bluuebunny/crossref_metadata_2025_split huggingface dataset:
Growth, Productivity, and Essential Oil Content of Fennel Plants Treated with Spirulina Platensis Extract and Compost Tea Under Low Nitrogen Doses
Authors: Shawky Aya A. , Khalifa Gamal S. , Hegazi Amira , ElSherif Moustafa 
Published: April 2023

AbstractThe aim of this study was to find alternatives (even partial) to mineral nitrogen fertilization to reduce its harmful accumulation in the edible parts of fennel. Fennel (Foeniculum vulgare) is a perennial plant of the Apiaceae family grown for its edible shoots, leaves, and seeds. The present work was conducted in two successive seasons, in 2018/2019 and 2019/2020, at the farm of the Agricultural Botany Department, Faculty of Agriculture, Ain Shams University, Cairo, Egypt, to investigate the efficiency of application of two biostimulants—Spirulina platensis algae extract (SP) at 10% and compost tea (CT) at 6.72 ml L−1—with partial doses (75 and 50%) of the recommended mineral nitrogen fertilizer dose on growth, photosynthetic pigments, fruit yield, and essential oil productivity. The results indicated that vegetative parameters, leaf number, branch number, shoot and bulb fresh weight, and shoot dry weight, as well as yield components such as 100-fruit weight and number of umbels per plant were significantly enhanced with application of SP as a foliar spray coupled with 75% of the recommended dose of nitrogen fertilizer (SP + 75% N) as compared to other treatments. Photosynthetic pigments and total soluble carbohydrate were improved significantly when CT was applied as soil drenching coupled with 75% of the recommended N dose (CT + 75% N). In this regard, a clear effect was noticed in terms of oil yield and fruit yield using SP and CT with a 75% N dose. It is concluded that using Spirulina platensis algae extract and compost tea as biostimulants coupled with only 75% of the recommended nitrogen fertilizer dose can be recommended for economical fennel production.
Video description: A comprehensive survey of deep learning approaches
Authors: Rafiq Ghazala , Rafiq Muhammad , Choi Gyu Sang 
Published: April 2023

AbstractVideo description refers to understanding visual content and transforming that acquired understanding into automatic textual narration. It bridges the key AI fields of computer vision and natural language processing in conjunction with real-time and practical applications. Deep learning-based approaches employed for video description have demonstrated enhanced results compared to conventional approaches. The current literature lacks a thorough interpretation of the recently developed and employed sequence to sequence techniques for video description. This paper fills that gap by focusing mainly on deep learning-enabled approaches to automatic caption generation. Sequence to sequence models follow an Encoder–Decoder architecture employing a specific composition of CNN, RNN, or the variants LSTM or GRU as an encoder and decoder block. This standard-architecture can be fused with an attention mechanism to focus on a specific distinctiveness, achieving high quality results. Reinforcement learning employed within the Encoder–Decoder structure can progressively deliver state-of-the-art captions by following exploration and exploitation strategies. The transformer mechanism is a modern and efficient transductive architecture for robust output. Free from recurrence, and solely based on self-attention, it allows parallelization along with training on a massive amount of data. It can fully utilize the available GPUs for most NLP tasks. Recently, with the emergence of several versions of transformers, long term dependency handling is not an issue anymore for researchers engaged in video processing for summarization and description, or for autonomous-vehicle, surveillance, and instructional purposes. They can get auspicious directions from this research.
Robotic hand synergies for in-hand regrasping driven by object information
Authors: Dimou Dimitrios , Santos-Victor José , Moreno Plinio 
Published: April 2023

AbstractWe develop a conditional generative model to represent dexterous grasp postures of a robotic hand and use it to generate in-hand regrasp trajectories. Our model learns to encode the robotic grasp postures into a low-dimensional space, called Synergy Space, while taking into account additional information about the object such as its size and its shape category. We then generate regrasp trajectories through linear interpolation in this low-dimensional space. The result is that the hand configuration moves from one grasp type to another while keeping the object stable in the hand. We show that our model achieves higher success rate on in-hand regrasping compared to previous methods used for synergy extraction, by taking advantage of the grasp size conditional variable.
Incorporating potential environmental impacts in building seismic design decisions
Authors: Gonzalez Rosa E. , Stephens Max T. , Toma Charlotte , Dowdell David 
Published: April 2023

AbstractSeismic losses due to earthquakes have been shown to have significant economic, social and environmental consequences. Over recent years, research to predict potential economic and social impact due to seismic risk has been increasing. Recognizing that the traditional philosophy of life safety design can lead to extensive damage and demolition which has a large environmental cost, incorporating environmental impacts associated with the expected seismic damage over a building’s life is a key step as the building industry moves towards both sustainable and seismically resilient design. This paper introduces a framework that uses environmental indicators quantifying losses from seismic response that can then be used to advocate for a change in seismic performance objectives. First, existing literature and previously developed approaches for quantifying potential environmental impact due to seismic damage are summarized. Next, performance based earthquake engineering concepts are used to demonstrate a probabilistic approach to quantify potential environmental impacts using a range of environmental and resource use indicators over the life span of a case study building. In addition, a case study is presented to compare different environmental indicators between a Code Minimum building and the same building redesigned for a higher seismic performance. The majority of the composition of the environmental indicator values are from the inclusion of the non-repairable scenario, and from the repair activities, the majority of the impacts are from damage to drift sensitive components including curtains walls, partitions and elevators. For the Code Minimum building the non-repairable scenario contributes to between 8 to 11% the total seismic cost. For the Stronger Stiffer building, the non-repairable scenario contributes around 3% of the initial impact. Neglecting non-repairable scenarios does significantly reduce the potential environmental impacts when analyzing buildings designed for current code minimum structural standards.
On doubly symmetric periodic orbits
Authors: Frauenfelder Urs , Moreno Agustin 
Published: April 2023

AbstractIn this article, for Hamiltonian systems with two degrees of freedom, we studydoubly symmetricperiodic orbits, i.e., those which are symmetric with respect to two (distinct) commuting antisymplectic involutions. These are ubiquitous in several problems of interest in mechanics. We show that, in dimension four, doubly symmetric periodic orbits cannot be negative hyperbolic. This has a number of consequences: (1) All covers of doubly symmetric orbits aregood, in the sense of Symplectic Field Theory (Eliashberg et al. Geom Funct Anal Special Volume Part II:560–673, 2000); (2) a non-degenerate doubly symmetric orbit is stable if and only if its CZ-index is odd; (3) a doubly symmetric orbit doesnotundergo period doubling bifurcation; and (4) there is always a stable orbit in any collection of doubly symmetric periodic orbits with negativeSFT-Euler characteristic(as coined in Frauenfelder et al. in Symplectic methods in the numerical search of orbits in real-life planetary systems. PreprintarXiv:2206.00627). The above results follow from: (5) A symmetric orbit is negative hyperbolic if and only its twoB-signs(introduced in Frauenfelder and Moreno 2021) differ.
Does Fear of Crime Moderate the Relationship Between Low Self-Control and Victimization? An Empirical Study
Authors: Guedes Inês Sousa , Santos Margarida , Santos Gilda , Machado Mariana , Teixeira Nuno , Cardoso Carla Sofia 
Published: April 2023

Abstract
Taking into consideration the framework of the general theory of crime, research has been exploring the role of low self-control in the explanation of both crime perpetration and personal victimization. Moreover, while several studies have been analysing the role of victimization experiences in the fear of crime, less is known about how an individuals’ fear of crime influences the likelihood of being victimized. Therefore, using a sample of 243 individuals (55.6% women) from the University of Porto, through a self-reported survey, we tested (i) the effects of low self-control on victimization and (ii) the moderating role of fear of crime on the relationship between self-control and victimization. Results showed that low self-control was positively correlated with victimization. It was observed that fear of crime moderated the effect of low self-control on victimization, thus suggesting that the relationship between low self-control and victimization is not observed when fear of crime is high. Implications of the results are discussed.
Results of an open-label phase 1b study of the ERK inhibitor MK-8353 plus the MEK inhibitor selumetinib in patients with advanced or metastatic solid tumors
Authors: Stathis Anastasios , Tolcher Anthony W. , Wang Judy S. , Renouf Daniel J. , Chen Lin-Chi , Suttner Leah H. , Freshwater Tomoko , Webber Andrea L. , Nayak Tapan , Siu Lillian L. 
Published: April 2023

AbstractAim: We evaluated MK-8353 (small molecule inhibitor of extracellular signal-regulated kinase 1/2) plus selumetinib (mitogen-activated extracellular signal-regulated kinase 1/2 inhibitor) in patients with advanced solid tumors. Methods: This phase 1b, open-label, dose-escalation study (NCT03745989) enrolled adults with histologically/cytologically documented, locally advanced/metastatic solid tumors. MK-8353/selumetinib dose combinations were intended to be investigated in sequence: 50/25, 100/50, 150/75, 200/75, 200/100, and 250/100. Each agent was administered orally BID 4 days on/3 days off in repeating cycles every 21 days. Primary objectives were safety and tolerability and to establish preliminary recommended phase 2 doses for combination therapy. Results: Thirty patients were enrolled. Median (range) age was 61.5 (26−78) years and 93% had received previous cancer therapy. Among 28 patients in the dose-limiting toxicities [DLT]-evaluable population, 8 experienced DLTs: 1/11 (9%) in the MK-8353/selumetinib 100/50-mg dose level experienced a grade 3 DLT (urticaria), and 7/14 (50%) in the 150/75-mg dose level experienced grade 2/3 DLTs (n = 2 each of blurred vision, retinal detachment, vomiting; n = 1 each of diarrhea, macular edema, nausea, retinopathy). The DLT rate in the latter dose level exceeded the prespecified target DLT rate (~30%). Twenty-six patients (87%) experienced treatment-related adverse events (grade 3, 30%; no grade 4/5), most commonly diarrhea (67%), nausea (37%), and acneiform dermatitis (33%). Three patients (10%) experienced treatment-related adverse events leading to treatment discontinuation. Best response was stable disease in 14 patients (n = 10 with MK-8353/selumetinib 150/75 mg). Conclusion: MK-8353/selumetinib 50/25 mg and 100/50 mg had acceptable safety and tolerability, whereas 150/75 mg was not tolerable. No responses were observed.
Teachers’ online preparedness in times of crises: trends from Norway and US
Authors: Hathaway Dawn M. , Gudmundsdottir Greta B. , Korona Matthew 
Published: April 2023

AbstractThe closing of schools world-wide in March 2020 due to the COVID-19 pandemic resulted in a rapid and unexpected shift from predominantly in-person teaching to online teaching practices. As teacher educators in the field of educational technology, we wondered about the preparedness of teachers for making the transition to fully online environments. Through an internationally distributed survey consisting of predominantly open-ended questions, we captured teachers’ perceptions of this transition. We aimed to inform our practice and that of other teacher educators about the strengths and weaknesses of professional development designed to develop teachers’ digital competence. In this paper, we present data from Norwegian (n = 574) and US (n = 239) teachers related to their elaborations on readiness. We qualitatively examined data for evidence of extent of preparedness and alignment to the pedagogical, ethical, attitudinal, and technical dimensions of digital competence. Findings indicated themes related to extent of preparedness, trends in preparation, focus on digital tools, teacher agency without autonomy, collaboration/networks, and challenges for work and learning lives. Findings informed implications and recommendations for the professional development of teachers’ digital competence at the teacher education, K-12 schools, and school policy/leadership levels.
University teachers’ profiles based on digital innovativeness and instructional adaptation to COVID-19: Association with learning patterns and teacher demographics
Authors: Aldahdouh Tahani Z. , Murtonen Mari , Riekkinen Jere , Vilppu Henna , Nguyen Trang , Nokelainen Petri 
Published: April 2023

Abstract
The purpose of this study was to understand the factors behind university teachers’ ability to implement instructional changes during the COVID-19 pandemic. An online questionnaire comprised of open-ended and Likert-scale questions was administered to teachers at a Finnish university in April 2020. The sample consisted of 378 university teachers who were categorised into four groups based on their digital innovativeness and the extent to which they implemented changes to adapt their teaching practices to COVID-19 restrictions: Avoider Survival Adapters, Avoider Ambitious Adapters, Embracer Survival Adapters, and Embracer Ambitious Adapters. We examined the association between the teacher groups and their learning patterns and background characteristics. The findings showed that Embracer Ambitious Adapters have significantly more meaning-oriented and application-oriented learning patterns than Embracer Survival Adapters, though Avoider Survival Adapters have more problematic learning patterns. Furthermore, the results indicated that pedagogical training and having more teaching experience helped innovative teachers embrace more changes in their teaching practices during the COVID-19 pandemic. In terms of discipline, the results showed that teachers working in hard disciplines (e.g., physics) were more likely to belong to the Embracer Survival Adapters group, while teachers working in soft disciplines (e.g., history) were more likely to belong to the Embracer Ambitious Adapters group. Possible interpretations of these findings and perspectives for further research are discussed.
Children’s Classroom Experiences in Building Peer Relationships
Authors: Neri Tejada Jillianne , Li Liang , Hammer Marie 
Published: April 2023

AbstractThis study examines a child’s personal experiences with peer relationship building in the classroom and is guided by Vygotsky’s cultural historical concepts of the social situation of development and cultural tools and Hedegaard’s (2012) model for learning and development. Hedegaard’s (2012) dialectical—interactive approach was adopted to analyse the data for this study which was gathered using digital video observations of a grade one classroom within the western suburbs of Melbourne, Victoria, Australia over a five week period. This paper explores the strategies used by children that enables them to balance their desire for peer relationships while remaining within what is considered acceptable behaviours by their teachers within the classroom. It is argued that it is important for schools to continue to strive to find the balance between institutional demands while creating social situations that foster peer relationships.
Integrating numerical modelling and scenario-based sensitivity analysis for saltwater intrusion management: case study of a complex heterogeneous island aquifer system
Authors: Sharan Ashneel , Datta Bithin , Lal Alvin 
Published: April 2023

AbstractPopulation growth, industrialisation and increasing agricultural demands have significantly stressed groundwater resources in Pacific Island countries (PICs). Climate change and sea-level rise also affect the groundwater resources in PICs. These anthropogenic and natural factors give rise to saltwater intrusion (SWI), a major growing environmental problem in the PICs. SWI is a highly non-linear process which makes it more complex to manage. However, with the help of numerical modelling, SWI can be monitored, managed and controlled. In the present study, we used an illustrative study area where the hydrogeological parameters and other boundary conditions used are similar to the PICs aquifer systems in Vanuatu. The scenarios include changing the barrier wells, injection wells, recharge, hydraulic head, hydraulic conductivity and grid size. The numerical simulation model of the study area was developed, and different scenarios were tested using SEAWAT modules. Apart from salt, we also modelled leachate and engine oil present in the investigated study area to see how it affects the freshwater wells over time. The scenario-based sensitivity analysis tests indicate that injection wells, recharge and hydraulic conductivities are highly sensitive, and with the proper modification, SWI can be managed or regulated. The sensitivity of grid size showed that the simulated results varied within the 10% range of different gird sizes. Moreover, it was also found that the rise in sea level or coastal heads by 0.3–1 m does not significantly cause further SWI encroachment in aquifers. The results from this study are very crucial in this modern era when freshwater needs in coastal areas, especially PICs, are rapidly increasing, and fresh groundwater resources are declining. The novel outcome presented in this study opens pathways for further detailed modelling and numerical studies in the field of SWI management strategy development and is, therefore, beneficial for policymakers, groundwater modellers and general scientific communities.
Accuracy of UAV-based DEMs without ground control points
Authors: Szypuła Bartłomiej 
Published: April 2023

AbstractUnmanned aerial vehicles (UAVs) are increasingly used in various environmental research projects and other activities that require accurate topography images. The quality of elevation models derived from UAV measurements varies depending on many variables (e.g. UAV equipment used, terrain conditions, etc.). In order to improve the quality of digital models based on UAV image data, additional GNSS-RTK measurements are usually made at ground control points. The aim of this article is to evaluate the mathematical accuracy of terrain models created without ground control points. The accuracy of the models is considered in two directions: vertical and horizontal. Vertical (elevation) accuracy is calculated based on airborne laser scanning (ALS) data and horizontal (location) accuracy is calculated through comparison with high-resolution orthophotomaps. The average elevation accuracy of all created UAV-based DEMs is found to be 2.7–2.8 m (MAE), 3.1–3.3 m (RMSE), and the average horizontal accuracy is 2.1 m. Despite the low accuracy of the UAV models, the topography is reflected very well in the spatial images. This may be related to the regular and symmetrical distribution of height errors. To improve the accuracy parameters of UAV-based DEMs, it is proposed that they be rapidly georeferenced based on orthophotomaps.
A Logical Modeling of Severe Ignorance
Authors: Bonzio S. , Fano V. , Graziani P. , Pra Baldi M. 
Published: April 2023

AbstractIn the logical context, ignorance is traditionally defined recurring to epistemic logic. In particular, ignorance is essentially interpreted as “lack of knowledge”. This received view has - as we point out - some problems, in particular we will highlight how it does not allow to express a type of content-theoretic ignorance, i.e. an ignorance of φ that stems from an unfamiliarity with its meaning. Contrarily to this trend, in this paper, we introduce and investigate a modal logic having a primitive epistemic operator I, modeling ignorance. Our modal logic is essentially constructed on the modal logics based on weak Kleene three-valued logic introduced by Segerberg (Theoria, 33(1):53–71, 1997). Such non-classical propositional basis allows to define a Kripke-style semantics with the following, very intuitive, interpretation: a formula φ is ignored by an agent if φ is neither true nor false in every world accessible to the agent. As a consequence of this choice, we obtain a type of content-theoretic notion of ignorance, which is essentially different from the traditional approach. We dub it severe ignorance. We axiomatize, prove completeness and decidability for the logic of reflexive (three-valued) Kripke frames, which we find the most suitable candidate for our novel proposal and, finally, compare our approach with the most traditional one.
Improved Fracture Segmentation from Unwrapped Drill-Core Images Using an Innovative Two-Stage Segmentation Approach
Authors: Alzubaidi Fatimah , Nalinakumar Harikrishnan , Clark Stuart R. , Lie Jan Erik , Mostaghimi Peyman , Armstrong Ryan T. 
Published: April 2023

AbstractWhile machine learning (ML) provides a great tool for image analysis, obtaining accurate fracture segmentation from high-resolution core images is challenging. A major reason is that the segmentation quality of large and detailed objects, such as fractures, is limited by the capacity of the segmentation branch. This challenge can be seen in the Mask Region-based Convolutional Neural Network (Mask R-CNN), which is a common and well-validated instance segmentation model. This study proposes a two-stage segmentation approach using Mask R-CNN to improve fracture segmentation from unwrapped-core images. Two CNN models are used: the first model processes full-size unwrapped-core images to detect and segment fractures; the second model performs a more detailed segmentation by processing smaller regions of the images that include the fractures detected by the first model. In addition, the procedure uses a new architecture of Mask R-CNN with a point-based rendering (PointRend) neural network module that can increase segmentation accuracy. The method is evaluated on approximately 47 m of core from four boreholes and results in an improvement to previous fracture segmentation methods. It achieves an increase in the average intersection over union of approximately 27% from the baseline (one-stage segmentation with standard Mask R-CNN). The enhanced fracture segmentation provides a mean for obtaining an accurate fracture aperture with an average error of less than 1 mm, which represents a reduction of 0.5 mm from the baseline method. This work presents a novel contribution towards developing an ML-based workflow for core-image analysis.