Miracle-dz/UniFAR-FTUs huggingface dataset:
PENUMBUHAN LAPIS LINDUNG NITRIDA PADA PERMUKAAN BAHAN STRUKTUR REAKTOR PADUAN FeCrNi

ABSTRAK PENUMBUHAN LAPIS LINDUNG NITRIDA PADA PERMUKAAN BAHAN STRUKTUR REAKTOR PADUAN FeCrNi. Percobaan penumbuhan lapis lindung nitrida pada paduan FeCrNi telah dilakukan dengan metode plasma nitridasi untuk meningkatkan nilai kekerasannya, ketahanan korosi dalam lingkungan air pendingin. Percobaan dilakukan pada temperatur 400 °C dengan variasi nitridasi waktu pada 2 jam, 3 jam dan 4 jam. Karakterisasi meliputi pola difraksi menggunakan X-Ray Diffractometer (XRD), struktumikro menggunakan Scanning Electron Microscope - Energy Dispersive Spectrometer (SEM-EDS), kekerasan menggunakan Vicker’s Hardness Tester dan uji korosi menggunakan Potensiostat Galvanostat Hasil nitridasi dari pengamatan strukturmikro menunjukkan terbentuknya lapisan tipis pada permukaan paduan FeCrNi. Pola difraksi sinar X menunjukkan bahwa terbentuk senyawa Fe2N dan Fe4N pada permukaan paduan FeCrNi. Hasil uji kekerasan menunjukkan adanya peningkatan nilai kekerasannya setelah proses nitridasi dari 235,97 VHN menjadi 271,76 VHN dan hasil uji korosi dalam larutan air pendingin pH 6,3 juga ada peningkatan yaitu dari 0,0357 mpy menjadi 0,0032 mpy. Kata kunci: plasma nitridasi, paduan FeCrNi, bahan struktur reaktor. ABSTRACT PROTECTING SURFACE LAYER OF NITRIDE ON STRUCTURE REACTOR MATERIAL OF FeCrNi ALLOY. Experiment on the growth of protecting nitride layer on non standard FeCrNi alloy has been done with plasma nitriding method. The research aims to increase the value of its hardness and corrosion resistance in cooling water environment. The plasma nitriding was carried out at a temperature of 400 °C with a variation of nitriding time, i.e. 2, 3 and 4 hours. Included in the characterization are X-ray diffraction pattern using X-Ray Diffractometer (XRD), microstructure - elemental composition examination using SEM-EDS, hardness (Vicker’s hardness), and corrosion test (Potensiostat Galvanostat). The results show that the thin layer of Fe-N coating was formed on the surface of FeCrNi alloy. The X-ray diffraction pattern shows that the compounds of the thin layer are Fe2N and Fe4N. Characterization of hardness indicates that the hardness increases after the nitriding process from 235.97 VHN to 271.76 VHN, while the corrosion test in cooling water with a solution pH of 6.3 shows enhanced corrosion resistance of FeCrNi when compared to the condition before the formation of the thin layer, i.e. from 0.0357 mpy to 0.0032 mpy. Keywords: plasma nitridation, FeCrNi, material structure.
Data Privacy in Trigger-Action IoT Systems

Trigger-action platforms (TAPs) allow users to connect independent IoT or web-based services to achieve useful automation. TAPs provide a simple interface that helps users to program trigger-compute-action rules that pass data between disparate services through the TAPs. Unfortunately, TAPs introduce a large-scale security risk: if they are compromised, attackers will gain access to all sensitive data for millions of users. Towards that end, we propose eTAP, a privacy-enhancing trigger-action platform that executes trigger-compute-action rules without accessing users' private data in plaintext or learning anything about the results of the computation. We use garbled circuits as a primitive, and leverage the unique structure of trigger-compute-action rules to make them practical. We formally state and prove the security guarantees of our protocols. We prototyped eTAP, which supports the most commonly used operations on popular commercial TAPs like IFTTT and Zapier. Specifically, we support boolean, arithmetic, and string operations on private trigger data and can run 100% of the top-500 rules of IFTTT users and 93.4% of all rules published on Zapier. We run ten existing user-created rules that exercise a variety of operations on trigger data. Performance tests show that the overhead is modest: on average rule execution latency increases by 70 ms (55%) and throughput reduces by 59%.
Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes

Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. While many efforts have been made on generating realistic and natural human motion, most approaches neglect the importance of modeling human-scene interactions and affordance. On the other hand, affordance reasoning (e.g., standing on the floor or sitting on the chair) has mainly been studied with static human pose and gestures, and it has rarely been addressed with human motion. In this paper, we propose to bridge human motion synthesis and scene affordance reasoning. We present a hierarchical generative framework to synthesize long-term 3D human motion conditioning on the 3D scene structure. Building on this framework, we further enforce multiple geometry constraints between the human mesh and scene point clouds via optimization to improve realistic synthesis. Our experiments show significant improvements over previous approaches on generating natural and physically plausible human motion in a scene.1
Concept Generalization in Visual Representation Learning

Measuring concept generalization, i.e., the extent to which models trained on a set of (seen) visual concepts can be leveraged to recognize a new set of (unseen) concepts, is a popular way of evaluating visual representations, especially in a self-supervised learning framework. Nonetheless, the choice of unseen concepts for such an evaluation is usually made arbitrarily, and independently from the seen concepts used to train representations, thus ignoring any semantic relationships between the two. In this paper, we argue that the semantic relationships between seen and unseen concepts affect generalization performance and propose ImageNet-CoG,1 a novel benchmark on the ImageNet-21K (IN-21K) dataset that enables measuring concept generalization in a principled way. Our benchmark leverages expert knowledge that comes from WordNet in order to define a sequence of unseen IN-21K concept sets that are semantically more and more distant from the ImageNet-1K (IN-1K) subset, a ubiquitous training set. This allows us to benchmark visual representations learned on IN-1K out-of-the box. We conduct a large-scale study encompassing 31 convolution and transformer-based models and show how different architectures, levels of supervision, regularization techniques and use of web data impact the concept generalization performance.
Moduli spaces of sheaves on Fano threefolds and K3 surfaces of genus 9

A complex smooth prime Fano threefold $X$ of genus $9$ is related via projective duality to a quartic plane curve $\Gamma$. We use this setup to study the restriction of rank $2$ stable sheaves with prescribed Chern classes on $X$ to an anticanonical $K3$ surface $S\subset X$. Varying the threefold $X$ containing $S$ gives a rational Lagrangian fibration $$\mathcal{M}_S(2,1,7) \dashrightarrow \mathbb{P}^3$$ with generic fibre birational to the moduli space $\mathcal{M}_X(2,1,7)$ of sheaves on $X$. Moreover, we prove that this rational fibration extends to an actual fibration on a birational model $\mathcal{M}$ of $\mathcal{M}_S(2,1,7)$. In a last part, we use Bridgeland stability conditions to exhibit all $K$-trivial smooth birational models of $\mathcal{M}_S(2,1,7)$, which consist in itself and $\mathcal{M}$. We prove that these models are related by a flop, and we describe the positive, movable and nef cones of $\mathcal{M}_S(2,1,7)$.
Wakefield decay in a radially bounded plasma due to formation of electron halo

There is a new effect that can limit the lifetime of a weakly non-linear wakefield in a radially bounded plasma. If the drive beam is narrow, some of the plasma electrons fall out of the collective motion and leave the plasma radially, forming a negatively charged halo around it. These electrons repeatedly return to the plasma under the action of the charge separation field, interact with the plasma wave and cause its damping. The lowest-energy halo electrons take the energy from the wave more efficiently, because their trajectories are bent by the plasma wave towards the regions of the strongest acceleration. For correct accounting of the wave damping in simulations, it is necessary to take the simulation window at least twice as wide as the plasma.
The significance of ovarian fibrosis

Ovarian aging is associated with significant changes in the structural organization of collagen, resulting in ovarian fibrosis. In many other tissues, fibrosis increases risks associated with tumorigenesis and metastasis. Thus, it is possible that ovarian fibrosis increases the risk of ovarian cancer by creating a microenvironment more permissive to tumor growth. In this research perspective, we review the impact of female reproduction on the development of ovarian fibrosis and the contributions of genetic and hormonal disruptions such as BRCA mutation, polycystic ovarian syndrome, and infertility to structural changes in the ovary and their relative risk of ovarian cancer. We also explore new fundamental questions in the field of ovarian fibrosis and possible prevention strategies such as metformin.
A Study of Condition Numbers for First-Order Optimization

The study of first-order optimization algorithms (FOA) typically starts with assumptions on the objective functions, most commonly smoothness and strong convexity. These metrics are used to tune the hyperparameters of FOA. We introduce a class of perturbations quantified via a new norm, called *-norm. We show that adding a small perturbation to the objective function has an equivalently small impact on the behavior of any FOA, which suggests that it should have a minor impact on the tuning of the algorithm. However, we show that smoothness and strong convexity can be heavily impacted by arbitrarily small perturbations, leading to excessively conservative tunings and convergence issues. In view of these observations, we propose a notion of continuity of the metrics, which is essential for a robust tuning strategy. Since smoothness and strong convexity are not continuous, we propose a comprehensive study of existing alternative metrics which we prove to be continuous. We describe their mutual relations and provide their guaranteed convergence rates for the Gradient Descent algorithm accordingly tuned. Finally we discuss how our work impacts the theoretical understanding of FOA and their performances.
Cardiac Computed Tomography as an Alternative Technique in the Assessment of Acute Myocardial Ischemia in the Context of COVID-19 Infection

Coronavirus disease 2019 (COVID-19) has been shown to result in coagulation abnormalities and predisposes patients to thrombotic status, both in the venous and arterial circulations. Herein, we report the case of a 60-year-old patient with COVID-19 pneumonia confirmed by polymerase chain reaction (PCR) who experienced signs and symptoms of myocardial ischemia. A cardiac computed tomography (CT) demonstrated an extensive coronary artery multivessel disease and ischemic dilated cardiomyopathy in a non-invasively approach allowing to define the coronary obstructive involvement in the acute stage of the disease.
Using symmetry forbidden interactions to create small band gap polymers: Poly‐aminosquaraine and related compounds

Materials with low band gaps, down to 0.5 eV, can be obtained from polymers containing squaraine or croconaine moieties. The properties of poly‐aminosquaraine, a representative of this novel class of materials, and related compounds have been investigated by both semiempirical and ab initio quantum mechanical methods. Formally, poly‐aminosquaraine is a donor/acceptor compound. The band gap in these compounds is usually determined by the interaction between the highest occupied molecular orbital (HOMO) of the donor and the lowest unoccupied molecular orbital (LUMO) of the acceptor. If the interaction is weak a small band gap results, but it will also lead to an unfavorably small dispersion of the bands. In poly‐aminosquaraine the levels around the gap are well described by a three‐level model, involving two acceptor levels and one donor level. Symmetry forbidden interactions allow for a small band gap and a significant dispersion at the same time. In principle, by choosing different donors and acceptors, the band gap can be manipulated. This is illustrated by results on poly‐aminocroconaine and poly‐amino‐(2‐propenolate).
Assessment of Fatigue Using Wearable Sensors: A Pilot Study

Background: Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts patient health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are severely limited. Methods: After using a recurrent neural network-based algorithm to impute missing time series data form a multisensor wearable device, we compared supervised and unsupervised machine learning approaches to gain insights on the relationship between self-reported non-pathological fatigue and multimodal sensor data. Results: A total of 27 healthy subjects and 405 recording days were analyzed. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters, and daily questionnaires on fatigue. The best results were obtained when using the causal convolutional neural network model for unsupervised representation learning of multivariate sensor data, and random forest as a classifier trained on subject-reported physical fatigue labels (weighted precision of 0.70 ± 0.03 and recall of 0.73 ± 0.03). When using manually engineered features on sensor data to train our random forest (weighted precision of 0.70 ± 0.05 and recall of 0.72 ± 0.01), both physical activity (energy expenditure, activity counts, and steps) and vital signs (heart rate, heart rate variability, and respiratory rate) were important parameters to measure. Furthermore, vital signs contributed the most as top features for predicting mental fatigue compared to physical ones. These results support the idea that fatigue is a highly multimodal concept. Analysis of clusters from sensor data highlighted a digital phenotype indicating the presence of fatigue (95% of observations) characterized by a high intensity of physical activity. Mental fatigue followed similar trends but was less predictable. Potential future directions could focus on anomaly detection assuming longer individual monitoring periods. Conclusion: Taken together, these results are the first demonstration that multimodal digital data can be used to inform, quantify, and augment subjectively captured non-pathological fatigue measures.
Comment on “Forest microclimate dynamics drive plant responses to warming”

Zellweger et al. (Reports, 15 May 2020, p. 772) claimed that a microclimatic debt, mainly controlled by canopy buffering, evolved in European forest understories. However, their analysis is based on circularity, as they explained the sum of three components by one of these components. The response of the understory to the thermal environment is generally weak.
How to Bring Surgery to the Next Level: Interpretable Skills Assessment in Robotic-Assisted Surgery

Introduction: A surgeon’s technical skills are an important factor in delivering optimal patient care. Most existing methods to estimate technical skills remain subjective and resource intensive. Robotic-assisted surgery (RAS) provides a unique opportunity to develop objective metrics using key elements of intraoperative surgeon behavior which can be captured unobtrusively, such as instrument positions and button presses. Recent studies have shown that objective metrics based on these data (referred to as objective performance indicators [OPIs]) correlate to select clinical outcomes during robotic-assisted radical prostatectomy. However, the current OPIs remain difficult to interpret directly and, therefore, to use within structured feedback to improve surgical efficiencies. Methods: We analyzed kinematic and event data from da Vinci surgical systems (Intuitive Surgical, Inc., Sunnyvale, CA, USA) to calculate values that can summarize the use of robotic instruments, referred to as OPIs. These indicators were mapped to broader technical skill categories of established training protocols. A data-driven approach was then applied to further sub-select OPIs that distinguish skill for each technical skill category within each training task. This subset of OPIs was used to build a set of logistic regression classifiers that predict the probability of expertise in that skill to identify targeted improvement and practice. The final, proposed feedback using OPIs was based on the coefficients of the logistic regression model to highlight specific actions that can be taken to improve. Results: We determine that for the majority of skills, only a small subset of OPIs (2–10) are required to achieve the highest model accuracies (80–95%) for estimating technical skills within clinical-like tasks on a porcine model. The majority of the skill models have similar accuracy as models predicting overall expertise for a task (80–98%). Skill models can divide a prediction into interpretable categories for simpler, targeted feedback. Conclusion: We define and validate a methodology to create interpretable metrics for key technical skills during clinical-like tasks when performing RAS. Using this framework for evaluating technical skills, we believe that surgical trainees can better understand both what can be improved and how to improve.
A genomic epidemiology study of multidrug-resistant Escherichia coli, Klebsiella pneumoniae and Acinetobacter baumannii in two intensive care units in Hanoi, Vietnam

Background Vietnam has high rates of antimicrobial resistance (AMR) but limited genomic surveillance, impeding our ability to assess transmission dynamics. This study aimed to use whole genome sequencing (WGS) to examine the transmission of key AMR pathogens in two intensive care units in Hanoi, Vietnam. Methods A prospective surveillance study of all adults admitted to two intensive care units (ICUs) at the National Hospital for Tropical Diseases (NHTD) and Bach Mai Hospital (BMH) was conducted between June 2017 and January 2018. Clinical and environmental samples were cultured on selective media, characterised using MALDI TOF MS, and Illumina sequenced. Phylogenies based on the de novo assemblies (SPAdes) were constructed using Mafft (PARsnp), Gubbins and RAxML. Resistance genes were detected using Abricate against the NCBI database. Findings 3,153 Escherichia coli, Klebsiella pneumoniae and Acinetobacter baumannii isolates from 369 patients were analysed. Phylogenetic analysis revealed predominant lineages within A. baumannii (global clone [GC]2, sequence types [ST]2, ST571) and K. pneumoniae (ST15, ST16, ST656, ST11, ST147) isolates. Colonisation was most common with E. coli (88.9%) followed by K. pneumoniae (62.4%). 91% of E. coli carried a blaCTX-M variant, while 81% of K. pneumoniae isolates carried blaNDM (54%) and/or blaKPC (45%). Transmission analysis using single nucleotide polymorphisms (SNPs) identified 167 clusters involving 251 (68%) patients, in some cases involving patients from both ICUs. There were no significant differences between the lineages or AMR genes recovered between the two ICUs. Interpretation This study represents the largest prospective surveillance study of key AMR pathogens in Vietnamese ICUs. Clusters of closely related isolates in patients across both ICUs suggests recent transmission prior to ICU admission in other healthcare settings or in the community.
When a pandemic intensifies racial terror

Bolivian urban spaces witnessed dramatic racialized power struggles in the context of the ouster of the indigenous President Evo Morales in a coup in November 2019 and the current lockdown of the country due to the coronavirus pandemic. Repression of indigenous protests against the usurpation of power by racist extreme right-wing forces led to massacres, forced disappearances and severe human rights violations. Furthermore, the outbreak of the Covid-19 pandemic intensified the process of racist stigmatization as urban indigenous sectors were vilified as a threat to the lives of white-mestizo middle class citizens. Besides examining the impacts of anti-indigenous structural racism on the vulnerabilities of Bolivian indigenous people in the context of the pandemic outbreak, this article also highlights the forms in which the pandemic is turned into an opportunity by racist political forces to intensify racial stigmatization of indigenous people. By showing the striking continuities between the racial terror inflicted on indigenous people after the usurpation of power by extreme right wing forces in 2019 and the stigmatization of the same social sectors in the wake of the COVID-19 outbreak, this article underlines how the abandonment and stigmatization of indigenous people during the pandemic, rather than being an aberration, is yet another manifestation of long term historical processes underlying colonialism, indigenous dispossession, and deracination. In response, indigenous activists produced alternative narratives and policy proposals to counter those of the state and the dominant society, (re)imagining the city in the process. This article examines the implications of these urban spatial struggles in dialog with an interdisciplinary body of literature on racialized urban geographies and the relationship between the biopolitical and the necropolitical.
Molybdenum Enzymes and How They Support Virulence in Pathogenic Bacteria

Mononuclear molybdoenzymes are highly versatile catalysts that occur in organisms in all domains of life, where they mediate essential cellular functions such as energy generation and detoxification reactions. Molybdoenzymes are particularly abundant in bacteria, where over 50 distinct types of enzymes have been identified to date. In bacterial pathogens, all aspects of molybdoenzyme biology such as molybdate uptake, cofactor biosynthesis, and function of the enzymes themselves, have been shown to affect fitness in the host as well as virulence. Although current studies are mostly focused on a few key pathogens such as Escherichia coli, Salmonella enterica, Campylobacter jejuni, and Mycobacterium tuberculosis, some common themes for the function and adaptation of the molybdoenzymes to pathogen environmental niches are emerging. Firstly, for many of these enzymes, their role is in supporting bacterial energy generation; and the corresponding pathogen fitness and virulence defects appear to arise from a suboptimally poised metabolic network. Secondly, all substrates converted by virulence-relevant bacterial Mo enzymes belong to classes known to be generated in the host either during inflammation or as part of the host signaling network, with some enzyme groups showing adaptation to the increased conversion of such substrates. Lastly, a specific adaptation to bacterial in-host survival is an emerging link between the regulation of molybdoenzyme expression in bacterial pathogens and the presence of immune system-generated reactive oxygen species. The prevalence of molybdoenzymes in key bacterial pathogens including ESKAPE pathogens, paired with the mounting evidence of their central roles in bacterial fitness during infection, suggest that they could be important future drug targets.