopy but not with X-ray diffraction. It is reported that Sum Frequency Generation Vibrational Spectroscopy, another surface-limited analytical method, may also be employed for detection of mercerization type, but FTIR-ATR spectroscopy has the advantages of ease of operation and the devices are less expensive.
Air quality in post-mining towns: tracking potentially toxic elements using tree leaves
Authors: Monaci Fabrizio , Ancora Stefania , Paoli Luca , Loppi Stefano , Franzaring Jürgen 
Published: March 2022

AbstractIn this study, leaves of the evergreen holm oak Quercus ilex were used to assess airborne contamination of potentially toxic elements (PTEs) at five towns located on the slopes of the Mt. Amiata (central Italy), an area with a long history of mining and, more recently, an important district for the industrial exploitation of geothermal energy. PTE composition and covariance of washed and unwashed Q. ilex leaves of three different ages (6, 12 and 24 month-old) were used to identify atmospheric inputs of PTEs at residential areas, evaluate long-term adsorption and retention of PTEs by the leaves, thus providing an indication of potential human exposure. Moreover, the determination of foliar concentrations of major elements (C, N, S and P) allowed an assessment of the nutritional status of the investigated urban tree stands which excluded the existence of stress condition caused by air pollution or other disturbances. Results indicated that overall Pb, Cu, and Cd concentration were low in the investigated urban sites, if compared with similar studies conducted in larger Italian cities, denoting a low contribution of vehicular traffic to the atmospheric pathway. The five urban settlements were characterized by a specific profile of elements (Al, Ba, Hg and Sb) enriched in unwashed leaves, resulting from the distinct geochemical characteristics of the area and from diffuse (i.e., urban activity) and point sources of PTEs emission (i.e., brownfields, geothermal power plants). The latter sources primarily govern the distribution of Hg, whose contamination was found to be very localized close to a major abandoned mining area. Our data provided quantitative evidence of the spectrum of PTEs potentially impacting resident population and may prove useful in support of follow-up instrumental monitoring campaigns of air quality, as well as for human health and ecological risk assessments.
Graphical abstract
AMAIX In-Depth: A Generic Analytical Model for Deep Learning Accelerators
Authors: Zurstraßen Niko , Jünger Lukas , Kogel Tim , Keding Holger , Leupers Rainer 
Published: March 2022

AbstractIn recent years the growing popularity of Convolutional Neural Network(CNNs) has driven the development of specialized hardware, so called Deep Learning Accelerator (DLAs). The large market for DLAs and the huge amount of papers published on DLA design show that there is currently no one-size-fits-all solution. Depending on the given optimization goals such as power consumption or performance, there may be several optimal solutions for each scenario. A commonly used method for finding these solutions as early as possible in the design cycle, is the employment of analytical models which try to describe a design by simple yet insightful and sufficiently accurate formulas. The main contribution of this work is the generic Analytical Model for AI accelerators (AMAIX) for the estimation of CNN execution time on DLAs. It is based on the popular Roofline model. To show the validity of our approach, AMAIX was applied to the Nvidia Deep Learning Accelerator (NVDLA) as a case study using the AlexNet and LeNet CNNs as workloads. The resulting performance predictions were verified against an RTL emulation of the NVDLA using a Synopsys ZeBu Server-based hybrid prototype. By refining the model following a divide-and-conquer paradigm, AMAIX predicted the inference time of AlexNet and LeNet on the NVDLA with an accuracy 98%. Furthermore, this work shows how to use the obtained results for root-cause analysis and as a starting point for design space exploration.
Quadratic Growth Conditions and Uniqueness of Optimal Solution to Lasso
Authors: Bello-Cruz Yunier , Li Guoyin , Nghia Tran Thai An 
Published: March 2022

AbstractIn the previous paper Bello-Cruz et al. (J Optim Theory Appl 188:378–401, 2021), we showed that the quadratic growth condition plays a key role in obtaining Q-linear convergence of the widely used forward–backward splitting method with Beck–Teboulle’s line search. In this paper, we analyze the property of quadratic growth condition via second-order variational analysis for various structured optimization problems that arise in machine learning and signal processing. This includes, for example, the Poisson linear inverse problem as well as the$$\ell _1$$ℓ1-regularized optimization problems. As a by-product of this approach, we also obtain several full characterizations for the uniqueness of optimal solution to Lasso problem, which complements and extends recent important results in this direction.
The costs of collaborative innovation
Authors: Vivona Roberto , Demircioglu Mehmet Akif , Audretsch David B. 
Published: March 2022

AbstractCollaborations between actors from different sectors (governments, firms, nonprofit organizations, universities, and other societal groups) have been promoted or mandated with increasing frequency to spur more innovative activities. This article argues that there is an essential gap in evaluating the issues of these collaborative arrangements on innovation and a need to theorize the costs of these arrangements systematically. This article identifies three implicit assumptions in current research that prevent a sound analysis of the costs of collaborative innovation and advances a new cost theory based on the integration of studies from several research fields and explanations provided by three main economic theories: transaction cost economics, game theory, and the knowledge-based view. In particular, four overarching factors are posited to impact the effectiveness of collaboration for innovation:governance(the number of collaborators and the hierarchical relationships among them);compactness(the degree of relationship formality that binds collaborators together);reliability(the quality of the relationships); andinstitutionalization(the extent to which the relationships have been pre-established by practice). We discuss the importance of leveraging these factors to determine an optimal governance structure that allows collaborating actors to minimize transaction, cooperation, and knowledge costs, and to reward participants proportionally to the cost they bear, in order to foster conditions of reciprocity, fair rates of exchange, and distributive justice.
On the tax efficiency of startup firms
Authors: Allen Eric J. , Allen Jeffrey C. , Raghavan Sharat , Solomon David H. 
Published: March 2022

AbstractWe examine the choice of organizational structure for VC-backed startup firms. These firms overwhelmingly organize as C-corporations rather than as tax advantaged limited liability companies (LLCs). This results in foregone tax savings of $43.9 billion, or 4.9% of the total equity invested in the sample firms. The decision is puzzling, given plausible estimates of the direct costs involved, but appears related to “hassle” and other transition costs generated by participants implementing a new form. Firms with more employees and investors are likely to choose the C-corporation. VCs appear to prefer the C-corporation form, as receiving VC money is associated with most LLC firms switching to a C-corporation within 30 days. Greater VC preferences for C-corporations are linked to a preference for familiarity, and less attention to taxes.
Video-Based Action Learning and Research: Increased Transformative Capacity among Team Leaders of a Youth Care Protection Agency
Authors: Bunders Arnout Ernst , de Wit Emma Emily , Dinkgreve Marcus Antonius Henricus Maria , Broerse Jacqueline Elisabeth Willy , Regeer Barbara Johanna 
Published: March 2022

AbstractPublic organizations need to learn and evolve continuously to keep up with emerging complexities. This may require a transformational organizational change, including culture, strategy, structure and working methods for service delivery. The aim of the study is to understand how a video-based reflection method could support an action learning (AL) and action research (AR) process to enhance transformative capacity among a group of team leaders in a changing youth care organization. Sixteen team leaders participated in the video-reflection process. The steps were: (1) all leaders were filmed leading a team meeting; (2) each leader reflected on a peer’s video; and (3) reflections were analyzed, and themes were abstracted. (4) Related to these themes, video fragments were compiled to share with the leaders in Focus Group Discussions (FGDs), where the leaders discussed the videos and formulated key lessons. (5) Key lessons were summarized in a report. Data collection included: video-recordings, evaluation sheets and field notes acquired during the process of video-reflection and seven months later, interviews with team leaders (n = 11) about their learning experiences. This study shows greater transformative capacity in most of the team leaders. Awareness of their work practice, as well as the process of reflexive monitoring during the FGDs, contributed to widening the scope of their reflective reality, and thus their sense of agency in adapting their practices, such as moderating their occasionally controlling behavior, being able to structure the team meetings effectively, and understanding when to use specific leadership roles.
HDNN: a cross-platform MLIR dialect for deep neural networks
Authors: Martínez Pablo Antonio , Bernabé Gregorio , García José Manuel 
Published: March 2022

AbstractThis paper presents HDNN, a proof-of-concept MLIR dialect for cross-platform computing specialized in deep neural networks. As target devices, HDNN supports CPUs, GPUs and TPUs. In this paper, we provide a comprehensive description of the HDNN dialect, outlining how this novel approach aims to solve the $$P^3$$

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3

problem of parallel programming (portability, productivity, and performance). An HDNN program is device-agnostic, i.e., only the device specifier has to be changed to run a given workload in one device or another. Moreover, HDNN has been designed to be a domain-specific language, which ultimately helps programming productivity. Finally, HDNN relies on optimized libraries for heavy, performance-critical workloads. HDNN has been evaluated against other state-of-the-art machine learning frameworks on all the hardware platforms achieving excellent performance. We conclude that the ideas and concepts used in HDNN can be crucial for designing future generation compilers and programming languages to overcome the challenges of the forthcoming heterogeneous computing era.
Effects of DGAT1 on milk performance in Sudanese Butana × Holstein crossbred cattle
Authors: Elzaki Salma , Korkuć Paula , Arends Danny , Reissmann Monika , Brockmann Gudrun A. 
Published: March 2022

AbstractThe improvement of milk production of indigenous Sudanese cattle such as Bos indicus Butana and its cross with Holstein is a major goal of the Sudanese government to ensure sufficient healthy nutrition in the country. In this study, we investigated the K232A polymorphism of diacylglycerol acyltransferase (DGAT1), a well-known modulator of milk production in other breeds. We determined allele frequencies and the allele effects on milk production. Therefore, 93 purebred Butana and 203 Butana × Holstein crossbred cattle were genotyped using competitive allele-specific PCR assays. Association analysis was performed using a linear mixed model in R. In purebred Butana cattle, the lysine DGAT1 protein variant K232, which is found to be associated with higher fat and protein contents, as well as higher fat yield was highly frequent at 0.929, while its frequency in Butana × Holstein crossbred cattle was 0.394. Significant effects were found on milk yield (P = 7.6 × 10−20), fat yield (P = 2.2 × 10−17), protein yield (P = 2.0 × 10−19) and lactose yield (P = 4.0 × 10−18) in crossbred cattle. As expected, the protein variant K232 was disadvantageous since it was decreasing milk, protein, and lactose yields by 1.741 kg, 0.063 kg and 0.084 kg, respectively. No significant effects were found for milk fat, protein, and lactose contents. The high frequency of the lysine DGAT1 protein variant K232 in Butana cattle could contribute to their high milk fat content in combination with low milk yield. In Butana × Holstein crossbred cattle, the DGAT1 marker can be used for effective selection and thus genetic improvement of milk production.
Eliminating Temporal Illumination Variations in Whisk-broom Hyperspectral Imaging
Authors: Funatomi Takuya , Ogawa Takehiro , Tanaka Kenichiro , Kubo Hiroyuki , Caron Guillaume , Mouaddib El Mustapha , Matsushita Yasuyuki , Mukaigawa Yasuhiro 
Published: March 2022

AbstractWe propose a method for eliminating the temporal illumination variations in whisk-broom (point-scan) hyperspectral imaging. Whisk-broom scanning is useful for acquiring a spatial measurement using a pixel-based hyperspectral sensor. However, when it is applied to outdoor cultural heritages, temporal illumination variations become an issue due to the lengthy measurement time. As a result, the incoming illumination spectra vary across the measured image locations because different locations are measured at different times. To overcome this problem, in addition to the standard raster scan, we propose an additional perpendicular scan that traverses the raster scan. We show that this additional scan allows us to infer the illumination variations over the raster scan. Furthermore, the sparse structure in the illumination spectrum is exploited to robustly eliminate these variations. We quantitatively show that a hyperspectral image captured under sunlight is indeed affected by temporal illumination variations, that a Naïve mitigation method suffers from severe artifacts, and that the proposed method can robustly eliminate the illumination variations. Finally, we demonstrate the usefulness of the proposed method by capturing historic stained-glass windows of a French cathedral.
Speciation in nanosecond laser ablation of zinc in water
Authors: Reich Stefan , Klügl Yannis , Ziefuss Anna , Streubel René , Göttlicher Jörg , Plech Anton 
Published: March 2022

AbstractIn situ experimental methods have been applied to resolve mass flow and chemical speciation in the pulsed laser ablation of zinc in water. The chemical speciation has been resolved by time-resolved μ-X-ray absorption spectroscopy and mapped onto the macroscopic mass flow during material ejection from the metallic target and bubble dynamics of evaporated water. Large particles and agglomerates have been detected via dark-field X-ray imaging with a Shack-Hartmann sensor. The characteristic of the dynamics is that the vapor bubble is nearly homogeneously filled with ablated material. This persists during bubble collapse, which means that the ablated particles are captured and retracted towards the target. Limited mass escape is indicated by the X-ray absorption signal. Importantly, the near-edge structure at the Zn-Kα transition delivers information on the chemical state of the ejected material. It clearly confirms that oxidation is not present within the bubble phase and the following sub-millisecond time scale. The oxidation proceeds on Zn nanoparticles in suspension on a second to minute course. Within the first microseconds, a Zn atom phase is detected that resembles Zn vapor. The addition of either reductive NaBH4 or oxidative HAuCl4 to the water phase influences the quantity of the atom contribution moderately, but does not influence the initial atom phase. Such behavior must be understood in terms of the nanosecond pulse excitation. After ejected material and a plasma is formed within the pulse duration of 7 ns the laser is able to further heat the ejecta and transform it partly into vapor. Correspondingly, the coupling of energy into the ablation zone as followed by plasma intensity and bubble size follows a threshold behavior as a function of laser fluence, marking the onset of laser-plasma heating. The reaction conditions inside the bubble are probably reductive due to the concomitant formation of excess hydrogen.
Mechanics of wedge turns in alpine skiing
Authors: Komissarov Serguei S. 
Published: March 2022

AbstractA simple approximate theory of snow machining is applied to modelling successive wedge turns of alpine skiing. The model involves predefined control functions describing skier’s control over the turns via angle of attack, edge angle and loading of the skis. To demonstrate the model’s potential, reasonable control functions with a small number of free parameters are designed and used in attempt to reproduce the data obtained in a previous field study by other researches. The results are in semi-quantitative agreement with the data. In particular, the model explains the nature of the abnormally high values for the “coefficient of friction” deduced in that study. Future field studies of wedge turns should aim at measuring the angle of attack, edge angle and loading of the skis. This will allow to determine the control functions from the experimental data and hence to conduct a more stringent verification of the model.