Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Finite-Time Trajectory Tracking Control of Quadrotor Aircraft via Terminal Sliding Mode-Based Active Antidisturbance Approach: A PIL Experiment

This paper presents an accurate solution of finite-time Cartesian trajectory tracking control problem of a quadrotor system by designing and implementing a novel robust flight-control algorithm. The quadrotor is subject to nonlinearities, unmodeled dynamics, parameters’ uncertainties, and external time-varying disturbances. To reject the disturbances and enhance the control system’s robustness, a terminal sliding mode-based active antidisturbance control (TSMBAADC) approach is proposed for rotational and translational subsystems. To improve the tracking performance, a nonlinear continuous terminal sliding manifold and a fast reaching law are proposed in this work to quickly drive the systems’ states to the equilibrium point even in the presence of lumped disturbances. The convergence time of the states can be pretuned based on the parameters of the sliding manifold and the reaching law. Lyapunov theorem is used to provide a rigorous stability proof for the feedback control system. Numerical simulations and processor-in-the-loop (PIL) experiments are conducted to validate and implement the designed flight control algorithm on real autopilot hardware. The novelty of the proposed research lies in hardware implementation of a sophisticated version of modern control technique that exhibits a multitude of distinguishing features including but not limited to (i) finite-time tracking stability featuring fast convergence is ensured, (ii) chattering and singularity problems in sliding mode control (SMC) are avoided, and (iii) null steady-state error is achieved along with enhanced robustness. Finally, the proposed control law is compared with two recently reported research works. Results of performance comparison in term of the integral of square error (ISE) and the absolute value of the derivative of the input (IADU) dictate that the proposed technique overperforms by precision and chattering alleviation.
Human resource practices, employee competencies and firm performance: a 2-1-2 multilevel mediational analysis

PurposeThis study aims to understand the role of employee competencies in terms of the relationship between Human Resource Practices (HRPs) and firm performance.Design/methodology/approachA sample of 60 HR managers and 546 employees from large-scale food processing firms were considered for the study. The study presents a 2-1-2 multilevel mediational analysis in which HRPs and firm performance are measured at the firm level (Level-2) and employee competencies are measured at employee level (Level-1). FindingsPositive relationship was found between HRPs and firm performance, which was partially mediated by employee competencies.Practical implicationsThe study highlights the importance of employee-related factors by focusing on the wider dimensions of human capital (e.g. academic qualification, job experience) in HRPs–performance relationship.Originality/valueThe study undertakes a 2-1-2 multilevel mediational analysis, which is rarely applied in HRM studies; however, this interaction between macro- and microlevel effects will create a better understanding of organization studies from an integrated and multilevel context.
Automatic Extraction and Labelling of Memorial Objects From 3D Point Clouds

This research addresses the problem of automatic extraction of memorial objects from cultural heritage sites represented as scenes of 3D point clouds. Point clouds provide a fine spatial resolution and accurate proxy of the real world. However, how to use them directly is not always obvious. This is especially true for applications where extensive training data or computational resources are not available. In this paper, we present a methodology for automatic segmentation and labelling of cultural heritage objects from 3D point cloud scenes. The proposed methodology is based on machine learning techniques and, in particular, makes use of the concept of transfer learning. Memorial objects are segmented from the scene based on their geometric shape characteristic through a conditional multi-scale partitioning scheme. Then, high-level latent feature descriptors are extracted by a convolutional neural network pre-trained on different 3D object models from a standard dataset (e.g., ModelNet). Based on these descriptors, a classification model (multilayer perceptron) is trained and applied to obtain semantic labels. Experiments demonstrated that the proposed methodology is effective for the extraction and labelling of grave marker objects from cultural heritage sites.
Research Fronts of Computer Science: A Scientometric Analysis

Computer science and technology have developed rapidly in the past few decades and shown an increasing tendency of interdisciplinary research in the community. Research fronts of Computer Science (CS) have attracted the attention of scientists from different background and it is a big challenge for them to discover the development trends. The study uses scientometric methods and a combination of macro and micro analysis to detect the research fronts of CS based on the data from Scopus and Scival database. Macro analysis focuses on leading countries and institutions by scholarly output and citation count. Micro analysis pays attention to the performance of institutions and their competitors in research fronts and helps researchers understand frontier topics of specific research field. This paper provides a comprehensive and finer-grained analysis about the research frontier topics of CS domain. The insights obtained from the analysis are for both researchers and policy makers.
Attend to Where and When: Cascaded Attention Network for Facial Expression Recognition

Recognizing human expression in videos is a challenging task due to dynamic changes in facial actions and diverse visual appearances. The key to design a reliable video-based expression recognition system is to extract robust spatial features and make full use of temporal modality characteristics. In this paper, we present a novel network architecture called Cascaded Attention Network (CAN) which is a cascaded spatiotemporal model incorporating with both spatial and temporal attention, tailored to video-level facial expression recognition. The cascaded fundamental model consists of a transfer convolutional network and Bidirectional Long Short-Term Memory (BiLSTM) network. Spatial attention is designed from the facial landmarks since facial expressions depend on the actions of key regions (eyebrows, eyes, nose, and mouth) on the face. Focusing on these key regions can help to decrease the effect of person-specific attributes. Meanwhile, the temporal attention is applied to automatically select the peak of expressions and aggregate the video-level representation. Our proposed CAN achieves the state-of-the-art performance on the three most widely used facial expression datasets: CK+ (99.03%), Oulu-CASIA (88.33%), and MMI (83.55%). Moreover, we conduct an extended experiment on a much more complex wild dataset AFEW and the experimental results further verify the generality of our attention mechanisms.
Radiofrequency Energy Harvesting for Wireless Sensor Node: Design Guidelines and Current Circuits Performance

Given their omnipresence, electromagnetic energy offers the most attractive and recent energy supply solutions for low consumption power devices. The most targeted application is the wireless Sensor (WS) node, which is indispensable in all computing systems. This work proposes the design guideline for harvesting radiofrequency (RF) energy using the Rectifying Antenna circuit known as rectenna. The rectenna design issues are then developed to introduce new solutions for optimizing the performance of the circuits. Note that the end-to-end efficiency analysis must incorporate both receiving antenna characteristics, rectifying diode parameters, and matching filter components. However, in most studies, only one or at most two of these aspects are treated. We then want to overcome this lack by offering a global view highlighting all the design issues for optimal RF/DC conversion efficiency. The specific case of rectennas based on patch antennas and Schottky diodes, easily integrated into the circuit boards, is considered. The results of this chapter show that although the harvestable energy levels of ambient RF waves are low, some recent designs offer solutions to take advantage of these ambient waves.
When Their Voices Are Heard: Bilingual Paraeducators and Organizational Dynamics

Paraeducators play a marginal yet essential role in fulfilling teaching-related tasks in many international classrooms in China. In order to meet high standards of education, schools often provide professional training to paraeducators. However, in the training process, schools primarily focus on their own needs and rarely consider the personal condition or positionality of paraeducators. Informed by positioning theory, this study argues that overlooking paraeducators’ needs and positions can be detrimental to the success of institutionally provided training. This case study, which spans the course of 2 years, explores how Chinese bilingual paraeducators’ self-positioning impacts their responses to professional training, their daily teaching practices, and their subsequent professional development. Findings suggest that paraeducators should not remain peripheral figures in an organization because hearing and understanding their self-positioning practices and concerns can be beneficial to both paraeducators’ professional learning and schools’ organizational development.
A Machine Learning–Based Fall Risk Assessment Model for Inpatients

Supplemental digital content is available in the text. Falls are one of the most common accidents among inpatients and may result in extended hospitalization and increased medical costs. Constructing a highly accurate fall prediction model could effectively reduce the rate of patient falls, further reducing unnecessary medical costs and patient injury. This study applied data mining techniques on a hospital's electronic medical records database comprising a nursing information system to construct inpatient-fall-prediction models for use during various stages of inpatient care. The inpatient data were collected from 15 inpatient wards. To develop timely and effective fall prediction models for inpatients, we retrieved the data of multiple-time assessment variables at four points during hospitalization. This study used various supervised machine learning algorithms to build classification models. Four supervised learning and two classifier ensemble techniques were selected for model development. The results indicated that Bagging+RF classifiers yielded optimal prediction performance at all four points during hospitalization. This study suggests that nursing personnel should be aware of patients' risk factors based on comprehensive fall risk assessment and provide patients with individualized fall prevention interventions to reduce inpatient fall rates.
Possible connection between declining Barents Sea ice and interdecadal increasing northeast China precipitation in May

Northeast China is a major agricultural production base in East Asia, and spring is the agricultural ploughing season throughout the region. The precipitation in spring is therefore significant to agricultural activity and production in the region. In this study, the interdecadal variation in spring precipitation across northeast China is investigated. In the past four decades, there has been a significant increase in spring precipitation in northeast China, and the increase in May precipitation made a dominant contribution to that increase. Further analysis indicates that the increase in May precipitation is concurrent with the decline in Arctic sea ice concentration (SIC) over the Barents Sea; both variables show an interdecadal shift around the early 2000s. Physical analysis based on observations and numerical simulations demonstrates that a decline in the Barents Sea SIC could excite a Rossby wave train propagating southeastward across the Mongolia‐Baikal region and reaching northeast China, thereby inducing a zonal anticyclone–cyclone–anticyclone teleconnection pattern over mid‐ to high‐latitude Eurasia. Under the influence of such a teleconnection pattern, significant southerlies prevail over northeast China, bringing warm and wet air to the region; in addition, the local atmospheric instability and upward motion are also increased. The changes in the moisture and dynamic conditions favour the increase in May precipitation over northeast China. Furthermore, SIC anomalies in the Barents Sea persist from late winter to spring; therefore, the SIC signal could provide signals for predictions of the interdecadal changes in May precipitation over northeast China.
Effective attributed network embedding with information behavior extraction

Network embedding has shown its effectiveness in many tasks, such as link prediction, node classification, and community detection. Most attributed network embedding methods consider topological features and attribute features to obtain a node embedding but ignore its implicit information behavior features, including information inquiry, interaction, and sharing. These can potentially lead to ineffective performance for downstream applications. In this article, we propose a novel network embedding framework, named information behavior extraction (IBE), that incorporates nodes’ topological features, attribute features, and information behavior features within a joint embedding framework. To design IBE, we use an existing embedding method (e.g., SDNE, CANE, or CENE) to extract a node’s topological features and attribute features into a basic vector. Then, we propose a topic-sensitive network embedding (TNE) model to extract a node’s information behavior features and eventually generate information behavior feature vectors. In our TNE model, we design an importance score rating algorithm (ISR), which considers both effects of the topic-based community of a node and its interaction with adjacent nodes to capture the node’s information behavior features. Eventually, we concatenate a node’s information behavior feature vector with its basic vector to get its ultimate joint embedding vector. Extensive experiments demonstrate that our method achieves significant and consistent improvements compared to several state-of-the-art embedding methods on link prediction.
Prediction of drilled hole quality in bidirectional woven carbon fiber reinforced plastic using wavelet packets of force–torque signals

Drilling of fibrous composites like carbon fiber reinforced plastics (CFRPs) is always considered a grim job to perform owing to its abrasiveness and inhomogeneous properties. Compared to the available studies, the present work addresses the parametric investigation on drilling capability of TiAlN- and TiN-coated drills over the uncoated one. The delamination factor, surface roughness, and circularity error in CFRP were considered as the quality features. The acquired thrust and torque were analyzed in the time domain as well as the time–frequency wavelet field, followed by a sensitivity analysis between decomposed original force–torque signals in different phases. Finally, an attempt was made to improve the predictability of drilled hole quality using decomposed wavelets of force and torque extended to hybrid force–torque wavelets of the best features to the developed regression models. The feed rate was more predominant than spindle speed on delamination evidenced by higher axial thrust particularly using coated tools. However, the coated drills generated more uniform torque than the uncoated drills and improved the drilled hole quality as well. The torque wavelet was a better indicator of hole surface integrity than force wavelets in which intermediate frequency band torque and low-/high-frequency band force wavelets were found to be more significant.
Toll-like receptor 2 (TLR2) and TLR4 signaling in the innate response against bacterial components

Toll-like receptors (TLRs) are transmembrane proteins involved in the recognition of specific microbial structures and thus the activation of signaling cascades of innate immunity. Regulation of the innate immune response is a complex biological process involving the combined synergistic and antagonistic effects of distinct signaling mediators. Although TLR signaling has been widely studied in recent years, there remain many unexplored unique features of each TLR signaling pathway. The present study evaluated the activation and regulation of TLR4 and TLR2 signaling with the aim of better understanding the molecular mechanisms that control these inflammatory signaling pathways. In the present study, the signal transduction mechanisms of TLR4 and TLR2 in response to Escherichia coli LPS and Staphylococcus aureus LTA were evaluated in mouse macrophages. The inductions, interactions, and activations of the signaling molecules and mediators in the TLR pathways were studied by using several molecular biology and protein chemistry methods. In addition, the role of TLR4 and TLR2 in the regulation of the hepatic inflammatory reaction during endotoxemia was studied. Mouse macrophages were found to induce central proinflammatory mediators in response to LPS and LTA stimulation. Specific roles for PI 3-kinase and Btk were described. These kinases were found to be activated by LPS and LTA; moreover, PI 3-kinase and Btk were found to form specific interactions with TLRs and their intracellular signaling mediators. In addition, a unique IRF2 signaling pathway for LTA-induced TLR2 was found, resulting in the activation of signal transducers and activators of transcription (Stats) and IFN-α secretion. The secreted IFN-α was shown to regulate the LTA-induced inflammatory responses, thereby combining the LTA-induced IRF proteins into NF-κB pathway. The present study provides insight into the signal transduction mechanisms of TLRs. The understanding of these molecular mechanisms that control the activation of TLR signaling cascades will in the future help to predict predisposition and outcome in infectious diseases, and to control the course of disease at an earlier stage.
Enhancing empathy and understanding of disability by using poetry in research

Abstract Research poetry, valuable for its poignancy and creativity in analysing and presenting data, can enhance comprehension of research content, move political policy by drawing on readers’ emotional connection to participants, inspire social justice activism, and increase accessibility to disability research. The field of adult education acknowledges and strives to address issues of social justice by amplifying voices that are often unheard. Using research poetry, scholars can challenge hegemony not only through research content but also through their writing format, particularly in disability research that considers society’s traditionally accepted view of an ideal human body.
A Usability Study of the Central-Bantu Multilingual Keyboards

ABSTRACT Central-Bantu speakers usually use the predominant QWERTY keyboard on a combination of some local and European languages, such as Chichewa and English. There has been no research that looks into usability issues of Central-Bantu speakers typing text in English and in local languages on QWERTY. This article presents our experiments that assess the usability of multilingual keyboards for Central-Bantu language speakers. We first obtained a Central-Bantu layout that is partially optimized from QWERTY, based on the German-QWERTZ keyboard, and a highly optimized version based on the French-AZERTY keyboard. The two keyboard layouts (Entry and advanced-level) were optimized for both English and Chichewa, for a two-finger text-entry mode. Simulation tests measured text-entry rates for Chichewa and English. The usability experiments show that the text-entry rate of English is about higher than that of Chichewa, which is close to the prediction of the simulation test. The Bantu keyboards reduced this imbalance to about . While the simulation tests predicted an increase in text-entry rate, the experimental tests show a decrease in performance. Typing-error rates almost double from QWERTY to the Advanced-Bantu keyboard. In this study, we show that the Central-Bantu keyboards have high practical-performance potential for both Chichewa and English, at balanced usability levels.
Effects of Night Ventilation on Indoor Air Quality in Educational Buildings—A Field Study

Night ventilation methods have been used in educational buildings to guarantee indoor air quality at the beginning of occupied periods. A typical method has been to pre-start ventilation 2 h before the space usage. Another selection has been to ventilate a building continuously during the night with a minimum airflow rate that can dilute material emissions. In this study, the pre-started, continuous, and intermittent ventilation methods were compared by assessing indoor air quality in field measurements. The daytime ventilation was operating normally. The test periods lasted for 2 weeks. Indoor air quality was assessed by measuring the total volatile organic compounds and microbial concentrations using the quantitative polymerase chain reaction method. Additionally, the thermal conditions, carbon dioxide, and pressure differences over the building envelope were measured. The results show that the night ventilation strategy had negligible effects on microbial concentrations. In most cases, the indoor air microbial concentrations were only a few percent of those found outdoors. The averaged concentration of total volatile organic compounds was at the same level with all the night ventilation methods at the beginning of the occupied periods in the mornings. The concentrations reached a minimum level after 2-h ventilation. The concentrations of total volatile organic compounds were higher during the day than at night. This reveals that space usage had the largest effect on the total volatile organic compounds. Generally, the results show that continuous night ventilation does not significantly affect the biological and chemical contaminants. Consequently, a 2-h flushing period is long enough to freshen indoor air before occupancy.