Miracle-dz/UniFAR-FTUs huggingface dataset:
Best Practice: Application Frameworks

While frameworks can be a powerful tool, they have some disadvantages and may not make sense for all organizations. Framework maintainers need to provide standardization and well-defined behavior while not being overly prescriptive. When frameworks strike the right balance, however, they can offer large developer productivity gains. The consistency provided by widespread use of frameworks is a boon for other teams such as SRE and security that have a vested interest in the quality of applications. Additionally, the structure of frameworks provides a foundation for building higher-level abstractions such as microservices platforms, which unlock new opportunities for system architecture and automation. At Google, such frameworks and platforms have seen broad organic adoption and have had a significant positive impact.
How do Users interact with Mobile Health Apps?: A Markov Chain Analysis

Intelligent virtual coaches for healthcare require sufficient insights about the user. Data from interactions with a mobile health app have previously been overlooked as a source of valuable insights. In this paper, traces of interaction with a mobile app are analysed to identify the needs and goals of the user. The mobile app is part of a blended care solution for anxiety therapy. The paper illustrates how Markov Chain analysis proves to be a useful tool in classifying user interaction based on the user's goal.
Daily Journals: Extracting Insights for Well-being

Journals and diaries are the mediums through which individuals express and self report their routines, daily activities and experiences. Analyzing such self reported activities from journals and diaries over a period of time uncovers significant activities that affected the individual's sentiments. Such analysis, aids better self-reflection and improvement, through a better understanding of events and activities that affect her well-being. We present an automated tool, WEACT(Well-being-Emotions-ACTivities) to detect, extract and classify one's self reported activities and to gauge her sentiment from journal text using Natural Language Processing and Deep Learning techniques. This paper discusses the WEACT system and showcases WEACT's results through a study, where WEACT helps to extract activities that have had a significant impact on the individual's sentiments in the study period. We believe, WEACT is the first of its kind tool, provisioning automatic extraction of self reported activities from text of journals and diaries with 92.84% precision and 71.8% recall. It classifies extracted activities with an overall 70.16% precision and 76.6% recall. WEACT is able to uncover study participants' activities that significantly affected their emotional valence with an accuracy of 74.6%. The system will be deployed through mobile and web interfaces, that will enable users to create journal entries anytime anywhere.
Recrystallization of magadiite into offretite in the presence of tetramethylammonium cations

BACKGROUND: This study was aimed at investigating the recrystallization of magadiite into offretite in the presence of tetramethylammonium cations (TMA). The offretite was characterized by X-ray powder diffraction, scanning electron microscopy, infrared spectroscopy, N2 adsorption analysis, thermogravimetry and differential thermal analysis. The influence of key reaction parameters on the formation of offretite, including reaction time, TMA cations, potassium cations and the SiO2/Al2O3 reactant ratio, were examined. RESULTS: Well-crystallized offretite, which was composed of agglomerated samples, was prepared from a dispersion with the molar composition: 3.6 K2O: 3.6 Na2O: 1.0 Al2O3: 6.5 SiO2: 320 H2O: 1.5 TMA+ by heating at 140 °C for 3 days. The structure of offretite collapsed when the temperature rose above 1000 °C. The organic template was removed when the sample was calcined at 550 °C for 10 h. CONCLUSION: Magadiite has been used to synthesize offretite with a high purity and well crystallized in the presence of tetramethylammonium cations. The results of the present study indicate the feasibility of a synthetic route to offretite through recrystallization of magadiite in the presence of tetramethylammonium cations. Copyright © 2009 Society of Chemical Industry
In Situ Determination of the Active Catalyst in Hydrosilylation Reactions Using Highly Reactive Pt(0) Catalyst Precursors

The mechanism of hydrosilylation using the highly active precatalyst Karstedt's precatalyst (Ptx(MvinylMvinyl)y, MvinylMvinyl = divinyltetramethyldisiloxane) was investigated using extended X-ray absorption fine structure (EXAFS), small-angle X-ray scattering (SAXS), and ultraviolet−visible (UV−vis) spectroscopy. Contrary to previous reports suggesting colloidal catalysts, we find that regardless of the stoichiometric ratio of hydrosilane to olefin, the catalyst is a monomeric platinum compound with silicon and carbon in the first coordination sphere. The platinum end product, however, is a function of the stoichiometry of the reactants. At excess olefin concentration, the platinum end product contains only platinum−carbon bonds, whereas at high hydrosilane concentration, the platinum end product is multinuclear and also contains platinum−silicon bonds. The two end products can interconvert by adding additional aliquots of the stoichiometrically deficient reagent. An explanation of the “oxygen” effect is .
Correction of eIF2-dependent defects in brain protein synthesis, synaptic plasticity, and memory in mouse models of Alzheimer’s disease

Treatments that promote protein synthesis in the brain may preserve cognition in Alzheimer’s disease. Restoring protein synthesis and cognition The loss of protein synthesis caused by chronic activation of the integrated stress response (ISR) is observed in hippocampal neurons and correlates with cognitive loss in mouse models of Alzheimer’s disease. The compound ISRIB blocks the translation inhibition arm of the ISR but failed to confer cognitive benefit and caused mortality in mouse models of Alzheimer’s disease in previous studies. However, Oliveira et al. found that daily systemic administration of low-dose ISRIB, which did not cause mortality or obvious side effects, rescued protein synthesis and synaptic plasticity in the hippocampus and restored performance on long-term memory tests both in wild-type mice in which translation had been pharmacologically inhibited and in two mouse models of Alzheimer’s disease, although there was no effect on amyloid plaque load. The findings suggest that restoring protein synthesis in the brain may delay cognitive deficits in Alzheimer’s disease patients. Neuronal protein synthesis is essential for long-term memory consolidation, and its dysregulation is implicated in various neurodegenerative disorders, including Alzheimer’s disease (AD). Cellular stress triggers the activation of protein kinases that converge on the phosphorylation of eukaryotic translation initiation factor 2α (eIF2α), which attenuates mRNA translation. This translational inhibition is one aspect of the integrated stress response (ISR). We found that postmortem brain tissue from AD patients showed increased phosphorylation of eIF2α and reduced abundance of eIF2B, another key component of the translation initiation complex. Systemic administration of the small-molecule compound ISRIB (which blocks the ISR downstream of phosphorylated eIF2α) rescued protein synthesis in the hippocampus, measures of synaptic plasticity, and performance on memory-associated behavior tests in wild-type mice cotreated with salubrinal (which inhibits translation by inducing eIF2α phosphorylation) and in both β-amyloid-treated and transgenic AD model mice. Thus, attenuating the ISR downstream of phosphorylated eIF2α may restore hippocampal protein synthesis and delay cognitive decline in AD patients.
Covalent immobilization of glucose oxidase to poly(O‐amino benzoic acid) for application to glucose biosensor

A biosensor for glucose utilizing glucose oxidase (GOX) covalently coupled to poly(o-amino benzoic acid) (PAB; a carboxy-group-functionalized polyaniline) is described. Amperometric response measurements conducted via unmediated and mediated (with ferrocene carboxylic acid and tetrathiafulvalene) reoxidation of GOX show that glucose can be detected over a wide range of concentrations. An enzyme-conducting polymer-mediator model provides for better charge transport in a biosensor. The optimal response, obtained at pH 5.5 and 300 K, lies in the 1–40 mM range. A kinetic plot yields the value of the apparent Michaelis–Menten constant, Kmapp. The operational stability of the PAB-based glucose biosensor was experimentally determined to be about 6 days. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 78: 662–667, 2000
TRICE: A Channel Estimation Framework for RIS-Aided Millimeter-Wave MIMO Systems

We consider the channel estimation problem in point-to-point reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) MIMO systems. By exploiting the low-rank nature of mmWave channels in the angular domains, we propose a non-iterative Two-stage RIS-aided Channel Estimation (TRICE) framework, where every stage is formulated as a multidimensional direction-of-arrival (DOA) estimation problem. As a result, our TRICE framework is very general in the sense that any efficient multidimensional DOA estimation solution can be readily used in every stage to estimate the associated channel parameters. Numerical results show that the TRICE framework has a lower training overhead and a lower computational complexity, as compared to benchmark solutions.
Accumulation Features of Micro and Macroelements in Indigenous and Alien Molluscs in Saldanha Bay, South Africa

Abstract Alien Mediterranean mussels Mytilus galloprovincialis, Pacific oysters Crassostrea gigas and indigenous Cape mussels Choromytilus meridionalis were used as sentinel biomonitors of inorganic pollutants in Saldanha Bay, South Africa. Neutron activation analysis was used to determine the concentrations of 33 macro and microelements in the soft tissues of molluscs. The Mediterranean mussels significantly demonstrated the higher accumulation ability to S, Se and Br than the Pacific oysters, whereas the Pacific oysters - to Fe, Cu, Zn and As. The Cape mussels are more sensitive to Mn and As and the Mediterranean mussels were sensitive to Zn and Se.
An Angular 3D Path Selection Protocol in Wireless Sensor Networks

Abstract Geographical routing is an area of interest for wireless sensor networks because of its scalability and local decision making capability. Usually, geographical routing protocols rely on greedy approach and suffer from the void node problem (VNP). This paper presents an angular three-dimensional routing protocol (A3DR) for wireless sensor networks. In A3DR, the next hop is selected from nodes available in solid angle decided dynamically based on network density, i.e., larger solid angle for sparse networks while a smaller angle for dense one. The delay based contention mechanism has been proposed to select a suitable forwarding node among nodes lying in a particular solid angle. A3DR has a provision to adaptively tune the solid angle to resolve the void node problem. Further, the proposed protocol A3DR will take care of traffic congestion problem suffered by beaconless routing protocol. The A3DR tackle the concave void problem by allowing conditional backtracking to find another suitable path and prevent looping. Additionally, A3DR address overhear problem by utilizing the concept of request-to-forward and clear-to-forward for the nodes lying outside the solid angle 60°. The performance of proposed A3DR is compared with existing protocols by implementing it on the INET framework under OMNET++ simulator. The simulation results indicate that A3DR achieves a high packet reception rate while maximizing the residual energy and minimizing traffic overhead.
Using Network Embedding to Obtain a Richer and More Stable Network Layout for a Large Scale Bibliometric Network

Abstract Purpose The goal of this study is to explore whether deep learning based embedded models can provide a better visualization solution for large citation networks. Design/methodology/approach Our team compared the visualization approach borrowed from the deep learning community with the well-known bibliometric network visualization for large scale data. 47,294 highly cited papers were visualized by using three network embedding models plus the t-SNE dimensionality reduction technique. Besides, three base maps were created with the same dataset for evaluation purposes. All base maps used the classic OpenOrd method with different edge cutting strategies and parameters. Findings The network embedded maps with t-SNE preserve a very similar global structure to the full edges classic force-directed map, while the maps vary in local structure. Among them, the Node2Vec model has the best overall visualization performance, the local structure has been significantly improved and the maps’ layout has very high stability. Research limitations The computational and time costs of training are very high for network embedded models to obtain high dimensional latent vector. Only one dimensionality reduction technique was tested. Practical implications This paper demonstrates that the network embedding models are able to accurately reconstruct the large bibliometric network in the vector space. In the future, apart from network visualization, many classical vector-based machine learning algorithms can be applied to network representations for solving bibliometric analysis tasks. Originality/value This paper provides the first systematic comparison of classical science mapping visualization with network embedding based visualization on a large scale dataset. We showed deep learning based network embedding model with t-SNE can provide a richer, more stable science map. We also designed a practical evaluation method to investigate and compare maps.
Gas–liquid mass transfer using advanced optical probe in a mimicked FT slurry bubble column

Abstract Gas-liquid volumetric liquid-phase mass transfer coefficient (kLa) was studied in a slurry bubble column at the conditions mimicking Fischer–Tropsch synthesis. To avoid the hydrodynamic disturbances due to the gas switching, oxygen enriched air dynamic absorption method was used. Influence of reactor models (CSTR, ADM and RCFD) on the volumetric mass transfer coefficient was investigated. Effect of operating pressure, superficial gas velocity and solids loading were investigated. From the reactor models investigated, it is recommended to use ADM model for kLa study. If the CSTR model is used, applicability of the model should be checked. With increase in the superficial gas velocity and operating pressure, volumetric liquid-phase mass transfer coefficient increases, while it decreases with the solids loading corroborating with the literature.
Femtosecond time-resolved ionization spectroscopy of ultrafast internal-conversion dynamics in polyatomic molecules : theory and computational studies

A framework for the theoretical description of two‐pulse time‐resolved ionization spectroscopy of ultrafast excited‐state dynamics of polyatomic molecules is developed. The radiation–matter interaction as well as intramolecular couplings in the excited‐state manifold are treated nonperturbatively by solving the time‐dependent Schrodinger equation. The numerical solution is based on a discretization of the ionization continua which becomes particularly efficient for ultrashort laser pulses. With this method converged computations of ionization signals become possible even for complex molecular systems. Computer simulations are performed for a model system representing three‐dimensional non‐Born–Oppenheimer excited‐state dynamics on conically intersecting potential‐energy surfaces (the S1 and S2 surfaces of pyrazine). The dependence of the observable time‐resolved ionization signals (total ion yield as well as photoelectron spectrum) on the properties of the laser pulses (carrier frequency and pulse duratio.
Towards a Formal Specification of Production Processes Suitable for Automatic Execution

Abstract Technological advances and increasing customer need for highly customized products have triggered a fourth industrial revolution. A digital revolution in the manufacturing industry is enforced by introducing smart devices and knowledge bases to form intelligent manufacturing information systems. One of the goals of the digital revolution is to allow flexibility of smart factories by automating shop floor changes based on the changes in input production processes and ordered products. In order to make this possible, a formal language to describe production processes is needed, together with a code generator for its models and an engine to execute the code on smart devices. Existing process modeling languages are not usually tailored to model production processes, especially if models are needed for automatic code generation. In this paper we propose a research on Industry 4.0 manufacturing using a Domain-Specific Modeling Language (DSML) within a Model-Driven Software Development (MDSD) approach to model production processes. The models would be used to generate instructions to smart devices and human workers, and gather a feedback from them during the process execution. A pilot comparative analysis of three modeling languages that are commonly used for process modeling is given with the goal of identifying supported modeling concepts, good practices and usage patterns.
Fast, Accurate Barcode Detection in Ultra High-Resolution Images

Object detection in Ultra High-Resolution (UHR) images has long been a challenging problem in computer vision due to the varying scales of the targeted objects. When it comes to barcode detection, resizing UHR input images to smaller sizes often leads to the loss of pertinent information, while processing them directly is highly in-efficient and computationally expensive. In this paper, we propose using semantic segmentation to achieve a fast and accurate detection of barcodes of various scales in UHR images. Our pipeline involves a modified Region Proposal Network (RPN) on images of size greater than $10k \times10k$ and a newly proposed Y-Net segmentation network, followed by a post-processing workflow for fitting a bounding box around each segmented barcode mask. The end-to-end system has a latency of 16 milliseconds, which is $2.5 \times$ faster than YOLOv4 and $5.9 \times$ faster than Mask R-CNN. In terms of accuracy, our method outperforms YOLOv4 and Mask R-CNN by a mAP of 5.5% and 47.1% respectively, on a synthetic dataset. We have made available the generated synthetic barcode dataset and its code at http://www.github. com/viplabB/SBD/.
The Relationship Between Mental Toughness, Job Loss, and Mental Health Issues During the COVID-19 Pandemic

Concerns toward public well-being and mental health are increasing considering the COVID-19 pandemic's global societal and individual impact. The present study builds on the current body of COVID-19 literature by examining the role of mental toughness (MT) in predicting negative affective states (depression, anxiety and stress) during the pandemic. The study also examined the effects of changes in employment on mental health and MT. Participants (N = 723) completed a battery of questionnaires including the Mental Toughness Questionnaire 48-item, The State-Trait Anxiety Inventory, and the Depression, Anxiety and Stress Scale – 21 items. Participants reported relatively higher levels of depression, stress and anxiety in comparison to pre-COVID-19 samples from previous research, with respondents who had lost their jobs during the pandemic reporting higher levels of negative affective states. Despite this, mentally tough individuals appeared to report lower levels of depression, anxiety and stress. Moreover, moderation analyses identified some interaction between MT and employment status when predicting depression, anxiety and stress. Our findings suggest that MT may have some utility in reducing the adverse mental health effects of the pandemic on individuals, however, further longitudinal research is needed to support these implications.
Epstein-Barr Functional Mimicry: Pathogenicity of Oncogenic Latent Membrane Protein-1 in Systemic Lupus Erythematosus and Autoimmunity

Systemic lupus erythematosus (SLE) and other autoimmune diseases are propelled by immune dysregulation and pathogenic, disease-specific autoantibodies. Autoimmunity against the lupus autoantigen Sm is associated with cross-reactivity to Epstein-Barr virus (EBV) nuclear antigen 1 (EBNA-1). Additionally, EBV latent membrane protein-1 (LMP1), initially noted for its oncogenic activity, is an aberrantly active functional mimic of the B cell co-stimulatory molecule CD40. Mice expressing a transgene (Tg) for the mCD40-LMP1 hybrid molecule (containing the cytoplasmic tail of LMP1) have mild autoantibody production and other features of immune dysregulation by 2–3 months of age, but no overt autoimmune disease. This study evaluates whether exposure to the EBV molecular mimic, EBNA-1, stimulates antigen-specific and concurrently-reactive humoral and cellular immunity, as well as lupus-like features. After immunization with EBNA-1, mCD40-LMP1 Tg mice exhibited enhanced, antigen-specific, cellular and humoral responses compared to immunized WT congenic mice. EBNA-1 specific proliferative and inflammatory cytokine responses, including IL-17 and IFN-γ, were significantly increased (p<0.0001) in mCD40-LMP1 Tg mice, as well as antibody responses to amino- and carboxy-domains of EBNA-1. Of particular interest was the ability of mCD40-LMP1 to drive EBNA-1 associated molecular mimicry with the lupus-associated autoantigen, Sm. EBNA-1 immunized mCD40-LMP1 Tg mice exhibited enhanced proliferative and cytokine cellular responses (p<0.0001) to the EBNA-1 homologous epitope PPPGRRP and the Sm B/B’ cross-reactive sequence PPPGMRPP. When immunized with the SLE autoantigen Sm, mCD40-LMP1 Tg mice again exhibited enhanced cellular and humoral immune responses to both Sm and EBNA-1. Cellular immune dysregulation with EBNA-1 immunization in mCD40-LMP1 Tg mice was accompanied by enhanced splenomegaly, increased serum blood urea nitrogen (BUN) and creatinine levels, and elevated anti-dsDNA and antinuclear antibody (ANA) levels (p<0.0001 compared to mCD40 WT mice). However, no evidence of immune-complex glomerulonephritis pathology was noted, suggesting that a combination of EBV and genetic factors may be required to drive lupus-associated renal disease. These data support that the expression of LMP1 in the context of EBNA-1 may interact to increase immune dysregulation that leads to pathogenic, autoantigen-specific lupus inflammation.
Ritual in Therapy for Prolonged Grief: A Scoping Review of Ritual Elements in Evidence-Informed Grief Interventions

The aim of this article of to analyze ritual in evidence-informed treatments for prolonged and traumatic grief. A scoping review is conducted in order to give an overview of existing literature on ritual and symbolic interventions in grief therapies for prolonged grief and the type of evidence supporting these interventions. The 22 studies reported in this review reveal a variety of ritual elements ranging from symbolic expression and interaction, writing assignments, dialogue with the deceased or an imaginary person, to farewell ceremonies at the end of the treatment. The interventions are studied within different populations (e.g., bereaved spouses, perinatal loss, grief after violent death, and genocide). Almost all studies show significant effects of the grief treatment, trauma and related symptoms. However, the effects are mostly measured for the entire treatment and not separately for the ritual intervention. In the discussion we focus on the role of ritual and culture in prolonged grief treatment.