Miracle-dz/UniFAR-FTUs huggingface dataset:
X-ray to MR: the progress of flexible instruments for endovascular navigation

Interventional radiology and cardiology are rapidly growing areas of minimally invasive surgery, covering multiple diagnostic and interventional procedures. Treatment via endovascular techniques has become the go-to approach, thanks to its minimally invasive nature and its effectiveness in reducing hospitalisation and total time to recovery when compared to open surgery. Although x-ray fluoroscopy is currently the gold standard imaging technique for endovascular interventions, it presents occupational safety hazards to medical personnel and potential risks to patients, especially paediatric patients, because of its inherent ionising radiation. Magnetic resonance imaging (MRI), with its unique ability to provide radiation-free imaging, and acquiring morphologic and functional information, holds great promise in the advancement of image-guided navigation through the vasculature. Moreover, MRI has the potential to combine diagnosis, therapy and early evaluation of therapy in the same intervention. However, MR-guided interventions face a major challenge due to the presence of a large magnetic field (1.5/3 Tesla), which limits the set of materials suitable for the construction of key instrumentation (sheaths, catheters and guidewires). Despite these challenges, in recent years, significant progress has been made in the development of interventional devices, which comprise biocompatible, MR safe and MR visible materials. In an attempt to encourage and accelerate the development of MR-guided endovascular instrumentation, we present a systematic and illustrated overview of the plethora of work targeting to overcome the aforementioned limitations which are underpinned by the interdependent advancements in science, technology, engineering, mathematics and medicine (STEMM).
Effect of different concentrations of sodium chloride and soy protein isolate with mono- and diglycerides/corn oil on physical properties and stability of w/o/w double emulsion

The successful fabrication of double emulsion is determined by the ingredients or the process. The aim of the study was to determine the effects of sodium chloride and soy protein isolate with addition of mono-and diglycerides/corn oil on physical and stability of emulsion. Model emulsions with different concentration of sodium chloride (0.5% and 1.5%) and soy protein isolate (1% and 3%) with addition of mono-and diglycerides/corn oil (ratio 1:1) were prepared. Physical properties and stability of double emulsion were characterized by viscosity, emulsion stability, pH, and color. The results showed that the stability of double emulsion was significantly affected (p < 0.05) by the interaction between sodium chloride and soy protein isolate, whereas pH and viscosity were affected only by the concentration of soy protein isolate. However, double emulsion containing 1.5% of sodium chloride and 3% of soy protein isolate (SPI) with addition of mono-and diglycerides/corn oil showed the highest stability in comparing with emulsion containing 0.5% of sodium chloride and 3% of SPI.
The Use of Chitosan as Non-toxic Flocculant for Harvesting Microalgae Spirulina sp

Spirulina is a type of microalgae widely consumed as a food supplement due to its high nutritious benefits. Furthermore, it is very small and lives floating in water, making it quite difficult to harvest. One effective method of harvesting Spirulina is by coagulation/flocculation. In order to successfully harvest Spirulina, choosing the right flocculant material is very important. Chitosan, a natural polymer that has a cationic amine group, is often used in the food industry. This is because colloidal particles (polymer) that exist in nature are mostly negatively charged (including microalgae) and electrostatic interactions between cationic polymers with anionic polymers lead to the formation of flocculants. Therefore, this study was carried out to measure the feasibility of chitosan as a flocculant in the flocculation process of harvesting microalgae. The experiments were carried out with the variation of the concentration of chitosan, pH and slow-stirring speed, using efficiency of flocculation and sedimentation speed as parameters. The result showed that optimum condition was achieved at a slow-stirring speed of 40 rpm, sedimentation time of 2 hours, chitosan concentration of 100 mg/L and pH of 7-8. In this condition, the flocculation efficiency was 99.57%.
On the mechanics of MQXFB—the low-beta quadrupole for the HL-LHC

The High Luminosity Large Hadron Collider (LHC) Project target is to reach an integrated luminosity of the LHC of 3000 fb−1, corresponding to a factor 10 increase in collisions with respect to the current accelerator. One of the main components is the superconducting quadrupole called MQXF. It is based on Nb3Sn technology and has a 150 mm single aperture with a field gradient of 132.6 T m−1. The MQXF magnets are currently in preseries production in a joint collaboration between CERN and the US-LHC Accelerator Upgrade Project. The first prototype magnet based on 7 m-long coils (MQXFBP1) was assembled and preloaded in 2019. The testing and disassembly was done in 2020. The coils were equipped with optical fiber Bragg grating (FBG) sensors. Some of the FBG values exhibited strain jumps during preload and their signals were lost during cool-down. The magnet did not reach the nominal current, quenching at 15.15 kA. At the time of writing three MQXFB magnets have been preloaded from which the second prototype is soon to be tested. The objective of this paper is to analyze the mechanical behavior of MQXFB magnets in the light of current knowledge, thanks to earlier short model experiments, as well as describe in detail the process of assembly and preload. We synthesize the mechanical theory of preload and present new considerations on symmetric/asymmetric bladder-key operations and their effects. An extensive comparison between long and short model magnets is presented. In our analysis and FE modeling we take into account the measured coil sizes that vary over position. We introduce a novel technique that allows preload homogenization over the length of the magnet after preload has been done. We analyze the role of the stainless steel shell and show its mechanical behavior. Finally, the relevant data at cold is presented and analyzed.
Effect of Phosphorus and Calcium Availability as Influenced by Manure, Rock Phosphate, and Dolomite on Soybean Yield

Soybean (Glycine max) contains nutritional components such as high protein which support health needs and improve the quality of life. However, to achieve this standard, soybean productivity needs to be increased. Manure, rock phosphate, and dolomite are potential alternatives in improving soybean production. This study aims to examine phosphorus and calcium availability as Influenced by manure, rock phosphate, and dolomite to increase soybean yield. The experiment consisted of six treatments groups with four replications using a completely randomized block design. Furthermore, the groups include T0 (Control), T1 (5 t ha− 1 manure + 2.5 t ha−1 rock phosphate), T2 (5 t ha−1 manure + 5 t ha−1 rock phosphate), T3 (5 t ha− 1 + 2.5 t ha−1 dolomite), T4 (5 t ha−1 manure + 5 t ha−1 dolomite), and T5 (5 t ha−1 manure + 5 t ha− 1 rock phosphate + 5 t ha−1 dolomite). The results showed that phosphorus and calcium availability were significantly influenced by manure, rock phosphate, dolomite, and simultaneously increase soybean yield. Also, the best number of filled pods, 100-seed weight and yield were obtained with the application of 5 t ha−1 manure + 5 t ha−1 rock phosphate + 5 t ha−1 dolomite (20 pods plot−1, 0.62 g, and 260.89 g plot−1 respectively). Therefore, based on the results, phosphorus and calcium obtained from manure, rock phosphate and dolomite potentially increase soybean yield.
Validation of a Synchronized Smartphone-Based Gait Analysis System

Citation: Mitsuaki Noda. , et al. “Validation of a Synchronized Smartphone-Based Gait Analysis System”. EC Orthopaedics 12.4 (2021): 7486. Abstract Mitsuaki Noda1*, Hironori Ohhigashi2, Takehiko Matsushita3, Atsuyuki Inui3, Takahiro Niikura3 and Shin Ohsawa4 1Department of Orthopaedics, Nishi Hospital, Kobe, Japan 2Osaka University Graduate School of Engineering Science, Japan 3Department of Orthopedics, Kobe University Graduate School of Medicine, Japan 4Department of Orthopedics, Konan Hospital, Japan
First triple-wavelength lidar observations of depolarization and extinction-to-backscatter ratios of Saharan dust

Abstract. Two Saharan dust layers observed over Leipzig in February and March 2021 were used to provide the first ever lidar measurements of the extinction coefficient at 1064 nm for desert dust. The advanced multiwavelength Raman polarization lidar was able to provide, for the first time, the lidar ratio (extinction-to-backscatter ratio) and particle linear depolarization ratio at all three classical lidar wavelengths (355, 532 and 1064 nm). The pure dust conditions during the first event exhibit lidar ratios of 47±8, 50±5 and 63±13 sr and particle linear depolarization ratios of 0.260±0.026, 0.298±0.017 and 0.214±0.025 at the wavelengths of 355, 532 and 1064 nm, respectively. The second, slightly polluted dust case shows a similar spectral behavior with values of the lidar ratio of 52±8, 47±5 and 61±10 sr and the depolarization ratio of 0.188±0.053, 0.270±0.017 and 0.242±0.007 at 355, 532 and 1064 nm, respectively. The results were compared to AERONET v3 inversions and GRASP retrievals at six and seven wavelengths, which could reproduce the spectral slope of the lidar ratio from 532 to 1064 nm. The spectral slope of the particle linear depolarization ratio could not be reproduced by the AERONET inversions, especially at 1064 nm.
ENRICHMENT OF ONLINE AND PHYSICAL COLLABORATION

Prolonged online collaboration decreases employee productivity. Furthermore, the initial productivity gains from telecommuting are lost over time, and teams see their efficiency and effectiveness decline even with sustained remote collaboration. The current study evaluates the underlying pains and gains of prolonged remote collaboration. In addition, information from relevant studies like behavioural science and new technologies are explored. Based on interviews, observation and co-creation, opportunities are listed to inspire and provide direction for innovation in a hybrid work environment.
FDI Flows in Europe: Endogeneity and Credibility

The European sovereign debt crisis has increased the uncertainty regarding the benefits and costs of membership in the Economic and Monetary Union. In this article, we focus on one of the important components of the European financial integration, notably foreign direct investments. In particular, we develop a structural gravity model, influenced by some very recent theoretical and econometric advancements. This new structural gravity approach provides needed theoretical underpinnings as well as strong support for the econometric estimation of gravity models.
ADVANCED CONCEPTS FOR SEEDED FELS

The experiments enabled by ultrashort XUV/X-rays FELs, e.g. coherent control of quantum phenomena, nonlinear optics, etc., require the knowledge, and possibly the control, of the spectro-temporal content of individual pulses. While spatial coherence is also a property of FELs based on SASE, the capability of generating temporally coherent pulses is a distinctive feature of seeded FELs. Indeed, this is a natural consequence of the principle on which a seeded FEL relies: before emitting radiation, electrons interact with a coherent source, the seed, and, under given conditions, the latter transmits its coherence properties to the FEL light. In the following, we demonstrate the use of interferometry in the frequency domain to investigate the properties of the seeded FEL pulses. Moreover, we provide the first direct evidence of the temporal coherence of a seeded FEL working in the extreme ultraviolet spectral range and show the way to control the light generation process to produce Fourier-limited pulses.
Smart Glasses for Improving Mobility of Low Vision People (Phase Two)

The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation’s University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. FINAL RESEARCH REPORT
BUILDING SUSTAINABLE MATERIAL NARRATIVES WITH MATERIAL PATHWAYS

This paper introduces Material Pathways, a learning tool developed to strengthen awareness, initiate reflection and support discussion on the multifarious roles, materials can have within the design for sustainability field in a common frame. Its objective has thus been to create a common pedagogical ground for notions and concepts used on materials and sustainability in fields such as art and craft, biology, engineering, geography, economy, culture studies and sociology. The Material Pathways tool is a physical deck of 22 cards, each describing a ‘material position’ that can be used to unfold understandings of materials in a design project. The deck has been developed as a follow up and complement to the Sustainable Design Cards, a learning tool developed to articulate and activate approaches to sustainable design [1]–[3]. It builds on the same kinds of information and visual layout, making the two decks compatible with each other in the process. Consequently, the decks can be used independently, but also together, to both cover sustainable design broadly and the role of materials herein more specifically. First, the paper presents the structure of content of each card and second it describes the development of the cards and as a collection. Finally, the paper discusses, how the cards might be used to create a language on and around materials, to frame and guide a process focusing on materials and to strengthen communication of material-driven ideas and intentions.
Ubiquity of human-induced changes in climate variability

While climate change mitigation targets necessarily concern maximum mean state changes, understanding impacts and developing adaptation strategies will be largely contingent on how climate variability responds to increasing anthropogenic perturbations. Here we present a new 100-member large ensemble of climate change projections conducted with the Community Earth System Model version 2 to examine the sensitivity of internal climate fluctuations to greenhouse warming. Our unprecedented simulations reveal that changes in variability, considered broadly in terms of probability distribution, amplitude, frequency, phasing, and patterns, are ubiquitous and span a wide range of physical and ecosystem variables across many spatial and temporal scales. Greenhouse warming will in particular alter variance spectra of Earth system variables that are characterized by non-Gaussian probability distributions, such as rainfall, primary production or fire occurrence. Our modeling results have important implications for climate adaptation efforts, resource management, and for seasonal predictions.
Unsupervised marine vessel trajectory prediction using LSTM network and wild bootstrapping techniques

Increasing intensity in maritime traffic pushes the requirement in better preventionoriented incident management system. Observed regularities in data could help to predict vessel movement from previous vessels trajectory data and make further movement predictions under specific traffic and weather conditions. However, the task is burden by the fact that the vessels behave differently in different geographical sea regions, sea ports, and their trajectories depends on the vessel type as well. The model must learn spatio-temporal patterns representing vessel trajectories and should capture vessel’s position relation to both space and time. The authors of the paper proposes new unsupervised trajectory prediction with prediction regions at arbitrary probabilities using two methods: LSTM prediction region learning and wild bootstrapping. Results depict that both the autoencoder-based and wild bootstrapping region prediction algorithms can predict vessel trajectory and be applied for abnormal marine traffic detection by evaluating obtained prediction region in an unsupervised manner with desired prediction probability.
Synthesis and Characterization of Iron(II), Cobalt(II), Nickel(II), Copper(II), and Zinc(II) Complexes Using Diphenylmethyl Xanthate Ligand

Potassium Diphenylmethyl Xanthate and its monomeric complexes were synthesized at room temperature under inert gas and stirring condition. The ligand and its complexes of the general formula [M(L) 2 ] (where M= Fe II , Co II , Ni II , Cu II , Zn II and Cd II ) were characterized by spectroscopic methods (IR, UV-Vis, 1 H-, 13 C-, DEPT-, HQMC- and COSY-NMR), elemental analysis, metal content, magnetic susceptibility measurement and molar conductance. These studies revealed the formation of four coordinate complexes.
D EEP LTRS: A D EEP L ATENT R ECOMMENDER S YS - TEM BASED ON U SER R ATINGS AND R EVIEWS

We introduce a deep latent recommender system named deepLTRS in order to provide users with high quality recommendations based on observed user ratings and texts of product reviews. The underlying motivation is that, when a user scores only a few products, the texts used in the reviews represent a significant source of information. The addition of review information can alleviate data sparsity, thereby enhancing the predictive ability of the model. Our approach adopts a variational auto-encoder architecture as a generative deep latent variable model for both an ordinal matrix encoding users scores about products, and a documentterm matrix encoding the reviews. Moreover, different from unique user-based or item-based models, deepLTRS assumes latent representations for both users and products. An alternated user/product mini-batching optimization structure is proposed to jointly capture user and product preferences. Numerical experiments on simulated and real-world data sets demonstrate that deepLTRS outperforms the state-of-the-art, in particular in contexts of extreme data sparsity.
Narratives, Organizational Ethics and Social Dysfunction

This paper presents a model of the determinants of organizational culture, based on narratives and associated identities. Contractual relations between employees and managers in any organization are incomplete, and thus managers gain an incentive to use narratives to shape worker identities in order to reach the organization’s goals. Different narratives give rise to different managerial moral sensitivities, inducing managers to adjust their corporate culture, in terms of the manager’s efforts to instill the organizations’s overt moral standard. Thereby managers affect workers’ internal moral standards and consequently affect the workers’ propensity to engage in socially harmful activities. Dysfunctional organizational cultures can persist in equilibrium. The model explains why cross-sectionally we see high wages in organizations with strong cultures and worker attachment, while for managers the two are substitutable incentives. We explore policy implications with an eye towards strengthening organizational cultures which discourage unethical behavior.
O NLINE C ONTINUAL L EARNING U NDER C ONDITIONAL D OMAIN S HIFT

Existing continual learning benchmarks often assume each task’s training and test data are from the same distribution, which may not hold in practice. Towards making continual learning practical, in this paper, we introduce a novel setting of online continual learning under conditional domain shift, in which domain shift exists between training and test data of all tasks: P (X,Y ) 6= P (X,Y ), and the model is required to generalize to unseen domains at test time. To address this problem, we propose Conditional Invariant Experience Replay (CIER) that can simultaneously retain old knowledge, acquire new information, and generalize to unseen domains. CIER employs an adversarial training to correct the shift in P (X,Y ) by matching P (X|Y ), which results in an invariant representation that can generalize to unseen domains during inference. Our extensive experiments show that CIER can bridge the domain gap in continual learning and significantly outperforms state-of-the-art methods. We will release our benchmarks and implementation upon acceptance.
Deep Domain Adaptation through Inter-modal Self-supervision

Computer vision in robotics makes heavy use of RGB-D data. However, collecting large manually annotated datasets is extremely time-consuming and therefore costly. A potential solution is to automatically generate synthetic datasets and to use them in order to make predictions on the real data. Nevertheless, the domain shift between the synthetic dataset (source domain) and the real data (target domain) partially invalidates the effectiveness of this solution, yielding an accuracy significantly lower than the one that would be obtained using labelled real data. In order to overcome this issue, multiple domain adaptation methods have been developed. These methods can also be employed on multimodal data, such as RGB-D data, but they do not explicitly exploit the natural relationship between modalities. We propose a novel domain adaptation method which allows reducing the domain shift by forcing the convolutional neural network to learn the connection between RGB and Depth images through a secondary self-supervised task. Extensive experiments on object categorization and instance recognition show that the exploitation of inter-modal relation can significantly enhance the performance of the main classification task.
L EARNING O NE - HIDDEN - LAYER N EURAL N ETWORKS ON G AUSSIAN M IXTURE M ODELS WITH G UARAN - TEED G ENERALIZABILITY

We analyze the learning problem of fully connected neural networks with the sigmoid activation function for binary classiﬁcation in the teacher-student setup, where the outputs are assumed to be generated by a ground-truth teacher neural network with unknown parameters, and the learning objective is to estimate the teacher network model by minimizing a non-convex cross-entropy risk function of the training data over a student neural network. This paper analyzes a general and practical scenario that the input features follow a Gaussian mixture model of a ﬁnite number of Gaussian distributions of various mean and variance. We propose a gradient descent algorithm with a tensor initialization approach and show that our algorithm converges linearly to a critical point that has a diminishing distance to the ground-truth model with guaranteed generalizability. We characterize the required number of samples for successful convergence, referred to as the sample complexity, as a function of the parameters of the Gaussian mixture model. We prove analytically that when any mean or variance in the mixture model is large, or when all variances are close to zero, the sample complexity increases, and the convergence slows down, indicating a more challenging learning problem. Although focusing on one-hidden-layer neural networks, to the best of our knowledge, this paper provides the ﬁrst explicit characterization of the impact of the parameters of the input distributions on the sample complexity and learning rate