Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between medical narrative ability and humanistic care quality among oncology nurses

： Objective To analyze the relationship between medical narrative ability and hu⁃ manistic care quality among oncology nurses. Methods By convenience sampling method ， 192 oncology nurses in Wuhan were selected as survey subjects. The general information questionnaire ， Narrative Competence Scale （ NCS ） of medical staff and the Humanistic Care Quality Scale of nurses were conduct online. Results The average score of NCS of oncology nurses was （ 148 . 18 ± 21 . 03 ） points. The average score of Humanistic Care Quality Scale of oncology nurses was （ 134 . 44 ± 11 . 79 ） points. There was a significant positive correlation between medical narrative ability and humanistic care quality among oncology nurses （ r = 0 . 577 ） . Conclusion The medical narrative ability and the quality of humanistic care of oncology nurses were above the average. The quality of humanistic care of oncology nurses was positively correlated with the level of medical narrative ability. The hospital administrators should pay attention and take actions to improve medical narrative ability ， in order to improve the level of humanistic care among oncology nurses.
Bistatic scattering from one-dimensional random rough homogeneous layers in the high-frequency limit with shadowing effect

Many fast asymptotic models of electromagnetic scattering from a single rough interface have been developed over the last few years, but only a few have been developed on stacks of rough interfaces. The specific case of very rough surfaces, compared to the incident wavelength, has not been treated before, which is the context of this paper. The model starts from the iteration of the Kirchhoff approximation to calculate the fields scattered by a rough layer, and is reduced to the high-frequency limit in order to rapidly obtain numerical results. The shadowing effect, important under grazing angles, is taken into account. The model can be applied to any given slope statistics. Then, the model is compared with a reference numerical method based on the method of moments, which validates the model in the high-frequency limit for lossless and lossy inner media.
Classifying Cardiovascular Disease Using Gradient Boosting Method Risk Factor Analysis using Shapely Additive exPlanations

One of the worst diseases in the world is heart disease or cardiovascular disease. Heart disease is on the rise globally, and doctors are becoming quite concerned about it. Therefore, it’s crucial to accurately identify heart disease kinds when you require them. The complete understanding of cardiac disease has become one of the most popular scientific topics. In order to determine the best feature for accurately identifying heart disease, we worked with the "statlog" dataset. There, we trained our model using the LightGMB Python module. It is the Boosting Machine framework. We utilized the auROC score together with a 5-fold cross-validation for performance measurements. Figure 3 displays the results on the test data, which were 0.85 auROC with a 95% confidence interval of 0.70-0.99. However, it obtained 0.81 auPRC with a 95% confidence interval of 0.59 to 0.99. It obtained recall scores of 91.83% and precision scores of 77.52%. Shapely Additive exPlanations or shape value is utilized to discover the best features in this case. We categorize our heart disease datasets by identifying the most effective characteristics. As a consequence, it will assist medical professionals in identifying cardiac disease through practical application and help save countless lives
MO-Transformer: A Transformer-Based Multi-Object Point Cloud Reconstruction Network

This paper proposes a new network for reconstructing multi-object point cloud. Different from previous networks which reconstruct multi-object point cloud as a whole, our network iteratively reconstructs each individual object point cloud from a frame of multi-object point cloud. To achieve this goal, we have designed MO-Transformer, a transformer-based autoregressive network. During training, MO-Transformer takes a frame of multi-object point cloud and individual object point clouds as input. During testing, MO-Transformer iteratively reconstructs individual object point clouds only based on the input multi-object point cloud. To train the proposed MO-Transformer, we design a new loss function called separate Chamfer distance (SCD). In addition, we prove that SCD is an upper bound of the traditional Chamfer distance calculated based on the entire multi-object point cloud. The reconstruction experiment verifies the efficacy of our network in multi-object point cloud reconstruction. Furthermore, the reconstruction experiment also investigates the effect of different dimensions using a series of datasets. The ablation study experiment verifies the necessity of SCD in training MO-Transformer.
Relationships between auditory impressions and onomatopoeic features for environmental sounds

In order to clarify the features of environmental sounds, psychoacoustical experiments using onomatopoeic representations were conducted. The onomatopoeic representations obtained for each sound and participant were expressed using phonetic parameters, such as place, manner of articulation, and vowels. Onomatopoeic representations were classified based on similarities of phonetic parameters using a hierarchical cluster analysis. As a result, similar acoustic properties were identified in the stimuli expressed by onomatopoeic representations classified into the same clusters. Furthermore, the auditory impressions associated with stimuli were measured by a semantic differential method using 13 adjective pairs. Factor analysis was applied to the average ratings for each sound for each scale. The three factors obtained were the emotion factor, the clearness factor, and the powerfulness factor. From these results, the relationships among the acoustic properties of sound stimuli, the impressions associated with them, and their onomatopoeic features were discussed. For example, onomatopoeic representations including voiced consonants were frequently used for sounds displaying components in the frequency region below about 1 kHz in their spectrum, inducing “powerfulness” and “darkness, dullness, and muddiness” impressions. Furthermore, similar relationships were found in supplementary experiments using various band noises.
A Signal-Domain Object Segmentation Method for Ultrasound and Photoacoustic Computed Tomography

Image segmentation is important in improving the diagnostic capability of ultrasound computed tomography (USCT) and photoacoustic computed tomography (PACT), as it can be included in the image reconstruction process to improve image quality and quantification abilities. Segmenting the imaged object out of the background using image domain methods is easily complicated by low contrast, noise, and artifacts in the reconstructed image. Here, we introduce a new signal domain object segmentation method for USCT and PACT which does not require image reconstruction beforehand and is automatic, robust, computationally efficient, accurate, and straightforward. We first establish the relationship between the time-of-flight (TOF) of the received first arrival waves and the object’s boundary which is described by ellipse equations. Then, we show that the ellipses are tangent to the boundary. By looking for tangent points on the common tangent of neighboring ellipses, the boundary can be approximated with high fidelity. Imaging experiments of human fingers and mice cross sections showed that our method provided equivalent or better segmentations than the optimal ones by active contours. In summary, our method greatly reduces the overall complexity of object segmentation and shows great potential in eliminating user dependency without sacrificing segmentation accuracy. The method can be further seamlessly incorporated into algorithms for other processing purposes in USCT and PACT, such as high-quality image reconstruction.
Systemising Data-driven Methods for Predicting Throughput Time within Production Planning & Control

Predicting throughput times is of particular interest to production planners to schedule the production flow or communicate reliable delivery times to customers. Most established prediction methods are based on general assumptions, expert knowledge or simple statistical techniques. With the increasing use of data mining in production management, it is possible to provide more sophisticated predictions of throughput time. However, current research often does not describe the application or locate the particular prediction approach within the time and task structure of Production Planning and Control (PPC). Therefore, this paper aims to develop a systematisation approach to classify prediction models within the PPC task structure. To this end, applications along the order fulfilment process are first defined and then elaborated. A systematic literature review is conducted to classify current throughput time prediction approaches within the previously described application domains. In a case study, the application possibilities of throughput time predictions based on the provided systematisation are demonstrated, and differences in data availability and prediction quality are highlighted.
Improved Task Space Locomotion Controller for a Quadruped Robot with Parallel Mechanisms

In this work, an advanced quadruped robot with abundant kinematic loops and passive joints is introduced. Due to the existence of many closed chains, the robot dynamic model is quite complex, and is derived using the Gauss's principle of least constraint. To explicitly consider the loop-closure constraints, we propose a task-space inverse dynamics based approach to obtain the robot locomotion controller. Besides, to meet the demand of high frequency (≥ 500Hz) in controller, an alternative method is provided. It uses the projected dynamics to find an analytical mapping from the desired contact force to the desired torque of actuators under full consideration of passive joints and loop-closure constraints. The effectiveness and efficiency of the proposed algorithms in this paper have been validated by simulation with a reliable physical engine MuJoCo.
POPULATION TRAPPING AND LASER-INDUCED CONTINUUM STRUCTURE IN HELIUM : EXPERIMENT AND THEORY

We report the observation of laser-induced structure (LICS) in the flat photoionization continuum of helium. The structure is a strong and spectrally sharp resonance, showing both enhanced and diminished ionization (a Beutler-Fano profile). Our observations show, in an otherwise unstructured continuum, pronounced ionization suppression (as much as 70{percent}), which is due to population trapping associated with LICS. We also show the effect of dynamic Stark shifts upon the LICS profile. A discussed theoretical model, incorporating significant dynamic Stark shifts, gives a quantitatively accurate description of the line profile for a wide range of pulse intensities. {copyright} {ital 1998} {ital The American Physical Society}
Reproductive biology of harvestmen (Arachnida: Opiliones): a review of a rapidly evolving research field

Abstract Harvestmen are a major arachnid order that has experienced a dramatic increase in biological knowledge in the 21st century. The publication of the book Harvestmen: The Biology of Opiliones in 2007 stimulated the development of many behavioral studies. Although the book is relatively recent, our understanding of the reproductive biology of harvestmen is already outdated due to the fast accumulation of new data. Our goal is to provide an updated review of the subject to serve as a benchmark for the following years. In the pre-copulatory phase, we explore the evolution of facultative parthenogenesis, the factors that may affect the types of mating system, and the role of nuptial gifts in courtship. Regarding the copulatory phase, harvestmen are unique arachnids because they have aflagellate spermatozoa and a penis with complex morphology. We discuss the implications of these two features for sperm competition and cryptic female choice. In the post-copulatory phase, we connect oviposition site selection and climate conditions to the widespread occurrence of resource defense polygyny, alternative reproductive tactics, and sexual dimorphism in several clades of tropical harvestmen. Finally, we present the different forms of parental care in the order, and discuss the benefits and costs of this behavior, which can be performed either by females or males. Throughout the review, we indicate gaps in our knowledge and subjects that deserve further studies. Hopefully, the information synthesized here will stimulate researchers worldwide to embrace harvestmen as a study system and to improve our effort to unravel the mysteries of their reproductive biology.
Characterisation of extended spectrum β-lactamase among Escherichia coli and Klebsiella pneumoniae associated with skin and urogenital tract infections in dog

Thus, ESBL producing organisms pose a major problem for clinical therapeutics. Because of the close contact between dogs and man, and aimless utilization of antibiotics especially cephalosporins in veterinary sector, these animals have turned into potential source of ESBL producing bacteria, thereby prompting the transfer of multidrug resistant strains in human. So far no studies have been conducted regarding the prevalence of ESBL among animals in Kerala. Because of the alarming rise in the number of these resistant strains, there is a urgent need for regular area based monitoring of the antibiogram profile of the various bacterial organisms associated with clinical infections in animals which could fluctuate throughout the years and from site to site. This could provide adequate data prompting the authorities to formulate strict antibiotic usage guidelines in veterinary and human sector.
Multiple Fano interferences in a plasmonic metamolecule consisting of asymmetric metallic nanodimers

We theoretically explore signatures of plasmonic Fano interferences in a subwavelength plasmonic metamolecule consisting of closely packed asymmetric gold nanodimers, which lead to the possibility of generating multiple Fano resonances in the scattering spectrum. This spectral feature is attributed to the interference between bright and dark plasmonic modes sustained by the constituent nanodimers. The excited Fano dips are highly sensitive in both wavelength and amplitude to geometry and background dielectric medium. The tunability of induced Fano resonances associated with enhanced electric fields from the visible to infrared region provides promising applications, particularly in refractive index sensing, light-trapping, and photon up-converting.
Towards Robust Visual-Inertial Odometry with Multiple Non-Overlapping Monocular Cameras

We present a Visual-Inertial Odometry (VIO) algorithm with multiple non-overlapping monocular cameras aiming at improving the robustness of the VIO algorithm. An initialization scheme and tightly-coupled bundle adjustment for multiple non-overlapping monocular cameras are proposed. With more stable features captured by multiple cameras, VIO can maintain stable state estimation, especially when one of the cameras tracked unstable or limited features. We also address the high CPU usage rate brought by multiple cameras by proposing a GPU-accelerated frontend. Finally, we use our pedestrian carried system to evaluate the robustness of the VIO algorithm in several challenging environments. The results show that the multi-camera setup yields significantly higher estimation robustness than a monocular system while not increasing the CPU usage rate (reducing the CPU resource usage rate and computational latency by 40.4% and 50.6% on each camera). A demo video can be found at https://youtu.be/r7QvPth1m10.
Reproductive status of a female white cockatoo (Cacatua alba) based on relationships among urofecal steroid hormone dynamics, molting, and body weight

The detailed reproductive physiology of cockatoos based on gonadal hormone dynamics is unclear. In this study, we aimed to investigate ovarian activity by monitoring urofecal sex steroid hormone profiles in a captive female white cockatoo (Cacatua alba) and to noninvasively reveal basic reproductive physiology by comparing the hormone profiles with the laying dates, body mass changes, and molt progress. Urofeces were collected regularly for approximately 4 years from one female that frequently laid unfertilized eggs under single-rearing conditions. Urofecal progesterone (P4) and estradiol-17β (E2) were measured by enzyme immunoassay. In addition, body mass and the number of fallen feathers were measured periodically. The urofecal P4 concentration peaked at an average of 17.7 days after the start of the rise in urofecal E2 concentration, and egg laying began on the day after the peak urofecal P4 concentration. The clutch size was usually two eggs, with an average interval of 4.5 days between eggs in each egg-laying cycle. There was a significant correlation between the dynamics of E2 concentration in urofeces and body mass. The results strongly suggest that E2 and P4 reflect the follicle growth and ovulation status, respectively, and that noninvasive monitoring of hormone dynamics using urofeces can accurately capture ovarian activity in the white cockatoo. Furthermore, changes in body mass can predict follicular growth, and reproduction and molt are antagonistic.
Collaborative Teleoperation with Haptic Feedback for Collision-Free Navigation of Ground Robots

We propose a collaborative teleoperation algorithm which utilizes haptic force feedback to guide users around oncoming obstacles while accounting for non-holonomic constraints. The proposed algorithm predicts the user's goal, plans a path using a modified RRT* algorithm to the predicted goal, and provides haptic guidance to the path and away from obstacles when the user is in an unsafe pose. We show that the vehicle cannot collide with obstacles under the proposed algorithm following the haptic commands. We assess the per-formance of our algorithm with a virtual pilot in simulations and hardware experiments, demonstrating its ability to prevent collisions while reaching the goal location. Additionally, we demonstrate human-in-the-loop navigation with a Geomagic Touch haptic device providing force feedback to the user. These simulations and experiments show that the proposed haptic guidance system is a useful and effective tool for co-navigation of non-holonomic vehicles via teleoperation.
A Page-mapping Consistency Protecting Method for Soft Error Damage in Flash-based Storage

A soft error in flash-based storage might impair a host system. For instance, if the soft error infiltrates the storage mapping function, the host system could experience severe operation failures, such as data corruption or a drive freeze. To harden the storage against soft errors, we propose a novel page-mapping consistency checker (PCK) method implemented with a lightweight redundancy. Our PCK exploits a small page tracing table written previously and only performs mapping-related functions again with the time redundant. Then, with that redundancy result, the storage detects page mapping corruption and finally recovers it. Consequently, the flash-based storage keeps the page-mapping consistency and improves the host system's reliability.
iPos-5G: Indoor Positioning via Commercial 5G NR CSI

The fifth-generation (5G) networks have been massively deployed in commerce. The new features introduced by 5G networks are beneficial to wireless positioning. In this study, the performance of indoor positioning with commercial 5G new radio (NR) signals is investigated, and the channel state information (CSI) extracted from the downlink synchronization signal block is utilized. Considering the limited 5G NR base station (known as gNodeB) is hearable indoors, the fingerprint method is used, and an indoor positioning system termed iPos-5G is developed. The system consists of four components. First, a module of quality control is applied for CSI preprocessing. Second, an unsupervised deep-autoencoder network is utilized to reconstruct CSI features. Third, by supervised learning, a radial basis function is improved to optimize the probability model for similarity calculations. Finally, an amplitude-phase probability fusion function is proposed for positioning by weighting the coordinates of reference points. To verify the effectiveness of iPos-5G, indoor field tests are carried out in the scenarios of an office and a corridor. The test results show that iPos-5G achieves mean absolute errors of 2.14 and 2.81 m and standard deviation of the errors of 1.07 and 1.66 m, which outperforms the compared CSI fingerprint methods in terms of positioning accuracy and stability.
Social Behavior Prediction using Ensemble Learning Algorithms

In this modern era, social media users are spreading daily. Most social media users spend much time on these platforms daily. While using social networks, people can connect within a few seconds and attain information from any part of the world. Nevertheless, using social networks also have some adverse effects. Recently, researchers have shown that social networks negatively affect our behavior. In this work, a method was presented to analyze social behavior. Our analysis was started with data collection in the form of questionnaires from different individuals. The dataset was preprocessed by handling missing values and applying SMOTE technique. After that, Genetic search for the feature selection technique, ranking features with Information Gain, and Chi-square test with the p-value were used. Finally, three machine learning algorithms were applied for the classification algorithm with Rotation Forest, Dagging, and Decorate. In our analysis, Decorate ensemble classifier achieved the satisfactory performance.
Application of Edge Detection Algorithm for Self-Driving Vehicles

Self-driving vehicle is not one simple technology but rather a huge system integrating many technologies, which means that the algorithm of autonomous driving is a challenging task. Indeed, most existing autonomous driving classes focus on one of the technologies involved. Vehicle detection road would be contributed algorithm in this process. Various classes of vehicles are to be detected from the surveillance video, and then they need to be classified based on various feature points. This paper brings out the edge detection methods used for vehicle detection from a camera. Lane edge detection is an important foundation in the development of Self-Driving vehicles.
Characterization of Different-Al-Content AlGaN/GaN Heterostructures and High-Electron-Mobility Transistors Grown on 100-mm-Diameter Sapphire Substrates by Metalorganic Vapor Phase Epitaxy

Different-Al-content AlxGa1-xN/GaN (0.26≤x≤0.52) high-electron-mobility transistor (HEMT) structures were grown on large-area 100-mm-diameter sapphire substrates by metalorganic vapor phase epitaxy (MOVPE). A low sheet resistance of approximately 380 Ω/sq. and an in-wafer total variation of 7.6% with a standard deviation of 1.6% were obtained for the sample with the highest Al content of 0.52. The two-dimensional electron gas (2DEG) density and room-temperature Hall mobility of the Al0.52Ga0. 48N/GaN HEMT structures were 1.76×1013/cm2 and 971 cm2/Vs with in-wafer total variations of 7.8% and 8.1%, respectively. HEMTs were successfully fabricated and they exhibited good dc characteristics. The dependence of maximum source–drain current density on Al content was consistent with the Al content dependence of sheet carrier concentration. The highest source–drain current density of 1033 mA/mm with a maximum extrinsic transconductance of 228 mS/mm was obtained for 2-µm-gate-length Al0.52Ga0. 48N/GaN HEMTs.