Academic literature on the topic 'Mosgiel Limited (N.Z.)'

Abstract A splittable good provided in n pieces shall be divided as evenly as possible among m agents, where every agent can take shares from at most F pieces. We call F the fragmentation and mainly restrict attention to the cases $$F=1$$F=1 and $$F=2$$F=2. For $$F=1$$F=1, the max–min and min–max problems are solvable in linear time. The case $$F=2$$F=2 has neat formulations and structural characterizations in terms of weighted graphs. First we focus on perfectly balanced solutions. While the problem is strongly NP-hard in general, it can be solved in linear time if $$m\ge n-1$$m≥n-1, and a solution always exists in this case, in contrast to $$F=1$$F=1. Moreover, the problem is fixed-parameter tractable in the parameter $$2m-n$$2m-n. (Note that this parameter measures the number of agents above the trivial threshold $$m=n/2$$m=n/2.) The structural results suggest another related problem where unsplittable items shall be assigned to subsets so as to balance the average sizes (rather than the total sizes) in these subsets. We give an approximation-preserving reduction from our original splitting problem with fragmentation $$F=2$$F=2 to this averaging problem, and some approximation results in cases when m is close to either n or n / 2.

AbstractWhether net primary productivity in an aquatic ecosystem is limited by nitrogen (N), limited by phosphorus (P), or co-limited by N & P is determined by the relative supply of N and P to phytoplankton compared to their elemental requirements for primary production, often characterized by the “Redfield” ratio. The supply of these essential nutrients is affected by both external inputs and biogeochemical processes within the ecosystem. In this paper, we examine external sources of nutrients to aquatic systems and how the balance of N to P inputs influences nutrient limitation. For ocean subtropical gyres, a relatively balanced input of N and P relative to the Redfield ratio from deep ocean sources often leads to near co-limitation by N and P. For lakes, the external nutrient inputs come largely from watershed sources, and we demonstrate that on average the N:P ratio for these inputs across the United States is well above that needed by phytoplankton, which may contribute to P limitation in those lake that experience this average nutrient loading. Watershed inputs are also important for estuaries and coastal marine ecosystems, but ocean sources of nutrients are also significant contributors to overall nutrient loads. The ocean-nutrient sources of N and P are very often at or below the Redfield ratio of 16:1 molar, and can be substantially so, particularly in areas where the continental shelf is wide. This large input of coastal ocean nutrients with a low N:P ratio is one factor that may make N limitation more likely in many coastal marine ecosystems than in lakes.

Abstract In this paper, we explore orthogonal systems in $$\mathrm {L}_2({\mathbb R})$$L2(R) which give rise to a real skew-symmetric, tridiagonal, irreducible differentiation matrix. Such systems are important since they are stable by design and, if necessary, preserve Euclidean energy for a variety of time-dependent partial differential equations. We prove that there is a one-to-one correspondence between such an orthonormal system $$\{\varphi _n\}_{n\in {\mathbb Z}_+}$${φn}n∈Z+ and a sequence of polynomials $$\{p_n\}_{n\in {\mathbb Z}_+}$${pn}n∈Z+ orthonormal with respect to a symmetric probability measure $$\mathrm{d}\mu (\xi ) = w(\xi ){\mathrm {d}}\xi $$dμ(ξ)=w(ξ)dξ. If $$\mathrm{d}\mu $$dμ is supported by the real line, this system is dense in $$\mathrm {L}_2({\mathbb R})$$L2(R); otherwise, it is dense in a Paley–Wiener space of band-limited functions. The path leading from $$\mathrm{d}\mu $$dμ to $$\{\varphi _n\}_{n\in {\mathbb Z}_+}$${φn}n∈Z+ is constructive, and we provide detailed algorithms to this end. We also prove that the only such orthogonal system consisting of a polynomial sequence multiplied by a weight function is the Hermite functions. The paper is accompanied by a number of examples illustrating our argument.

Background: This randomized clinical trial was conducted to assess whether sleep bruxism (SB) is associated with an increased rate of technical complications (ceramic defects) in lithium disilicate (LiDi) or zirconia (Z) molar single crowns (SCs). Methods: Adult patients were classified as affected or unaffected by SB based on structured questionnaires, clinical signs, and overnight portable electromyography (BruxOff) and block randomized into four groups according to SB status and crown material (LiDi or Z): LiDi-SB (n = 29), LiDi-no SB (n = 24), Z-SB (n = 23), and Z-no SB (n = 27). Differences in technical complications (main outcome) and survival and success rates (secondary outcomes) one year after crown cementation were assessed using Fisher’s exact test with significance level α = 0.05. Results: No technical complications occurred. Restoration survival rates were 100% in the LiDi-SB and LiDi-no SB groups, 95.7% in the Z-SB group, and 96.3% in the Z-no SB group (p > 0.999). Success rates were 96.6% in the LiDi-SB group, 95.8% in the LiDi-no SB group (p > 0.999), 91.3% in the Z-SB group, and 96.3% in the Z-no SB group (p ≥ 0.588). Conclusions: With a limited observation time and sample size, no effect of SB on technical complication, survival, and success rates of molar LiDi and Z SCs was detected.

AbstractThe chronology of bulge and disk formation is a major unsolved issue in galaxy formation, which impacts on our global understanding of the Hubble sequence. We present colours of the nuclear regions of intermediate-redshift disk galaxies, with the aim of obtaining empirical information of ages of bulges at intermediate z. We work with a sample of 248 galaxies (123 inclined + 125 face-on) from the HST Groth Strip Survey (Groth et al. 1994) and another one with 404 objects (214 inclined + 190 face-on) from the HST GOODS-N field (Giavalisco et al. 2004), covering redshifts 0.1 < z < 1.3. Those samples are apparent-diameter limited at R > 1.4″. We find that, as in the Local Universe, the minor axis color profiles are negative (bluer outward), and fairly gentle, indicating that bulge colours are not distinctly different from disk colours. We apply a conservative criterion to identify bulges and potential precursors of present-day bulges, based on nuclear excess surface brightness above the exponential profile of the outer parts. For galaxies with central brightness excesses, rest-frame colour distributions show a red sequence that confirms the finding of very red bulges by Koo et al. (2005), using independent methods. In contrast, galaxies without central brightness excesses show typical colours of star-forming populations. Clearly, something had truncated star formation in many high-density cores, already at z ~ 1. The truncation epoch is uncertain, 1.5 < z < 10. The colour-magnitude distribution of intermediate-z bulges shows more colour dispersion than that of bulges in the Local Universe. About 50% of bulges are as red as local bulges, while the remainder are significantly bluer, a possible sign of late bulge formation. We also find that bulge colours correlate with integrated galaxy colours and with their disk colours.

Novel, functionalized piperazine derivatives were successfully synthesized and fully characterized by 1H/13C/19F NMR, MS, elemental analysis and lipophilicity. All piperazine compounds occur as conformers resulting from the partial amide double bond. Furthermore, a second conformational shape was observed for all nitro derivatives due to the limited change of the piperazine chair conformation. Therefore, two coalescence points were determined and their resulting activation energy barriers were calculated using 1H NMR. To support this result, single crystals of 1-(4-nitrobenzoyl)piperazine (3a, monoclinic, space group C2/c, a = 24.587(2), b = 7.0726(6), c = 14.171(1) Å, β = 119.257(8)°, V = 2149.9(4) Å3, Z = 4, D obs = 1.454 g/cm3) and the alkyne derivative 4-(but-3-yn-1-yl)-1-(4-fluorobenzoyl)piperazine (4b, monoclinic, space group P21/n, a = 10.5982(2), b = 8.4705(1), c = 14.8929(3) Å, β = 97.430(1)°, V = 1325.74(4) Å3, Z = 4, D obs = 1.304 g/cm3) were obtained from a saturated ethyl acetate solution. The rotational conformation of these compounds was also verified by XRD. As proof of concept for future labeling purposes, both nitropiperazines were reacted with [18F]F–. To test the applicability of these compounds as possible 18F-building blocks, two biomolecules were modified and chosen for conjugation either using the Huisgen-click reaction or the traceless Staudinger ligation.

Electrochemical impedance spectroscopy analysis for porous electrodes in energy conversion and storage, described as resistance and capacitance, is widely used to understand their electrochemical processes. These processes include: (i) electronic resistance inside the electrode (R e), (ii) ionic resistance in the electrolyte bulk and inside the porous electrode (R ion), (iii) electrical double layer formation (C dl) and charge transfer resistance at the electrode electrolyte interface (R ct) , and (iv) diffusion for charge compensation. As one of the mathematical models in electrochemical impedance spectroscopy for porous electrodes, a transmission line model is known, in which the pore structure is described as a cylinder geometry.1 We have proposed an analytical method that combines the transmission line model with electrochemical impedance spectroscopy using a symmetric cell,2 and have investigated resistance separation in lithium-ion batteries and its effect on battery performances.3-5 Due to mathematical constraints, the transmission line model is limited to a representation of an ideal, uniform process model. Here we propose a mathematical model based on a network model consisting of a staircase structure for electrochemical impedance spectroscopy at porous electrodes that can describe not only ideal and uniform processes but also non-ideal and non-uniform processes (Fig. 1), and aims to help elucidate their borderline behavior.6 The proposed staircase model (Z SCM) gives a series equivalent circuit consisting of Z 0, which is a series circuit of electrode/electrolyte interface impedance (Z int0) and electronic resistance (R e0), and electrolyte resistance (R ion0) as an initial step without pores (Z SCM0 = R ion(0) + Z 0). The one-step model (Z SCM1) is then given a series equivalent circuit consisting of the parallel circuit of Z SCM0 and Z 1 and the electrolyte resistance R ion(1) in series (Z SCM1 = R ion(1) + (Z SCM0 −1+Z1 −1)−1). The staircase model is calculated by incorporating the 1 prior step (Fig. 2). Thus, the model with n steps (Z SCM(n)) is composed of a series equivalent circuit of the parallel circuit of Z SCM(n−1) and Z n and the electrolyte resistance R ion(n) (Z SCM(n) = R ion(n) + (Z SCM(n−1) −1 +Z n −1)−1). Fig. 3 shows Nyquist plots computed for each input parameter at 50 steps for non-Faradic and Faradic processes in the staircase model. The results are in good agreement with the profiles calculated for the transmission electric model.2 The presentation will further show examples of the calculated results using the staircase model for non-ideal and inhomogeneous processes and discuss the analysis of impedance behavior using real electrodes reflecting non-uniform processes. References R. de Levie, Electrochim. Acta, 9, 1231-1245 (1964). N. Ogihara, S. Kawauchi, C. Okuda, Y. Itou, Y. Takeuchi and Y. Ukyo, J. Electrochem. Soc., 159, A1034-A1039 (2012). N. Ogihara, Y. Itou, T. Sasaki and Y. Takeuchi, J. Phys. Chem. C, 119, 4612-4619 (2015). N. Ogihara, Y. Itou and S. Kawauchi, J. Phys. Chem. Lett., 10, 5013-5018 (2019). Y. Itou, N. Ogihara and S. Kawauchi, J. Phys. Chem. C, 124, 5559-5564 (2020). N. Ogihara and Y. Itou, Phys. Chem. Chem. Phys., 24, 21863-21871 (2022). Figure 1

This research employed various statistical techniques, including linear regression, nonparametric regression, Naive Bayes classification, decision tree analysis, Support Vector Machine (SVM) analysis, k-means clustering, and Bayesian regression, to analyze nuclear data. The research aims to explore the relationships between variables, predict binding energy, classify nuclear data, and identify similar groups. The research results revealed that linear regression indicated a significant influence of the intercept and predictor variable 'n' on the variable 'BE4DBE2,' while the variable 'z' was not significant. However, the overall model had limited explanatory power. Nonparametric regression with smoothing functions effectively modeled the relationship between 'BE4DBE2' and variables 'n' and 'z,' explaining approximately 11% of the variability in the response variable. Classification using Naive Bayes successfully categorized nuclear data based on 'n' and 'z,' revealing their relationship. Decision tree analysis evaluated the performance of this classification model and provided insights into accuracy, agreement, sensitivity, specificity, precision, and negative predictive value. SVM analysis successfully built an accurate SVM model with a linear kernel, classifying nuclear data while depicting decision boundaries and support vectors. K-means clustering grouped nuclear data based on 'n' and 'z,' revealing distinct characteristics and enabling the identification of similar clusters. The Bayesian regression model predicted binding energy using 'n' and 'z' as independent variables, capturing the Gaussian distribution of 'BE4DBE2' and providing statistical measures for parameter estimation. Ccomprehensives nuclear data analysis using various statistical approaches provides valuable insights into relationships, predictions, classification, and clustering, contributing to the advancement of nuclear science and facilitating further research in this field.

Water has an exceptional ability to dissolve minerals. It is safe and chemically stable, and it remains liquid over a wide temperature range. Thus, it is the best solvent and reaction medium for both laboratory and industrial purposes. Water is able to dissolve ionic and ionocovalent solids because of the high polarity of the molecule (dipole moment μ = 1.84 Debye) as well as the high dielectric constant of the liquid (ε = 78.5 at 25°C). This high polarity allows water to exhibit a strong solvating power: that is, the ability to fix onto ions as a result of electrical dipolar interactions. Water is also an ionizing liquid able to polarize an ionocovalent molecule. For example, the solvolysis phenomenon increases the polarization of the HCl molecule in aqueous solution. Finally, owing to the high dielectric constant of the liquid, water is a dissociating solvent that can decrease the electrostatic forces between solvated cations and anions, allowing their dispersion as H+solvated and Cl−solvated through the liquid. (The attractive force F between charges q and q′ separated by the distance r is given by Coulomb’s law, F = qq′/εr2.) These characteristics are rarely found together in common liquids. The dipole moment of the ethanol molecule (μ = 1.69 Debye) is close to that of water, but the dielectric constant of ethanol is much lower (ε = 24.3). Ethanol is a good solvating liquid, but a poor dissociating one; consequently, it is considered a bad solvent of ionic compounds. Dissolution in water of an ionic solid such as sodium chloride is limited to dipolar interactions with Na+ and Cl− ions and their dispersion in the liquid as solvated ions, regardless of the pH of the solution. Cations with higher charge, especially cations of transition metals, retain a fixed number of water molecules, thereby forming a true coordination complex [M(OH2)N]z+ with a well-defined geometry. In addition to the dipolar interactions, water molecules behave as true ligands because they are Lewis bases exerting an electron σ-donor effect on the empty orbitals of the cation.

Resistivity log is used to measure the formation resistivity from which the water saturation is estimated. Because of the spatial resolution, the log resistivity Rapp (the apparent resistivity) is not the true formation resistivity when the bed is thin and/or the bed is neighbored with conductive beds. Iterative forward modeling inversion is applied to estimate the formation resistivity from the log resistivity. The inversion has to assume an earth model. Iterative forward modeling inversion is time consuming by nature. The inversion is empirical as the regularization coefficient has to be fixed empirically. We developed a deconvolution method using Machine Learning technique to invert the resistivity log accurately and fast while the bed boundaries are automatically inverted accurately within 0.5-ft that is the sampling interval of the log data. We used Machine Learning regressors to minimize errors in the true resistivity at depth z, R(z), from 2N+1 sets of log data {Rapp(z  k) , k=0, 1, …,N} where  is the sampling interval of log data, 0.5 ft. For training dataset, an earth model extending over 7,500 ft was created where the bed thickness and the bed resistivity were randomly selected in log-uniform distribution. There were more than 15,000 data points. For testing datasets three earth models extending over 1,200 ~ 1,500 ft were similarly created. The 2C40 induction log was considered for the resistivity log and the deconvolution window size N was varied from 3 to 60. Several ML regressors were used for comparison and for consistency. We found the deconvolution method to invert the resistivity log accurately and fast. Furthermore, the bed boundaries were automatically inverted very accurately within 0.5 ft, the sampling interval of log data. For 2C40 induction log, the 21-point (N=10) window over 10ft around the center of the tool worked satisfactorily for the log data in vertical wells. We used different training datasets and testing datasets, and reached the same conclusion. The Machine Learning technique has not been applied to deconvolution method. This empirical deconvolution method can invert the resistivity log accurately and fast. Most remarkably, the bed boundaries are automatically inverted very accurately within 0.5 ft. The method is not limited to resistivity logs.

Background: There is limited information about effects of transcranial Direct Current Stimulation(tDCS), delivered within the first weeks post-stroke, on performance of the paretic upper limb and on connectivity between motor areas in the affected and unaffected hemispheres. Objectives: We compared changes in Fugl-Meyer Assessment of Motor Recovery(FMA) scores, connectivity between the primary motor cortex of the unaffected(M1UH) and the affected hemisphere(M1AH), as well as between M1UH and the premotor cortex of the unaffected hemisphere(PMUH) before and after 6 sessions of cathodal tDCS targeting the primary motor cortex of the unaffected hemisphere(M1UH) early after stroke in 13 patients. Methods: This hypothesis-generating substudy was a randomized parallel, two-arm, double-blind, sham-controlled clinical trial performed at the Albert Einstein Hospital. Subjects were randomized active(N=6) or sham(N=7) groups. Results: Clinically relevant differences in FMA scores(≥ 9 points) were observed more often in the sham than in the active group. Between-group differences in changes in FMA scores were not statistically significant(Mann-Whitney test, p=0.133) but the effect size was -0.619(rank biserial correlation). Connectivity measures(Fisher’s z- transform of ROI-to-ROI correlations) between M1AH-M1UH increased in 5/6 participants in the active, and in 2/7 in the sham group after treatment. Between-group differences in changes in connectivity(M1UH-M1AH or PMUH-M1AH) were not statistically significant. In contrast with M1AH-M1UH connectivity, improvements in motor performance were more frequent in the active than in the sham group. Conclusions: Effects of cathodal tDCS on motor performance and on Resting-state Functional Magnetic Resonance Imaging may have distinct underpinnings in subjects at an early stage after stroke.

Driver monitoring systems (DMS) are designed to track drivers’ attention status, accumulate real-time data, and intervene when symptoms of fatigue or distraction are observed, thereby enhancing driving safety [1]. In vehicles equipped with partial driving automation [2], the driver’s role necessitates constant attention to road conditions and monitoring of dynamic driving task (DDT). These vehicles usually feature an advanced driver-assistance system (ADAS), but due to limited understanding of ADAS functionalities, drivers might develop an overreliance on these systems. This could potentially lead to misuse or distractions [3]. Consequently, DMS assists in preventing traffic accidents by supporting drivers in their responsibilities, which includes providing responses in instances of driver negligence.The effectiveness of a DMS is directly related to the user’s willingness to share data such as facial images and vehicle behavior. Moreover, it’s often seen that privacy concerns inversely affect the readiness of users to disclose personal information [4]. Unlike technological domain, users’ perspective regarding DMS utilization has been under-explored. This study aims to gather insights into Chinese drivers’ attitudes towards DMS privacy issues and their willingness to adopt this technology. These findings will help evaluate the future prospects of DMS implementation. To facilitate this goal, the existing DMS systems are categorized into four types based on their primary features: facial image-based DMS, electroencephalogram signals-based DMS, electrocardiogram signals-based DMS, and vehicle behavior-based DMS.A one-way between-subjects design was conducted to investigate the influence of various DMS types on psychological perception and behavioral intention using an online survey (N = 486). Each participant was randomly assigned to one of four DMS type conditions. The questionnaire commenced with a succinct introduction to the relevant DMS type, including its name, functions, and methods of data collection. Subsequently, participants were asked to express their agreement or disagreement with 19 items across seven dimensions (data sensitivity, collection concern, secondary use, perceived insecurity, perceived usefulness, trust, and behavioral intention) about the involved DMS on a Likert scale ranging from 1 (totally disagree) to 7 (totally agree). The questionnaire ended up with demographic questions.All demographic variables did not differ significantly among different DMS type conditions. An exploratory factor analysis was conducted on the 19 items, revealing that three factors emerged, named “privacy concern,” “general acceptance,” and “data sensitivity.” Privacy concern is composed of the predetermined factor collection concern, secondary use, and perceived insecurity; general acceptance is composed of perceived usefulness, trust, and behavioral intention. Subsequently, we examined if DMS types influenced participants’ ratings on privacy concern, general acceptance, and data sensitivity. These analyses yielded no significant effects for DMS types on privacy concern and data sensitivity. Regarding general acceptance, participants displayed a positive attitude and significantly preferred vehicle behavior-based DMS. Further, we investigated the effect of DMS types on predetermined factors. The results showed that there was no significant effect for DMS types on collection concern, secondary use, and perceived insecurity. Participants believed that vehicle behavior-based DMS was more useful and trustworthy. Regression analysis indicated that data sensitivity was a positive explanatory variable for general acceptance, however, the privacy concern was a negative one. This study examined data sensitivity, privacy concern, and general acceptance of various DMS among drivers, and explored the factors influencing general acceptance. It was observed that Chinese drivers, in general, hold a favorable view of DMS and express a degree of willingness to use them. They are less worried about privacy and data insecurity. Further exploration is necessary to ascertain the readiness to use DMS in real-world scenarios.References:1. Dong, Y., Hu, Z., Uchimura, K., Murayama, N.: Driver inattention monitoring system for intelligent vehicles: A review. IEEE Trans. Intell. Transport. Syst. 12(2), 596–614 (2011)2. SAE International: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. Society of Automotive Engineering, USA (2021)3. de Winter, J. C. F., Petermeijer, S. M., Abbink, D. A.: Shared control versus traded control in driving: A debate around automation pitfalls. Ergonomics (in press) 1–43 (2022)4. Hoffman, D., Novak, T. P., & Peralta, M.: Building consumer trust online. Commun. ACM. 42(4), 80–85 (1999)

Many studies have shown the trend that students’ conceptualisations of scientific phenomena are limited to the ontological category of objects. The ontological category of processes, on the other hand, is not developed enough to build valid scientific mental models (Chi , 2005; Ferrari and Chi, 1998; Vosniadou, 1991). A correct conceptualisation of any scientific concept results from a proper understanding of object properties and processes involving these objects. Moreover, Pata and Sarapuu (2003) have raised the problem that the traditional way to teach science is usually focused on the ontological category of objects. The active-learning idea, coming from constructivism, predicts that virtual manipulations improve deeper learning (Evans and Gibbons, 2007). With interactive activities, learners are the main actors of their own construction of scientific concepts. According to this approach, deeper conceptualisation will be enhanced when manipulations are possible. In the context of web-based learning, some manipulatives, which are defined as movable representations or movable objects on the computer screen (Moyer et al., 2002), can be used to create interactivity. This study investigates the students’ ontological understanding in a virtual learning environment and the effect of manipulatives on the students’ construction of ontological conceptions. More precisely, this work attempts to answer the following research questions: (1) What is the learners’ understanding about ontological category of objects comparing to ontological category of processes? (2) How do manipulatives and non-manipulatives help learners to conceptualise through ontological categories? A process-based virtual learning environment, “Cell World” (http://bio.edu.ee/models/fr/), was applied by 59 French students of junior year of high school in sciences (aged 16-17). The sample consisted of students from three average level French schools. The results presented in this paper were drawn from a pilot survey using the model translation of “Cell World”. In this model, students can move each manipulative from the store (on the left of the screen) to the area of animations where non-manipulatives are (on the right of the screen). If the selected manipulative is incorrect, learners receive a feedback which instructs them to drag the correct manipulative in order to continue the translation process. If the manipulative is correct, the animation continues so that students can observe interaction processes involving directly manipulatives (e.g. interaction between tRNA and mRNA) and/or only non-manipulatives (e.g. the fixing process of ribosome onto mRNA molecule). For leading students to use the environment correctly, a worksheet containing instructions was developed. Moreover, the worksheet also consisted of ten questions to investigate students’ understanding about ontological categories of objects (four questions) and processes (six questions). The questions about processes lead students to think in term of emergent interactions between objects. Questions about objects lead students to use factual knowledge about the properties of molecules. These questions involve also either manipulatives or non-manipulatives to investigate the role of interactivity on students’ conceptions. In order to obtain results about learners’ understanding of ontological categories, students were grouped according to their results about the two categories of questions, objects and processes: 43 students out of 59 expressed a high level of objects’ understanding – they obtained at least two-third of maximum points, whereas only 29 students expressed a high level of processes’ understanding. The Wilcoxon signed-ranks test also revealed significant differences (Z=-3.803, P<0.001) between the answers of the two categories of questions (objects and processes): 45 students demonstrated better performances concerning questions about objects than about processes, 13 answered better about objects and one was equal in both categories. Thus, the results provide evidence that the ontological category of processes is significantly less constructed and understood than the objects’ category. For studying the role of manipulatives on the student’s ontological categories of objects and processes, a Wilcoxon signed-ranks test was used. The test compared questions about objects involving manipulatives and questions about non-manipulatives: 45 students obtained better results about manipulable molecules, 10 students about non-manipulable and only 4 learners performed with non-manipulable as well as manipulable molecules (Z=-3.868, P<0.001). Another Wilcoxon signed-ranks test was used to compare students’ answers about processes when manipulatives or non-manipulatives were available in theses processes: 47 students obtained better results concerning category of processes when these processes involved manipulable molecules. Seven students performed better when processes involved non-manipulable molecule and 5 learners got the same results for manipulable and non-manipulable molecules (Z=-5.070, P<0.001). Thus, the great majority of the students showed better understanding in both ontological categories – objects and processes, when manipulatives were involved. In conclusion, these outcomes confirm precedent works: the ontological category of processes is not developed enough to build valid scientific mental models. Thus, the main challenge for teaching scientific phenomena is to lead learners to think through ontological category of processes. One possibility to help students to improve understanding in both ontological categories is to introduce manipulatives at most crucial aspects of a scientific model. -References: -Chi, M. T. H. (2005). Common sense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14, 161–199. -Evans, C., Gibbons N. J. (2007). The interactivity effect in multimedia learning. Computers & Education, 49, 1147–1160. -Ferrari, M. & Chi, M. T. H. (1998). The nature of naive explanations of natural selection. International Journal of Science Education, 20(10), 1231-1256. -Moyer, P. S., & Bolyard, J. J. (2002). Exploring Representation in the Middle Grades: Investigations in Geometry with Virtual Manipulatives. The Australian Mathematics Teacher, 58(1), 19-25. -Pata, K. & Sarapuu, T. (2003). Framework for scaffolding the development of problem representationsby collaborative design. In B. Wasson, S. Ludvigsen & U. Hoppe (Eds.), Designing for Change in Networked Learning Environments. Proceedings of CSCL’ 2003 Conference. Kluwer Academic Publishers, Dordrecht, 189-198. -Vosniadou, S. (1991). Conceptual development in astronomy. In S. M. Glynn & e. al. (Eds.), The Psychology of Learning Science. Hillsdale, N.J.: Lawrence Erlbaum Associates Publishers, 149-177.

Abstract The number of spacecraft launched per year is increased dramatically in the last decades granting access to private companies and public actors. Space assets are becoming crucial to asses disaster monitoring, precise agriculture, and global network interconnections. This trend is not limited to Earth observation applications, but it extends beyond Earth's orbit to support space exploration and exploitation. The current paradigm to operate interplanetary spacecraft strongly relies on the Deep-Space Network (DSN) which communicates with the spacecraft to obtain range and range-rate measurements. These data are then processed by large teams of engineers on ground to solve the orbit determination problem and the required maneuvers. Although this process is extremely precise and has been used since the beginning of space exploration, the increasing number of spacecraft and the riskier operations needed to support compelling science are making it outdated. First, the DSN has a limited number of communication slots which implies that a small number of spacecraft can be operated. Second, the process is extremely costly as large teams of individuals are involved in it. Finally, the delayed communications between the spacecraft and the ground station make some operations, such as landing or sampling, infeasible as the spacecraft does not have the needed reactivity. Because of these reasons, autonomous navigation is becoming a crucial technology for present and future missions. Among all the navigation sensors, cameras are generally preferred because they are light, compact, and low-priced. For this reason, Vision-Based Navigation (VBN), i.e., the combination of camera and image processing (IP) algorithms, is generally employed as an autonomous solution to solve the navigation problem. When a spacecraft is on an interplanetary cruise, it can determine its position by using known planet position within the Solar System. When the planet lines of sight (LoS) measurements are available, the spacecraft can triangulate its position in the inertial reference frame by knowing the planet ephemeris. This can be performed statically [1, 2], when more than one planet is available, or dynamically, by providing the LoSes measurements history to a navigation filter [3, 4]. The planet LoS determination can be performed by extracting the planet position from images by performing attitude determination and by knowing the planet ephemeris [5, 6]. This is a fully autonomous solution as the spacecraft does not require any piece of information from ground. The proposed solution is thus composed of an IP pipeline, which determines autonomously its attitude and extracts the planet LoSes, and a navigation filter, which determines the spacecraft state by taking into account light aberrations [7]. An important step to be performed is the algorithm validation process which is generally performed by increasing the simulation framework complexity and by including hardware-in-the-loop (HIL) components. Andreis et al. [4] develops and analyses the navigation filtering strategy to be deployed on board by assuming IP behavioral model, while Andreis et al. [6] and Andreis et al. [5] develop the IP pipeline and test it on synthetic images from a custom-designed rendering engine [8]. Andreis et al. [7] further develop the VBN algorithm by proposing an integrated solution to compensate for light aberrations. Finally, Panicucci et al. [9] assesses the IP performances on images acquired on RETINA, a HIL optical navigation test bench. In this context, a high-resolution screen stimulates a camera to acquire images as they would be taken in orbit. Standing on previous work, this paper presents the validation of the VBN algorithm on HIL simulation. First, a series of images are acquired on RETINA by simulating the reference trajectory and the attitude profile of the spacecraft. These images are processed sequentially by the VBN algorithm. Spacecraft state estimates are compared against the true value to assess navigation accuracy. Acknowledgments This research is part of EXTREMA, a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 864697). References [1] V. Franzese and F. Topputo. Optimal beacons selection for Deep-Space optical navigation. The Journal of the Astronautical Sciences, 67(4):1775–1792, 2020. doi: 10.1007/s40295-020-00242-z. [2] S. B. Broschart, N. Bradley, and S. Bhaskaran. Kinematic approximation of position accuracy achieved using optical observations of distant asteroids. Journal of Spacecraft and Rockets, 56 (5):1383–1392, 2019. doi: 10.2514/1.A34354. [3] R. R. Karimi and D. Mortari. Interplanetary autonomous navigation using visible planets. Journal of Guidance, Control, and Dynamics, 38(6):1151–1156, 2015. doi: 10.2514/1.G000575. [4] E. Andreis, V. Franzese, and F. Topputo. Onboard Orbit Determination for Deep-Space CubeSats. Journal of Guidance, Control, and Dynamics, pages 1–14, 2022. doi: 10.2514/1.G006294. [5] E. Andreis, P. Panicucci, and F. Topputo. An Image Processing Pipeline for Autonomous Deep-Space Optical Navigation. Journal of Spacecraft and Rockets, Under Review. [6] E. Andreis, P. Panicucci, V. Franzese, and F. Topputo. A Robust Image Processing Pipeline for Planets Line-Of-sign Extraction for Deep-Space Autonomous Cubesats Navigation. In 44th AAS Guidance, Navigation and Control Conference, pages 1–19, 2022. [7] E. Andreis, P. Panicucci, V. Franzese, and F. Topputo. A Vision-Based Navigation algorithm for Autonomous Deep-Space Cruise. In 3rd Space Imaging Workshop, 2022. [8] S. Bella, E. Andreis, V. Franzese, P. Panicucci, and F. Topputo. Line-of-Sight Extraction Algorithm for Deep-Space Autonomous Navigation. In 2021 AAS/AIAA Astrodynamics Specialist Conference, pages 1–18, 2021. [9] P. Panicucci, Andreis E., V. Franzese, and F. Topputo. An Overview of the EXTREMA Deep-Space Optical Navigation Experiment. In 3rd Space Imaging Workshop, 2022.

Abstract The Earth-Moon system is constantly bombarded by meteoroids of different sizes. They are fragments of asteroids, comets, and major celestial bodies which provide information about the formation and evolution of the Solar System. Meteor showers have been studied for at least 50 years to construct Solar System meteoroid models which can be exploited to understand the spatial distribution of objects near the Earth-Moon system, to predict the degradation of spaceborne equipment, and to forecast large impact on Earth [1]. These models have been constructed by performing Earth-based observations of meteor showers and lunar flashes due to meteoroid impacts. As Earth-based observations are limited by geometrical, illumination, and meteorological constraints, a Moon-orbiting observatory could increase the quality and quantities of meteoroid impact detection to improve current meteoroid models. To answer these questions, the Lunar Meteoroid Impacts Observer (LUMIO) mission has been designed and is currently in development under ESA funding [2]. LUMIO is a CubeSat mission to a halo orbit at Earth-Moon L2 that shall observe, quantify, and characterize meteoroid impacts on the Lunar farside by detecting their flashes, complementing Earth-based observations on the Lunar nearside, to provide global information on the Lunar Meteoroid Environment and contribute to Lunar Situational Awareness [3, 4]. The LUMIO mission foresees two modes during its operative phase [5]: the Science Cycle and the Navigation & Engineering Cycle. The former lasts approximately 14 days and occurs when the Moon far side has the optimal illumination to perform flash detection (i.e., half of the Moon is not illuminated). The latter is defined as the complementary of the Science Cycle. During the Navigation & Engineering Cycle, the CubeSats cannot carry out scientific observations and performs reaction wheel desaturation, communication with the Earth, station-keeping, and technological demonstration. The technological demonstration performed by LUMIO is the Optical Navigation Experiment (ONX) [6]. The ONX aims at proving the feasibility of CubeSats to autonomously navigate in the cislunar environment without communication with the ground by exploiting images of the Moon. Indeed, the spacecraft-Moon range is computed by determining the apparent size of the Moon in the image [7, 8]. This piece of information is then provided to the navigation filter to determine the spacecraft state [6]. The computation of the spacecraft-Moon range is determined by the image processing (IP) algorithm which determines the Moon limb location in the image at subpixel precision [9] and estimates the spacecraft-Moon range with a non-iterative method [10]. To make the algorithm robust to possible outliers in the limb location points, the RANSAC algorithm is used [11]. This IP pipeline provides position measurements to the navigation filter by exploiting attitude information from the ADCS subsystem. The navigation filter is an Extended Kalman Filter (EKF) in ECI J2000 taking into account the Moon and Earth gravitational attraction and the Sun third-body perturbation. This work presents the current status of the ONX by focusing on the image processing (IP) algorithm, the navigation filter, and the expected results during the experiment. Acknowledgment This work has been conducted under ESA Contract No. 4000139301/22/NL/AS within the General Support Technology Programme (GSTP) through the support of the national delegations of Italy (ASI) and Norway (NOSA). The authors would like to acknowledge the members of the LUMIO team for their support and the ESA experts for reviewing the Phases 0 and A design. References [1] Z. Ceplecha, J. Borovička, W. G. Elford, D. O. ReVelle, R. L. Hawkes, V. Porubčan, and M. Šimek. Meteor phenomena and bodies. Space Science Reviews, 84(3):327–471, 1998. doi: 10.1023/A:1005069928850. [2] A. Cervone, F. Topputo, S. Speretta, A. Menicucci, E. Turan, P. Di Lizia, M. Massari, V. Franzese, C. Giordano, G. Merisio, D. Labate, G. Pilato, E. Costa, E. Bertels, A. Thorvaldsen, A. Kukharenka, J. Vennekens, and R. Walker. LUMIO: A CubeSat for observing and characterizing micro-meteoroid impacts on the Lunar far side. Acta Astronautica, 195:309–317, 2022. doi: 10.1016/j.actaastro.2022.03.032. [3] F. Topputo, G. Merisio, V. Franzese, C. Giordano, M. Massari, G. Pilato, D. Labate, A. Cervone, S. Speretta, A. Menicucci, E. Turan, E. Bertels, J. Vennekens, R. Walker, and D. Koschny. Meteoroids detection with the LUMIO lunar CubeSat. Icarus, 389:115213, 2023. doi: 10.1016/j.icarus.2022.115213. [4] G. Merisio and F. Topputo. Present-day model of lunar meteoroids and their impact flashes for LUMIO mission. Icarus, 389:115180, 2023. doi: 10.1016/j.icarus.2022.115180. [5] A. M. Cipriano, D. A. Dei Tos, and F. Topputo. Orbit design for LUMIO: The lunar meteoroid impacts observer. Frontiers in Astronomy and Space Sciences, 5:29, 2018. doi: 10.3389/fs-pas.2018.00029. [6] V. Franzese, P. Di Lizia, and F. Topputo. Autonomous optical navigation for the lunar meteoroid impacts observer. Journal of Guidance, Control, and Dynamics, 42(7):1579–1586, 2019. doi: 10.2514/1.G003999. [7] D. Mortari, C. N. D’Souza, and R. Zanetti. Image processing of illuminated ellipsoid. Journal of Spacecraft and Rockets, 53(3):448–456, 2016. doi: 10.2514/1.A33342. [8] J. A. Christian. Optical navigation using planet’s centroid and apparent diameter in image. Journal of guidance, control, and dynamics, 38(2):192–204, 2015. doi: 10.2514/1.G000872. [9] J. A. Christian. Accurate planetary limb localization for image-based spacecraft navigation. Journal of Spacecraft and Rockets, 54(3):708–730, 2017. doi: 10.2514/1.A33692. [10] John A Christian and Shane B Robinson. Noniterative Horizon-Based optical navigation by Cholesky factorization. Journal of Guidance, Control, and Dynamics, 39(12):2757–2765, 2016. doi: 10.2514/1.G000539. [11] R. Hartley and A. Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003.