Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pilot Study Exploring Age Differences in Presence, Workload, and Cybersickness in the Experience of Immersive Virtual Reality Environments

Research is often focused on understanding barriers to the use and adoption of technology to support older adults’ (65+) instrumental activities of daily living (IADLs), such as communication, banking, and transportation. Less attention is paid to technology to support enhanced activities of daily living (EADLs), activities that enrich our daily lives, even though they have the potential to improve wellbeing, promote physical and emotional health, and reduce stress. Here, we explored how older adults interacted with commercial virtual reality (VR) to investigate the feasibility of using VR as an EADL support system. Older adults navigated different VR environments, including environments that were meditation, exploration, and game-oriented. Of particular interest was whether older adults (N = 20) psychologically experienced differing degrees of presence within virtual environments compared to younger adults (N = 20), and potential barriers to use as assessed by measures of workload and system usability. Given previously observed age-related differences in cybersickness, this was also assessed as a potential barrier. Compared to younger adults, older adults expressed a greater sense of presence in virtual environments, with nonsignificant differences in perceived workload and usability according to most measures. Contrary to expectations, older adults reported significantly less cybersickness compared to younger adults. Results suggest that VR is a promising means to support older adults’ EADLs.
Exploration of a Novel Prognostic Risk Signature and Its Effect on the Immune Response in Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is a highly invasive and metastatic carcinoma with different molecular characteristics and clinical outcomes. In this work, we aimed to establish a novel gene signature that could predict the prognosis of NPC patients. A total of 13 significant genes between the recurrence/metastasis (RM) group and the no recurrence/metastasis (no-RM) group were identified by machine learning from RNA-Seq data including 60 NPC tumor biopsies. Based on these genes, a 4-mRNA signature (considering U2AF1L5, TMEM265, GLB1L and MLF1) was identified. Receiver operating characteristic (ROC) and Kaplan-Meier (K-M) analyses indicated that this signature had good prognostic value for NPC. The overall survival (OS) and progression-free survival (PFS) of the patients in the high-risk group were significantly shorter than those of the patients in the low-risk group (p = 0.00126 and p = 0.000059, respectively). The area under the ROC curve (AUC) values of the 4-mRNA signature were higher than those of T stage and N stage for OS (0.893 vs 0.619 and 0.582, respectively) and PFS (0.86 vs 0.538 and 0.622, respectively). Furthermore, the 4-mRNA signature was closely associated with cell proliferation and the immune response. The expression of GLB1L and TMEM265 was associated with the level of tumor-infiltrating immune cells (r > 0.4, p < 0.05). We have validated the model through measuring the expression levels of the 4-mRNA signature by qRT-PCR, in an independent cohort of NPC patients. Here, we report a novel gene signature that can serve as a new tool for predicting the prognosis of NPC patients.
Examining Job Complexity on Job Crafting Within Conservation of Resources Theory: A Dual-Path Mediation Model

This study examined the different ways in which job complexity influences employees’ job crafting. Specifically, we draw on conservation of resources (COR) theory to hypothesize that job complexity is positively related to approach crafting via work engagement (i.e., resource gain process). At the same time, job complexity may also induce employees to engage in avoidance crafting (i.e., resource loss process) as employee energy resources are depleted. Our data consist of 251 employees working in Macau. We used structural equation modeling (SEM) in Mplus software to test the proposed hypotheses. Our findings confirm that job complexity has differential effects on approach and avoidance crafting through work engagement and energy depletion. These findings highlight the importance of costs and benefits of job complexity and the importance of resources in the employees’ job crafting process. We discuss the practical implications for modern organizations in which complex jobs are prevalent.
White Matter Correlates of Hostility and Aggression in the Visuospatial Function Network in Patients With Schizophrenia

Background: Violent acts in patients with schizophrenia are often associated with their hostility and aggression levels. Poor visuospatial processing has been suggested as a possible risk factor of violence in schizophrenia. However, studies investigating the relationship between hostility, aggression, and the visuospatial function have been lacking. Here, we aimed to investigate brain dysconnectivity associated with hostility and aggression in schizophrenia, particularly focusing on the visuospatial function network. Methods: Eighty-eight participants with schizophrenia and 42 healthy controls were enrolled. The visuospatial function network regions of interest were analyzed using Tract-Based Spatial Statistics. The hostility item from the Positive and Negative Syndrome Scale (PANSS), aggressive, and agitated behavior item from the Scale for the Assessment of Positive Symptoms (SAPS), and the Rey Complex Figure Test (R-CFT) were measured. Results: Among the participants with schizophrenia, the SAPS aggressive and agitated behavior scores were significantly correlated with fractional anisotropies (FAs) of the white matter regions in the splenium of the corpus callosum (CC), left posterior thalamic radiations (PTR), and left posterior corona radiata (PCR). Exploratory correlational analysis revealed significant negative correlations between FAs of the splenium of the CC and R-CFT copy and immediate recall scores. In addition, three regions including CC, PTR, and PCR that significantly correlated with the aggression scores showed significant correlations with the total PANSS scores. Conclusion: Our main finding suggests that aggression of patients with schizophrenia may be associated with poor visuospatial ability and underlying white matter dysconnectivity. These may help enhance understanding aggression in patients with schizophrenia.
Approximation-based Adaptive NN Control for Nonstrict-Feedback Nonlinear Systems with Input Delay

This paper focuses on the problem of adaptive neural network (NN) control for a class of nonstrict-feedback nonlinear systems with input delay. A novel auxiliary system is introduced to compensate the input delay. The variable separation technique is utilized to overcome the difficulty from the nonstrict-feedback structures. Furthermore, Barrier Lyapunov Functions (BLFS) are employed to ensure that the unknown functions remain within a given compact superset where the NN approximation is valid. With the aid of NN approximation method and backstepping technique, an adaptive NN controller is designed which can guarantee all the states in the closed-loop systems are semi-globally uniformly ultimately bounded. The stability of the closed- loop system are proved by using the Lyapunov stability theorem and a simulation example is given to illustrate the effectiveness of the proposed method.
Training visuospatial skills for medical ultrasound imaging with a desktop-based learning game

Ultrasound is becoming increasingly important in almost all medical fields. However, there are still large gaps in ultrasound education. A novel educational game for ultrasound beginners is presented which aims at training the visuospatial skills needed for understanding cross-sectional imaging. The game can be played at home on a standard PC with mouse and keyboard as input devices, thereby opening up new teaching options. We investigated training efficacy in an exploratory study. Results suggest that participants who played the game can improve their spatial abilities more than participants who did not. This improvement was not statistically significant, possibly due to the small sample size and a strong ceiling effect. The concept of the game, however, was rated positively by the participants.
Online Routing and Scheduling for Time-Sensitive Networks

Recent proposals leverage Time-Aware Shaper (TAS) to achieve precise transmission in Time-Sensitive Networking (TSN). However, most of the proposals require the information of all time-triggered flows to be known in advance and synthesize the gate control list of each switch offline, making the mechanisms they designed inapplicable to industrial automation scenarios where the devices are changed dynamically and the flows should be scheduled online. In this paper, we propose an online routing and scheduling mechanism of TAS for time-sensitive networks. In order to maximize the number of schedulable flows and reduce bandwidth waste, we devise the variable time slot mechanism and minimize the sending start time of each flow. Based on these mechanisms, a novel incremental routing and scheduling (IRAS) algorithm is designed to achieve per-flow deployment, with a pre-routing algorithm to reduce synthesis time. The evaluations show that the IRAS algorithm approaches 96.5 % of the optimal solution in scheduling 2000 flows, and has a feasible per-flow computational time from sub-seconds to less than ten seconds.
SpaceTransformers: Language Modeling for Space Systems

The transformers architecture and transfer learning have radically modified the Natural Language Processing (NLP) landscape, enabling new applications in fields where open source labelled datasets are scarce. Space systems engineering is a field with limited access to large labelled corpora and a need for enhanced knowledge reuse of accumulated design data. Transformers models such as the Bidirectional Encoder Representations from Transformers (BERT) and the Robustly Optimised BERT Pretraining Approach (RoBERTa) are however trained on general corpora. To answer the need for domain-specific contextualised word embedding in the space field, we propose SpaceTransformers, a novel family of three models, SpaceBERT, SpaceRoBERTa and SpaceSciBERT, respectively further pre-trained from BERT, RoBERTa and SciBERT on our domain-specific corpus. We collect and label a new dataset of space systems concepts based on space standards. We fine-tune and compare our domain-specific models to their general counterparts on a domain-specific Concept Recognition (CR) task. Our study rightly demonstrates that the models further pre-trained on a space corpus outperform their respective baseline models in the Concept Recognition task, with SpaceRoBERTa achieving significant higher ranking overall.
Traversability Analysis by Semantic Terrain Segmentation for Mobile Robots

Mobile robots have the potential to be used in many outdoor tasks, such as search and rescue, patrolling, and delivery services. To enable robots to safely navigate through outdoor environments, it is important to analyse the terrain. We present a novel approach using Semantic Terrain Segmentation (STS), which relies on an adaptation of Deeplabv3+. Our goal is to segment the terrain according to different height thresholds, using only aerial RGB images. We collected and labelled a dataset, Vale, consisting of four categories based on the traversability constraints of three types of mobile robots: wheeled, tracked, legged and non-traversable. We trained our deep convolutional neural network (DCNN) on Vale, with transfer learning using a network pre-trained on Cityscapes. Our findings suggest that the proposed DCNN approach can identify and differentiate different height thresholds in the terrain, i.e. can segment based on mobile robot traversability.
Coupled Bending Inwards Motion and Control Strategy Analysis of a Cable-Driven Underactuated Finger

Motion strategy analysis at the pre-bending stage is a fundamental component of underactuated finger grasp research. This study presents the pre-bending motion strategy and corresponding analysis of cable driving forces a 3-DOF underactuated finger comprising cable truss units. The structure and four motion strategies at the pre-bending stage are illustrated. The equivalent joint-driven and quasi-static motion models are established in the case where one or two cable driving forces drive the finger. Quasi-static motion model is established, and four typical quasi-static coupled motion space and distributions of corresponding cable driving forces are analysed. In accordance with the kinetic energy theorem and Lagrange’s dynamical method, a sampled and general dynamic model is established. Through matrix transformation, a hypothetical “PPA” type dynamic model is obtained. On the basis of the “PPA” dynamic model, three grasping control strategies at pre-bending stage are carried out. Valid simulations and experiments are conducted to verify the accuracy of the coupled bend inwards motion and control strategy.
Deterministic 5G mmWave Large-Scale 3D Path Loss Model for Lagos Island, Nigeria

5G millimeter wave (mmWave) application in mobile connectivity to realize high-speed, reliable communication is attributed with high path loss. This paper presents a detailed 3D ray-tracing technique at 28 GHz for Lagos Island to investigate five unique path loss scenarios: path loss, free space path loss with antenna pattern, free space path loss without antenna pattern, excess path loss with antenna pattern, and the excess path loss without antenna pattern for an urban environment. The Close-In (CI) model, Floating Intercept (FI) path loss model, and a root mean square error (RMSE) are used to model and evaluate the best path loss model for Lagos Island. The average achieved FI ( $\alpha,\beta, \sigma $ ) parameters were 189.92352, 0.1654, and 0.66948, While the average CI ( $\eta,X\sigma $ ) parameters were 2.309355 and 56.236425. From all the scenarios evaluated, the lowest path loss exponent achieved was 0.45, while the highest path loss exponent was 3.8. We have established that the FI path loss model accurately characterizes path loss for the Lagos Island environment with the lowest RMSE of 0.0359 dB and the highest RSME of 0.0997 dB. In contrast, the CI model over-predict the path loss at 28 GHz with the lowest RMSE of 0.0495 dB and the highest RMSE of 2.2547 dB. This work opens up a new area of research on mm-Wave at 28 GHz in Lagos Island, and the results obtained from this work can be used to benchmark future studies on mmWave in a similar environment.
A Coupled Regulation and Flexible Ramp Product Marginal Pricing Rule

The high integration levels of non-dispatchable generation have necessitated more efficient formulations to cope with the increasing requirements for operating reserves and ramping capability. One aspect that has captured particular attention is the representation of opportunity costs derived from flexibility constraints, in order to propose efficient pricing rules for regulating reserves and flexible ramp products. In this work, a price rule for regulating reserves and flexible ramp products is proposed as a function of the opportunity cost and the available ramp capacity of the units. An example considering the IEEE RTS 24-Bus System is presented to show numerical results.
Development of droplet based microfluidic system for agglutination assays

DEVELOPMENT OF DROPLET BASED MICROFLUIDIC SYSTEM FOR AGGLUTINATION ASSAYS Merve Marçalı M.S. in Material Science and Nanotechnology Advisor: Asst. Prof. Dr. Çağlar Elbüken October, 2015 Agglutination reactions have been carried out for several applications such as assessment of bacterial infection, blood typing or detecting non-infectious diseases. To observe agglutination reactions, several approaches have been developed. Although microfluidic methods are more costly compared to the common methods in clinical practice, microfluidic systems provide a reliable and more controlled environment. Due to the ability of droplet based systems to manipulate minute volumes of fluids, microfluidic systems allow screening of several agglutination reactions at a single run. In comparison to standard microfluidic devices, droplet based microfluidic systems provide efficient mixing which is a crucial parameter for agglutination reactions. This study reports detection of agglutination reactions of whole blood in microdroplets using impedimetric measurement method. As a proof of concept demonstration of agglutination reaction, blood typing method was implemented in microdroplets. Using label free impedimetric measurement approach, agglutination reaction in microdroplets was monitored. Impedance monitoring was achieved using microelectrodes in the microchannel to measure the impedance signal generated by droplets in the continuous phase. Besides monitoring the reaction, an empirical approach was applied to find the lumped element model of the system. This model can be used as a guidance to design a detection system and to test the sensitivity of the detection system that is used for other type of immuno-diagnostic assays in microdroplets.
Tailored design of renewable copolymers based on poly(1,4-butylene 2,5-furandicarboxylate) and poly(ethylene glycol) with refined thermal properties

In the recent years, search for innovative polymers derived from renewable resources resulted in an intense research and development of 2,5-furandicarboxylic acid-based polyesters. Special emphasis has been placed in high-performance polyesters, such as the poly(1,4-butylene 2,5-furandicarboxylate) (PBF)-based structures. In this study, both thermal and crystallisation-thermal properties of PBF have been enlarged simply by the incorporation of other renewable soft moieties in the polymer structure, namely, poly(ethylene glycol) (PEG) moieties. In particular, these novel copolymers can be designed to show some advantageous processing features as revealed by the lower melting temperature (in particular, it could be 107 °C) and higher thermal stability (up to 352–380 °C) as compared with PBF. Moreover, fast scanning calorimetric (FSC) studies of these novel copolymers indicated that crystallisation could be prevented even using relatively slow cooling rates (e.g., 0.1 °C s−1). The judicious selection and balance between hard PBF and soft PEG units enabled a segmented copolymer behaviour.
LayoutExOmizer Interactive Exploration and Optimization of 2D Data Layouts - Supplemental Material

Supplemental material document for the paper LayoutExOmizer: Interactive Exploration and Optimization of 2D Data Layouts . We provide more context and details about the optimization of 2D data layouts in Section 1, as well as about the two applied sets of layout quality measures in Section 2. Furthermore, we show more ﬁgures for the better understanding of LayoutExOmizer and its interface as used in the two usage scenarios
Mapping of the Covid-19 Vaccine Uptake Determinants From Mining Twitter Data

Opinion polls on vaccine uptake clearly show that Covid-19 vaccine hesitancy is increasing worldwide. Thus, reaching herd immunity not only depends on the efficacy of the vaccine itself, but also on overcoming this hesitancy of uptake in the population. In this study, we revealed the determinants regarding vaccination directly from people’s opinions on Twitter, based on the framework of the 6As taxonomy. Covid-19 vaccine acceptance depends mostly on the characteristics of new vaccines (i.e. their safety, side effects, effectiveness, etc.), and the national vaccination strategy (i.e. immunization schedules, quantities of vaccination points and their localization, etc.), which should focus on increasing citizens’ awareness, among various other factors. The results of this study point to areas for potentially improving mass campaigns of Covid-19 immunization to increase vaccine uptake and its coverage and also provide insight into possible directions of future research.
Combining Neural Gas and Reinforcement Learning for Adaptive Traffic Signal Control

Travel time of vehicles in urban traffic networks can be reduced by using Adaptive Traffic Signal Control (ATSC) to change the signal program according to the current traffic situation. Modern ATSC approaches based on Reinforcement Learning (RL) can learn the optimal signal control policy. While there are multiple RL based ATSC implementations available, most suffer from high state-action complexity leading to slow convergence and long training time. In this paper, the state-action complexity of ATSC based RL is reduced by implementing Growing Neural Gas learning structure as an integral part of RL, leading to high convergence rate and system stability. The presented approach is evaluated on a simulated signalized intersection, and compared with self-organizing map RL-based ATSC systems. Obtained results prove that the reduction of state-action complexity in this manner improves the effectiveness of RL based ATSC not needing to have an a priory analysis of needed number of neurons for state representation.
Transformed Dynamic Feature Pyramid for Small Object Detection

The low resolution and less feature information of small targets make it difficult to recognize and locate, which greatly hinders the improvement of object detection accuracy. In this paper, an object detection model (TDFP) based on CNN and transformer was established, which combines local and global context to establish the connection between features. In the proposed transformed dynamic feature pyramid network, a transformer module was designed to dynamically transform and fuse the multi-scale features generated by the backbone to generate a transformed feature pyramid with richer multi-scale features and context information. In this transformation process, gate block is used to dynamically select single-scale transformation or cross-scale transformation to achieve an optimal style of transformation and fusion of multi-scale features. The experimental results show that the model improves the small targets detection accuracy based on CNN and transformer. Based on the backbone ResNeXt-101, TDFP achieves 46.2% AP and 26.3% APS on MS COCO, and takes the amount of computation as a loss constraint to achieve a better balance between detection accuracy and computational complexity.
Detection of Weak Signal Amplitude of Winding Deformation Based on Duffing Oscillator

In order to solve the problem that it is difficult to detect the weak sinusoidal response signal when the frequency response method is used to detect the transformer winding deformation fault online, this paper proposes a method to detect the amplitude of the response signal by using Duffing oscillator. Secondly, in order to solve the influence of the target signal phase shift on the amplitude detection in the actual detection process, a method of first detecting the signal phase using a vibrator array composed of Duffing vibrators and then detecting the amplitude is proposed. Finally, using this method to process the two sets of measured response signals of online detection, the detection results show that the maximum error between the detected signal amplitude and the standard signal is 5. 69%, which meets the requirements of the power industry standard, which proves that the method used in this article is It is feasible in winding deformation detection.