Miracle-dz/UniFAR-FTUs huggingface dataset:
A study of the relationship between ethical sensitivity and emotional intelligence in nursing, anesthesia, and operating room students

When providing healthcare services, healthcare students face a variety of ethical issues and conflicts daily. Such characteristics as ethical sensitivity and emotional intelligence are influential in determining an individual’s performance and can prove useful in coping with challenges in life. Given the significance of these two variables in the career prospects and educational and professional lives of students, the present cross-sectional study with 486 students majoring in nursing, operating room, and anesthesia, aims to investigate the relationship between ethical sensitivity and emotional intelligence. Data were collected using demographics, ethical sensitivity, and emotional intelligence. The students’ ethical sensitivity level was high, while their emotional intelligence was average. Regression analysis revealed that students’ ethical sensitivity was related to their emotional intelligence and gender. There is also a significant correlation between ethical sensitivity and emotional intelligence on one hand and gender on the other: female students possess higher levels of ethical sensitivity, while male students have greater emotional intelligence. Students in nursing schools may benefit from greater insight into the concepts of ethical sensitivity and emotional intelligence and ways to enhance them. Keywords: ethical sensitivity, emotional intelligence, nursing students, operating room, anesthesia.
Double image encryption based on the random fractional transform

This paper proposes a method for double image encryption based on a proposed random fractional transform. Firstly, we produce a pseudorandom sequence to randomize the eigenvalue of the discrete fractional Fourier transform based on logistic map. Secondly, a random matrix is produced based on two-dimension logistic map. Then, the eigenvector of the proposed random matrix is used to replace the eigenvector of the discrete fractional Fourier transform. At the last, we present a double image encryption algorithm based on the proposed random fractional transform. Numerical simulations have been performed to verify the efficiency of the proposed algorithm.
Aprendizagens Expansivas Reveladas pela Pesquisa sobre a Atividade Matemática na Sala de Aula

In this article we examine the theoretical and methodological possibilities offered by Expansive Learning theory to analyze learning resulting from a problem solving classroom activity. This activity involved the use of a school procedure, rule of three, and its subsequent unfolding when students' everyday situations were confronted with a school procedure. The field research, in two 8th-grade classes from a public school, revealed a range of learning instances not anticipated by the teacher related with the meaning expansion of a school procedure (rule of three) and with the different ways of solving a problem when everyday situations are introduced by means of a cultural artifact (water bill). It also showed how research in mathematics education may influence the subjects' learning.
Loss Mitigation in Self-Biased Microstrip Circulators

Integration of the ferrite devices in the RF front-end and active antennas is hindered by the need for external magnets, biasing soft microwave ferrites. The hexaferrite-based self-biased nonreciprocal devices can operate without external magnets at mm-wave frequencies but the currently available hexaferrite materials inflict high RF losses at lower frequencies, particularly in the wireless communication bands. In this paper, the parameters of La-Co-substituted hexaferrite compounds are used for the self-biased circulators in the low GHz frequency bands, and a means of the dissipation loss reduction are discussed.
Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis

Background Social media has transformed the way health messages are communicated. This has created new challenges and ethical considerations while providing a platform to share nutrition information for communities to connect and for information to spread. However, research exploring the web-based diet communities of popular diets is limited. Objective This study aims to characterize the web-based discourse of popular diets, describe information dissemination, identify influential voices, and explore interactions between community networks and themes of mental health. Methods This exploratory study used Twitter social media posts for an online social network analysis. Popular diet keywords were systematically developed, and data were collected and analyzed using the NodeXL metrics tool (Social Media Research Foundation) to determine the key network metrics (vertices, edges, cluster algorithms, graph visualization, centrality measures, text analysis, and time-series analytics). Results The vegan and ketogenic diets had the largest networks, whereas the zone diet had the smallest network. In total, 31.2% (54/173) of the top users endorsed the corresponding diet, and 11% (19/173) claimed a health or science education, which included 1.2% (2/173) of dietitians. Complete fragmentation and hub and spoke messaging were the dominant network structures. In total, 69% (11/16) of the networks interacted, where the ketogenic diet was mentioned most, with depression and anxiety and eating disorder words most prominent in the “zone diet” network and the least prominent in the “soy-free,” “vegan,” “dairy-free,” and “gluten-free” diet networks. Conclusions Social media activity reflects diet trends and provides a platform for nutrition information to spread through resharing. A longitudinal exploration of popular diet networks is needed to further understand the impact social media can have on dietary choices. Social media training is vital, and nutrition professionals must work together as a community to actively reshare evidence-based posts on the web.
Adaptive Submanifold-Preserving Sparse Regression for Feature Selection And Multiclass Classification

In this paper, we propose a novel embedded feature selection method, which is able to select the informative and discriminative features with the underlying submanifolds of data in intra-class being well preserved so as to improve the classification performance. Specifically, we first impose the l2,1-norm on both loss function and projection matrix with the aim to suppress the influence of noise and achieve the sparse features selection. Then, a retargeted learning technique is introduced into our model to further enhance the discriminant ability of the projection matrix. Moreover, we design an adaptive graph regularizer to fully utilize the intra-class local submanifold information, which can endow the projection matrix with more locality preserving power and prevent overfitting. What is more, our method jointly optimizes problems of the projection learning, target learning and graph learning instead of separately learning them as in most of existing works, which guarantees an overall optimality in algorithmic performance. Experimental results on both synthetic and real-world image data sets demonstrate the superiorities of our method on local submanifold structure exploration and classification task.
Monitoring of Volatile Organic Compounds in Strawberry Genotypes over the Harvest Period

Volatile Organic Compounds (VOCs) over the harvest period have been assessed in twenty-five strawberry genotypes cultivated in western Greece. Using liquid–liquid extraction and gas chromatography–mass spectrometry (GC–-MS), twenty-eight volatiles were monitored at early (T1) and mid-harvest (T3) time points to investigate the effect of the genotype and harvest time on strawberry volatilome. A quantitative impact of both harvest date and genotype on VOCs associated with aroma was demonstrated, with the most significant VOCs being terpenes, esters, and acids, followed by lactones and furanones. Harvest date was crucial for terpenoid and phenylpropanoid content, and important for esters, short-chain acids, and lactones. Six out of the twenty-five genotypes (four commercial varieties, including ‘Rociera’, ‘Victory’, ‘Leyre’, and ‘Inspire’, and two advanced selection genotypes (G2 and G8) were evaluated at two additional time points, covering the entire harvest season. The volatile levels were higher in fruits harvested at early stages (T1–T2) for most of the genotypes examined. The G2 genotype turned out to have a less ample but more stable volatile profile throughout harvesting, while ‘Victory’, ‘Leyre’, and ‘Inspire’ exhibited less abrupt changes than ‘Rociera’. This study demonstrates that the determination of VOCs provides significant information regarding the differences in strawberry genotypes related to aroma and enables the selection of genotypes based on specific VOCs content and/or volatile stability over the harvest period. Furthermore, this study pinpoints that growers could opt for optimal harvest dates based on the genotypes and the VOC content.
The emergence and dominance of Planktothrix rubescens as an hypolimnetic cyanobacterium in response to re‐oligotrophication of a deep peri‐alpine lake

Toxic cyanobacteria, such as Planktothrix rubescens, came to dominate temperate lakes in the mid‐to‐late 20th century, as a result of eutrophication. Even after decades of re‐oligotrophication, where phosphorus levels were reduced by 1–2 orders of magnitude, P. rubescens remains present in various lakes. In this study, we examine the persistence and changes of P. rubescens deep chlorophyll maximum (DCM) in Lake Hallwil (Switzerland) over 35 yr of steadily decreasing phosphorous concentrations. Although lake transparency increased and the euphotic layer deepened during this period, the P. rubescens population maximum moved even deeper. It is now found ca. 7.7 m deeper than its shallowest position in the 2000s, and this depth no longer coincides with the depth of maximal water column stability. P. rubescens neutral buoyancy has now driven it beneath the stable metalimnion into the hypolimnion, where buoyancy regulation is restricted due to reduced metabolic activity at low light and low temperature. If P. rubescens DCM continues to deepen each year, it will eventually reach a region of lower stability in the hypolimnion where turbulent conditions are strong enough to disperse the DCM. We also explore the mechanisms that ensure P. rubescens ongoing presence in peri‐alpine lakes despite strong re‐oligotrophication and ongoing climate change. We find that P. rubescens in the lake is mainly sustained by growth during fully mixed conditions in winter, not during stratification in summer. This may contradict another commonly made prediction that periods of longer stratification will promote future blooms of this cyanobacterium.
Integrated watershed management solutions for healthy coastal ecosystems and people

Tropical coastal ecosystems are in decline worldwide due to an increasing suite of human activities, which threaten the biodiversity and human wellbeing that these ecosystems support. One of the major drivers of decline is poor water quality from land-based activities. This review summarises the evidence of impacts to coastal ecosystems, particularly coral reefs, from sediments, nutrients, chemicals and pathogens entering
L’anaphore associative

Depois de dedicar a anafora varios trabalhos importantes, e depois de participar por varios anos num projeto de pesquisa de grande envergadura dedicado ao tratamento do mesmo tema, Georges Kleiber (Universidade Max Bloch, Estrasburgo) retorna a anafora com este alentado estudo monografico, que enfoca especificamente uma de suas formas mais intrigantes, a chamada anafora associativa. (daqui para frente, AA).
Detecting Malicious Migration on Edge to Prevent Running Data Leakage

With the popularity of the Internet of Things (IoT) applications, for instance, smart homes and smart medical, edge servers have become increasingly critical infrastructures. Nevertheless, the loose management puts the edge server under the threat of malicious administrators, which causes the leaking risks of the user’s data security. We first give a Data Sniffing Attack that malicious administrators can use live migration to complete without being discovered. To resist the attack, the transparency of live migration to users is the most severe difficulty, where there has not been an effective solution yet. In this paper, we propose a live migration detection model to simulate the migration process, namely observing the indicator values that can obtain without high authorities and calculating the possibility of state transition. Then through many migration experiments, we present the immediate indicators represented by the OS interrupts and the persistent indicators represented by the IO speed, and sort these indicator values into datasets. Next, we train ten frequently-used classifiers and show their accuracy. Eventually, we analyze the advantages and disadvantages of different algorithms in predicting migration and provide the weight recommendation if applied in the detection model.
KINETIC ANALYSIS OF THE SUPPORT LEG IN SOCCER INSTEP KICKING

The purpose of the present study was to illustrate the kinetic aspect of the support leg during soccer instep kicking. The motion was captured together with the ground reaction force using a motion capture system. Moments and angular velocities of ankle, knee and hip joint for the support leg were calculated. The ankle joint was passively rotated threedimensionally, during which these motions were counteracted to the joint moments. The knee joint motion, likewise, counteracted to the joint moment from the instant of the touch-down to just before ball impact. It is likely that the ankle and knee joints have a role in attenuating the impact of landing. In contrast, positive power due to the knee and hip extension moments appeared just before ball impact. It can be assumed that those motions may serve to lift the body thereby indirectly contributing the kick leg swing.
Robust Binaural Sound Localisation with Temporal Attention

Despite there being clear evidence for attentional effects in biological spatial hearing, relatively few machine hearing systems exploit attention in binaural sound localisation. This paper addresses this issue by proposing a novel binaural machine hearing system with temporal attention for robust localisation of sound sources in noisy and reverberant conditions. A convolutional neural network is employed to extract noise-robust localisation features, which are similar to interaural phase difference, directly from phase spectra of the left and right ears for each frame. A temporal attention layer operates on top of these frame-level features by incorporating outputs of a temporal mask estimation module that indicate target dominance within each frame. The combined features are then exploited by fully connected layers, which map them to the corresponding source azimuth. Both the temporal mask estimation module and the sound localisation module are trained jointly in a multi-task learning manner. Our evaluation shows that the proposed system is able to accurately estimate the azimuth of a sound source in various reverberant and noisy conditions.
Semi-Supervised Remote Sensing Image Change Detection Using Mean Teacher Model for Constructing Pseudo-Labels

In recent years, deep learning has ushered in great developments in remote sensing image change detection. Practically, it is labor-intensive and time-consuming to label images for co-registration. In this paper, we propose a semi-supervised training that uses the mean teacher model to construct pseudo-labels to increase the generalizability of the model trained with a handful of data supervision. More specifically, we first supervise the training of a student change detection model with a few labeled data, while migrating the model parameters from each training round to a teacher model with the same structure through Exponential Moving Average (EMA). The weakly augmented output produced by the teacher model was preferable to the strongly augmented prediction produced by the student to penalize the latter. We explore the paradigm of Strong-to-Weak Consistency in change detection. Experiments on the LEVIR-CD and WHU-CD have been extensively conducted and state-of-the-art performance has been achieved1.
Joint Unmixing And Demosaicing Methods For Snapshot Spectral Images

Recent technological advances in design and processing speed have successfully demonstrated a new snapshot mosaic imaging sensor architecture (SSI), allowing miniaturized platforms to efficiently acquire the spatio-spectral content of the dynamic scenes from a single exposure. However, SSI systems have a fundamental trade-off between spatial and spectral resolution because they associate each pixel with a specific spectral band. In this paper, we introduce the problem of joint "demosaicing" and "unmixing" for the hyperspectral images acquired by the SSI camera that we formulate as a low-rank matrix factorization and completion problem. For that reason— and in addition to a "naive" approach—we extend the "pure pixel" framework to the SSI sensor patch level and propose a dedicated method which (i) assumes the observed data to be locally rank-1 in some SSI "patches" to find, (ii) estimates endmembers in these patches which are (iii) clustered to derive the actual spectra. The abundances are then recovered using nonnegative least squares in each patch. The experiments show that our proposed scheme provides a slightly better demosaicing performance than state-of-the-art methods and a much higher unmixing enhancement.
Correction and restoration of images obtained through turbulent media

Wavefront distortion introduced as light passes through the atmosphere results in short exposure images which exhibit, amongst other effects, blurring which varies from region to region. Our aim is to remove the position dependency of the blurring and to achieve this, we need to understand the effect of atmospheric turbulence on short exposure images. The individual images are dewarped, summed and then iteratively deblurred using an estimate of the now position independent point spread function introduced by the telescope optics and the atmosphere and the resultant image approaches the theoretical resolution limit of our optical/imaging system. An important by-product of the process is a sequence of detailed shift maps which provide, in effect, a visualisation of the instantaneous turbulence field.
Local update of support vector machine decision boundaries

This paper presents a new adaptive sampling technique for the construction of locally reflned explicit decision functions. The decision functions can be used for both deterministic and probabilistic optimization, and may represent a constraint or a limit-state function. In particular, the focus of this paper is on reliability-based design optimization (RBDO). Instead of approximating the responses, the method is based on explicit design space decomposition (EDSD), in which an explicit boundary separating distinct regions in the design space is constructed. A statistical learning tool known as support vector machine (SVM) is used to construct the boundaries. A major advantage of using an EDSD-based method lies in its ability to handle discontinuous responses. A separate adaptive sampling scheme for calculating the probability of failure is also developed, which is used within the RBDO process. The update methodology is validated through several test examples with analytical decision functions.
Machine Learning Based Early Debris Detection Using Automotive Low Level Radar Data

Road safety for automated vehicles requires accurate and early detection of stationary objects in the vehicle’s path. Radar can use doppler to effectively identify stationary objects and make these identifications at long range and in severe weather and poor light conditions. In this paper, we propose a radar-based stationary object detection system that combines signal processing techniques with machine learning technology to detect stationary in-path objects from the low level spectra of front looking radars. The proposed system consists of novel signal and image processing methods to extract key features from the raw data, which are fed into a long short-term memory (LSTM) to determine the probability of a stationary object in-lane at each range. Experiments with collected data in controlled and uncontrolled scenarios demonstrate the effectiveness of our approach.
Object Affordance-Based Implicit Interaction for Wheelchair-Mounted Robotic Arm Using a Laser Pointer

With the growth of the world’s population, limited healthcare resources cannot provide adequate nursing services for all people in need. The wheelchair-mounted robotic arm (WMRA) with interactive technology could help to improve users’ self-care ability and relieve nursing stress. However, the users struggle to control the WMRA due to complex operations. To use the WMRA with less burden, this paper proposes an object affordance-based implicit interaction technology using a laser pointer. Firstly, a laser semantic identification algorithm combined with the YOLOv4 and the support vector machine (SVM) is designed to identify laser semantics. Then, an implicit action intention reasoning algorithm, based on the concept of object affordance, is explored to infer users’ intentions and learn their preferences. For the purpose of performing the actions about task intention in the scene, the dynamic movement primitives (DMP) and the finite state mechanism (FSM) are respectively used to generalize the trajectories of actions and reorder the sequence of actions in the template library. In the end, we verified the feasibility of the proposed technology on a WMRA platform. Compared with the previous method, the proposed technology can output the desired intention faster and significantly reduce the user’s limb involvement time (about 85%) in operating the WMRA under the same task.
Acoustic Field Imaging of Pipeline Turbulence for Noninvasive and Distributed Gas Flow Measurement

Precise control and measurement of the flow are essential for pipeline safety monitoring and economic accounting. Nowadays, the measurement of pipeline flow relies on point measurement schemes such as mechanical flowmeters, which invade the pipeline and can irreversibly damage the fluid pressure, especially when multipoint measurements are required. To overcome these problems, we have explored a novel technique in which the acoustic field properties of pipeline turbulence are reflected by distributed backscattering light field, from which the flow can be measured via acoustic field imaging. The acoustic field information is recorded by a helical deployment scheme using backscattering-enhanced optical fiber (BEOF) encapsulated in the polyethylene anticorrosive layer, thereby achieving highly sensitive, noninvasive, and distributed online monitoring of gas flows. We obtained 24-h flow fluctuation distributions with only one sensing fiber for two operating gas pipelines, and the flow measurement accuracy reached higher than 98% within the error range of −15% to 15%. Moreover, the minimum detectable flow is evaluated and it is demonstrated that the proposed scheme is extremely sensitive to weak flows. We successfully demonstrated noninvasive and distributed online measurement of operational pipeline flow, which will exhibit great potential for pipeline integrity management in the future.