bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cost-effectiveness of lures in attracting mammals: a large scale camera-trapping field test on European species
Authors: Mortelliti Alessio , Bergamin Riccardo , Bartolommei Paola , Greco Ilaria , Manzo Emiliano , Rovero Francesco , Fonda Federica 
Published: August 2024

AbstractThe cost-effectiveness of different attractants during camera trapping surveys has been seldom evaluated. To contribute in filling this knowledge gap we (1) compare the effectiveness of a suite of attractants in detecting widely distributed mammals in Europe and (2) evaluate the cost-effectiveness of these attractants, by calculating the costs associated to reach a specific monitoring objective. We conducted a large-scale field experiment across four study areas in central and northern Italy, encompassing a variety of environments, from lowland forest to alpine beech forest. We focused on comparing the following low cost and readily available attractants: sardines, peanut butter, a commercial lure and we used a camera with no attractant as control, collecting data on a suite of small to large mammals. We found that for seven of our 13 target species detectability varied with the type of attractant used. Specifically, sardines proved to be the most effective attractant for canids and the porcupine, peanut butter was most effective for mustelids but was avoided by the roe deer, whereas the commercial lure was the most effective with red deer. Through a power analysis combined with a cost function analysis we were able to show striking differences in the cost-effectiveness of the different methods, sometimes in the order of magnitude of tens of thousands of euros, which strongly emphasizes the critical importance played by the choice of whether to use an attractant or not and the type of attractant to be used.
Planning under uncertainty for safe robot exploration using Gaussian process prediction
Authors: Stephens Alex , Budd Matthew , Staniaszek Michal , Casseau Benoit , Duckworth Paul , Fallon Maurice , Hawes Nick , Lacerda Bruno 
Published: August 2024

AbstractThe exploration of new environments is a crucial challenge for mobile robots. This task becomes even more complex with the added requirement of ensuring safety. Here, safety refers to the robot staying in regions where the values of certain environmental conditions (such as terrain steepness or radiation levels) are within a predefined threshold. We consider two types of safe exploration problems. First, the robot has a map of its workspace, but the values of the environmental features relevant to safety are unknown beforehand and must be explored. Second, both the map and the environmental features are unknown, and the robot must build a map whilst remaining safe. Our proposed framework uses a Gaussian process to predict the value of the environmental features in unvisited regions. We then build a Markov decision process that integrates the Gaussian process predictions with the transition probabilities of the environmental model. The Markov decision process is then incorporated into an exploration algorithm that decides which new region of the environment to explore based on information value, predicted safety, and distance from the current position of the robot. We empirically evaluate the effectiveness of our framework through simulations and its application on a physical robot in an underground environment.
Low-density, water-repellent, and thermally insulating cellulose-mycelium foams
Authors: Amstislavski Philippe , Pöhler Tiina , Valtonen Anniina , Wikström Lisa , Harlin Ali , Salo Satu , Jetsu Petri , Szilvay Géza R. 
Published: August 2024

AbstractThis work explored whether partial cellulose bioconversion with fungal mycelium can improve the properties of cellulose fibre-based materials. We demonstrate an efficient approach for producing cellulose-mycelium composites utilizing several cellulosic matrices and show that these materials can match fossil-derived polymeric foams on water contact angle, compression strength, thermal conductivity, and exhibit selective antimicrobial properties. Fossil-based polymeric foams commonly used for these applications are highly carbon positive, persist in soils and water, and are challenging to recycle. Bio-based alternatives to synthetic polymers could reduce GHG emissions, store carbon, and decrease plastic pollution. We explored several fungal species for the biofabrication of three kinds of cellulosic-mycelium composites and characterized the resulting materials for density, microstructure, compression strength, thermal conductivity, water contact angle, and antimicrobial properties. Foamed mycelium-cellulose samples had low densities (0.058 – 0.077 g/cm3), low thermal conductivity (0.03 – 0.06 W/m∙K at + 10 °C), and high water contact angle (118 – 140°). The recovery from compression of all samples was not affected by the mycelium addition and varied between 70 and 85%. In addition, an antiviral property against active MS-2 viruses was observed. These findings show that the biofabrication process using mycelium can provide water repellency and antiviral properties to cellulose foam materials while retaining their low density and good thermal insulation properties.
Graphical Abstract
Entropy optimization of a FENE-P viscoelastic model: a numerically guided comprehensive analysis
Authors: Khan Razi , Rossi di Schio Eugenia , Valdiserri Paolo 
Published: August 2024

AbstractThe influence of polymers on entropy generation processes is substantial, particularly in the fields of fluid dynamics and rheology. The FENE-P (Finitely Extensible Nonlinear Elastic-Peterlin) model describes the polymer’s dynamics as a result of the interaction between the stretching caused by the velocity gradient and the elastic force that restores the polymer to its equilibrium position. Models such as FENE-P aid in understanding and predicting polymer flow behaviour allowing for the reduction of entropy generation by optimizing system designs. A continuum approach is employed to express the heat flux vector and polymer confirmation tensor of the model. The study investigates the complex relationship between polymer conformation, flow dynamics, and heat transfer taking into account the thermophoresis (Soret effect) and mass diffusion-thermal diffusion coupling (Dufour effect) phenomena to optimize processes by reducing entropy. This study illuminates polymer’s significance in entropy minimization, improving engineering design methodologies and applications in materials science, chemical engineering, and fluid dynamics. As result, the presence of polymers leads to a substantial decrease in the total entropy of the system. This understanding provides opportunities for enhancing heat transfer systems, thereby facilitating the development of more efficient and sustainable technology.
A performance indicator and its decomposition according to the impacts of different aspects based on distributional data
Authors: Crocetta Corrado , Irpino Antonio , Antonucci Laura , Marin Claudia 
Published: August 2024

AbstractIn this paper, we present a novel approach to customer satisfaction analysis of airport services based on the analysis of distributional data for constructing a bivariate performance indicator. Distributional data was introduced for describing macro-data coming from the aggregation of micro-data observed at the individual level. We use them to represent the distribution of the ratings given by 165 classes (macro-units) of airport customers for twelve observed aspects. We describe the trend of passenger satisfaction over time by extracting 165 macro units from a survey conducted among 13,047 passengers at Bari and Brindisi airports during the peak and off-peak seasons of 2015, 2016 and 2017. To obtain a performance indicator, we performed a multiple factor analysis for distributional data. To our knowledge, no other methods exist for the factor analysis of multiple distributional variables. Further, we propose a new visualization tool called Green Eye Iris plot, which allows a joint visualization of our set of distributional values. The obtained results show that the distributional data analysis approach can provide valuable information at macro level that could be hidden when analyzing micro-data or when macro data are represented only by some features coming from summary statistics of groups.
Electrical brain networks before and after transcranial pulsed shockwave stimulation in Alzheimer’s patients
Authors: Wojtecki Lars , Cont Celine , Stute Natalie , Galli Anastasia , Schulte Christina , Trenado Carlos 
Published: August 2024

AbstractAlzheimer’s disease (AD) is a neurodegenerative disorder that dramatically affects cognitive abilities and represents the most common cause of dementia. Currently, pharmacological interventions represent the main treatment to deal with the symptoms of AD; however, alternative approaches are readily sought. Transcranial pulse stimulation (TPS) is an emerging non-invasive neuromodulation technique that uses short, repetitive shockwaves with the potential to provide a wide range of vascular, metabolic, and neurotrophic changes and that has recently been shown to improve cognitive abilities in AD. This exploratory study aims to gain insight into the neurophysiological effect of one session of TPS in AD as reflected in electroencephalographic measures, e.g., spectral power, coherence, Tsallis entropy (TE), and cross-frequency coupling (cfc). We document changes in power (frontal and occipital), coherence (frontal, occipital and temporal), and TE (temporal and frontal) as well as changes in cfc (parietal-frontal, parietal-temporal, frontal–temporal). Our results emphasize the role of electroencephalographic measures as prospective markers for the neurophysiological effect of TPS.
Aerothermal characterization of the CALLISTO vehicle during descent
Authors: Ecker Tobias , Ertl Moritz , Klevanski Josef , Krummen Sven , Dumont Etienne 
Published: August 2024

AbstractAerothermal loads are a design driving factor during launcher development as the thermal loads directly influence thermal protection system (TPS) design and successively the possible flight trajectory and mission profiles. Recent developments in reusable launch vehicles (RLV) (e.g. SpaceX, Blue Origin) have added the dimension of refurbishment to the challenges the thermal design must consider. With the current European launcher roadmap moving towards a reusable first stage aerothermal loads may significantly change. The CALLISTO vehicle is a flight demonstrator for future reusable launcher stages and their technologies developed by a tri-national consortium and planned to fly in 2025. For this vehicle the highest heat fluxes are mainly due to heating from hot exhaust gases and heated air in the proximity of the aft bay and on the exposed structures like legs and fins. In the presented study we conducted computational fluid dynamics (CFD) studies to determine the aerothermal loads on the vehicle during descent through the landing approach corridor for both phase B and phase C aeroshapes. A defining difference to previous aerothermal databases (ATD) is that the CALLISTO demonstrator is planned to execute a series of test flights with different energy levels, as well as a final demo flight. This leads to an large parameter space the final aerothermal database needs to cover. The database development is described in detail and analysed for integral and local loads, as well as interpolation uncertainties. The final phase C database allows interpolation of interface heatfluxes for the entire flight domain (Mach number, density $$\rho )$$

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at varying angle of attack (AoA). Further the sensitivity of the plume-vehicle interaction to angle of attack, chemistry, thrust vector control (TVC) and engine throttling are investigated for a critical Mach number indicating further areas of improvement for future databases.
Defining and measuring quality in students’ mathematical writing: a systematic literature review
Authors: Teledahl Anna , Kilhamn Cecilia , Ahl Linda Marie , Helenius Ola 
Published: August 2024

AbstractMathematical communication, encompassing writing in, about, and for mathematics, is a critical competency. Defining excellent mathematical writing standards, however, remains challenging. To address this, we conducted a systematic review of 48 scholarly works on quality in mathematical writing. Our findings reveal mathematical writing for different purposes under scrutiny, including general mathematical writing, proof writing, reflective writing, expository writing, and descriptive writing during problem solving. To assess quality, researchers explore a variety of facets, such as syntax and semantics. Progression pathways vary, with both quantitative and qualitative evaluations—analysing text structure, writing style, and the use of different semiotic elements. It seems that in mathematics education, a consensus on quality measurement remains elusive. Proof writing is a notable exception. Among reviewed articles examining proof writing, a common set of standards emerges and provides valuable guidance. We propose that mathematical writing, perhaps especially in the context of reporting solutions in problem solving, can draw from proof writing standards. ‘Good’ mathematical writing would then require students to focus on (1) defining assumptions and assigning variables; (2) producing a coherent narrative, including relevant calculations (semantic issues); (3) using correct language, representations, and mathematical symbols (syntax issues); and (4) attending to what is appropriate in the context.
One-click annotation to improve segmentation by a convolutional neural network for PET images of head and neck cancer patients
Authors: Rainio Oona , Liedes Joonas , Murtojärvi Sarita , Malaspina Simona , Kemppainen Jukka , Klén Riku 
Published: August 2024

AbstractA convolutional neural network (CNN) can be used to perform fully automatic tumor segmentation from the positron emission tomography (PET) images of head and neck cancer patients but the predictions often contain false positive segmentation caused by the high concentration of the tracer substance in the human brain. A potential solution would be a one-click annotation in which a user points the location of the tumor by clicking the image. This information can then be given either directly to a CNN or an algorithm that fixes its predictions. In this article, we compare the fully automatic segmentation to four semi-automatic approaches by using 962 transaxial slices collected from the PET images of 100 head and neck cancer patients. According to our results, a semi-automatic segmentation method with information about the center of the tumor performs the best with a median Dice score of 0.708.
Aerogel-based collection of ejecta material from asteroids from libration point orbits: Dynamics and capture design
Authors: Burattini Carlo , Colombo Camilla , Trisolini Mirko 
Published: August 2024

AbstractScientific interest in asteroids and their physical characteristics is growing. These bodies provide insights into the primordial solar system and represent a valuable source of metals, silicates, and water. Several missions over the past few years have aimed to improve and better identify the main properties of these poorly known celestial bodies. However, these missions relied on touchdown(s) on the target asteroid to gather samples, which is complicated owing to the difficulty of accurately reaching and rendezvousing with the body. This study aims to assess the feasibility of an in-orbit asteroid sample collection mission. Such a strategy could prevent complex operations related to landing and touchdown maneuvers and avoid the dead times present in a mission requiring several landings. The presented collection scenario, which focuses on the asteroid Ryugu, proposes gathering samples using a spacecraft injected into a halo orbit around the second libration point, L2. For this purpose, the orbits in the neck region of the zero velocity curves are analyzed. A novel methodology to characterize bouncing behavior is introduced. An interpolation-based approach was used to recover the appropriate restitution coefficients for each collision occurring at a specific impact angle. This was applied to both the rigid body model and the point mass approximation studied for two different sites on the asteroid. Furthermore, the study enlarged the region of interest from only L2 to its neighboring zones to return a more global and realistic point of view. Considering the solar radiation pressure and asteroid aspherical potential, particles of different sizes ejected from different longitudes and with different ejection angles were classified according to their trajectories to finally build a database. Based on this analysis, an aerogel-based collection strategy inspired by that used in the Stardust-NExT (NASA) mission was investigated to assess its possible applicability to the analyzed scenario.
Predicting the compressive strength of self-compacting concrete using artificial intelligence techniques: a review
Authors: Terlumun Sesugh , Onyia M. E. , Okafor F. O. 
Published: August 2024

AbstractConcrete is one of the most common construction materials used all over the world. Estimating the strength properties of concrete traditionally demands extensive laboratory experimentation. However, researchers have increasingly turned to predictive models to streamline this process. This review focuses on predicting the compressive strength of self-compacting concrete using artificial intelligence (AI) techniques. Self-compacting concrete represents an advanced construction material particularly suited for scenarios where traditional vibrational methods face limitations due to intricate formwork or reinforcement complexities. This review evaluates various AI techniques through a comparative performance analysis. The findings highlight that employing Deep Neural Network models with multiple hidden layers significantly enhances predictive accuracy. Specifically, artificial neural network (ANN) models exhibit robustness, consistently achieving R2 values exceeding 0.7 across reviewed studies, thereby demonstrating their efficacy in predicting concrete compressive strength. The integration of ANN models is recommended for formulating various civil engineering properties requiring predictive capabilities. Notably, the adoption of AI models reduces both time and resource expenditures by obviating the need for extensive experimental testing, which can otherwise delay construction activities.
Application of machine learning in predicting mechanical properties of sandcrete blocks made from quarry dust: a review
Authors: Braimah John Igeimokhia , Ajagbe Wasiu Olabamiji , Olonade Kolawole Adisa 
Published: August 2024

AbstractQuarry dust, conventionally considered waste, has emerged as a potential solution for sustainable construction materials. This paper comprehensively review the mechanical properties of blocks manufactured from quarry dust, with a particular focus on the transformative role of machine learning (ML) in predicting and optimizing these properties. By systematically reviewing existing literature and case studies, this paper evaluates the efficacy of ML methodologies, addressing challenges related to data quality, feature selection, and model optimization. It underscores how ML can enhance accuracy in predicting mechanical properties, providing a valuable tool for engineers and researchers to optimize the design and composition of blocks made from quarry dust. This synthesis of mechanical properties and ML applications contributes to advancing sustainable construction practices, offering insights into the future integration of technology for predictive modeling in material science.
Evaluating the performance of implementing regionally coordinating bus priority signals under different control schemes
Authors: Al-Hyasat Esra’a , Alhadidi Taqwa I. 
Published: August 2024

AbstractBus Rapid Transit (BRT) proves its effectiveness in alleviating traffic congestion, especially in urban areas. The implementation of Transit Signal Priority (TSP) for BRT has shown a significant reduction in delays. However, in densely populated urban areas, this priority can inadvertently cause additional delays for other modes of transportation. In this paper, we propose a control strategy for Regionally Coordinating Bus Priority Signals Control (RCBPSC) at urban intersections. The aim is not only to reduce bus delays but also to consider minimizing delays for pedestrians and other vehicles. To achieve this, we modeled two consecutive intersections along the Amman BRT. Essentially, we evaluated three different control scenarios in addition to the current base scenario. These scenarios include adaptive traffic signal control, RCBPSC with no signal timing optimization, and RCBPSC with signal optimization. Simulation results indicate that the adaptive traffic signal timing has the worst operational performance in terms of average delay and Level of Service (LOS) compared to the base scenario. Additionally, the results show that BRT delays significantly decrease at both intersections when we implement RCBPSC scenarios. When implementing RCBPSC with optimization scenarios, the results show an average reduction of more than 60% in intersection delay, a decrease in emissions of more than 50%, and an improved LOS for system users compared to the base scenario. The findings of this work can help agencies improve the current operational condition of BRT when implementing RCBPSC.
RH-ECBS: enhanced conflict-based search for MRPP with region heuristics
Authors: Pan Zhangchao , Wang Runhua , Bi Qingchen , Zhang Xuebo , Yu Jingjin 
Published: August 2024

Abstract
This paper proposes a novel two-layer framework based on conflict-based search and regional divisions to improve the efficiency of multi-robot path planning. The high-level layer targets the reduction of conflicts and deadlocks, while the low-level layer is responsible for actual path planning. Distinct from previous dual-level search frameworks, the novelties of this work are (1) subdivision of planning regions for each robot to decrease the number of conflicts encountered during planning; (2) consideration of the number of robots in the region during planning in the node expansion stage of A*, and (3) formal proof demonstrating the nonzero probability of the proposed method in obtaining a solution, along with providing the upper bound of the solution in a special case. Experimental comparisons with Enhanced Conflict-Based Search demonstrate that the proposed method not only reduces the number of conflicts but also achieves a computation time reduction of over 30%.