Miracle-dz/UniFAR-FTUs huggingface dataset:
Work Disability and Return to Work After Lymphoma: A Danish Nationwide Cohort Study

Purpose Many patients diagnosed with lymphoma are of working age. Cancer patients are known to have a higher risk of sick leave and disability pension, but this has only been delineated for certain subtypes of lymphoma. Therefore, this study aimed at investigating the overall risk of disability pension for all lymphoma subtypes and at quantifying return to work for patients with lymphoma in work before diagnosis. Patients and Methods Patients aged 18–60 years with lymphoma in complete remission (CR) diagnosed between 2000 and 2019 were included in the study. Using national registers, each patient was matched with five comparators from the general population with same sex, birth year, and level of Charlson Comorbidity Index. Risk of disability pension was calculated from 90 days after CR or end of treatment with competing events (death, retirement pension, early retirement pension, relapse for patients, or lymphoma diagnosis for comparators). Return to work for patients was calculated annually until 5 years after diagnosis for patients employed before diagnosis. Results In total, 4072 patients and 20,360 comparators were included. There was a significant increased risk of disability pension for patients with all types of lymphoma compared to the general population (5-year risk difference: 5.3 (95% confidence interval (CI): 4.4;6.2)). Patients with non-Hodgkin lymphoma were more likely to get disability pension than patients with Hodgkin lymphoma (sex- and age-adjusted 10-year risk difference: 2.9 (95% CI: 0.3;5.5)). One year after diagnosis, 24.5% of the relapse-free patients were on sick leave. Return to work was highest 2 years after diagnosis (82.1%). Conclusion Patients with lymphoma across all subtypes have a significantly higher risk of disability pension. Return to work peaks at 2 years after diagnosis.
LE-7A Engine Nozzle Problems during Transient Operations

Under development the LE-7A engine encountered two major troubles of nozzle extension. These two problems are the large side loads and the damage of some regenerative cooling tubes during start up and shut down processes. In the investigation results, it was clear that there are two kinds of large side loads and their origins are different. One kind of side load origin is the transition between Free Shock Separation (FSS) and Restricted Shock Separation (RSS) during start up and shut down processes. This RSS occurrence is also the reason of the damage of some regenerative cooling tubes. The second large side load origin is the stagnation and sudden movement (‘jump’) phenomena of the separation point. This paper presents a focus on the LE-7A engine separation phenomenon caused by the two kinds of the large side loads and the damage of some regenerative cooling tubes of the upper nozzle extension during the startup and shutdown transients. Nomenclature Symbols Pa ambient pressure Pc main combustion chamber pressure e nozzle expansion ratio Abbreviations FSS Free Shock Separation RSS Restricted Shock Separation CTP Compressed Truncated Perfect TP Truncated Perfect NPR Nozzle Pressure Ratio (=Pc/Pa)
New Country Records for Dung Beetles (Coleoptera: Scarabaeidae: Scarabaeinae) in Bolivia and Paraguay

Abstract A species is newly recorded from Paraguay, Sylvicanthon monnei Cupello and Vaz-de-Mello, 2018, and the previously dubious record of Sylvicanthon candezei (Harold, 1869) from the country is confirmed. Agamopus viridis Boucomont, 1928, Pseudocanthon chaquensis Nazaré-Silva and Silva, 2021, and Pseudocanthon pantanensis Nazaré-Silva and Silva, 2021 are newly recorded from Paraguay, and Pseudocanthon xanthurus (Blanchard, 1846), already known from humid southern areas of the country, is newly recorded from its northern Dry Chaco. The publication date of P. xanthurus—1846, not 1843, 1845, or 1847 as previously mentioned—is clarified, and additional data on the chromatic population structure of Bolbites onitoides Harold, 1868 in Paraguay are presented.
A Multi-stage Optimization based Model for Component Defect Detection of Trains

Long runs of railway trains inevitably lend to fault conditions. Automatic detection of defects in train components is very important for train safety and maintenance. For a long time, our detection of images still required to some extent human visual interpretation. ln recent years, convolutional neural networks have achieved great success in object detection, instance segmentation and image classification. In order to reduce the need of manual inspection, in this paper a multi-level optimized instance detection model (MOIDM) is proposed for instance-level defect detection. In MOIDM, we propose a two-stage instance segmentation method for defect detection. First, the feature pyramid network and the depth residual neural network are used to extract the multi-scale features of the image, and then the region proposal neural network is used to generate the rough defect region with the extracted hierarchical features, and the defect classification is carried out. Finally, the obtained features are used for instance-level prediction of coarse defect areas. In addition, we propose an Improved Pyramid Pooling Module (IPPM) and a multi-level optimisation module CPSPMask to improve the performance. IPPM can effectively establish long-range connections between pixels, further increasing the network’s ability to extract feature and spatial context information. CPSPMask can aggregate the original image and the mask for obtaining a more accurate mask. Experiment on a dataset containing two kinds of defect verifies the effectiveness of the algorithm. Our method can achieve 86.8% accuracy in terms of segmentation. In addition, which proves its promising performance comparison with state-of-the-art methods.
Recombination and unsupervised learning: effects of crossover in the genetic optimization of neural networks

Genetic algorithms have been successfully used for optimizing complex functions over multidimensional domains, such as the space of the connection weights in a neural network. A feed-forward layered network is used to simulate the life cycle of a synthetic animal that moves in an environment and captures food objects. The adaptation of the animal (i.e. of the network's weight matrix) to the environment can be measured by the amount of reached food objects in a given lifetime. We consider this amount as a fitness function to be optimized by a genetic algorithm over the space of the connection weights. The network can learn the weights that solve the survival task only by means of its genetic evolution. The recombination genetic operator (crossover) can be seen as a model of sexual recombination for the population, while mutation models agamic reproduction. The central problem in trying to apply crossover is the difficult mapping between the genetic code string (genotype) and the network's weight matrix (ph.
On the Use of Discriminant Functions in Taxonomy

The most effective use of discrimninant functions presumes an expedient selection of character for measurement. Such characters should possess (a) individually, a high coefficient of discrimination, K, relating interspecific and intraspecific variability, and (b) taken in pairs, a high intraspecific correlation, with the interspecific correlation of opposite sign. A considerable increase of discrimination can be attained graphically following V. I. Romanovsky. The consideration of scatter-diagrams and correlation-ellipses suggests a new criterion, the rank of discrimination, R, whose relation to K is R = VK/2. For any two out of three species of Chaetocnema, a pair of discriminant functions, each based on three characters, provides excellent discrimination: discrimination between the species used to construct the functions and the third species is less satisfactory. Corresponding coefficients in the three pairs of functions differ appreciably, but when correlation ellipses for the three pairs are plotted, the relative positions of the ellipses for the three species are the same. New pairs of functions, whose coefficients are arithmetic or (Figure 9) geometric means of the coefficients in the separate pairs permit a clean separation of all three species in one figure. The results obtained indicate the possibility of measuring taxonomic likeness, and of developing objective criteria for the construction of a natural system of
Adaptive Formation Framework with Obstacle Avoidance Based on Reinforcement Learning

Multi-robot formation control is a control technology that enables robots to maintain a specific formation and adapt to environmental constraints during task execution. In practical application, it is important to maintain structure and adaptively avoid obstacles. Therefore, a multi-robot adaptive formation obstacle avoidance framework is proposed in this paper. There are two pieces to this framework. An obstacle avoidance navigation algorithm built on reinforcement learning makes up the first section. Additionally, we optimize the system’s reward function using the artificial potential field method (APF). In the second part, we design a new formation control strategy based on the leader-follower model and propose the virtual obstacle avoidance point (VOAP) to solve the overlapping problem between virtual target points and obstacles in the leader-follower model. The results of the simulations demonstrate that the proposed formation obstacle avoidance framework can make unmanned ground vehicles (UGV) carry out formation keeping and adaptive obstacle avoidance independently.
Gender Contribution to the Innovation-Productivity Relationship in the Wake of COVID-19: Evidence for the Caribbean

This study presents new findings on the impact of the COVID-19 pandemic on productivity and innovation for Caribbean firms, with particular focus on the effects on firm gender diversity and workforce composition. Research on the impact of women's participation on firm performance and innovation has so far produced mixed results, though there is some evidence to suggest that for Latin America, larger shares of women in the knowledge creation and innovation process may increase innovative behavior and, as a consequence, lead to greater labor productivity. In the current context, female participation in firms becomes an even more pressing issue, given the early indications of the pandemics disproportionately negative burden on women s income and jobs in different regions. We found that the gender composition of the personnel has an interesting direct effect on productivity. At the same time, our results show that the expected reductions in female personnel due to the pandemic, have a negative effect in the shares of female participation which, in turn, have the potential to nullify the mentioned productivity channel. This suggests the existence of a minimum threshold of female participation to profit from diversity. Hence, it seems that policy should focus particularly on protecting female jobs, particularly in the wake of dramatic shocks affecting revenues and/or employment.
FOUR NEBULOUS OBJECTS IN THE OUTER PARTS OF THE ANDROMEDA NEBULA

Through the kindness of Dr. Edwin Hubble, a Palomar 48inch schmidt plate, taken in 1948 and centered on the Andromeda nebula (Messier 31), was sent to the Lick Observatory for examination. Because of the uniformly excellent quality of the images in the 6° X 6° field, this plate was searched over its entire area for outlying objects possibly associated with M31. The survey disclosed six new nebulous objects, comparable in their nonstellar appearance with those previously found by Hubble,1 and provisionally identified by him as globular star clusters in the spiral. During the 1952 observing season for M31, spectrograms of all six objects were obtained with the Crossley nebular spectrograph. These spectra show that two are field nebulae having red shifts greater than 10,000 km/sec, and four are objects having radial velocities consistent with membership in the spiral. Plate II, A, reproduced from the 48-inch schmidt plate, shows the four new outlying objects in relation to the whole spiral. Approximate positions and rectangular co-ordinates measured from the nucleus along the major axis, X, and along the minor axis, Y, are given in Table I. For comparison, Hubble's object most distant from the nucleus, his No. 140, is included in the table and also marked in Plate II, A. * Plate II, B, C, and D are enlargements from Crossley photographs for better identification of the objects, while E shows spectra of the two brightest ones, II and IV. Table II gives the observational data, and the radial velocities, from the spectrograms obtained for the four objects. The Crossley reflector was also used on two nights to obtain
Simulation of a Midlatitude Convective Storm Initialized with Bistatic Doppler Radar Data

Abstract An analysis is developed to initialize a cloud-resolving model from an explicit structure of a precipitating convective system derived from multiple-Doppler radar observations. The different fields of the model prognostic variables that compose the initial state of the simulation are estimated or retrieved using a 4DVAR assimilation method in which the model is used as a weak constraint using two time level observations. This allows for the retrieval of physical fields consistent with the observations and the equations of the model. This method is applied on a midlatitude summer storm sampled by the McGill bistatic Doppler radar network that occurred on 2 August 1997. During the 30-min-forward simulation, the model succeeds in representing the observed features of the three main cells that compose the storm in terms of precipitation distribution and evolution of the convective activity. After this period, the model produces less stratiform precipitations. Comparisons with a Lagrangian persistency.
Integral Robust Control of Electro-hydraulic Force Servo System Based on ESO

Electro-hydraulic servo forced control system is a nonlinear control system with nonlinear compound disturbances such as unmodelled friction force and parameter uncertainty. But the traditional linear control can not meet the performance requirements of this system. Aiming at the above problems, taking the electro-hydraulic servo force control system as the research object, a nonlinear model of the electro-hydraulic servo force control is constructed, and an integral robust control method based on the extended state observer (ESO) is designed based on Lyapunov theory. Firstly, the nonlinear unknown disturbance in the system is estimated and compensated online by using the extended state observer (ESO). Secondly, based on the estimated value of the output of the extended state observer, an integral robust controller is designed, and its stability is analyzed. Finally, to verify the effectiveness of the algorithm, the proposed algorithm is simulated by using Matlab, and the results show that the algorithm has good robustness and tracking accuracy.
Fast Cloud Detection in Remote Sensing Image Preprocessing

As the innovation of remote sensing sensors, the resolution and size of remote sensing images are getting larger and larger, which put enormous pressure on data transmission and processing. To improve the timeliness of remote sensing tasks, it is necessary to preprocess the source data to remove useless data, such as pixels obscured by clouds. Through the extraction of cloud texture and luminance features, a fast cloud detection method based on feature fusion is proposed. Its computation is much less than that of the deep learning method. After a series of parallel optimizations, the deployment of the proposed method is implemented on Zynq 7020. The experiment on the calm sea condition remote sensing dataset shows that the detection latency of this method for processing an image with a size of $\boldsymbol{256}\times \boldsymbol{256}$ is 1.31 ms at a frequency of 100 MHz, which is much faster than the existing complex method. Meanwhile, the pixel accuracy (PA) of segmentation is maintained at 94.15%, and the intersection over union (IoU) of the proposed method is 89.06%. Results could meet the expected goal of a fast preprocessing process.
High accuracy refractive index measurement system for germanium and silicon using the channelled spectrum method in the range of 3 to 15 μm

The refractive index of germanium is known only up to the third decimal according to publicly available sources. This data from various authors shows deviations in the order of several 10-3 not to be explained by experimental errors of the refractive index measurement. This is a strong indication that there is optically relevant material property variation. We present an interferometric method to measure the refractive index and its temperature dependency on etalon samples, which are cheaper to prepare with high quality than prism samples needed for the classical method of index measurement. Resolution and stability of our method is better than 10-4. The method can be used for both germanium and silicon. Our goal is to be able to produce material with optically relevant specifications. This is in contrast to the conventional method of specifying these important IR-optical materials in terms of electrical properties such as dopant type and concentration.
A novel burst assembly scheme for optical burst switching networks

Proposed in this paper is a novel burst assembly scheme called longest queue first scheduled assembly (LQF-SA) mechanism, where burst assembly and burst scheduling for optical burst-switching networks are integrated. In the proposed mechanism, ingress edge nodes have multiple assembly queues where IP packets are stored according to their egresses and QoS classes. Among these queues, the longest one is scheduled to assemble bursts by a scheduler at a fixed interval. In real networks, traffic usually distributes non-uniformly and there exists heavier traffic between some source and destination pairs. Simulation results show that LQF-SA could adapt well to the non-uniform traffic profiles. Furthermore, even under uniform traffic, LQF-SA is also super to round-robin scheduled assembly (RR-SA) in terms of burst size distribution, assembly efficiency, and burst loss rate.
Contrast pattern kernel for strings

In this work we present a contrast pattern kernel for strings. In this kernel, the feature extraction is based on contrast patterns, that are substrings (patterns) common for the positive class but rare for the negative one. The presented idea can be also illustrated as the extension of a spectrum kernel, investigated in previous work [11]. This extension consists on incorporating feature selection based on contrast patterns to the spectrum kernel. Therefore, presentation of contrast pattern kernel was referenced to the spectrum kernel and the results of tests done were compared with those obtained for the spectrum kernel. Kernels were tested on the data sets obtained from the Phospho.ELM database. These data incoroporate amino acids sequences to which specified enzymes (protein kinases) were assigned. The enzymes catalyze reaction of phosphorylation for these sequences.
A Catalogue of carbon stars in the LMC

A catalogue of 7760 carbon stars in the Large Magellenic Cloud is presented. The stars were identified during a systematic survey of objective-prism plates taken with the UK 1.2 m Schmidt Telescope. The catalogue is compared with other lists of carbon stars and the distribution of the carbon stars is discussed.
Fluid-like cathode enhances valuable biomass production from brewery wastewater in purple phototrophic bacteria

The climate crisis requires rethinking wastewater treatment to recover resources, such as nutrients and energy. In this scenario, purple phototrophic bacteria (PPB), the most versatile microorganisms on earth, are a promising alternative to transform the wastewater treatment plant concept into a biorefinery model by producing valuable protein-enriched biomass. PPB are capable of interacting with electrodes, exchanging electrons with electrically conductive materials. In this work, we have explored for mobile-bed (either stirred or fluidized) cathodes to maximize biomass production. For this purpose, stirred-electrode reactors were operated with low-reduced (3.5 e−/C) and high-reduced (5.9 e−/C) wastewater under cathodic polarization (−0.4 V and –0.8 V vs. Ag/AgCl). We observed that cathodic polarization and IR irradiation can play a key role in microbial and phenotypic selection, promoting (at –0.4 V) or minimizing (at –0.8 V) the presence of PPB. Then, we further study how cathodic polarization modulates PPB biomass production providing a fluid-like electrode as part of a so-called photo microbial electrochemical fluidized-bed reactor (photoME-FBR). Our results revealed the impact of reduction status of carbon source in wastewater to select the PPB photoheterotrophic community and how electrodes drive microbial population shifts depending on the reduction status of such carbon source.
Application of Mussel Biomimetic Materials in Biomedical Field

Marine mussels have strong adhesion ability in humid environments. A biomimetic mussel adhesive with similar adhesion ability can be prepared by modifying catechol groups, or by combining polydopamine with peptides or targeted small molecules to prepare drug carriers. Because these materials have unique adhesion, biocompatibility, and excellent surface properties, they are widely used in the field of biomedicine. This article reviews the research progress of biomimetic mussel adhesives and biomimetic mussel drug carriers in the world in recent years and summarizes their applications and development trends in the field of biomedicine.
Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities

Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more interest nowadays. They more closely resemble actual neural networks in the brain than their second-generation counterparts, artificial neural networks (ANNs). SNNs have the potential to be more energy efficient than ANNs on event-driven neuromorphic hardware. This can yield drastic maintenance cost reduction for neural network models, as the energy consumption would be much lower in comparison to regular deep learning models hosted in the cloud today. However, such hardware is still not yet widely available. On standard computer architectures consisting mainly of central processing units (CPUs) and graphics processing units (GPUs) ANNs, due to simpler models of neurons and simpler models of connections between neurons, have the upper hand in terms of execution speed. In general, they also win in terms of learning algorithms, as SNNs do not reach the same levels of performance as their second-generation counterparts in typical machine learning benchmark tasks, such as classification. In this paper, we review existing learning algorithms for spiking neural networks, divide them into categories by type, and assess their computational complexity.
Particle Image Velocimetry Study and Numerical Simulation of Turbulent near Wake of Square Cylinder

The result of the experiment of vortex shedding from a square cylinder is important in comparing the precision of different models including Reynolds averaged Navior-Stokes(RANS) models in computational fluid dynamics(CFD). The experiment of vortex shedding from a square cylinder with the Reynolds number of 2.2×104 is done using particle image velocimetry(PIV). A comparison with the results of Laser Doppler Velocimetry(LDV) experiments carried out by other researchers in the same Reynolds number shows that the PIV instruments is effective in obtaining turbulent wake of bodies.Comparisons are also conducted with different RANS models(Realizable k-e model,k-ω shear stress transport(SST) model and Reynolds stress model) of the coefficients of damping ratio,strouhal number and the velocity profiles and turbulent kinetic energy profiles.It is discovered that the result of the k-ω SST model is much more precise in the aerodynamic parameter identification and flow field simulation mentioned above.