Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning for the prediction of drug-induced toxicity

The knowledge of toxicological properties of compounds (e.g. drugs, chemicals, and contaminants) is crucial for drug development, definition of toxicological thresholds and exposure limits. However, toxicological testing, either in vitro or in vivo, is time-consuming, labour intensive and expensive. An alternative to the classic experiments is the use of computational (in silico) approaches, such as machine learning. For machine learning, it is assumed that substances with comparable structure or molecular features also exhibit the comparable pharmacological or toxicological action. Based on the comparison of substances with known pharmacological or toxicological action to substances with unknown properties, models, which were generated using machine learning methods, are able to predict the action of the latter substances. The aim of this work was the development of predictive machine learning models for the estimation of risk of hepatotoxicity and genotoxicity. These models were then applied on two different substance groups and the outcome was compared to available literature data. The acute hepatotoxic potential of over 600 different pyrrolizidine alkaloids (PAs) was evaluated using the methods Random Forest and artificial Neural Networks. The predicted qualitative hepatotoxicity of both models was highly correlated. Furthermore, specific structural motives showed different hepatotoxic potential. Overall, the obtained results fitted well with already published in vitro and in vivo data on the acute hepatotoxic properties of PAs. The genotoxic/ mutagenic potential of PAs was addressed using six different machine learning methods (LAZAR (Lazy Structure-Activity Relationships), Support Vector Machines, Random Forest and two Deep Learning Networks). Even though the models achieved only low to moderate accuracy rates, the best model clearly showed structural specific differences in the predicted genotoxic potential. Furthermore, the acute hepatotoxic potential of 165 protein kinase inhibitors (PKIs) was predicted using Random Forest and artificial Neural Networks. The models confirmed clinical observations that PKIs have in general a high probability for inducing hepatotoxicity. However, interestingly, there seemed to be a target specific difference, with inhibitors of Janus kinases having the lowest hepatotoxic probability of 60-67%. The greatest challenge is the performance of the models. This has to be validated e.g. by cross-validation before the model can be used on the substances of interest. Although group statements could be easily obtained, due caution has to be taken while interpreting the results of predictive models for single compounds and if possible, comparison to already published data is advisable, as a form of external validation.
Hybrid Zadoff‐Chu and multilateral piecewise exponential companding transform–based PAPR reduction technique in OFDM systems

The orthogonal frequency‐division multiplexing (OFDM) is a multicarrier modulation system that is used to transmit the large volume of data to the receiver. Reducing the peak‐to‐average power ratio (PAPR) in OFDM system is one of the demanding and crucial task in recent days. For this reason, various precoding and companding mechanisms are developed in the traditional works, but it remains with the limitations of increased complexity, reduced performance, and nonlinear distortion. The reduction of PAPR is achieved by minimizing the companding distortion with the enhancement of the bit error rate (BER) performance significantly. Then, in order to avoid clipping in OFDM, a multilateral piecewise exponential companding transform (MPECT) method has been utilized rather than using piecewise exponential companding transform (PEC) where PAPR is getting reduced. The OFDM is very sensitive to synchronizing error. To overcome this sensitivity, employ the Zadoff‐Chu sequence to carrier frequency offsets. Zadoff‐Chu matrix transform (ZCMT) has numerous merits among the other ODFM systems such as the improvement in the performance of the channels that are fading away and provides an ideal periodic autocorrelation and a constant magnitude periodic cross correlation. Both of these techniques provide improvement in the ODFM systems. To get more efficiency, this paper aims to develop a hybrid technique by integrating the ZCMT and MPECT techniques for reducing the PAPR in OFDM systems. Further, convolutional encoding is applied for better BER and PAPR. The simulation results of the proposed ZCMT‐MPECT technique are evaluated and compared with the conventional OFDM and other precoding methods.
Supporting learning situations connecting formal and informal learning in Smart Learning Environments

The recent advances in technology have increased the proliferation of informal learning experiences in both physical and virtual spaces. In this sense, the connection between formal and informal learning can potentially help students reﬂect and experience in real life settings the learning contents and goals deﬁned by teachers in learning designs. To this end, Smart Learning Environments (SLEs) have been proposed as a promising solution to achieve such connection, considering the learning designs and the students’ context to better personalize learning interventions in informal settings. This PhD. thesis addresses the problem of how SLEs can provide automatic and personalized support in informal settings considering students’ context across learning spaces, being aligned with the goals deﬁned by teachers. This paper presents a brief motivation, the methodology and the current state of the thesis.
Application and Implementation of CNN in Artillery Countermeasure Training System

Blasting point recognition is the basic task in artillery countermeasure training system. In order to solve the inefficiency and inaccuracy caused by manual recognition, this paper proposes a blast point area recognition algorithm based on convolution neural network. Aiming at the relatively small area of blasting point, the design idea of transfer learning is adopted. Firstly, based on the Mask-RCNN model, the structure of feature pyramid network is modified. By comparing the recognition effects of different feature fusion methods, the optimal one is selected. The anchor parameters are adjusted according to the target characteristics as well. Finally combining the mask branch network and the target detection network, the contour of the blasting point area and the center of it are calculated. The experimental results show that the method proposed in this paper achieve 97.6% mAP on the blasting points test dataset we built at 4.7 FPS, which solved a basic problem in artillery countermeasure training system well.
Early Window Tailoring: A New Approach to Increase the Number of TCP Connections Served

In the absence of losses, TCP constantly increases the amount of data sent per instant of time. This behavior leads to problems that affect its performance, especially when multiple devices share the same gateway. Several studies have been done to mitigate such problems, but many of them require TCP side changes or a meticulous configuration. Some studies have shown promise, such as the use of gateway techniques to change the receiver’s advertised window of ACK segments based on the amount of memory in the gateway; in this work, we use the term “network-return” to refer to these techniques. In this paper, we present a new network-return technique called early window tailoring (EWT). For its use, it does not require any modification in the TCP implementations at the sides and does not require that all routers in the path use the same congestion control mechanism, and the use in the gateway is sufficient. With the use of the simulator ns-3 and following the recommendations of RFC 7928, the new approach was tested in multiple scenarios. The EWT was compared to drop-tail, RED, ARED, and the two network-return techniques—explicit window adaptation (EWA) and active window management (AWM). In the results, it was observed that EWT was shown to be efficient in congestion control. Its use avoided losses of segments, bringing expressive gains in the transfer latency and goodput and maintaining fairness between the flows. However, unlike other approaches, the most prominent feature of EWT is its ability to maintain a very high number of active flows at a given level of segment loss rate. The EWT allowed the existence of a number of flows, which is on average 49.3% better than its best competitor and 75.8% better when no AQM scheme was used.
Robust Visual-Inertial Navigation and Control of Fixed-Wing and Multirotor Aircraft

Robust Visual-Inertial Navigation and Control of Fixed-Wing and Multirotor Aircraft Jerel Bendt Nielsen Department of Electrical and Computer Engineering, BYU Doctor of Philosophy With the increased performance and reduced cost of cameras, the robotics community has taken great interest in estimation and control algorithms that fuse camera data with other sensor data. In response to this interest, this dissertation investigates the algorithms needed for robust guidance, navigation, and control of fixed-wing and multirotor aircraft applied to target estimation and circumnavigation. This work begins with the development of a method to estimate target position relative to static landmarks, deriving and using a state-of-the-art EKF that estimates static landmarks in its state. Following this estimator, improvements are made to a nonlinear observer solving part of the SLAM problem. These improvements include a moving origin process to keep the coordinate origin within the camera field of view and a sliding window iteration algorithm to drastically improve convergence speed of the observer. Next, observers to directly estimate relative target position are created with a circumnavigation guidance law for a multirotor aircraft. Taking a look at fixed-wing aircraft, a state-dependent LQR controller with inputs based on vector fields is developed, in addition to an EKF derived from error state and Lie group theory to estimate aircraft state and inertial wind velocity. The robustness of this controller/estimator combination is demonstrated through Monte Carlo simulations. Next, the accuracy, robustness, and consistency of a state-of-the-art EKF are improved for multirotors by augmenting the filter with a drag coefficient, partial updates, and keyframe resets. Monte Carlo simulations demonstrate the improved accuracy and consistency of the augmented filter. Lastly, a visual-inertial EKF using image coordinates is derived, as well as an offline calibration tool to estimate the transforms needed for accurate, visual-inertial estimation algorithms. The imaged-based EKF and calibrator are also shown to be robust under various conditions through numerical simulation.
Joint Optimization of Power Splitting and Beamforming in Energy Harvesting Cooperative Networks

A new joint best relay and jammer selection (JBRJS) scheme is conceived for enhancing the physical layer security (PLS) of cooperative networks relying on multiple energy harvesting (EH) aided intermediate nodes, which accumulate energy based on the power splitting (PS) protocol. Specifically, we select the best intermediate node as the relay, whilst exploiting all the remaining nodes as friendly jammers. Furthermore, we investigate the joint optimization of the PS ratio and the relay-aided beamforming for maximizing the system’s secrecy rate and present a full channel state information (CSI) based joint PS and beamforming (<inline-formula> <tex-math notation="LaTeX">$\it {f}$ </tex-math></inline-formula>JPSB) scheme as the optimal solution by converting the optimization problem formulated into a single-variable optimization problem. We also propose a partial-CSI based JPSB (<inline-formula> <tex-math notation="LaTeX">$\it {p}$ </tex-math></inline-formula>JPSB) method for the scenario where only the main link’s CSI is available. Our numerical results show that the proposed JBRJS scheme beneficially enhances the PLS compared to the joint random relay and jammer selection (JRRJS) and to the pure best relay selection (PBRS) schemes. Moreover, the secrecy rate of the proposed <inline-formula> <tex-math notation="LaTeX">$\it {f}$ </tex-math></inline-formula>JPSB and <inline-formula> <tex-math notation="LaTeX">$\it {p}$ </tex-math></inline-formula>JPSB schemes is obviously higher than that of the fixed PS and beamforming (FPSB) method, and it further increases with the number of the relay’s transmit antennas.
Minimizing Misinformation Profit in Social Networks

The widespread and effective online social networks may cause misinformation to diffuse in the networks, which could lead to public panic and even serious economic consequences. The classical misinformation containment (MC) problem aims to select a small node set as positive seeds to compete against the misinformation and limit the influence of misinformation as much as possible, where the misinformation seed set is given. Most of the prior works concentrate on either minimizing the number of users infected by misinformation or maximizing the number of users protected by the positive cascade. That is, they only concentrate on optimizing the number of nodes. However, the interaction effects between nodes differ from user to user and the related profit obtained from interaction activities may also be different. This article proposes a novel problem, called profit minimization of misinformation (PMM), which is the first to analyze the profit of activity in the MC problem. Given a misinformation seed set, the PMM problem aims at selecting a node set satisfying the cardinality constraint to minimize the profit of edges starting from infected nodes but ending at infected or protected nodes. Based on the sandwich method, we design a data-dependent approximation scheme for the PMM problem. We approximate the upper and lower bounds of the objective in the equivalent problem by the reverse influence sampling technique. Our algorithm is verified on realistic data sets, which demonstrate the superiority of our method.
Effects of strain on cardiac electrophysiology and microstructure

Does strain induce changes in the electrical properties of the heart? Does strain affect the microstructure of cardiac myocytes? Others have considered these questions, but have been limited in their findings. I addressed the first question by measuring conduction velocity in papillary muscles in rest conditions and during applied strain. I also applied streptomycin, a nonselective stretch ion channel blocker, in the above conditions. In control, conduction velocity increased with strain before conduction block occurred. When streptomycin was applied conduction velocity peaked at a higher strain, but conduction block remained unchanged. Changes in electrical properties of papillary muscle allowed for changes in conduction velocity. Although streptomycin did not alter the strain at which conduction block occurred, it did shift the peak conduction velocity to a higher strain. The second question was addressed by imaging isolated cardiac ventricular myocytes in varying degrees of contraction and strain using confocal microscopy. The length of transverse tubules (t-tubules), along with cross-section ellipticity, and orientation in myocytes were analyzed for cells in 16% contraction, rest, and 16% strain. Cells in contraction showed an increase in length of t-tubules with less elliptical cross-sections compared to cells in rest. Strained cells showed a decrease in length of t-tubules with less elliptical cross-sections than cells at rest. The orientation of t-tubule cross-sections changed in a similar manner when comparing contracted and strained cells with cells at rest. The transfer of strain to the t-tubule system supports the hypothesis that the motion of t-tubules during contraction and stretch may constitute a mechanism for pumping extracellular fluid.
Shifu2: A Network Representation Learning Based Model for Advisor-Advisee Relationship Mining

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2.
Forecasting Social Network Reaction to Disruption: Current Practices and New Directions

Intervening in networks can lead to complex and unexpected outcomes. This paper introduces reviews current analytical approaches for forecasting how a network might react to an intervention or disruption: reviewing studies from fields of network science including sociology, computer science, neuroscience, and logistics management. We find a wide range of conflicting theories about how networks recover from disruption but little empirical research on how networks react to disruption, and none at all on how social networks react to disruptions. We suggest several approaches to empirically studying network reactions and using this information to forecast network reactions including the use of Exponential Random Graph Models and Stochastic Actor Oriented Models.
Word order variation and dependency length minimisation : a cross-linguistic computational approach

Word order is one of the most readily observed aspects of the syntax of human language. This thesis focuses on intralinguistic word order variation and, in particular, on a tendency - known as dependency length minimisation (DLM) - for words and phrases that are close in the syntactic structure to be linearly adjacent. Our approach to DLM phenomena in word order variation is computational: the use of syntactically-annotated corpora and statistical methods allows for generalisations of the variation principles across dozens of languages. This thesis presents three cross-linguistic computational studies at three levels of linguistic representation. First, we look at word order variation at the language level and compare the extent to which languages follow the DLM principle. Secondly, we investigate DLM effects in adjective variation across five Romance languages. Finally, we present a model of incremental production of word order which incorporating word order variation and dependency length effects.
Power spectral parameter variations after transcranial direct current stimulation in a bimanual coordination task

Transcranial direct current stimulation (tDCS) can shift neuronal membrane excitability by applying a weak slow electric current to the brain through the scalp. Attendant electroencephalography (EEG) can provide valuable information about the tDCS mechanisms. This study investigated the effects of anodal tDCS on parietal cortex and cerebellum activity to reveal possible modulation of spontaneous oscillatory brain activity. Timing of the tDCS priming protocol in relation to the intervention especially with respect to bimanual coordination task was also studied. EEG activity was measured in 120 healthy participants before and after sessions of anodal stimulation of the parietal cortex and cerebellum to detect the tDCS-induced alterations. Variations of the delta, theta, alpha, beta, and sensorimotor rhythm (SMR) power bands were analyzed using a MATLAB program. The results showed that anodal parietal and cerebellar tDCS cause changes in brain wave frequencies. They also showed an increase in alpha, beta, and SMR power bands during stimulation sessions for during stimulation parietal group (p ≤ .01). Also, theta, alpha, beta, and SMR power bands were increased in during stimulation cerebellum group in stimulation sessions and 48 h later (p ≤ .01). Moreover, the results revealed that the tDCS intervention led to a variety of activations in some areas of the brain. Altogether, the cerebellar tDCS during motor task had a significant improvement in off-line learning.
Hospital-Based Views and Practices Related to Incident Reporting and Patient Safety: A Qualitative Comparative Study of Two Divisions

Patient safety has been on the research agenda since 2000, when unnecessary harm to patients in providers’ care came to light. In 2005, the improvements in patient safety fell short of expectations, and the patient safety research community recognized that the issues are more difficult to resolve than first thought. One of the tools to address this vexing problem has been voluntary incident reporting systems, although the literature has given incident reporting systems mixed reviews. This qualitative comparative case study comprises 85 semi-structured interviews in two separate divisions of a tertiary care hospital, General Internal Medicine (GIM) and Obstetrics and Neonatology (OBS/NEO). The main line of questioning concerned incident reporting; general views of patient safety were also sought. This is a thesis by publication. The thesis consists of a general introduction to patient safety, a literature review, a description of the methods and cases, followed by the manuscripts. The thesis concludes with a summarization of the findings, and implications of the study. Manuscript one focuses on the reporter end of incident reporting systems. It asks what frames underlie GIM nurse and physician self reporting and peer reporting practices. The findings showed that frames that inhibit reporting are shared by physicians and nurses, such as the fear of blame frame regarding self reporting, and the tattletale frame regarding peer reporting. These frames are underpinned by a focus on the individual, despite the organisational message of reporting for learning. A learning frame is an enabler to incident reporting. Viewing the objective of voluntary incident reporting as learning allows practitioners to depersonalize incident reporting. The focus becomes preventing recurrence and not the individual reporting or reported on. Manuscript two again focuses on the reporter end, and on one type of reportable incident – a problem that healthcare practitioners can fix themselves. The study asks: when faced with such a problem, do practitioners fix it in the moment and forget about it, or do they fix it in the moment and report it? We found that “fixing and forgetting” was the main choice that most GIM practitioners made in situations where they faced problems that they themselves could resolve. These situations included a) handling near misses, which were seen as unworthy of reporting since they did not result in actual harm to the patient, b) prioritizing solving individual patients’ safety problems, which were viewed as unique or one-time events, and c) encountering re-occurring safety problems, which were framed as inevitable, routine events. The paper argues that fixing and forgetting patient safety problems encountered may not serve patient safety as well as fixing and reporting. The latter approach aligns with a preventive view of patient safety.