Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-deformation for assembly performance of machine centers

The current research of machine center accuracy in workspace mainly focuses on the poor geometric error subjected to thermal and gravity load while in operation, however, there are little researches focusing on the effect of machine center elastic deformations on workspace volume. Therefore, a method called pre-deformation for assembly performance is presented. This method is technically based on the characteristics of machine tool assembly and collaborative computer-aided engineering (CAE) analysis. The research goal is to enhance assembly performance, including straightness, positioning, and angular errors, to realize the precision of the machine tool design. A vertical machine center is taken as an example to illustrate the proposed method. The concept of travel error is defined to obtain the law of the guide surface. The machine center assembly performance is analyzed under cold condition and thermal balance condition to establish the function of pre-deformation. Then, the guide surface in normal direction is processed with the pre-deformation function, and the machine tool assembly performance is measured using a laser interferometer. The measuring results show that the straightness deviation of the Z component in the Y-direction is 158.9% of the allowable value primarily because of the gravity of the spindle head, and the straightness of the X and Y components is minimal. When the machine tool is processed in pre-deformation, the straightness of the Z axis moving component is reduced to 91.2%. This research proposes a pre-deformation machine center assembly method which has sufficient capacity to improving assembly accuracy of machine centers.
Validation of Large-Eddy Simulation for the Prediction of Compressible Flow in an Axial Compressor Stage

A better understanding of unsteady flows (including turbulence) is a necessary step towards a breakthrough in the design of modern gas turbine components. LES emerges as a very promising method to improve both knowledge of complex physics and reliability of flow solver predictions. However, there is still a lack of evidences in the literature that LES is applicable for rotor/stator configurations at industrial Reynolds numbers. In that context, the objective of the present work is to investigate the capability of LES to predict the turbulent flow in a stage of an axial compressor. The studied configuration operates at conditions relevant to industrial applications (M = 0.5, Re = 5 × 105). The whole 3D flow is considered, including the rotor tip clearance, as well as the natural periodicity of the compressor (i.e. the exact ratio between the number of rotor blades and stator vanes). First a validation of LES is obtained by comparing results on different mesh densities, in order to evaluate the grid requirements necessary to perform a wall-resolved LES (the finest grid is made of o(109) points to represent 3 rotor and 4 stator passages). Typical mesh scales are compared to the Kolmogorov length scale to estimate the regions where the subgrid scale model acts. Results obtained with both URANS and LES are then compared at nominal operating conditions. The analysis focuses on two dataset: time-averaged quantities (boundary layer profiles, turbulent kinetic energy, etc). and unsteady flow data. An evaluation of the flow physics shows that URANS predicts much more losses than LES in this configuration (discrepancy on efficiency is about 6%). The analysis indicates that LES predicts a laminar to turbulent transition at 50% of the rotor chord, leading to thinner boundary layers in both rotor and stator than with URANS. Actually LES also predicts the development of high-energy frequencies in the tip region that are uncorrelated to the blade passing frequency, related to the pulsation of the tip leakage flow.Copyright © 2013 by ASME
A Residual Attention and Local Context-Aware Network for Road Extraction from High-Resolution Remote Sensing Imagery

Extracting road information from high-resolution remote sensing images (HRI) can provide crucial geographic information for many applications. With the improvement of remote sensing image resolution, the image data contain more abundant feature information. However, this phenomenon also enhances the spatial heterogeneity between different types of roads, making it difficult to accurately discern the road and non-road regions using only spectral characteristics. To remedy the above issues, a novel residual attention and local context-aware network (RALC-Net) is proposed for extracting a complete and continuous road network from HRI. RALC-Net utilizes a dual-encoder structure to improve the feature extraction capability of the network, whose two different branches take different feature information as input data. Specifically, we construct the residual attention module using the residual connection that can integrate spatial context information and the attention mechanism, highlighting local semantics to extract local feature information of roads. The residual attention module combines the characteristics of both the residual connection and the attention mechanism to retain complete road edge information, highlight essential semantics, and enhance the generalization capability of the network model. In addition, the multi-scale dilated convolution module is used to extract multi-scale spatial receptive fields to improve the model’s performance further. We perform experiments to verify the performance of each component of RALC-Net through the ablation study. By combining low-level features with high-level semantics, we extract road information and make comparisons with other state-of-the-art models. The experimental results show that the proposed RALC-Net has excellent feature representation ability and robust generalizability, and can extract complete road information from a complex environment.
Size Selective Characterization and Particle Emission Rates during a Simulated Medical Laser Procedure

Introduction Investigations on medical laser interaction with tissue have suggested laser operational parameter settings influence LGAC emission, but this has not been systematically explored. Characterization of medical LCAG particles may improve exposure control strategies and provide insight on the ability of particles to transport viable cellular material. Methods A laboratory-based simulated surgical procedure was designed to characterize LGAC and to estimate exposures in theoretical hospital settings. Two medical lasers (CO2, l =10,600 nm and Ho:YAG, l =2100 nm) were used, varying three operational parameters (beam diameter, PRF, and power) between two levels and the resultant plume was collected using two real-time size selective particle counters and polycarbonate filters in an emission chamber. ANOVA was used to determine the influence of operational parameter settings on size-specific particle emission rate, and particles collected on polycarbonate filters were imaged using a SEM to study particle characteristics. Emission rates were applied to a two-zone exposure model to estimate PM concentrations in ORs. Results Beam diameter was the most influential parameter for both lasers followed by power. An increase in power and decrease in beam diameter led to increased particle emissions for the Ho:YAG laser. For the CO2 laser, higher power led to decreased emission rates of small particles and an increase for large particles while a smaller beam diameter led to an increase of particle emissions for some size ranges (0.02 to 1 µm and 3 to 10 µm) and a decrease in others (0.3 to 0.5 µm and 1 to 3 µm). Particle size observed under an SEM varied between 1 and 10 mm for the Ho:YAG laser and between 1 and 50 mm for the CO2 laser. Visible particle shapes included fibers, foam, and conglomerate particles. Modeled concentrations for the near-field ranged between 0.03 to 0.5 mg/m3 and between 0.01 to 0.4 mg/m3 in the far-field. Discussion/Conclusion The methods used in this study provide a foundation for future investigations to better estimate particle-size dependent emission rates for additional laser operational parameters in order to inform occupational exposure control strategies.
Numerical Simulation Analysis and Engineering Applications on the Double-Side-Wall Heading Step Bench Excavation of Large-Cross Section and Shallow Tunnel

With the pace accelerating of development of urban rail traffic in large and medium-sized cities, urban subway and railway lines need to construct shallow and large-cross section tunnels and especially large-cross section tunnels. Large-cross section tunnel constructions make big influence on the surrounding buildings, roads and environment. How to build underground tunnel fast and safe in the downtown has become an problem which needs to be solved urgently. This article is for the technology of city large-cross section and shallow buried-tunnel, relying on the phase 1 of rail transit no.2 in Changsha, which combined with construction characteristics of the excavation method, double-side-wall method and step method, numerical simulation analysis on influence of the large-cross section tunnel disturbance in the surrounding rock, and provides the necessary technical support.
Metabolic Syndrome and Endocrine Disrupting Chemicals: An Overview of Exposure and Health Effects

Increasing prevalence of metabolic syndrome (MetS) is causing a significant health burden among the European population. Current knowledge supports the notion that endocrine-disrupting chemicals (EDCs) interfere with human metabolism and hormonal balance, contributing to the conventionally recognized lifestyle-related MetS risk factors. This review aims to identify epidemiological studies focusing on the association between MetS or its individual components (e.g., obesity, insulin resistance, diabetes, dyslipidemia and hypertension) and eight HBM4EU priority substances (bisphenol A (BPA), per- and polyfluoroalkyl substances (PFASs), phthalates, polycyclic aromatic hydrocarbons (PAHs), pesticides and heavy metals (cadmium, arsenic and mercury)). Thus far, human biomonitoring (HBM) studies have presented evidence supporting the role of EDC exposures on the development of individual MetS components. The strength of the association varies between the components and EDCs. Current evidence on metabolic disturbances and EDCs is still limited and heterogeneous, and mainly represent studies from North America and Asia, highlighting the need for well-conducted and harmonized HBM programmes among the European population. Rigorous and ongoing HBM in combination with health monitoring can help to identify the most concerning EDC exposures, to guide future risk assessment and policy actions.
Transcribing Lead Sheet-Like Chord Progressions of Jazz Recordings

Abstract The vast majority of research on automatic chord transcription has been developed and tested on databases mainly focused on genres like pop and rock. Jazz is strongly based on improvisation, however, and the way harmony is interpreted is different from many other genres, causing state-of-the-art chord transcription systems to achieve poor performance. This article presents a computational system that transcribes chords from jazz recordings, addressing the specific challenges they present and considering their inherent musical aspects. Taking the raw audio and minor manually obtained inputs from the user, the system can jointly transcribe chords and detect the beat of a recording, allowing a lead sheet–like rendering as output. The analysis is implemented in two parts. First, all segments with a repeating chord progression (the chorus) are aligned based on their musical content using dynamic time warping. Second, the aligned segments are mixed and a convolutional recurrent neural network is used to simultaneously detect beats and transcribe chords. This automatic chord transcription system is trained and tested on jazz recordings only, and achieves better performance than other systems trained on larger databases that are not jazz specific. Additionally, it combines the beat-detection and chord transcription tasks, allowing the creation of a lead sheet–like representation that is easy to interpret by both researchers and musicians.
Carbon nanotube based ultra-low voltage integrated circuits: Scaling down to 0.4 V

Carbon nanotube (CNT) based integrated circuits (ICs) including basic logic and arithmetic circuits were demonstrated working under a supply voltage low as 0.4 V, which is much lower than that used in conventional silicon ICs. The low limit of supply voltage of the CNT circuits is determined by the degraded noise margin originated from the process inducing threshold voltage fluctuation. The power dissipation of CNT ICs can be remarkably reduced by scaling down the supply voltage, and it is of crucial importance for the further developments of nanoelectronics ICs with higher integration density.
Enhancement of Predicting Students Performance Model Using Ensemble Approaches and Educational Data Mining Techniques

Student performance prediction is extremely important in today’s educational system. Predicting student achievement in advance can assist students and teachers in keeping track of the student’s progress. Today, several institutes have implemented a manual ongoing evaluation method. Students benefit from such methods since they help them improve their performance. In this study, we can use educational data mining (EDM), which we recommend as an ensemble classifier to anticipate the understudy accomplishment forecast model based on data mining techniques as classification techniques. This model uses distinct datasets which represent the student’s intercommunication with the instructive model. The exhibition of an understudy’s prescient model is evaluated by a kind of classifiers, for instance, logistic regression, naïve Bayes tree, artificial neural network, support vector system, decision tree, random forest, and k -nearest neighbor. Additionally, we used set processes to evolve the presentation of these classifiers. We utilized Boosting, Random Forest, Bagging, and Voting Algorithms, which are the normal group of techniques used in studies. By using ensemble methods, we will have a good result that demonstrates the dependability of the proposed model. For better productivity, the various classifiers are gathered and, afterward, added to the ensemble method using the Vote procedure. The implementation results demonstrate that the bagging method accomplished a cleared enhancement with the DT model, where the DT algorithm accuracy with bagging increased from 90.4% to 91.4%. Recall results improved from 0.904 to 0.914. Precision results also increased from 0.905 to 0.915.
QMIX Aided Routing in Social-Based Delay-Tolerant Networks

Delay-tolerant network (DTN) is a network that’s designed to operate effectively in heterogeneous networks that may lack continuous network connectivity. It is characterized by their lack of instantaneous end-to-end paths, resulting in difficulties in designing effective DTN routing protocols. Traditional routing algorithms largely rely on greedy schemes. Such schemes can not guarantee the packets will be eventually transmitted to their destinations, thereby presenting a poor transmission efficiency. Recently, the social-based method has attracted a large amount of attention in wireless network routing. It can use the community and the centrality information to increase the delivery rate of the whole network. Therefore, in this article, we introduce the social-based mechanism to our DTN routing design. Besides, how the distributed nodes can learn the collaboration strategies is another challenge. Inspired by the recent success of multi-agent learning in online control, we adopt a centralized training and distributed execution learning paradigm and design a hierarchical social-based DTN architecture. Based on this, we propose a collaborative multi-agent reinforcement learning (termed as QMIX) aided routing algorithm.
Morphological and ultrastructural characterization of neurospheres spontaneously generated in the culture from sheep ovarian cortical cells

Neurospheres (NS) derived from adult stem cells of non‐neural tissues represent a promising source of neural stem cells (NSCs) and neural progenitor cells (NPCs) for autologous cell therapy. Knowing the fine structure of NS cells is essential for characterizing them during differentiation or oncogenic transformation. NS are generated by culturing ovarian cortical cells (OCCs) under specific conditions. To establish whether these OCCs exhibited a similar morphophenotype as those from the central nervous system (CNS) reported in the literature, sheep OCCs were cultured for 21 days to generate NS. Expression levels of pluripotency (Nanog, octamer‐binding transcription factor 4 [Oct4], and SRY‐box transcription factor 2 [Sox2]) and NSCs/NPCs (nestin, paired box 6 [Pax6], and p75 neurotrophin receptor [P75NTR]) transcripts were analyzed by quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR), the NSC/NPC antigens were immunolocalized, and structural and ultrastructural analyses were performed in OCC‐NS on Days 10, 15, and 21 in culture. Spheroids expressed transcripts and antigens of pluripotency as well as NSCs/NPCs. Cells were arranged into an inner core, with frequent apoptotic and degenerative events, and a peripheral epithelial‐like cover with abundant cytoplasmic organelles, apical microvilli, and filament bundles of cytoskeleton elements. Adherens junctions and apical tight and lateral loose interdigitations were found in peripheral cells that eventually lost apical–basal polarization, which might indicate their disengaging/aggregating from/to the NS. We can conclude that OCC‐NS shares the most structural and ultrastructural characteristics with CNS‐NS.
Fixed node reduction technique using relative coordinate estimation algorithm

Abstract: Recently, with the rapid development of factory automation and logistics system, a few workers were able to manage the broad workplace such as large vessels and warehouse. To estimate the exact location of these work-ers in the conventional wireless indoor localization systems, three or more fixed nodes are generally used to recog-nize the location of a mobile node consisting of a single node. However, these methods are inefficient in terms of node deployment because the broad workplace requires a lot of fixed nodes compared to workers(mobile nodes). Therefore, to efficiently deploy fixed nodes in these environments that need a few workers, this paper presents a novel estimation algorithm which can reduce the number of fixed nodes by efficiently recognizing the relative coor-dinates of two fixed nodes through a mobile node composed of three nodes. Also, to minimize the distance errors between mobile node and fixed node, rounding estimation(RE) technique is proposed. Experimental results show that the error rate of localization is improved, by using proposed RE technique, 90.9% compared to conventional trilat-eration in the free space. In addition, despite the number of fixed nodes can be reduced by up to 50% in the in-door free space, the proposed estimation algorithm recognizes precise location which has average error of 0.15m.
Simultaneous measurement of thickness and refractive index by a single-channel self-mixing interferometer

The authors introduce a new method for the simultaneous measurement of thickness d and refractive index n of transparent slabs and thin films. The method is based on the optical phase shift measured by a single-channel, self-mixing interferometer (SMI) as a function of the angle of incidence on the sample. The authors use a motorised rotating stage to apply an angular scan up to ±65° to the sample. Then, the authors analyse the derivative of phase difference with respect to the rotation angle, apply a standardisation and fit it to the theoretical expression and after a few iterations they are able to simultaneously determine n and d, with a typical accuracy of 0.02 and 1%, respectively.
Brain Computer Interface -EEG Based Imagined Word Prediction Using Convolutional Neural Network Visual Stimuli for Speech Disability

Background: Brain Computer Interface (BCI) is one of the fast-growing technological trends, which finds its applications in the field of the healthcare sector. In this work, 16 electrodes of Electroencephalography (EEG) placed according to the 10-20 electrode system is used to acquire the EEG signals. A BCI with EEG based imagined word prediction using Convolutional Neural Network (CNN) is modeled and trained to recognize the words imagined through the EEG brain signal, where the CNN model Alexnet and Googlenet are able to recognize the words due to visual stimuli namely, up, down, right, left and up to ten words. The performance metrics are improved with the Morlet Continuous wavelet transform applied at the pre-processing stage, with seven extracted features such as mean, standard deviation, skewness, kurtosis, bandpower, root mean square, and Shannon entropy.Results: Based on the testing, Alexnet transfer learning model performed better as compared to Googlenet transfer learning model, as it achieved an accuracy of 90.3%, recall, precision, and F1 score of 91.4%, 90%, and 90.7% respectively for seven extracted features. However, the performance metrics decreased when the number of extracted features was reduced from seven to four, to 83.8%, 84.4%, 82.9%, and 83.6% respectively.Conclusions: The Alexnet transfer learning model is selected to be the best model as compared Googlenet, as it achieved an accuracy of 90.3% and the final training option of 80 epoch, 64 batch size, scalogram pre-processing method, ratio of 80:20 training and validation set and initial learning rate of 0.0001.
Organizational Justice and Workplace Bullying: Lessons Learned from Externally Referred Complaints and Investigations

Workplace bullying is a serious psychosocial risk which, when poorly managed, results in detrimental outcomes for individuals, organizations, and society. Some of the most common strategies for addressing bullying within the workplace centre on attempts to document and contextualise the bullying situation—that is, the internal complaint and investigation process. Scholarly inquiries of these investigative mechanisms, however, are limited, and most have neglected the influence of organisational justice as an underpinning mechanism in explaining complainant dissatisfaction. Using evidence from 280 real-life cases of workplace bullying lodged with a peak work, health, and safety agency, we identify how organizational justice manifests in externally referred cases of workplace bullying. Specifically, we match complainant evaluations of the internal complaint and investigation handling process to domains of organisational justice, thereby ascertaining potential threats to efforts to effectively manage and prevent bullying in the workplace. Four types of justice—distributive, procedural, interpersonal, and informational—were identified within the cases. Specifically, in cases of workplace bullying where distributive justice is not upheld (usually by virtue of unsubstantiated claims), the way in which information is gathered and decisions are made (procedural), the way in which the parties are treated (interpersonal), and the timeliness and validity of explanations provided (informational) are all cited by complainants as key factors in their decision to escalate the complaint to an external investigative body. These results signal the need for timely, clear, and compassionate investigative processes that validate complainants’ experiences and serve as a tool for rebuilding trust and repairing damaged relationships in the workplace.