Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparative study on mental health seeking behavior of university students in India and Taiwan

Mental health conditions such as depression and anxiety have become very common in young individuals which often go unnoticed easily these days. Every year there is a rise in the number of individuals who have undiagnosed anxiety or depression, which can negatively impact their ability to learn and enjoy time in universities. Within this context, the current pilot study aims to highlight students help seeking behavior in case of mental health conditions such as depression and anxiety. Data was collected from 112 individuals studying at universities in India and Taiwan. Moderation analysis and basic statistical methods were used for data analysis. Results show that depression caused a greater number of individuals to feel ashamed and embarrassed about themselves in Taiwan as compared to India which further raised their worries about hindering academic performance. Also, contrary to previous studies, a higher number of male students were found to experience anxiety and depression than their female student counterparts in this study. We also found that maximum participants were the most comfortable talking to their friends or seeking help from various online platforms and social media networks for their mental health problems, rather than seeking treatment from counseling services. There is an urgent need for the universities to intervene and educate various stakeholders in the universities about mental health problems and their impact on academic performance. It has also become crucial to create awareness around stigma of mental ill-health as it remains one of the key barriers to promote effective mental health care solutions. Fundamentally, there needs to be a shift in the culture in the world, especially in the Asian countries to eliminate stigma and ensure that individuals are not reluctant to timely and openly communicate about their mental health problems.
A spectral method for axisymmetric Stokes flow past a particle

Abstract We present a non-perturbative mesh-free spectral method for the axisymmetric scenario of a radially highly deformed sphere in Stokes flow. Spectra of harmonic and biharmonic Stokes flow modes are used to provide a general algebraic spectral solution to axisymmetric Stokes flow. A solution for the flow field around a sphere with prescribed surface velocity field is presented, and is used to obtain the velocity field around a deformed sphere. The method is demonstrated on two problems: hydrodynamic radii of radially deformed spheres for a range of strength and angular dependence of the deformation, with results in good agreement with the boundary element method; and self-phoretic velocity of spheroids with surface flux of the driving field in a source/sink or source/inert configuration.
Digital tongue image analyses for health assessment

Abstract Traditional Chinese Medicine (TCM), as an effective alternative medicine, utilizes tongue diagnosis as a major method to assess the patient’s health status by examining the tongue’s color, shape, and texture. Tongue images can also give the pre-disease indications without any significant disease symptoms, which provides a basis for preventive medicine and lifestyle adjustment. However, traditional tongue diagnosis has limitations, as the process may be subjective and inconsistent. Hence, computer-aided tongue diagnoses have a great potential to provide more consistent and objective health assessments. This paper reviewed the current trends in TCM tongue diagnosis, including tongue image acquisition hardware, tongue segmentation, feature extraction, color correction, tongue classification, and tongue diagnosis system. We also present a case of TCM constitution classification based on tongue images.
A Multiscale Study of Phosphorylcholine Driven Cellular Phenotypic Targeting

Phenotypic targeting requires the ability of the drug delivery system to discriminate over cell populations expressing a particular receptor combination. Such selectivity control can be achieved using multiplexed-multivalent carriers often decorated with multiple ligands. Here, we demonstrate that the promiscuity of a single ligand can be leveraged to create multiplexed-multivalent carriers achieving phenotypic targeting. We show how the cellular uptake of poly(2-methacryloyloxyethyl phosphorylcholine)-poly(2- (diisopropylamino)ethyl methacrylate) (PMPC-PDPA) polymersomes varies depending on the receptor expression among different cells. We investigate the PMPC-PDPA polymersome insertion at the single chain/receptor level using all-atom molecular modelling. We propose a theoretical statistical mechanics-based model for polymersome-cell association that explicitly considers the interaction of the polymersome with the cell glycocalyx shedding light on its effect on the polymersome binding. We validate our model experimentally and show that the binding energy is a non-linear function, allowing us to tune interaction by varying the radius and degrees of polymerisation. Finally, we show that PMPC-PDPA polymersomes can be used to target monocytes in vivo due to their promiscuous interaction with SRB1, CD36 and CD81.
PseudoNAM: A Pseudo Value Based Interpretable Neural Additive Model for Survival Analysis

Deep learning models have achieved the start-of-the-art performance in survival analysis as they can handle censoring while learning complex nonlinear hidden representations directly from the raw data. However, the covariate effects on survival probabilities are difﬁcult to explain using deep learning models. To address this challenge, we propose PseudoNAM - an interpretable model which uses pseudo values to efﬁciently handle censoring and uses neural additive networks to capture the nonlinearity in the covariates of the survival data. In particular, PseudoNAM uses neural additive models to jointly learn a linear combination of neural networks corresponding to each covariate and identiﬁes the effect of the individual covariate on the output, and thus, is inherently interpretable. We show that our PseudoNAM outputs can be used in other survival models such as random survival forests to obtain improved survival prediction performance. Our experiments on three real-world survival analysis datasets demonstrate that our proposed models achieve similar or better performance (in terms of C-index and Brier scores) than the state-of-the-art survival methods. We show-case that PseudoNAM provides overall feature importance scores and feature-level interpretations (covariate effect on survival risk) for survival predictions at different time points.
The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.
Modeling and Analysis of Urban Traffic System Based on Colored Petri Nets

Colored Petri nets can be employed to model, simulate, and analyze systems with discrete, asynchronous and concurrent characteristics that are commonly featured by road traffic systems. In traffic systems, the vehicle routing problem and traffic lights signal control are essential issues that should be investigated. This work mainly studies the automatic vehicle route planning problems in a small-scale urban traffic system, in which traffic lights signal controls vehicles in a queue to pass through an intersection orderly. Based on the Colored Petri nets, a small-scale urban traffic system model with a 3 × 3 chessboard pattern network is established, which can realize the functions: automatic routes planning that leads the vehicles to the destinations, traffic accidents dealing, and traffic lights control that directs vehicles to queue up at intersections. Owning to the modular and hierarchical methods, the proposed model can be expanded conveniently in scale and functions according to demands, i.e., the model is universalizable and applicative for general traffic systems. Finally, the established model’s theoretical and practical values are demonstrated by system simulation.
A Multi-sensor Gesture Interaction System for Human-robot Cooperation

Gestures are considered as a natural expression of the human body and are used to communicate with other people. The gesture-based human-robot interaction is natural, convenient, and applicable, and can be applied to complex interactive scenarios. In this paper, considering the real-time nature of the human-robot cooperation(HRC) system and the variability of the interaction range, we combine Kinect V2.0 (far-range sensor) and Leap Motion (short-range and high precision sensor), and propose a real-time multi-sensor gesture interaction system. Firstly, a reasonable layout of two sensors is discussed to realize far-range perception of natural gesture interaction. Then, nine gestures are defined that are easy for users to remember and operate. At the same time, a gesture interactive mechanism is proposed that can automatically switch two sensors according to the distance of the operator’s position. It can better improve the defects such as occlusion and confusion in the process of gesture controlling, and solve the minimum distance constraint of Kinect. Finally, the interactive experiment proves the stability and accuracy of the system.
Indoor User Movement Simulation with Markov Chain for Deep Learning Controlled Antenna Beam Alignment

5G networks utilize new high frequencies in the millimeter wavelength range to increase the wireless throughput. However, using this band requires antenna beam alignment. Artificial intelligence based solutions may use deep learning to predict the best antenna direction that is also applicable for beam forming. This paper presents an improved user movement simulation for indoor environment using Markov chain in order to train and test deep neural networks for direction prediction. The new procedure improves the efficiency of the former training methods by providing a more accurate description of a human movement.
Clinical Test of Surface Rejection Method for Microwave Breast Cancer Imaging

Microwave breast imaging modality is one of the most promising alternative techniques from X-ray mammography in terms of safety, painless, non-harmful exposure measurement. In order to maintain its imaging quality, an effective surface reflection algorithm should be implemented, which considers a frequency dependency due to coupling effect or dispersive breast media. As a solution for this issue, we introduce a fractional derivative based waveform compensation model, which would be validated through the clinical data using ultra-wideband radar equipment. These tests demonstrate that our method significantly reduce the interference from surface reflection, which would upgrade the reliability of post radar image.
Predicting Fuel Consumption in Power Generation Plants using Machine Learning and Neural Networks

The instability of power generation from national grids has led industries (e.g., telecommunication) to rely on plant generators to run their businesses. However, these secondary generators create additional challenges such as fuel leakages in and out of the system and perturbations in the fuel level gauges. Consequently, telecommunication operators have been involved in a constant need for fuel to supply diesel generators. With the increase in fuel prices due to socio-economic factors, excessive fuel consumption and fuel pilferage become a problem, and this affects the smooth run of the network companies. In this work, we compared four machine learning algorithms (i.e. Gradient Boosting, Random Forest, Neural Network, and Lasso) to predict the amount of fuel consumed by a power generation plant. After evaluating the predictive accuracy of these models, the Gradient Boosting model out-perform the other three regressor models with the highest Nash efficiency value of 99.1 %.
Ultra-Wideband Non-Reciprocal Micro-Acoustic Delay Lines with Slanted-Finger Interdigital Transducers

Ultra-wideband (UWB) Lamb wave delay lines with non-reciprocal transmission are demonstrated in a suspended thin-film lithium niobate (LN)-on-silicon (Si) platform. Using slanted-finger interdigital transducers (SFIT) to efficiently excite fundamental symmetric (S0) Lamb waves, up to 33% fractional bandwidth (6-dB) is realized at ~650 MHz. This is achieved through leveraging the strong interaction between drifting electrons in Si and piezoelectrically transduced acoustic waves - i.e. acoustoelectric (AE) effect - to compensate the problematic diffraction losses associated with SFIT and enable UWB non-reciprocal behavior. The non-reciprocity of the device is switched on and regulated by the bias applied along the device.
Grey Box Modelling of Supermarket Refrigeration Room

Aiming to enable robust large-scale fault diagnostics and optimized control for supermarket refrigeration systems, a data-driven grey box model for cooling rooms and cabinets was developed. The analysis scopes a single cold room in a supermarket in Otterup (Denmark) and was done using one-minute of sampling data. A resistance-capacitor diagram of the room was analyzed to derive three state-space equations for the model - the following were the states: the room temperature, the temperature of the goods and the refrigerant mass in the evaporator. The model parameters were then estimated using a Kalman filter and the maximum likelihood method. In the present paper, the resulting model is demonstrated through a five-hour simulation and the importance of ongoing re-estimation of parameters is highlighted, as the dynamics of the room constantly change, as goods are added and removed. Furthermore, the physical meaning of the parameters is discussed and a case where the parameter estimates became physically meaningless is highlighted - suggesting that robustness was an issue and further studies with simpler models and other solver algorithms are necessary for large-scale implementation.
Pinned Buried PIN Photodiode Type Solar Cell

This paper reviews the origin of Pinned Buried Photodiode and its historical development efforts. Three original Japanese Patent Applications filed by Hagiwara at Sony in 1975 are explained in details which defined the first triple junction type Pinned Buried Photo diode with the in-pixel vertical overflow drain (VOD) function with the electrical shutter capability, realizing the completely film-free and mechanical-parts-free all-digital solid-state image sensors. It is shown that the conventional PN junction depletion region is not the only place to have a barrier potential needed for photo electron hole pair separation. A clever doping-engineering of the pinned surface P+ P hole accumulation region can also create the surface barrier electric field to enhance drastically the photo electron and hole pair separations to increase the short-wave blue light sensitivity. It is concluded that this surface P+P doping-engineering possibly creates Pinned Buried PIN Photo diode Type Solar Cell with a better quantum efficiency.
Smart Connected Aircraft: Towards Security, Privacy, and Ethical Hacking

The Aviation Industry has always been adopting superior technology and state-of-art concept that includes Aero-dynamics, Avionics, and Jet Engine Technologies, etc. There is, however, a new frontier in the Aviation Industry, development of the “Connected Aircraft” or so-called “Smart Aircraft”. However, as with any other new frontier, there are possibilities of pitfalls that need to be discussed and addressed. This paper discusses Wi-Fi connectivity on Smart Aircraft, the technologies involved, and the threats to security. The research was conducted via several sources including online research, scholarly papers, pilot interviews, and consultation with various aviation academies that train pilots focus only on aircraft/airliner Wi-Fi connectivity from within the cabin during the flight. Findings reveal the industry as emerging and in infancy, there are still scores of issues related to security and cost that aviation organizations must work extensively towards secure and safe Smart Aircraft.
A Web Of Things Context-Aware IoT System leveraging Q-learning

The Internet of Things and more recently the Web of Things are changing how we interact with devices. The possibilities and novel services they provide enables the users to perform automatic operations and to monitor data of interest. Although many operations are performed autonomously by devices, there is still the need for the user to understand the data provided, and to configure their own services according to it. In this work we explore the possibility for devices to autonomously organize and understand the effects of the actions on the scenario, and provide a better status of the system. We do so by presenting a novel architecture, and developing a Q-learning algorithm which learns from the different statuses in which the system is. Our results indicate that devices with no prior knowledge of each other may eventually collaborate to provide a novel service to the end user, without any human intervention, and eventually achieve a better system status.
Comparison of Two Power Factor Correction Topologies on Conducted Emissions in Wireless Power Transfer Systems for Electric Vehicles

Electromagnetic interferences are problematic in high-power systems with high switching frequencies such as wireless power transfer (WPT). Using finite element analysis and system level simulations, a WPT system for electric vehicles (EVs) is investigated with regard to its conducted emissions (CEs). Two different power factor correction (PFC) topologies were considered, which are integrated into whole WPT system, simulated and analyzed for system perturbations. The corresponding PFC topologies are: bridgeless interleaved boost converter and interleaved totem-pole bridgeless boost converter.
Natural Frequency Measurements of Seal Whiskers Using A 3D-Printed MEMS Graphene-Based Cantilever Sensor

Seal whiskers have been well studied in view of their remarkable hydrodynamic trail tracking capability. The considerable variation in the whisker lengths indicates that the whiskers have different natural frequencies sensitive to various frequency bands of hydrodynamic stimuli. However, a thorough experimental investigation of the natural frequencies of seal whiskers has not been conducted before. Here, we develop a novel and low-cost methodology for easy measurements of the natural frequencies of seal whiskers using a 3D-printed MEMS cantilever sensor featuring a graphene nanoplatelet piezoresistor and a soft encapsulation of the whisker base at the distal end of the cantilever.
Determination of Waste Properties from Settlement Behaviour of a Full Scale Waste Cell Operated As a Landfill Bioreactor

ABSTRACT The proper engineering of landfill bioreactors require operation in a manner that maximizes waste decomposition and gas generation while making use of the settlement that occurs. Settlement in landfills consists of interacting multiphase media with each phase exhibiting variations both in time and space. There are mathematical models available to evaluate the processes of biodegradation, gas generation and transport and distribution of moisture within a landfill. At the same time, many existing waste settlement models mainly focus on mechanical compression of waste solids but ignore the contribution from other phases. An effective model for landfill settlement should be able to consider settlement, gas generation and fluid transport simultaneously. However, the phase interactions involved in landfill settlement are not well understood. Apart from the theoretical uncertainties in the phase interactions, a major obstacle to developing a multiphase settlement model is the amount of data required to calibrate and validate the model. This manuscript presents data from study of a full scale waste cell currently operated as a landfill bioreactor. The waste cell is equipped with gas collection and leachate recirculation systems, and is instrumented to collect time resolved data. This waste cell is being used as a research facility to study the impact of leachate recirculation on gas production and settlement.
Extracting football players video sprites from broadcast video

The main focus of this paper is to explore an alternative method in video sprite extraction. For the case of this work, the priority is creating a dataset of football player's video sprites - directly from footage of broadcast football matches. The quality of the generated dataset is essential, considering that the obtained dataset can be used for further accurate 2D and 3D reconstructions. At its core, our approach relies on Mask R-CNN to get the player's image masks. The process is prone to multiple errors, depending on the player's pose and position. If we have a semi-supervised dataset collecting process, we can leverage the curated images, and then we use a variational autoencoder (VAE). In the reconstruction phase, many uncommon image masks will be converted to more generic ones, thus, improving the process of creating the dataset.
Direct method and Bayesian inference for uncertainty quantification on stochastic NDT problem

In this paper, we present a framework to deal with uncertainty quantification in case where the ranges of variability of the random parameters are ill-known. Namely the physical properties of the corrosion product (magnetite) which frequently clogs the tube support plate of steam generator, which is inaccessible in nuclear power plants. The methodology is based on Polynomial Chaos (PC) direct approaches and on Bayesian inference for inverse problems. The direct Non Intrusive Spectral Projection (NISP) method is first employed by considering prior probability densities and therefore construct a PC surrogate model of the large-scale NDT finite element model. To face the prohibitive computational cost underlying the high dimensional random space, the adaptive sparse grid technique is applied on NISP resulting in a significant time reduction. The PC surrogate model, with reduced dimensionality, is used as a forward model in the Bayesian procedure. The posterior probability densities are then identified by inferring from few noisy experimental data. We demonstrate the effectiveness of the approach on the identification the permeability and conductivity of the magnetite using Sax ratio measurements.
Network Intrusion Prediction Model based on Bio-inspired Hyperparameter Search

In recent years, predicting network intrusions has been a serious research concern in the academia and industry due to the expanding attack surfaces. This unending trend of threat escalation implies that more robust approaches are required to accurately predict attacks. Extant prediction models are generally affected by the choice of hyperparameters based on expert knowledge. Consequently, in this paper we present a novel approach to predict cyberattacks using bio-inspired hyperparameter search technique to generate an optimal network structure using core components of a deep neural network as chromosomes. This optimal or bio-inspired network structure is further used to derive a novel prediction model called NetBiiDenns. Furthermore, we evaluated our model on two well-known benchmark datasets, which include the CICIDS2017 and NSL-KDD datasets and achieved a prediction accuracy of 99%. From findings, our model predicts cyberattacks with high accuracy, low error and false positive rates, and significantly outperforms state-of-the-art comparative models.