bluuebunny/crossref_metadata_2025_split huggingface dataset:
Contribution of Community-Based Ecotourism to Forest Conservation and Local Livelihoods
Authors: Ota Tetsuji , Lonn Pichdara , Mizoue Nobuya 
Published: January 2021

AbstractTropical forests significantly contribute to local livelihoods as well as global carbon storage and biodiversity conservation. Therefore, a strategy that harmonizes a better quality of life for local people with tropical forest conservation is required. In Community-based ecotourism (CBET), the local community participates in related economic activities. In this chapter, we summarize our current studies that quantified the contribution of CBET to the income and livelihoods of local people and forest conservation. We selected the Chambok CBET site in Cambodia for our case study. First, we quantified the effectiveness of CBET in forest conservation by analyzing forest cover change with published maps created from satellite images. Second, we evaluated the contribution of CBET to household income and livelihood changes through a household survey using a questionnaire. Analysis of the forest cover change maps revealed that deforestation had significantly decreased inside the CBET area as compared to outside it, although the reduction was not enough to stop net deforestation. The survey revealed that the total monthly income of CBET member households and non-CBET households was not significantly different. It also showed that the community members felt the livelihood change after the implementation of CBET. However, this change may have been caused by general socioeconomic changes in Cambodia. We conclude that CBET effectively contributed to forest conservation but in a limited capacity to household income.
Machinic Phylum and Architecture
Authors: Radman Andrej 
Published: January 2021

AbstractThe chapter draws on the anti-substantivist and anti-hylomorphic legacy of two significant Deleuze and Guattari’s interlocutors: Raymond Ruyer and Gilbert Simondon. Ruyer vehemently opposed the logic of mechanicism without regressing to (active) vitalism. His masterpieceNeofinalism, yet to be fully appreciated in architectural circles, is an ode to multiplicity or ‘absolute form’. The title is to be read as a challenge to the hegemony of the step-by-step causation and partes-extra-partes mereology. According to Ruyer, non-locality is the key, not only to the question of subjectivity, but to the problem of life itself. Simondon too shies away from the metaphysics of presence. For him, the process of individuation cannot be grasped on the basis of the fully formed individual. In other words, the knowledge of individuationisthe individuation of knowledge. Simondon’s highest ambition inOn the Mode of Existence of Technical Objectswas to integrate culture and technics (tekhne). The conviction that culture need not be antagonistic to technology is particularly pertinent to the ecologies of architecture. In the second half of the chapter, the affordance theory meets contemporary neurosciences.
A Performance-Based Urban Block Generative Design Using Deep Reinforcement Learning and Computer Vision
Authors: Han Zhen , Yan Wei , Liu Gang 
Published: January 2021

AbstractIn recent years, generative design methods are widely used to guide urban or architectural design. Some performance-based generative design methods also combine simulation and optimization algorithms to obtain optimal solutions. In this paper, a performance-based automatic generative design method was proposed to incorporate deep reinforcement learning (DRL) and computer vision for urban planning through a case study to generate an urban block based on its direct sunlight hours, solar heat gains as well as the aesthetics of the layout. The method was tested on the redesign of an old industrial district located in Shenyang, Liaoning Province, China. A DRL agent - deep deterministic policy gradient (DDPG) agent - was trained to guide the generation of the schemes. The agent arranges one building in the site at one time in a training episode according to the observation. Rhino/Grasshopper and a computer vision algorithm, Hough Transform, were used to evaluate the performance and aesthetics, respectively. After about 150 h of training, the proposed method generated 2179 satisfactory design solutions. Episode 1936 which had the highest reward has been chosen as the final solution after manual adjustment. The test results have proven that the method is a potentially effective way for assisting urban design.
The Development of ‘Agent-Based Parametric Semiology’ as Design Research Program
Authors: Duan Xuexin 
Published: January 2021

AbstractA new framework, agenda and practice is called for to address the challenges and opportunities architecture must confront in the age of our computationally empowered Post-Fordist network society. This paper introduces the research agenda of ‘agent-based parametric semiology’, and explains the necessity of introducing a new tool, agent-based life-process modelling, as part of the design process, in order to cope with the new complexity and dynamism of architecture’s social functionality. The paper reviews the development of this design research program over the last 10 years. Finally, the paper describes current efforts to move from the illustrative use of life-process modelling to a scientifically grounded quantitative analysis and generative design optimization.
Machine Learning Aided 2D-3D Architectural Form Finding at High Resolution
Authors: Zhang Hang , Huang Ye 
Published: January 2021

AbstractIn the past few years, more architects and engineers start thinking about the application of machine learning algorithms in the architectural design field such as building facades generation or floor plans generation, etc. However, due to the relatively slow development of 3D machine learning algorithms, 3D architecture form exploration through machine learning is still a difficult issue for architects. As a result, most of these applications are confined to the level of 2D. Based on the state-of-the-art 2D image generation algorithm, also the method of spatial sequence rules, this article proposes a brand-new strategy of encoding, decoding, and form generation between 2D drawings and 3D models, which we name 2D-3D Form Encoding WorkFlow. This method could provide some innovative design possibilities that generate the latent 3D forms between several different architectural styles. Benefited from the 2D network advantages and the image amplification network nested outside the benchmark network, we have significantly expanded the resolution of training results when compared with the existing form-finding algorithm and related achievements in recent years.
Research on Architectural Form Optimization Method Based on Environmental Performance-Driven Design
Authors: Song Jinghua , Sun Sirui 
Published: January 2021

AbstractIn the context of contemporary environment and society, the architectural form optimization based on Environmental performance-driven design is a method by using environmental performance data to optimize the architectural form. Its value lies in dealing with the interaction between architecture and environment, and developing architecture with environmental sustainability. This thesis summarizes the similarities and differences between performance-driven form design and traditional bionic form design. The traditional bionic design separates the bionic object from its complex living environment, and its simple imitation tends to fall into the local rather than the global optimum. However, performance-driven design is different from bionic design. It advocates environmental factors as a driving factor rather than a confrontational factor. It is a systematic global optimal method for studying architectural form. This paper puts forward the specific architectural form optimization simulation process based on the performance-driven thought. Taking the multilayer parking building design of the riparian zone on the south bank of Chongqing as an example, the parametric design method is used to obtain architectural optimization form adapted to the environment.
Machine Learning for Fabrication of Graded Knitted Membranes
Authors: Sinke Yuliya , Gatz Sebastian , Tamke Martin , Ramsgaard Thomsen Mette 
Published: January 2021

AbstractThis paper examines the use of machine learning in creating digitally integrated design-to-fabrication workflows. As computational design allows for new methods of material specification and fabrication, it enables direct functional grading of material at high detail thereby tuning the design performance in response to performance criteria. However, the generation of fabrication data is often cumbersome and relies on in-depth knowledge of the fabrication processes. Parametric models that set up for automatic detailing of incremental changes, unfortunately, do not accommodate the larger topological changes to the material set up. The paper presents the speculative case studyKnitVault. Based on earlier research projectsIsoropiaandOmbre, the study examines the use of machine learning to train models for fabrication data generation in response to desired performance criteria.KnitVaultdemonstrates and validates methods for shortcutting parametric interfacing and explores how the trained model can be employed in design cases that exceed the topology of the training examples.
On-Site BIM-Enabled Augmented Reality for Construction
Authors: Chernick Adam , Morse Christopher , London Steve , Li Tim , Ménard David , Cerone John , Pasquarelli Gregg 
Published: January 2021

AbstractWe describe a prototype system for communicating building information and models directly to on-site general contractors and subcontractors. The system, developed by SHoP Architects, consists of a workflow of pre-processing information within Revit, post-processing information outside of Revit, combining data flows inside of a custom application built on top of Unity Reflect, and delivering the information through a mobile application on site with an intuitive user interface. This system incorporates augmented reality in combination with a dashboard of documentation views categorized by building element.
Apophysenschäden im Sport
Authors: Neubauer Markus , Nehrer Stefan 
Published: January 2021

ZusammenfassungDie Zahl an Jugendlichen und Kindern im Spitzen- oder hochintensiven Breitensport ist – bezogen auf Industrienationen – im Steigen begriffen. Hochintensives Training kann Überlastungen durch die vermehrte Zugwirkung besonders auf Sehnen- und Muskelinsertionsstellen bedingen. Apophysen als Ossifikationskerne in Sehnen- und Muskelinsertionen sind bei Heranwachsenden besonders vulnerabel für überlastungsbedingte Pathologien. Zentrale Maßnahmen in der Prävention sind eine systematische Trainingsplanung und die Vermeidung mechanischer Überbeanspruchung im Wachstumsalter. Eine exakte Bildgebung ermöglicht die Diagnose von Frühstadien apophysärer Strukturschäden, die in dieser Phase durch Trainingspause und konservative Maßnahmen geheilt werden können.
Phosphorylated Tau protein in the myenteric plexus of the ileum and colon of normothermic rats and during synthetic torpor
Authors: Chiocchetti R , Hitrec T , Giancola F , Sadeghinezhad J , Squarcio F , Galiazzo G , Piscitiello E , De Silva M , Cerri M , Amici R , Luppi M 
Published: January 2021

AbstractTau protein is of primary importance for neuronal homeostasis and when hyperphosphorylated (PP-Tau), it tends to aggregate in neurofibrillary tangles, as is the case with tauopathies, a class of neurodegenerative disorders. Reversible PP-Tau accumulation occurs in the brain of hibernating rodents and it was recently observed in rats (a non-hibernator) during synthetic torpor (ST), a pharmacological-induced torpor-like condition. To date, the expression of PP-Tau in the rat enteric nervous system (ENS) is still unknown. The present study immunohistochemically investigates the PP-Tau expression in the myenteric plexus of the ileum and colon of normothermic rats (CTRL) and during ST, focusing on the two major subclasses of enteric neurons, i.e., cholinergic and nitrergic.Results showed that both groups of rats expressed PP-Tau, with a significantly increased percentage of PP-Tau immunoreactive (IR) neurons in ST vs. CTRL. In all rats, the majority of PP-Tau-IR neurons were cholinergic. In ST rats, the percentage of PP-Tau-IR neurons expressing a nitrergic phenotype increased, although with no significant differences between groups. In addition, the ileum of ST rats showed a significant decrease in the percentage of nitrergic neurons. In conclusion, our findings suggest an adaptive response of ENS to very low core body temperatures, with changes involving PP-tau expression in enteric neurons, especially the ileal nitrergic subpopulation. In addition, the high presence of PP-Tau in cholinergic neurons, specifically, is very interesting and deserves further investigation. Altogether, these data strengthen the hypothesis of a common cellular mechanism triggered by ST, natural hibernation and tauopathies occurring in ENS neurons.
Comparison of nine different commercially available molecular assays for detection of SARS-CoV-2 RNA
Authors:   for the Bavarian SARS-CoV-2-Public Health Laboratory Team, Eberle Ute , Wimmer Clara , Huber Ingrid , Neubauer-Juric Antonie , Valenza Giuseppe , Ackermann Nikolaus , Sing Andreas 
Published: January 2021

AbstractTo face the COVID-19 pandemic, the need for fast and reliable diagnostic assays for the detection of SARS-CoV-2 is immense. We describe our laboratory experiences evaluating nine commercially available real-time RT-PCR assays. We found that assays differed considerably in performance and validation before routine use is mandatory.
Fenchel Duality Theory and a Primal-Dual Algorithm on Riemannian Manifolds
Authors: Bergmann Ronny , Herzog Roland , Silva Louzeiro Maurício , Tenbrinck Daniel , Vidal-Núñez José 
Published: January 2021

AbstractThis paper introduces a new notion of a Fenchel conjugate, which generalizes the classical Fenchel conjugation to functions defined on Riemannian manifolds. We investigate its properties, e.g., the Fenchel–Young inequality and the characterization of the convex subdifferential using the analogue of the Fenchel–Moreau Theorem. These properties of the Fenchel conjugate are employed to derive a Riemannian primal-dual optimization algorithm and to prove its convergence for the case of Hadamard manifolds under appropriate assumptions. Numerical results illustrate the performance of the algorithm, which competes with the recently derived Douglas–Rachford algorithm on manifolds of nonpositive curvature. Furthermore, we show numerically that our novel algorithm may even converge on manifolds of positive curvature.
Coastal ocean acidification and nitrogen loading facilitate invasions of the non-indigenous red macroalga, Dasysiphonia japonica
Authors: Young Craig S. , Gobler Christopher J. 
Published: January 2021

AbstractCoastal ecosystems are prone to multiple anthropogenic and natural stressors including eutrophication, acidification, and invasive species. While the growth of some macroalgae can be promoted by excessive nutrient loading and/or elevatedpCO2, responses differ among species and ecosystems. Native to the western Pacific Ocean, the filamentous, turf-forming rhodophyte,Dasysiphonia japonica,appeared in estuaries of the northeastern Atlantic Ocean during the 1980s and the northwestern Atlantic Ocean during the late 2000s. Here, we report on the southernmost expansion of theD. japonicain North America and the effects of elevated nutrients and elevatedpCO2on the growth ofD. japonicaover an annual cycle in Long Island, New York, USA. Growth limitation of the macroalga varied seasonally. During winter and spring, when water temperatures were < 15 °C, growth was significantly enhanced by elevatedpCO2(p < 0.05). During summer and fall, when the water temperature was 15–24 °C, growth was significantly higher under elevated nutrient treatments (p < 0.05). When temperatures reached 28 °C, the macroalga grew poorly and was unaffected by nutrients orpCO2. The δ13C content of regional populations ofD. japonicawas −30‰, indicating the macroalga is an obligate CO2-user. This result, coupled with significantly increased growth under elevatedpCO2when temperatures were < 15 °C, indicates this macroalga is carbon-limited during colder months, when in situpCO2was significantly lower in Long Island estuaries compared to warmer months when estuaries are enriched in metabolically derived CO2. The δ15N content of this macroalga (9‰) indicated it utilized wastewater-derived N and its N limitation during warmer months coincided with lower concentrations of dissolved inorganic N in the water column. Given the stimulatory effect of nutrients on this macroalga and that eutrophication can promote seasonally elevatedpCO2, this study suggests that eutrophic estuaries subject to peak annual temperatures < 28 °C may be particularly vulnerable to future invasions ofD. japonicaas ocean acidification intensifies. Conversely, nutrient reductions would serve as a management approach that would make coastal regions more resilient to invasions by this macroalga.
Genetic worth of multiple sets of cowpea breeding lines destined for advanced yield testing
Authors: Ongom Patrick Obia , Fatokun Christian , Togola Abou , Oyebode Oluwaseye Gideon , Ahmad Mansur Sani , Jockson Ishaya Daniel , Bala Garba , Boukar Ousmane 
Published: January 2021

AbstractThe objective of this study was to determine genetic potentials in eight sets of cowpea lines for grain yield (GY), hundred seed weight (HSDWT) and days to 50% flowering (DT50FL). A total of 614 F6 genotypes constituting the sets, grouped by maturity, were evaluated across two locations in Northern Nigeria, in an alpha lattice design, two replications each. Data were recorded on GY, HSDWT and DT50FL.Variance components, genotypic coefficient of variation (GCV), and genetic advance (GA) were used to decode the magnitude of genetic variance within and among sets. Genetic usefulness (Up) which depends on mean and variance to score the genetic merits in historically bi-parental populations was applied to groups of breeding lines with mixed parentage. Principal component analysis (PCA) was used to depict contribution of traits to observed variations. GY and DT50FL explained the variance within and between sets respectively. Genotypes were significantly different, although genotype-by-location and set-by-location interaction effects were also prominent. Genetic variance (δ2G) and GCV were high for GY in Prelim2 (δ2G = 45,897; GCV = 19.58%), HSDWT in Prelim11 (δ2G = 7.137; GCV = 17.07%) and DT50F in Prelim5 (δ2G = 4.54; GCV = 4.4%). Heritability varied among sets for GY (H = 0.21 to 0.57), HSDWT (H = 0.76 to 0.93) and DT50FL (H = 0.20 to 0.81). GA and percentage GA (GAPM) were high for GY in Prelim2 (GAPM = 24.59%; GA = 269.05Kg/ha), HSDWT in Prelim11 (GAPM = 28.54%; GA = 4.47 g), and DT50F in Prelim10 (GAPM = 6.49%; GA = 3.01 days). These sets also registered high values of genetic usefulness, suggesting potential application in non-full sib populations. These approaches can be used during preliminary performance tests to reinforce decisions in extracting promising lines and choose among defined groups of lines.
Large Deviation Properties of the Empirical Measure of a Metastable Small Noise Diffusion
Authors: Dupuis Paul , Wu Guo-Jhen 
Published: January 2021

AbstractThe aim of this paper is to develop tractable large deviation approximations for the empirical measure of a small noise diffusion. The starting point is the Freidlin–Wentzell theory, which shows how to approximate via a large deviation principle the invariant distribution of such a diffusion. The rate function of the invariant measure is formulated in terms of quasipotentials, quantities that measure the difficulty of a transition from the neighborhood of one metastable set to another. The theory provides an intuitive and useful approximation for the invariant measure, and along the way many useful related results (e.g., transition rates between metastable states) are also developed. With the specific goal of design of Monte Carlo schemes in mind, we prove large deviation limits for integrals with respect to the empirical measure, where the process is considered over a time interval whose length grows as the noise decreases to zero. In particular, we show how the first and second moments of these integrals can be expressed in terms of quasipotentials. When the dynamics of the process depend on parameters, these approximations can be used for algorithm design, and applications of this sort will appear elsewhere. The use of a small noise limit is well motivated, since in this limit good sampling of the state space becomes most challenging. The proof exploits a regenerative structure, and a number of new techniques are needed to turn large deviation estimates over a regenerative cycle into estimates for the empirical measure and its moments.
Innovation drivers of external competitiveness in the great recession
Authors: Brancati Emanuele , Brancati Raffaele , Guarascio Dario , Zanfei Antonello 
Published: January 2021

AbstractThis paper explores the main drivers of firms’ external competitiveness in times of crisis. We focus on the aftermath of the Great Recession (2008–2015) and present evidence based on a comprehensive survey of Italian companies (the MET dataset). Overall, our results highlight not only the strict correlation between internationalization and innovative activities but also a positive change of attitude of Italian firms towards these strategies. We show that, while structural factors play a key role for external competitiveness, other critical aspects trigger superior performances, especially strategic profiles, technological capabilities, and proactive behaviors such as innovativeness and R&D investment. Importantly, we document disproportionate effects of innovation for smaller and less productive companies. This points at dynamic strategies as a potential tool to fill the gap between larger/more productive companies and the set of less structured firms, a segment representing an ideal target for policy measures.
Rethinking SME default prediction: a systematic literature review and future perspectives
Authors: Ciampi Francesco , Giannozzi Alessandro , Marzi Giacomo , Altman Edward I. 
Published: January 2021

AbstractOver the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007–2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment.
The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.