Miracle-dz/UniFAR-FTUs huggingface dataset:
Pulsar/CNS integrated navigation based on federated UKF

In order to improve the autonomous navigation capability of satellite, a pulsar/CNS (celestial navigation system) integrated navigation method based on federated unscented Kalman filter (UKF) is proposed. The celestial navigation is a mature and stable navigation method. However, its position determination performance is not satisfied due to the low accuracy of horizon sensor. Single pulsar navigation is a new navigation method,, which can provide highly accurate range measurements. The major drawback of single pulsar navigation is that the system is completely unobservable. As two methods are complementary to each other, the federated UKF is used here for fusing the navigation data from single pulsar navigation and CNS. Compared to the traditional celestial navigation method and single pulsar navigation, the integrated navigation method can provide better navigation performance. The simulation results demonstrate the feasibility and effectiveness of the navigation method.
A new formulation of sparse multiple kernel k$$ k $$ ‐means clustering and its applications

Multiple kernel k$$ k $$ ‐means (MKKM) clustering has been an important research topic in statistical machine learning and data mining over the last few decades. MKKM combines a group of prespecified base kernels to improve the clustering performance. Although many efforts have been made to improve the performance of MKKM further, the present works do not sufficiently consider the potential structure of the partition matrix. In this paper, we propose a novel sparse multiple kernel k$$ k $$ ‐means (SMKKM) clustering by introducing a ℓ1$$ {\ell}_1 $$ ‐norm to induce the sparsity of the partition matrix. We then design an efficient alternating algorithm with curve search technology. More importantly, the convergence and complexity analysis of the designed algorithm are established based on the optimality conditions of the SMKKM. Finally, extensive numerical experiments on synthetic and benchmark datasets demonstrate that the proposed method outperforms the state‐of‐the‐art methods in terms of clustering performance and robustness.
Entrainment into Mixed Layers. Part I: Sharp-Edged and Smoothed Tops

Abstract A pervasive issue in the analysis of clear and cloud-containing convectively mixed layers is how to predict their growth through entrainment. This paper deals largely with the definition of the interface with the upper environment, and how that definition leads to complexities in entrainment analysis. The interface is usually quite abrupt locally, but horizontally or temporally averaged properties exhibit a more gradual transition, due to fluctuation of the interface height. The data from these transition layers produce unreliable results when applied to a mixed layer model with a flat top and zeroth-order discontinuities. Several authors have developed models with first- or higher-order discontinuities, but these vary in their structure. Presented here is a return to the zeroth-order, or “sharp-edged,” model, but with allowance for a locally variable upper boundary by use of a transformation of the mixed layer equations into a coordinate scheme based on the local mixed layer height. The model is.
Diagnosis of Chest Wall Tuberculosis Using Fine Needle Aspiration: A Single-Center Experience

Introduction Minimally invasive diagnosis of chest wall tuberculosis remains a challenge. Fine needle aspiration (FNA) is a simple and safe method of sampling. However, previous studies have shown that conventional tuberculosis tests had poor diagnostic performance in the needle aspirates. With the popularization of molecular detection application, the value of FNA in the diagnosis of chest wall tuberculosis needs to be reevaluated. Methods We retrospectively studied patients with suspected chest wall tuberculosis at admission who underwent FNA for diagnosis and reported the diagnostic efficacy of acid-fast bacilli smear, mycobacterial culture, cytology and Xpert MTB/RIF (GeneXpert) when testing FNA specimens. A composite reference standard (CRS) was used as the diagnostic gold standard for this study. Results Among 89 included FNA specimens, acid-fast bacilli smear, mycobacterial culture, and GeneXpert were positive in 15 (16.85%), 23 (25.8%), and 61 (68.5%), respectively. Thirty-nine (43.8%) had cytologic features suggestive of tuberculosis. According to CRS, 75 (84.3%) cases were categorized as chest wall tuberculosis, 14 (15.7%) were not diagnosed with tuberculosis. Using CRS as the gold standard, acid-fast bacilli smear, mycobacterial culture, cytology and GeneXpert exhibited sensitivity of 20%, 30.7%, 52% and 81.3%, respectively. Specificity in these four tests was found to be 100%. Sensitivity of GeneXpert was significantly higher than that of smear, culture and cytology, X2=66.3, p<0.001. Conclusion GeneXpert achieved higher sensitivity as compared to cytology and conventional TB tests in the chest wall FNA specimens. The implementation of GeneXpert may increase the diagnostic efficacy of FNA in the diagnosis of chest wall TB.
How Interpersonal Sensitivity Affects Depression Under the COVID-19 Lockdown Among College Students in South China: A Moderated Mediation Model

Purpose Based on the interpersonal theory of depression and resilience framework theory, this study tested the association between interpersonal sensitivity and depression in college students in the context of the COVID-19 pandemic. Additionally, the mediating role of resilience and the moderating role of the COVID-19 lockdown were investigated. Methods A total of 5193 South Chinese college students (M=19.27, SD = 1.18) were included in the study. According to which campus they were living on, the subjects were categorized as members of a lockdown group or a nonlockdown group. They completed the interpersonal sensitivity subscales of the Symptom Checklist 90 (SCL-90), Beck Depression Inventory (BDI), and Connor-Davidson Resilience Scale (CD-RISC). SPSS 26.0 statistical software was used to analyse the descriptive statistics, reliability, and correlation. Specifically, a moderated mediation model was analysed by multivariate logistic regression. Results Interpersonal sensitivity was significantly associated with depression (r = 0.517, p < 0.01), which was mediated by resilience (β = 0.12, 95% CI = [0.10, 0.13]). Lockdown status was shown to have a moderating effect on the relationship between resilience and depression (β = 0.03, t = 2.71, p < 0.01). Conclusion The high levels of interpersonal sensitivity displayed by South Chinese college students caused low resilience and then facilitated depression. The COVID-19 lockdown strengthened the effect of low resilience on depression. Lower resilience was more strongly associated with higher depression for students under lockdown conditions compared to students who were not in lockdown.
Co-Circle: Energy-Efficient Collaborative Neighbor Discovery for IoT Applications

Device discovery or neighbor discovery, which enables devices to detect other devices in the vicinity, plays an important role in many IoT applications as it is a prerequisite for wireless communication and interaction with each other. In this article, we study collaborative neighbor discovery, where multiple sensors connected to a gateway collaborate to discover another mobile device. Such a scenario can be found in many applications, such as automotive keyless access system, smart home monitoring, asset tracking, and safety monitoring. We first build an analytical model called Co-Circle for collaborative neighbor discovery. Based on Co-Circle, we derive the lower bound of the expected discovery latency. Then, we show how the lower bound of the expected discovery latency varies with the energy budget (or duty cycle). Finally, we propose an optimal collaboration scheme that can achieve the lower bound of the expected discovery latency. Experimental results verify the performance of the proposed collaboration scheme.
Faculty engagement in university-industry research partnerships: findings from a developing country

ABSTRACT University-industry research partnerships are crucial for promoting university entrepreneurship. Faculty as key instigators play a critical role in developing such partnerships. Previous studies demonstrate that faculty engagement depends on a diverse set of factors. Drawing on individual interviews with 76 faculty members from eight public and private universities in Kazakhstan, this study explores factors affecting faculty decisions to engage in industry research partnerships in a developing country. Applying the personal engagement theory, we found that despite perceiving industry partnerships as personally meaningful and being driven by extrinsic and intrinsic motivations, faculty engagement in industry research partnerships in Kazakhstan is limited. The reasons for this are faculty members’ feelings of low safety and availability caused by structural-, organisational- and individual-level barriers and challenges. We also found that some of these barriers and challenges can be more specific for developing countries that tend to have immature economies, suffer from corruption, have limited research capacity, and invest less in research activities.
A Theory of Markovian Time Inconsistent Stochastic Control in Continuous Time

In this paper, which is a continuation of the discrete time paper, we develop a theory for continuous time stochastic control problems which, in various ways, are time inconsistent in the sense that they do not admit a Bellman optimality principle. We study these problems within a game theoretic framework, and we look for Nash subgame perfect equilibrium points. Within the framework of a controlled SDE and a fairly general objective functional we derive an extension of the standard Hamilton-Jacobi-Bellman equation, in the form of a system of non-linear equations, for the determination for the equilibrium strategy as well as the equilibrium value function. As applications of the general theory we study non exponential discounting as well as a time inconsistent linear quadratic regulator. We also present a study of time inconsistency within the framework of a general equilibrium production economy of Cox-Ingersoll-Ross type.
Regression-Based Camera Pose Estimation through Multi-Level Local Features and Global Features

Accurate and robust camera pose estimation is essential for high-level applications such as augmented reality and autonomous driving. Despite the development of global feature-based camera pose regression methods and local feature-based matching guided pose estimation methods, challenging conditions, such as illumination changes and viewpoint changes, as well as inaccurate keypoint localization, continue to affect the performance of camera pose estimation. In this paper, we propose a novel relative camera pose regression framework that uses global features with rotation consistency and local features with rotation invariance. First, we apply a multi-level deformable network to detect and describe local features, which can learn appearances and gradient information sensitive to rotation variants. Second, we process the detection and description processes using the results from pixel correspondences of the input image pairs. Finally, we propose a novel loss that combines relative regression loss and absolute regression loss, incorporating global features with geometric constraints to optimize the pose estimation model. Our extensive experiments report satisfactory accuracy on the 7Scenes dataset with an average mean translation error of 0.18 m and a rotation error of 7.44° using image pairs as input. Ablation studies were also conducted to verify the effectiveness of the proposed method in the tasks of pose estimation and image matching using the 7Scenes and HPatches datasets.
Strength of children’s European identity: findings from majority and minority groups in four conflict-affected sites

ABSTRACT The European Union (EU) aims to promote peace. This research investigates the saliency of a European identity for children from majority and minority groups in four conflict-affected societies in Europe (Croatia, Kosovo, Northern Ireland (NI), and Republic of North Macedonia (RNM)). These sites represent a range of relations with the EU (e.g., leaving the EU, an EU member, wanting to join the EU). Participants included 442 children aged 7 to 11 years, evenly split by gender and group status (Croatia n = 90; Kosovo n = 107; NI n = 60; RNM n = 185). After a draw-and-tell task to prime European identity (vs. ingroup or control condition), we measured children’s identification with Europe, outgroup attitudes and prosociality. Although the European identity prime was not effective, children’s strength of European identity varied by site and group status and related to more positive attitudes and prosociality towards the conflict-rival outgroup. Implications for the future of the European project are discussed.
Hydrogen Embrittlement of CrCoNi Medium-Entropy Alloy with Millimeter-Scale Grain Size: An In Situ Hydrogen Charging Study

In this study, we examined the effect of charging current density on the hydrogen embrittlement (HE) of MEA and the associated HE mechanisms using electron backscattered diffraction (EBSD). Results show that MEA is susceptible to HE, but is stronger than as-rolled and 3D-printed Cantor alloy and stainless steel. The HE susceptibility of MEA decreases with increasing current density. Ductile fracture with transgranular dimples switches to intergranular brittle fracture with clear slip bands in the interior of grains. EBSD results uncovered that hydrogen facilitates localized slips and deformation twins. Hydrogen-enhanced localized plasticity and hydrogen decohesion are the possible HE mechanisms.
Morula Kernel Cake (Sclerocarya birrea) as a Protein Source in Diets of Finishing Tswana Lambs: Effects on Nutrient Digestibility, Growth, Meat Quality, and Gross Margin

Simple Summary The inclusion of morula kernel cake (MKC) as a source of protein in finishing diets of Tswana lambs positively supported growth and enhanced sensory attributes of cooked meat steaks. Feed intake, average daily gain (ADG), feed conversion ratio (FCR), and carcass traits were similar for lambs fed on diets containing Lucerne (commercial diet), sunflower seedcake, or MKC. However, lambs from a commercial diet (CD) had a better nitrogen retention than lambs from a sunflower seedcake diet (SCD). Nitrogen retention in lambs fed a morula kernel cake diet (MKCD) was numerically better than those from SCD. The nutritional meat quality parameters were similar across the dietary treatments and were within the recommended reference values. Additionally, meat steaks of lambs that consumed MKCD were rich in oleic acid while meat steaks of lambs that ate SCD had no oleic acid. Furthermore, the gross margins from lambs fed either MKCD or SCD were better than those fed CD. This was attributed to lower variable costs of feed that accounted to 51% of production costs in lambs fed either MKCD or SCD when compared to lambs fed CD, which had variable costs of 71% apportioned to feed. Abstract A trial evaluated growth performance, carcass characteristics, meat quality, and economic returns of fattened lambs fed on diets containing different protein sources. Six castrated male Tswana lambs per treatment were used in a completely randomised design (CRD) trial and fed on complete diets containing Lucerne (CD; commercial diet), morula kernel cake (MKC), or sunflower seedcake (SC) as protein sources over a 103-day experiment. No significant differences (p > 0.05) were observed in the dry matter intake, final body weight, average daily gain, and FCR. This was attributable to all the diets providing an equal supply of nutrients to the lambs. Meat quality attributes and proximate composition values were similar (p > 0.05) across the treatments. Longissimus dorsi muscle organoleptic quality did not differ (p > 0.05) across the treatments. The gross margin analysis was significantly greater (p < 0.05) when feeding SCD than feeding CD and was intermediate for lambs fed MKCD. Morula kernel cake (Sclerocarya birrea) can be used for fattening lambs when common protein sources are either not available or expensive.
The antiquity of verb agreement in Trans-Himalayan (Sino-Tibetan)

Abstract This paper reviews the evidence and arguments for reconstructing a person–number agreement paradigm for the Proto-Trans-Himalayan (=Sino-Tibetan) verb, and assesses the counter-arguments which have been presented in the literature. We demonstrate the cognacy of verb agreement paradigms across the family, and show that there is no plausible subclassification of the family which would place all the attesting languages in a single branch of the family, and no case for a “Rung” branch. The agreement systems of Jinghpaw and Northern Naga and the archaic postverbal paradigms of South Central/Kuki-Chin are demonstrably cognate to those of Rgyalrongic and Kiranti, and these languages have no common ancestor more recent than PTH.
Optimal reinsurance-investment strategies for insurers under mean-CaR criteria

This paper considers an optimal reinsurance-investment problem for an insurer, who aims to minimize the risk measured by Capital-at-Risk (CaR) with the constraint that the expected terminal wealth is not less than a predefined level. The surplus of the insurer is described by a Brownian motion with drift. The insurer can control her/his risk by purchasing proportional reinsurance, acquiring new business, and investing her/his surplus in a financial market consisting of one risk-free asset and multiple risky assets. Three mean-CaR models are constructed. By transforming these models into bilevel optimization problems, we derive the explicit expressions of the optimal deterministic rebalance reinsurance-investment strategies and the mean-CaR efficient frontiers. Sensitivity analysis of the results and a numerical example are provided.
Solving Rational Expectations Models

In this chapter, we present theoretical foundations of main methods solving rational expectations models with a special focus on perturbation approaches. We restrict our attention to models with a finite number of state variables. We first give some insights on the solution methods for linear models. Second, we show how to use the perturbation approach for solving non-linear models. We then document the limits of this approach. The perturbation approach, while it is the most common solution method in the macroeconomic literature, is inappropriate in a context of large fluctuations (large shocks or regime switching) and of strong non-linearities (e.g. occasionally binding constraints). The former case is then illustrated extensively by studying regime switching models. We also illustrate the latter case by studying existing methods for solving rational expectations models under the Zero Lower Bound constraint, i.e. the condition of non negativity of the nominal interest rate. Finally, we end up with a brief presentation of global methods which are alternatives when the perturbation approach fails in solving models.
NUMERICAL INVESTIGATION AND OPTIMIZATION OF MIXED CONVECTION AND ENTROPY GENERATION IN SQUARE CAVITY WITH LID-DRIVEN

Original Research Paper Received 11 May 2015 Accepted 01 June 2015 Available Online 24 June 2015 Taguchi method has been used since 1980 as an effective way to optimize the design process engineering tests. In this paper, optimal conditions of the mixed convection and entropy generation in square cavity filled with Cu-water nanofluid is analyzed using Taguchi method. For this purpose an L16 (43) orthogonal Taguchi array is used. Discretization of the governing equations was achieved through finite volume method and solved with SIMPLE algorithm. The effect of Richardson number (0.1-100 ), the volume fraction of copper nanoparticles (0-10%) and the wavelength of the wavy surface (0 1) as effective parameters for analysis in four levels are considered. This analysis was performed for ixed Grashof number 1.4. The results show that the mean Nusselt number decreases by increase of the Richardson number, the volume fraction of nanoparticles and the wavelength of the wavy surface. It is found that the Flat plate (for wavy surface with the wavelength 0) and the volume fraction 0% in the Richardson number 0.1 is optimal design for heat transfer while the geometry with =5%, Ri=100 and =0.25 is optimal design for entropy generation. Finally for maximum heat transfer and minimum entropy generation the geometry with =0%, Ri =1 and =0.25 can be considered as an optimal design
COVID-19 Communication Management on Facebook Pages of Local Governments

We examine how Italian municipalities manage COVID-19 communication on their official Facebook pages. For this purpose, we apply an automatic topic modeling procedure on a sample of 76,139 posts published on the official Facebook pages of 103 Italian provincial capital municipalities from 1 March 2020 to 26 March 2021 of the COVID-19 pandemic period. We identify two topics related to COVID-19 consisting of restrictive measures and support measures. Using regression models with municipality and year-week fixed effects, we find that the prevalence of the topic on COVID-19 restrictive measures negatively affects the tone of the communication, computed through a sentiment analysis procedure, and it is negatively associated with the number of COVID-19 positive cases in the municipal area. In contrast, the prevalence of the topic on COVID-19 support measures positively affects the tone of the communication and it is positively associated with the number of positive cases. These associations are moderated by ideology, age, and political incentives of incumbent mayors. These results may reveal a strategic communication of municipalities to induce positive perceptions about the performance of municipal incumbents in responding to the pandemic and, therefore, enhance political consensus among voters. Finally, these findings may have practical implications for public regulators, public managers, and other followers of local governments on social media.
Hierarchical Affinity Propagation for Reliable Integrated Multilayer V2X Networks

The integration of IEEE802.11p and 5G New Radio for the backbone transmission of vehicle-to-everything (V2X) technology has served as a novel type of intelligent transportation system with an efficient low-cost and high-coverage area. This integrated scheme implements a V2X end-to-end data delivery system in a multilayer mechanism. However, the dynamic nature of vehicular networks leads to numerous network outages and frequent network renewal requests, which considerably diminishes the overall performance. Hence, efficient routing management is required to maximize reliable communication. Optimizing routing management is categorized as a nonlinear problem with a very high computational load, making a practical solution very challenging. To cope with the complicated routing problem, we develop a low-complexity distributed algorithm based on a hierarchical affinity propagation framework. The proposed algorithm exchanges small-sized messages among local vehicles to determine data routing more efficiently. The distributive optimization of the proposed algorithm requires a small computational load for each node. The main advantage of the proposed algorithm is that each vehicle autonomously determines its best routing options without the help of any network coordinators. Extensive simulation results verify that the proposed algorithm outperforms conventional algorithms in terms of vehicle mobility, network scalability, the level of network overhead, and the algorithm convergence rate.