Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 Associated Stress Among Dental Students

Background: The COVID-19 pandemic has exposed the global population to a high risk of infection that constitutes a major stress factor, especially for vulnerable groups such as dental students Objective: To assess levels of COVID-19 related stress and anxiety, changes in hygienic and social habits, as well as subjective worries regarding the dental profession among dental students Methods: A self-structured questionnaire was sent to students at the College of Dentistry, Umm Al-Qura University It included demographic data, questions about the level of change in personal hygiene and social habits, students’ perception about the sufficiency of information received during the outbreak and its source, subjective worries regarding the dental profession, and the Generalized Anxiety Disorder 7-item (GAD-7) scale Two hundred fifty-eight responses were received Results: Moderate and severe anxiety were reported in 17% and 4% of students, respectively, with non-significant associations between anxiety and gender or level of study A high percentage of students (46 5% to 62 4%) reported great change in most hygienic and social habits with a highly significant association between change in buying more sanitizers and anxiety levels Forty-six percent agreed that their job performance was not at its best, and 31% reported a possible intention to change profession, with the latter reported more by male students (63 8%) Conclusion: A considerable percentage of dental students in our college are anxious and changed their hygienic and social habits due to COVID-19 This anxiety reflects worries about the dental profession in one-third of participants that necessitate counselling services and psychological support
Development of a microbe domestication pod (MD Pod) for in situ cultivation of micro‐encapsulated marine bacteria

Microbial marine natural products hold significant potential for the discovery of new bioactive therapeutics such as antibiotics. Unfortunately, this discovery is hindered by the inability to culture the majority of microbes using traditional laboratory approaches. While many new methods have been developed to increase cultivability, a high‐throughput in situ incubation chamber capable of simultaneously isolating individual microbes while allowing cellular communication has not previously been reported. Development of such a device would expedite the discovery of new microbial taxa and, thus, facilitate access to their associated natural products. In this study, this concept is achieved by the development of a new device termed by the authors as the microbe domestication (MD) Pod. The MD Pod enables single‐cell cultivation by isolating marine bacterial cells in agarose microbeads produced using microfluidics, while allowing potential transmission of chemical signals between cells during in situ incubation in a chamber, or “Pod,” that is deployed in the environment. The design of the MD Pod was optimized to ensure the use of biocompatible materials, allow for simple assembly in a field setting, and maintain sterility throughout incubation. The encapsulation process was designed to ensure that the viability of marine sediment bacteria was not adversely impacted by the encapsulation process. The process was validated using representative bacteria isolated from temperate marine sediment samples: Marinomonas polaris, Psychrobacter aquimaris, and Bacillus licheniformis. The overall process appeared to promote metabolic activity of most representative species. Thus, microfluidic encapsulation of marine bacteria and subsequent in situ incubation in the MD Pod is expected to accelerate marine natural products discovery by increasing the cultivability of marine bacteria.
Leveraging the Power of Nondisruptive Technologies to Optimize Mental Health Treatment: Case Study

Regular assessment of the effectiveness of behavioral interventions is a potent tool for improving their relevance to patients. However, poor provider and patient adherence characterize most measurement-based care tools. Therefore, a new approach for measuring intervention effects and communicating them to providers in a seamless manner is warranted. This paper provides a brief overview of the available research evidence on novel ways to measure the effects of behavioral treatments, integrating both objective and subjective data. We highlight the importance of analyzing therapeutic conversations through natural language processing. We then suggest a conceptual framework for capitalizing on data captured through directly collected and nondisruptive methodologies to describe the client’s characteristics and needs and inform clinical decision-making. We then apply this context in exploring a new tool to integrate the content of therapeutic conversations and patients’ self-reports. We present a case study of how both subjective and objective measures of treatment effects were implemented in cognitive-behavioral treatment for depression and anxiety and then utilized in treatment planning, delivery, and termination. In this tool, called Eleos, the patient completes standardized measures of depression and anxiety. The content of the treatment sessions was evaluated using nondisruptive, independent measures of conversation content, fidelity to the treatment model, and the back-and-forth of client-therapist dialogue. Innovative applications of advances in digital health are needed to disseminate empirically supported interventions and measure them in a noncumbersome way. Eleos appears to be a feasible, sustainable, and effective way to assess behavioral health care.
A Deep Generative Model Imitating Predictive Coding in Human Brain

In recent years, the development of deep generative models for prediction has been attracting attention. In our study, we focus on predictive coding, a concept from the neuroscience literature that hypothesizes the brain is constantly making predictions of sensory input, and attempt to develop a human brain-like prediction model. PredNet is a deep learning model based on the concept of predictive coding, however, it can predict the next image with the same prediction interval, but not with any intervals. On the other hand, Temporal Differential Variational Auto-Encoder (TD-VAE) can predict the next images with any prediction intervals, although it is not a model reﬂecting human brain function. In this work, we develop a new human brain-like prediction model by unifying PredNet and TD-VAE, combining both predictive coding and ﬂexible interval prediction abilities in one single model. Through experiments on the KITTI Vision Benchmark, we conﬁrmed that our proposed model can predict the next images correctly with ﬂexible prediction intervals. We also investigated the correlation between the feature values of representation layers in the model architecture and human brain activity data evoked under natural video stimulation.
Pegasus: Tolerating Skewed Workloads in Distributed Storage with In-Network Coherence Directories

High performance distributed storage systems face the challenge of load imbalance caused by skewed and dynamic workloads. This paper introduces Pegasus, a new storage system that leverages new-generation programmable switch ASICs to balance load across storage servers. Pegasus uses selective replication of the most popular objects in the data store to distribute load. Using a novel in-network coherence directory, the Pegasus switch tracks and manages the location of replicated objects. This allows it to achieve load-aware forwarding and dynamic rebalancing for replicated keys, while still guaranteeing data coherence and consistency. The Pegasus design is practical to implement as it stores only forwarding metadata in the switch data plane. The resulting system improves the throughput of a distributed in-memory key-value store by more than 10x under a latency SLO — results which hold across a large set of workloads with varying degrees of skew, read/write ratio, object sizes, and dynamism.
SafeGuard: Congestion and Memory-aware Failure Recovery in SD-WAN

In software-defined WANs (SD-WAN), link failure can lead to congestion and packet loss, hence degrading application performance. State-of-the-art traffic engineering approaches can speed up failure recovery by proactively installing backup tunnels and redirecting affected traffic immediately in the data plane, which reduces the burden on the network controller. However, these approaches either lead to bandwidth waste because of reserved link capacity or impose restrictions on network topologies, e.g., the existence of link-disjoint routes or large switch memory resources. In this paper, we propose SafeGuard, a software-defined proactive recovery system that improves bandwidth allocation and switch-memory usage while working on any connected network. We formulate the failure recovery problem as a multi-objective MILP optimization problem for all possible single link failures, the most common case in current WANs as temporally-coinciding failures are rare. We then develop a heuristic to efficiently compute backup routes as the problem is NP-Hard. We implemented a prototype of SafeGuard using the Ryu SDN controller and extensively evaluate it in Mininet over two real topologies, Google B4 and ATT. Our results show that SafeGuard can reduce the number of congested links by up to 50% compared to the state-of-the-art failure recovery scheme.
Diamond Films: Chemical Vapor Deposition for Oriented and Heteroepitaxial Growth

1. Overview of Oriented Growth 2. Diamond - Structure and CVD Growth 3. Microwave Plasma CVD Reactors 4. Other CVD Reactors 5. Crystal Orientations and Film Surface Morphology 6. Formation of Twins 7. Homoepitaxial Growth 8. Surface Reconstruction 9. Epitaxial Growth on cBN, Ni, and Other Substrates 10. Diamond Nucleation 11. HOD Film Growth 12. Oriented Growth on Noble Metals 13. Properties and Applications of Heteroepitaxial Diamond Films 14. Conclusion APPENDICES Notations and units Plasma Properties of diamond and other semiconducting materials Reconstruction of diamond surfaces Materials constants Phase diagrams of carbon and metals Carbon solubilities in metals Biasing and growth conditions for diamond growth
ABCs of FT-NMR

Some NMR Basics Electromagnetic Basics of NMR How Do We Perform the NMR Fourier Transform? The Bloch Equations: Calculating What Happens in NMR Experiments The NMR Fourier Transform and its Problems Relaxation and the Nuclear Overhauser Effect Pulse Experiments in One-Dimensional NMR Spectra NMR Spectroscopy in Two Dimensions Some Thoughts on Spin-Spin Splitting Some Thoughts About Chemical Shifts Measurement of Rates by NMR Index
Dirhamnolipid ester – formation of reverse wormlike micelles in a binary (primerless) system

We report new dirhamnolipid ester forming reverse wormlike micelles in nonpolar solvents without the addition of any primer. Therefore, these compounds represent a rare case of a binary system showing this gel-like behavior. In this study, the influence of the concentration of the rhamnolipid ester and the ester alkyl chain length on the rheological properties of the reverse wormlike micelles in toluene was investigated in detail. Highly viscoelastic solutions were obtained even at a relatively low concentration of less than 1 wt %. The phase transition temperatures indicate that the formation of reverse wormlike micelles is favored for dirhamnolipid esters with shorter alkyl chain lengths. Oscillatory shear measurements for the viscoelastic samples reveal that the storage modulus (G') and the loss modulus (G'') cross each other and fit the Maxwell model very well in the low-ω region. As is typical for wormlike micelle systems, the normalized Cole–Cole plot of G''/G''max against G'/G''max was obtained as a semicircle centered at G'/G''max = 1. The formation of network structures was also verified by polarized light microscopy. The sample was birefringent at ambient temperature and anisotropic at an elevated temperature. Differential scanning calorimetry analysis yielded a transition enthalpy of about ΔHSG/GS = ±7.2 kJ/mol. This value corresponds to a strong dispersion energy and explains the formation of the highly viscous gels by the entanglement of wormlike micelles through the interaction of the alkyl chains.
Real Robot Challenge Phase 2

Our approach is based on residual policy learning. We create a hand-designed control policy and learn residual corrections to this policy. The hand-designed policy consists of a position controller that follows a planned motion and a torque controller that acts to apply corrective forces to the cube. We try a series of grasps until we ﬁnd one that is able to plan to the goal location. For Task 4, we additionally perform a set of scripted manipulations to align the orientation of the cube with the goal before executing our policy. Due to time constraints, our approach is only slightly changed from Phase 1 of the competition and we use the residual models trained for phase one without modiﬁcation on Levels 2, 3, and 4. See the future work section for details about improving our training pipeline for the real system.
6 - Roles, Stress and Coping Mechanisms among Middle-level Academic Leaders in Multi-campus Universities in Africa

Using a critical review of literature and existing data, this article explicates the roles of middle-level academics and the stress factors that hinder their efficient and effective delivery of academic and managerial roles in multi- campus universities in Africa. The article presents both plausible and actual coping mechanisms for middle-level academic leaders. It is noted that most middle- level academic leaders ascend to positions of responsibility without any formal training for these positions, which strengthens the call for the university to offer continuous training programmes for these leaders. The article argues that an effective identification and resolution of stress in the multi-campus system is key to winning and maintaining the morale and loyalty of staff at the university. This therefore calls for the adoption of appropriate theoretical paradigms of leadership in multi-campus universities for effective middle- level academic leadership. Recommendations are provided in the form of roles of the university in how to best enhance the productivity of this cadre of university leaders in achieving the functions of teaching, research, and community service.
Bot Detective: Explainable Bot Detection in Twitter

Bot accounts exploit social media networks in order to fulfill their malicious purposes; phishing, influencing public opinion and spreading hate speech, to name a few. Twitter, with more than 330 million active users, is one of the social networks that suffer the most from bot attacks. The research community is getting more and more involved in this subject ever since social networks started attracting more audience. The approach using Machine Learning has proven to be the most effective, since it helps in discovering of patterns that exist in bot-like behavior. A lot of work in the literature focuses on extracting user and content information from identified Twitter accounts and then training a classification model to use in predicting the likelihood of an account being a bot. The issue that lies there is that the reasons behind how and why a Machine Learning model reaches to decisions internally are not clear. The present thesis introduces a novel methodology in order to explain the decisions made from Machine Learning models about their results of identifying accounts in Twitter. To that end, first, a large in volume labelled dataset was created using the Twitter API, that lead to the development of an explainable regression model, able to produce accurate results. To assist the public, in the context of this thesis, a web application was also developed, which makes real-time predictions about the status of Twitter users accompanied with the reasons behind those predictions. The contribution of this work will hopefully support not only the research community, but users of Twitter as well, that can use the developed tool as their ally in identifying bots in their everyday interactions in the network. Bot Detective: Εντοπισμός κακόβουλων και αυτοματοποιημένων λογαριασμών στο Twitter με επεξηγηματικό χαρακτήρα
Adaptive Evolution in Response to Climate Change

Why is adaptive evolution important to this discussion? Adaptive evolution is an almost universal response of the global biota to environmental change. Theoretical models suggest that adaptive evolution can enhance the persistence of populations in a changing environment even when migration is possible (Pease et al. 1989; Lynch et al. 1991; Lynch and Lande 1993; Bürger and Lynch 1995). As climate continues to change into the future, natural selection on wild populations will be altered (Etterson 2004a). The potential of species to respond to such changes in selection depends upon a number of genetic considerations that must be examined at the population level. Species are not uniform across their ranges but comprise a series of populations that have been molded by evolutionary forces such as founder events, genetic drift, gene flow, and selection. Each population differs in the extent to which it is adapted to local conditions and each has its own ecological amplitude and evolutionary potential. Thus, the ultimate fate of a species depends upon the evolutionary response of these genetically differentiated populations across the species range, not just at the margins of climate tolerance.
Complementary near field technique for assessment of fiber reinforced composites

The paper present the possibility to use a sensor with metamaterial lens for the nondestructive evaluation of metallic strip gratings and carbon fiber reinforced plastics. The sensor has enhanced spatial resolution due to the apparition of evanescent waves in the space between strips and respectively between carbon fibers at the excitation with a transversal magnetic along z axis polarized electromagnetic field. The evanescent waves can be manipulated by a lens made from two conical Swiss rolls that act as a field concentrator. The detection is made with spatial resolution better than λ/2000.
Title : Loss of integration of brain networks after sleep deprivation relates to the worsening of cognitive 1 functions 2 3 Running title : Brain topology and sleep deprivation

21 The topology of brain networks changes according to environmental demands and can be described within the 22 framework of graph theory. We hypothesized that 24-hours long sleep deprivation (SD) causes functional 23 rearrangements of the brain topology so as to impair optimal communication, and that such rearrangements 24 relate to the performance in specific cognitive tasks, namely the ones specifically requiring attention. Thirty25 two young men underwent resting-state MEG recording and assessments of attention and switching abilities 26 before and after SD. We found loss of integration of brain network and a worsening of attention but not of 27 switching abilities. These results show that brain network changes due to SD affect switching abilities, 28 worsened attention and induce large-scale rearrangements in the functional networks. 29 30
Two-stage Semantic Answer Type Prediction for QA using BERT and Class-Speciﬁcity Rewarding

Answer type prediction is a key task in Question Answering (QA) that aims at predicting the type of the expected answer for a user query expressed in natural language. In this paper we focus on semantic answer type prediction where the candidate types come from a class hierarchy of a general-purpose ontology. We model the problem as a two-stage pipeline of sequence classiﬁcation tasks (answer category prediction , answer literal/resource type prediction ), each one making use of a ﬁne-tuned BERT classiﬁer. To cope with the harder problem of answer resource type prediction, we enrich the BERT classiﬁer with a rewarding mechanism that favors the more speciﬁc ontology classes that are low in the class hierarchy. The results of an experimental evaluation using the DBpedia class hierarchy ( ∼ 760 classes) demonstrate a superior performance of answer category prediction ( ∼ 96% accuracy) and literal type prediction ( ∼ 99% accuracy), and a satisfactory performance of resource type prediction ( ∼ 78% lenient NDCG@5).
Highly Robust Prediction of Lung Nodule Malignancy by Deep Learning Model: A Multiracial, Multinational Study

Purpose Although statistical models have been employed to detect and classify lung nodules using deep learning-extracted and clinical features, there is a lack of model validation in independent, multinational datasets from computed tomography (CT) scans and patient clinical information. To this end, we developed a deep learning-based algorithm to predict the malignancy of pulmonary nodules and validated its performance in three independent datasets containing multiracial and multinational populations. Methods In this study, a convolutional neural network-based algorithm to predict lung nodule malignancy was built based on CT scans and patient-wise clinical features (i.e. sex, spiculation, and nodule location). The model consists of three steps: (1) a deep learning algorithm to automatically extract features from CT scans, (2) clinical features were concatenated with the nodule features after dimension reduction by the principal component analysis (PCA), and (3) a multivariate logistic regression model was employed to classify the malignancy of the lung nodules. The model was trained by a dataset containing 1,556 nodules from 813 patients from the National Lung Screening Trial (NLST). The performance of the model was evaluated on three independent, multi-institutional datasets LIDC and Infervision Multi-Center (IMC) dataset, which contains 562 nodules from 293 patients, and 2044 nodules from 589 patients, respectively. The model accuracy was measured by the area under curve (AUC) of receiver operating characteristic (ROC) analysis. Results The study shows that the AUCs of ROCs on the NLST dataset, LIDC dataset, and IMC dataset are 0.91, 0.86, and 0.95, respectively. The inclusion of clinical features does not significantly improve the model performance. Quantitatively, the summed-up weight on the prediction accuracy of the 10 nodule features extracted by the deep learning algorithm equals to 0.091, while the weight of patient sex, nodule spiculation, and location is 0.031, 0.052, and 0.008, respectively. Conclusion The convolutional neural network-based model for lung nodule classification could be generalized to multiple datasets containing diverse populations. The addition of three patient clinical features to the nodule features extracted by deep learning does not boost the performance of the model.