Miracle-dz/UniFAR-FTUs huggingface dataset:
The Moral Permissibility of Digital Nudging in the Workplace: Reconciling Justification and Legitimation

Organisations increasingly use digital nudges to influence their workforces’ behaviour without coercion or incentives. This can expose employees to arbitrary domination by infringing on their autonomy through manipulation and indoctrination. Nudges might furthermore give rise to the phenomenon of “organised immaturity. ” Adopting a balanced approach between overly optimistic and dystopian standpoints, I propose a framework for determining the moral permissibility of digital nudging in the workplace. In this regard, I argue that not only should organisations provide pre-discursive justification of nudges but they should also ensure that employees can challenge their implementation whenever necessary through legitimation procedures. Building on Rainer Forst’s concept of the right to justification, this article offers a way to combine contract- and deliberation-based theories for addressing questions in business ethics. I further introduce the concept of meta-autonomy as a capacity that employees can acquire to counter threats of arbitrary domination and to mitigate organised immaturity.
Simulation study on assist-as-needed control of a rehabilitation robotic walker

BACKGROUND: Along with China entering an aging society, the percentage of people that over 60 will reach 34.9% in 2050, resulted in a significant increase in stroke patients. OBJECTIVE: This paper proposes a rehabilitation robotic walker for walking assistance during the daily life, and a control method for the motor relearning during the gait training. The walker consists of an omni-directional mobile platform (OMP) which ensures the walker can move on the ground, a body weight support system (BWS) which is capable of providing the desired unloading force, and a pelvic assist mechanism (PAM) to provide the user with four degrees of freedom and avoid the rigid impact. The study goal is to gain a better understanding of the assist-as-needed control strategy during the gait training. METHODS: For the man-machine interaction control, the assist-as-needed control strategy is adopted to guide the users’ motions and improve the interaction experience. To build the force field in the three-dimensional space, the dynamics of the system is derived to increase the accuracy of force control. RESULTS: The simulation results show that the force field around the motion trajectory was generated in the three-dimensional space. In order to understand the force field, we designed the simulation on sagittal plane and the controller can generate the appropriate force field. The preliminary experiment results were consistent with the simulation results. CONCLUSION: Based on the mathematical simulation and the preliminary test, the results demonstrate that the proposed system can provide the guide force around the target trajectory, the accuracy of force control still remains to be improved.
Porcine Epidemic Diarrhea Virus Replication in Human Intestinal Cells Reveals Potential Susceptibility to Cross-Species Infection

Various coronaviruses have emerged as a result of cross-species transmission among humans and domestic animals. Porcine epidemic diarrhea virus (PEDV; family Coronaviridae, genus Alphacoronavirus) causes acute diarrhea, vomiting, dehydration, and high mortality in neonatal piglets. Porcine small intestinal epithelial cells (IPEC-J2 cells) can be used as target cells for PEDV infection. However, the origin of PEDV in pigs, the host range, and cross-species infection of PEDV remain unclear. To determine whether PEDV has the ability to infect human cells in vitro, human small intestinal epithelial cells (FHs 74 Int cells) were inoculated with PEDV LJX and PEDV CV777 strains. The results indicated that PEDV LJX, but not PEDV CV777, could infect FHs 74 Int cells. Furthermore, we observed M gene mRNA transcripts and N protein expression in infected FHs 74 Int cells. A one-step growth curve showed that the highest viral titer of PEDV occurred at 12 h post infection. Viral particles in vacuoles were observed in FHs 74 Int cells at 24 h post infection. The results proved that human small intestinal epithelial cells are susceptible to PEDV infection, suggesting the possibility of cross-species transmission of PEDV.
Analysis of Dynamic Plantar Pressure and Influence of Clinical-Functional Measures on Their Performance in Subjects with Bimalleolar Ankle Fracture at 6 and 12 Months Post-Surgery

Recovery after ankle fracture surgery can be slow and even present functional deficits in the long term, so it is essential to monitor the rehabilitation process objectively and detect which parameters are recovered earlier or later. The aim of this study was (1) to evaluate dynamic plantar pressure and functional status in patients with bimalleolar ankle fracture 6 and 12 months after surgery, and (2) to study their degree of correlation with previously collected clinical variables. Twenty-two subjects with bimalleolar ankle fractures and eleven healthy subjects were included in the study. Data collection was performed at 6 and 12 months after surgery and included clinical measurements (ankle dorsiflexion range of motion and bimalleolar/calf circumference), functional scales (AOFAS and OMAS), and dynamic plantar pressure analysis. The main results found in plantar pressure were a lower mean/peak plantar pressure, as well as a lower contact time at 6 and 12 months with respect to the healthy leg and control group and only the control group, respectively (effect size 0.63 ≤ d ≤ 0.97). Furthermore, in the ankle fracture group there is a moderate negative correlation (−0.435 ≤ r ≤ 0.674) between plantar pressures (average and peak) with bimalleolar and calf circumference. The AOFAS and OMAS scale scores increased at 12 months to 84.4 and 80.0 points, respectively. Despite the evident improvement one year after surgery, data collected using the pressure platform and functional scales suggest that recovery is not yet complete.
Detection of atrial fibrillation using continuous wavelet transform and wavelet coherence

Atrial fibrillation AF is a type of heart ailment that occurs when atria beats quicker than normal to move blood from atria to the ventricles. Our present study proposes a technique to detect AF ECG patterns with the use of continuous wavelet transform CWT, wavelet coherence WTC is presented. The wavelet coherence function finds common frequencies between two signals and evaluates similarity of the two signals. The mother wavelet used is db4. The ECG variation of atrial fibrillation AF is observed in lead II of ECG. For the detection of normal and AF beats, WTC output values are given as the input features for the Levenberg-Marquardt neural network LMNN classifier. The data was collected from MIT/BIH AF database.
Constrained Nonnegative Matrix Factorization Based on Label Propagation for Data Representation

Nonnegative matrix factorization (NMF) algorithms are a series of dimensional reduction techniques widely used in data preprocessing. To improve the performance of clustering and the discrimination of the low-dimensional representation in NMF, we proposed a novel semisupervised constrained nonnegative matrix factorization based on label propagation (LpCNMF). Specifically, the proposed LpCNMF adopts graph and label propagation as regularization terms, then makes use of a small amount of labeled data to predict the label information of the unlabeled data and finally obtains a predictive membership matrix with more label information. At the same time, we introduce an efficient alternating iterative algorithm to solve the optimization problem of the objective function in the LpCNMF. Unlike other NMF algorithms that only update the basis and coefficient matrices, the LpCNMF algorithm increases the update of the predictive membership matrix obtained by label propagation. Experimental results on various benchmark datasets demonstrate the superiority of our algorithm over existing state-of-the-art NMF algorithms.
FATORES PSICOSSOCIAIS E SÍNDROME DE BURNOUT EM PROFESSORES DA EDUCAÇÃO BÁSICA

RESUMO A Síndrome de Burnout (SB) é uma doença resultante de estresse crônico no trabalho que acomete trabalhadores de diferentes áreas, inclusive professores. Este é um estudo de revisão sistemática com objetivo de verificar a associação entre os fatores psicossociais do trabalho (FPT), as características individuais e a SB em professores da rede de Educação Básica. Foram analisados artigos das bases de dados PubMed, Scielo e Periódicos Capes, publicados entre 2014 e 2018, e que utilizaram o Maslach Burnout Inventory na avaliação de SB. Os resultados evidenciaram prevalência variável de SB entre os docentes. Além dos problemas de infraestrutura, as elevadas demandas de trabalho, a falta de autonomia, a qualidade ruim dos relacionamentos e a violência física e psicológica vivenciada nas escolas foram os principais fatores de risco para a SB. Sugere-se considerar os fatores psicossociais e organizacionais do trabalho nas ações de prevenção da Síndrome de Burnout.
Versatile Silver-Nanoparticle-Impregnated Membranes for Water Treatment: A Review

Increased affordability, smaller footprint, and high permeability quality that meets stringent water quality standards have accelerated the uptake of membranes in water treatment. Moreover, low pressure, gravity-based microfiltration (MF) and ultrafiltration (UF) membranes eliminate the use of electricity and pumps. However, MF and UF processes remove contaminants by size exclusion, based on membrane pore size. This limits their application in the removal of smaller matter or even harmful microorganisms. There is a need to enhance the membrane properties to meet needs such as adequate disinfection, flux amelioration, and reduced membrane fouling. To achieve these, the incorporation of nanoparticles with unique properties in membranes has potential. Herein, we review recent developments in the impregnation of polymeric and ceramic microfiltration and ultrafiltration membranes with silver nanoparticles that are applied in water treatment. We critically evaluated the potential of these membranes in enhanced antifouling, increased permeability quality and flux compared to uncoated membranes. Despite the intensive research in this area, most studies have been performed at laboratory scale for short periods of time. There is a need for studies that assess the long-term stability of the nanoparticles and the impact on disinfection and antifouling performance. These challenges are addressed in this study and future directions.
The Psychosocial Model of Absenteeism: Transition from 4.0 to 5.0

The research provides insight into the factors that determine absenteeism in different types of organizations, in order to facilitate the adjustment of employees and organizations in the transition process from Industry 4.0 to Industry 5.0. The aim of the study is to predict the absenteeism of employees in the context of job characteristics and mental health. Additionally, the research investigated the effect of size, ownership, and sector of the companies on absenteeism, job characteristics, and mental health. The sample included responses from 502 employees of different sociodemographic characteristics that work in various types of organizations, performing white-collar and blue-collar jobs. A short mental health questionnaire—Mental Health Inventory, MHI-5—was used in order to measure mental health. The Job Characteristics Questionnaire was used to measure the employees’ perceptions of their job characteristics—job variety, autonomy, feedback, dealing with others, task identity, and friendship. The absenteeism is operationalized with the question: “During the past 12 months, how many days were you absent from work for any reason? ”. The findings suggest that mental health and job characteristics significantly reduce absenteeism among different sectors. The result showed that the size, ownership, and sector of the organization significantly affect the absenteeism, job characteristics, and the mental health of the employees. The results support the premises of Industry 5.0 and offer a new human-centric approach to absenteeism through the promotion of mental health through long-term organizational strategies and a more inclusive approach to employees’ preferences in relation to job characteristics. The study offers a new, double-sided model of absenteeism, determining causal factors from the perspective of personal and organizational factors.
A Scalable Compositional Falsification Approach for Identifying Challenging Scenarios in Cyber-Physical Systems

This article presents a scalable approach for identifying system inputs and trajectories that lead to undesirable scenarios in a cyber-physical system (CPS). The proposed approach falls under the broad class of falsification methods. The objective in falsification is to find initial conditions and input signals resulting in trajectories that violate a system property expressed as a formal specification. The existing falsification methods are not suitable for handling systems where the input at a specific time depends on the state history of the system. Autonomous vehicle platoons and certain multi-agent systems that are required to maintain connectivity are some examples of such systems. In the first part of this article, we employ a graph-search-based motion planning algorithm to develop a falsification method capable of identifying falsifying inputs for the aforementioned class of systems. We demonstrate the effectiveness of the approach through a case study involving a vehicle platoon consisting of three autonomous vehicles, wherein one of the vehicles is tasked to execute a lane-change maneuver. In the second part, we extend the proposed approach to analyze a network of three unmanned underwater vehicles, wherein our objective is to identify system inputs resulting in trajectories that lead to a deadlock. The approach leverages the component-based structure inherent in the considered multi-agent system and employs a surrogate model in place of computationally demanding components present in the system. These modifications are shown to improve the scalability of the approach and reduce the computational time in finding falsifying trajectories.
GAUSSIAN KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM FOR IMAGE SEGMENTATION

Image processing is an important research area in computer vision. clustering is an unsupervised study. clustering can also be used for image segmentation. there exist so many methods for image segmentation. image segmentation plays an important role in image analysis.it is one of the first and the most important tasks in image analysis and computer vision. this proposed system presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering algorithm(fcm). the kfcm algorithm that provides image clustering and improves accuracy significantly compared with classical fuzzy c-means algorithm. the new algorithm is called gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and image detail preservation. the objective of the work is to cluster the low intensity in homogeneity area from the noisy images, using the clustering method, segmenting that portion separately using content level set approach. the purpose of designing this system is to produce better segmentation results for images corrupted by noise, so that it can be useful in various fields like medical image analysis, such as tumor detection, study of anatomical structure, and treatment planning.
Detection of microcalcification clusters by 2D-mammography and narrow and wide angle digital breast tomosynthesis

The aim of this study was to compare the detection of microcalcification clusters by human observers in breast images using 2D-mammography and narrow (15°/15 projections) and wide (50°/25 projections) angle digital breast tomosynthesis (DBT). Simulated microcalcification clusters with a range of microcalcification diameters (125 μm-275 μm) were inserted into 6 cm thick simulated compressed breasts. Breast images were produced with and without inserted microcalcification clusters using a set of image modelling tools, which were developed to represent clinical imaging by mammography and tomosynthesis. Commercially available software was used for image processing and image reconstruction. The images were then used in a series of 4-alternative forced choice (4AFC) human observer experiments conducted for signal detection with the microcalcification clusters as targets. The minimum detectable calcification diameter was found for each imaging modality: (i) 2D-mammography: 164±5 μm (ii) narrow angle DBT: 210±5 μm, (iii) wide angle DBT: 255±4 μm. A statistically significant difference was found between the minimum detectable calcification diameters that can be detected by the three imaging modalities. Furthermore, it was found that there was not a statistically significant difference between the results of the five observers that participated in this study. In conclusion, this study presents a method that quantifies the threshold diameter required for microcalcification detection, using high resolution, realistic images with observers, for the comparison of DBT geometries with 2D-mammography. 2Dmammography can visualise smaller detail diameter than both DBT imaging modalities and narrow-angle DBT can visualise a smaller detail diameter than wide-angle DBT.
A Machine-Learning-Based Cyberattack Detector for a Cloud-Based SDN Controller

The rapid evolution of network infrastructure through the softwarization of network elements has led to an exponential increase in the attack surface, thereby increasing the complexity of threat protection. In light of this pressing concern, European Telecommunications Standards Institute (ETSI) TeraFlowSDN (TFS), an open-source microservice-based cloud-native Software-Defined Networking (SDN) controller, integrates robust Machine-Learning components to safeguard its network and infrastructure against potential malicious actors. This work presents a comprehensive study of the integration of these Machine-Learning components in a distributed scenario to provide secure end-to-end protection against cyber threats occurring at the packet level of the telecom operator’s Virtual Private Network (VPN) services configured with that feature. To illustrate the effectiveness of this integration, a real-world emerging attack vector (the cryptomining malware attack) is used as a demonstration. Furthermore, to address the pressing challenge of energy consumption in the telecom industry, we harness the full potential of state-of-the-art Green Artificial Intelligence techniques to optimize the size and complexity of Machine-Learning models in order to reduce their energy usage while maintaining their ability to accurately detect potential cyber threats. Additionally, to enhance the integrity and security of TeraFlowSDN’s cybersecurity components, Machine-Learning models are safeguarded from sophisticated adversarial attacks that attempt to deceive them by subtly perturbing input data. To accomplish this goal, Machine-Learning models are retrained with high-quality adversarial examples generated using a Generative Adversarial Network.
Prism Couplers with Convex Output Surfaces for Nonlinear Cherenkov Terahertz Generation

Sideways THz generation in Mg:LiNbO3 crystal is studied considering Si-prism-lens couplers with different output surface curvatures. A theoretical approach is developed for modeling the angular distributions of THz radiation power inside the crystal, inside the Si coupler and outside in free space. Our calculations show how the imposition of a plano-convex lens on the standard flat prism can substantially improve the THz generation efficiency. The ratio between the lens curvature radius and the distance from the curvature center to the point of generation on the lens axis is found to be one of the most important parameters. The developed general approach can be used for the further design of the optimal THz extraction elements of a different configuration.
Wideband High-Speed and High-Accuracy Instantaneous Frequency Measurement System

A new photonic-assisted instantaneous frequency measurement system is presented. It overcomes the latency problem in the reported structures based on the frequency-to-time mapping technique or the frequency-to-power mapping technique that involves a long length of fiber, and at the same time, enables the incoming microwave signal frequency to be measured over a wide frequency range with only small errors. The system generates three low-frequency signals. The phases of the three low-frequency signals are compared. One of the two low-frequency signal phase differences is used to estimate the incoming microwave signal frequency unambiguously over a wide frequency range and the other is used to provide accurate microwave signal frequency measurement. A proof-of-concept experiment is set up. Experimental results show, by measuring the phase difference of two low-frequency signals, the frequency of the input microwave signal can be determined unambiguously in 15 GHz and 500 MHz frequency ranges with errors below ±220 MHz and ±10 MHz respectively. Hence, by using two low-frequency signal phase differences, the input microwave signal frequency can be determined accurately over a wide frequency range. The new photonic-assisted frequency measurement system has a fast response time, which is an order of magnitude shorter than that of the systems based on the frequency-to-time mapping technique and the frequency-to-power mapping technique with a kilometer-long fiber.
Mesh-Based DGCNN: Semantic Segmentation of Textured 3-D Urban Scenes

Textured 3-D mesh is one of the final user products in photogrammetry and remote sensing. However, research on the semantic segmentation of complex urban scenes represented by textured 3-D meshes is in its infancy. We present a mesh-based dynamic graph convolutional neural network (DGCNN) for the semantic segmentation of textured 3-D meshes. To represent each mesh facet, composite input feature vectors are constructed by concatenating the face-inherent features, i.e., $XYZ$ coordinates of the center of gravity (CoG), texture values, and normal vectors (NVs). A texture fusion module is embedded into the proposed mesh-based DGCNN to generate high-level semantic features of the high-resolution texture information, which is useful for semantic segmentation. We achieve competitive accuracies when the proposed method is applied to the SUM mesh datasets. The overall accuracy (OA), Kappa coefficient (Kap), mean precision (mP), mean recall (mR), mean F1 score (mF1), and mean intersection over union (mIoU) are 93.3%, 88.7%, 79.6%, 83.0%, 80.7%, and 69.6%, respectively. In particular, the OA, mean class accuracy (mAcc), mIoU, and mF1 increase by 0.3%, 12.4%, 3.4%, and 6.9%, respectively, compared with the state-of-the-art method.