Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning for Energy Optimization Under Human Fatigue Constraints of Power-Assisted Wheelchairs

In the last decade, Power-Assisted Wheelchairs (PAWs) have been widely used for improving the mobility of disabled persons. The main advantage of PAWs is that users can keep a suitable physical activity. Moreover, the metabolic-electrical energy hybridization of PAWs provides more flexibility for optimal control design. In this context, we propose an optimal control for minimizing the electrical energy consumption under human fatigue constraints, including a human fatigue model. The electrical motor has to cooperate with the user over a given distance-to-go. As the human fatigue model is unknown in practice, we use model-free Policy Gradient methods to directly learn controllers for a given driving task. We verify that the model-free solution is near-optimal by computing the model-based controller, which is generated by Approximate Dynamic Programming. Simulation results confirm that the model-free Policy Gradient method provides near-optimal solutions.
Mouse Hepatomas with Ha-ras and B-raf Mutations Differ in Mitogen-Activated Protein Kinase Signaling and Response to Constitutive Androstane Receptor Activation

Nuclear receptors mediate the hepatic induction of drug-metabolizing enzymes by xenobiotics. Not much is known about enzyme induction in liver tumors. Here, we treated tumor-bearing mice with phenobarbital, an activator of the constitutive androstane receptor (CAR), to analyze the response of chemically induced Ha-ras- and B-raf-mutated mouse liver adenoma to CAR activation in vivo. Both tumor subpopulations possess almost identical gene expression profiles. CAR target gene induction in the tumors was studied at the mRNA and protein levels, and a reverse-phase protein microarray approach was chosen to characterize important signaling cascades. CAR target gene induction was pronounced in B-raf-mutated but not in Ha-ras-mutated tumors. Phosphoproteomic profiling revealed that phosphorylation-activated extracellular signal-regulated kinase (ERK) 1/2 was more abundant in Ha-ras-mutated than in B-raf-mutated tumors. ERK activation in tumor tissue was negatively correlated with CAR target induction. ERK activation is known to inhibit CAR-dependent transcription. In summary, profound differences exist between the two closely related tumor subpopulations with respect to the activation of mitogenic signaling cascades, and these dissimilarities might explain the differences in xenobiotic induction of CAR target genes.
Responding to mental health statutory drivers: experience of a low intensity cognitive behavioural therapy programme

Objectives Stress control (SC), a brief psycho-education course, was implemented to increase access to psychological therapies in line with Northern Irish mental health service statutory drivers. The first aim of this study was to gauge the efficacy of SC in a robust manner with clinical significance testing. The second aim was to assess whether demographics traditionally ‘hard-to-reach’ – males, younger adults and those from deprived areas – accessed SC. The third aim was to elucidate what prompted their access and the experiences of attendees at SC. Methods Attendees at SC were 170 adults over six iterations of the course. Pre- and post-questionnaires included the Depression Anxiety Stress Scales – 21, captured demographic details and qualitative feedback, which was subject to a mixed-methods analysis. Results SC attendees reported significant decreases on depression, anxiety and stress sub-scales post-intervention. Moreover, 38.71% (n=36) of attendees who completed SC exhibited clinically significant improvement afterwards on one or more sub-scale. Attendance figures for males, younger adults and those classified as socioeconomically deprived were modest. Patterns within the data suggested prospective success for targeting these cohorts. Conclusions SC attracted people in need of mental healthcare input and affected quantifiable change within those people’s lives, while satisfying statutory demands for service delivery in an accessible community context. Recommendations to increase engagement with those traditionally ‘hard-to-reach’ for psychological services are provided, which, if implemented, have the potential to achieve further compliance with Northern Irish mental health statutory drivers.
Intermittent Feedback Based Rejection of Persistent Bounded Disturbance

In this paper, an intermittent feedback based control policy is proposed for disturbance rejection in a continuous, linear time invariant (LTI) system with distinct and rational eigenvalues, subject to a bounded disturbance signal. According to the proposed policy, the feedback link is intermittently turned on and off, to maximize the average feedback-off time while maintaining the state trajectory inside a pre-specified safe region. When the feedback is on, the state trajectory is steered to the origin and then the feedback is turned off for some pre-computed duration. The feedback control and the open-loop control in the proposed policy, which are applied during feedback-on and feedback-off intervals respectively, are obtained by solving certain time-optimal control problems. The optimal control problems in the proposed policy need to be solved only once offline and hence, do not add to the burden of real-time computation.
Model Learning for Multistep Backward Prediction in Dyna- ${Q}$  Learning

A model-based reinforcement learning (RL) method which interplays direct and indirect learning to update <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> functions is proposed. The environment is approximated by a virtual model that can predict the transition to the next state and the reward of the domain. This virtual model is used to train <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> functions to accelerate policy learning. Lookup table methods are usually used to establish such environmental models, but these methods need to collect tremendous amounts of experiences to enumerate responses of the environment. In this paper, a stochastic model learning method based on tree structures is presented. To model the transition probability, an online clustering method is applied to equip the model learning method with the abilities to evaluate the transition probability. By the virtual model, the RL method produces simulated experience in the stage of indirect learning. Since simulated transitions and backups are more usefully focused by working backward from the state-action, the pair estimated <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> value of which changes significantly, the useful one-step backups are actions that lead directly into the one state whose value has already obviously been changed. This, however, may induce a false positive; that is, a backup state may be an invalid state, such as an absorbing or terminal state, especially in cases where the changes of <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> values at the planning stage are still needed to put back for ranking even though they are based on a simulated experience and are possibly erroneous. It is obvious that when the agent is attracted to generate simulated experience around the area of these absorbing states, the learning efficiency is deteriorated. This paper proposes three detecting methods to solve this problem. Moreover, the policy learning can speed up. The effectiveness and generality of our method is further demonstrated in three numerical simulations. The simulation results demonstrate that the training rate of our method is obviously improved.
Improving Physical Layer Security of NOMA Networks by Using Opportunistic Scheduling

In this paper, we study how to improve physical layer security capability of multiple near users and multiple far users non-orthogonal multiple access (NOMA) networks. To this end, we propose an opportunistic user scheduling scheme, named the best-secure-near-user best-secure-far-user (BSNBSF) scheme. The BSNBSF aims to select the best near-far user pair, whose data transmission is the most robust against the interception of an eavesdropper. In order to facilitate the performance analysis of the proposed user scheduling scheme in terms of secrecy outage probability (SOP), we derive an exact closed-form expression and a tight approximate closed-form expression for the SOP of the selected near and far users, respectively. Numerical results show that the BSNBSF scheme significantly improves the secrecy outage performance NOMA networks compared to that of the random near user and random far user selection scheme. Additionally, discussions on the complicate convex characteristic of the total SOP with respect to the power allocation coefficients and the impact the number of near and/or far users are provided.
Relationship Between Putative eps Genes and Production of Exopolysaccharide in Lactobacillus casei LC2W

Lactobacillus casei LC2W, a probiotic strain, can produce exopolysaccharide (EPS) with anti-hypertensive bioactivity. The relationship between eps genes and EPS synthesis in LC2W due to unclear regulation mechanism of EPS biosynthesis was investigated. The several relevant genes in EPS biosynthetic gene cluster were deleted, overexpressed and complemented. The results suggested that glucose-1-phosphate thymidyltranseferase gene (LC2W_2179), uncharacterized EPS biosynthesis protein (LC2W_2188), and EPS biosynthesis protein (LC2W_2189) were related to EPS biosynthesis. EPS titer decreased 15, 13, and 21% when the three genes were deleted, respectively. When they were overexpressed, EPS titer increased 16, 10, and 18%. When they were complemented, EPS titer was similar to the wild-type strain. This work showed the three eps genes from LC2W played important roles on EPS production.
Automatic Recognition of Gait Phases Using a Multiple-Regression Hidden Markov Model

This paper presents a new approach for automatic recognition of gait phases based on the use of an in-shoe pressure measurement system and a multiple-regression hidden Markov model (MRHMM) that takes into account the sequential completion of the gait phases. Recognition of gait phases is formulated as a multiple polynomial regression problem, in which each phase, called a segment, is modeled using an appropriate polynomial function. The MRHMM is learned in an unsupervised manner to avoid manual data labeling, which is a laborious time-consuming task that is subject to potential errors, particularly for large amounts of data. To evaluate the efficiency of the proposed approach, several performance metrics for classification are used: accuracy, F-measure, recall, and precision. Experiments conducted with five subjects during walking show the potential of the proposed method to recognize gait phases with relatively high accuracy. The proposed approach outperforms standard unsupervised classification methods (Gaussian mixture model, k-means, and hidden Markov model), while remaining competitive with respect to standard supervised classification methods (support vector machine, random forest, and k-nearest neighbor).
Transformation or continuity?: The impact of social media on information: implications for theory and practice

This panel debates whether the ways in which social media are changing the nature, creation, seeking, use and sharing of information constitute a transformation or are primarily marked by continuity. Ubiquitous and everyday access to social media (for some) seems to be bringing about changes in social practice, including of information-related activities, such that conceptualisations of information itself are potentially reshaped. Discussants draw inspiration from the pervasive impact on information activities of the everyday adoption of social media. At a theoretical level they also draw inspiration from the analytic resources of contemporary practice theory and its emphasis on materiality and embodiment, routine and change, social expectations and social identity, and knowledge as a process. All the participants of the panel have conducted new empirical research on social media use with a focus on its deep as well as broad impact. The audience members are invited to discuss with the panelists questions such as how social media relate to routinised daily practices and institutionalised practices and hierarchies, how their use refashions social relationships, how they turn information seekers and users into information managers, producers and creators and shape perceptions of information authority and trustworthiness, and how a new theorisation can help librarians, information professionals and researchers understand change and assume a proactive role in it.
Hallmarks of Cancer-Related Newly Prognostic Factors of Oral Squamous Cell Carcinoma

Head and neck cancer, including oral squamous cell carcinoma (OSCC), is the sixth leading malignancy worldwide. OSCC is an aggressive tumor and its prognosis has exhibited little improvement in the last three decades. Comprehensive elucidation of OSCC’s molecular mechanism is imperative for early detection and treatment, improving patient survival. Based on broadly accepted notions, OSCC arises from multiple genetic alterations caused by chronic exposure to carcinogens. In 2011, research revealed 10 key alterations fundamental to cancer cell development: sustaining proliferative signaling, evading growth suppressors, avoiding immune destruction, activating invasion and metastasis, tumor-promoting inflammation, enabling replicative immortality, inducing angiogenesis, genome instability and mutation, resisting cell death, and deregulating energetics. This review describes molecular pathological findings on conventional and novel hallmarks of OSCC prognostic factors. In addition, the review summarizes the functions and roles of several molecules as novel OSCC prognosticators.
Incorporating Virtual Reality in an Embodied Interaction Course

Engagement with virtual reality (VR) through movement is becoming increasingly important. Therefore, the VR developers should improve their bodily skills and learn how to use the movement as design material. In addition, first person accounts of the development and experience are necessary. We explore the education space in VR with attention to the first-person experiences, movement data and code, and present an approach for teaching and designing VR-based embodied interaction.
Control of a Relocatable Energy-Harvesting Autonomous Underwater Vehicle in a Spatiotemporally-Varying Gulf Stream Resource

This paper describes and evaluates, through data-driven simulation, a strategy for using a relocatable autonomous underwater vehicle (AUV) with on-board turbines to extract hydrokinetic energy out of a shifting Gulf Stream. The conceptual AUV generates energy while anchored, and can relocate itself when needed. In order to maintain an estimate of the Gulf Stream flow profile that can be referenced for control, an estimator blends a measurement of the flow speed at the AUV's location with intermittent secondary flow speed data (which is available through a High Frequency Radar Network (HFRNet)). Model predictive control (MPC)-based spatiotemporal optimization is then used to optimize the location of the AUV along a cross-stream transect whose velocity profile varies as a function of time and cross-stream location. The proposed MPC strategy is structured in a way that the need for instantaneous power maximization (termed exploitation) is balanced with the need to maintain an accurate map of ocean current speed versus location on transect (termed exploration). The control strategy is validated through data from a numerical model of coastal circulation dynamics.
Distributed Ledger

Distributed Ledger (DL) has gained huge attention in the last years and will shift the conduction of business in the future. Until today, because of the great dynamics in the field a systematic approach to structure and define the field of DL does not exist. This led to a heterogeneous landscape of DL definitions and thus, misleading discussions. This paper gives a systematic overview of principles relevant to understand and structure the DL field. Furthermore, based on this it describes DL concepts and shows examples of protocols. The result can then be used for further research but also as a basis for successful collaborations in the field of DL.
Fog-based file sharing for secure and efficient file management in personal area network with heterogeneous wearable devices

In a personal area network (PAN) with wearable devices, the security of a file stored is an important concern. Secret sharing has received considerable attention for handling security, as it guarantees confidentiality and integrity even when some devices, whose count when taken together less than the minimum threshold, are compromised, lost, or stolen. However, because traditional secret sharing has tremendous computational overhead and requires a large storage space, it is not appropriate for battery-powered wearable devices. To overcome this limitation, combinatorial-based file sharing is proposed. However, this incurs another efficiency problem, as preparation of file storage and retrieval involves computational costs. Additionally, this file sharing has been designed without considering an environment with heterogeneous wearable devices. To overcome the aforementioned problems, we first propose a new fog network model that delegates the calculation for the preparation of file storage and retrieval to a fog node, such as a smartphone or tablet PC. A fog node has relatively faster operational ability and larger battery capacity, and hence the resources of the devices in the PAN can be managed efficiently. Additionally, we propose a new algorithm that generates file shares by considering the heterogeneous characteristics of wearable devices. In particular, we consider the factors of storage capacity and network speed to determine the sizes of the file shares. We demonstrate that our proposed model manages the file stored efficiently, using mathematical analysis and simulations.
Teaching Design in Europe: Challenges and Trends

Design has recently gained much attention among practitioners and scholars as a source of innovation. Firms are increasingly investing in design and involving design firms in their innovation processes. This research is based on an ongoing research project that consists in a complete range of training courses focused on design and has the objective to discover how design-driven innovation can become the key to improve European SMEs competitiveness, efficiency and sustainability. In the meanwhile, focusing on the project data, the main aim of the paper is to take a picture of the trends at European level with respect to the main design phases, looking at the modules proposed by the course. Accordingly, this work will allow understanding which is the knowledge (in terms of design) most requested by European SMEs. In order to perform a first complete analysis, we focus on hierarchical cluster analysis (HCA), which is a statistical method able to build a hierarchy of clusters. The goal of the statistical analysis is to try to discover some hidden patterns existing in the dataset, which connect Modules and Countries, in order to understand if some specific relationships exist.
A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting

Short-term traffic forecasting plays an important part in intelligent transportation systems. Spatiotemporal k-nearest neighbor models (ST-KNNs) have been widely adopted for short-term traffic forecasting in which spatiotemporal matrices are constructed to describe traffic conditions. The performance of the models is closely related to the spatial dependencies, the temporal dependencies, and the interaction of spatiotemporal dependencies. However, these models use distance functions and correlation coefficients to identify spatial neighbors and measure the temporal interaction by only considering the temporal closeness of traffic, which result in existing ST-KNNs that cannot fully reflect the essential features of road traffic. This study proposes an improved spatiotemporal k-nearest neighbor model for short-term traffic forecasting by utilizing a multi-view learning algorithm named MVL-STKNN that fully considers the spatiotemporal dependencies of traffic data. First, the spatial neighbors for each road segment are automatically determined using cross-correlation under different temporal dependencies. Three spatiotemporal views are built on the constructed spatiotemporal closeness, periodic, and trend matrices to represent spatially heterogeneous traffic states. Second, a spatiotemporal weighting matrix is introduced into the ST-KNN model to recognize similar traffic patterns in the three spatiotemporal views. Finally, the results of traffic pattern recognition under these three spatiotemporal views are aggregated by using a neural network algorithm to describe the interaction of spatiotemporal dependencies. Extensive experiments were conducted using real vehicular-speed datasets collected on city roads and expressways. In comparison with baseline methods, the results show that the MVL-STKNN model greatly improves short-term traffic forecasting by lowering the mean absolute percentage error between 28.24% and 46.86% for the city road dataset and, between 53.80% and 90.29%, for the expressway dataset. The results suggest that multi-view learning merits further attention for traffic-related data mining under such a dynamic and data-intensive environment, which owes to its comprehensive consideration of spatial correlation and heterogeneity as well as temporal fluctuation and regularity in road traffic.
Oceanic Feature Boundary Mapping with an Autonomous Underwater Glider

In this paper, an unknown three-dimensional oceanic feature is analyzed using a single autonomous vehicle. The problem of mapping the three-dimensional underwater feature is posed from a boundary mapping perspective. An autonomous underwater vehicle, specifically a glider, is considered. A dynamic and a kinematic model of the glider is used in order to mimic its movement and to design the control actions to be applied. Differently from previous work, the glider is not requested to follow a predefined trajectory of waypoints, but is considered capable of ‘intelligently’ determining its own trajectory. In particular, the glider defines its direction of movement through a suboptimal sliding mode control method, using only the measurement of the analyzed feature at its current position, in order to achieve the boundary mapping objective. Knowledge of the vehicle position and of the gradient of the analyzed feature is not required. A set of simulations results are provided, based on a realistic dataset relating to a sea temperature front in the Iceland Faroes Front.