Miracle-dz/UniFAR-FTUs huggingface dataset:
Opinion Conflicts

In Twitter, there is a rising trend in abusive behavior which often leads to incivility. This trend is affecting users mentally and as a result they tend to leave Twitter and other such social networking sites thus depleting the active user base. In this paper, we study factors associated with incivility. We observe that the act of incivility is highly correlated with the opinion differences between the account holder (i.e., the user writing the incivil tweet) and the target (i.e., the user for whom the incivil tweet is meant for or targeted), toward a named entity. We introduce a character level CNN model and incorporate the entity-specific sentiment information for efficient incivility detection which significantly outperforms multiple baseline methods achieving an impressive accuracy of 93.3% (4.9% improvement over the best baseline). In a post-hoc analysis, we also study the behavioral aspects of the targets and account holders and try to understand the reasons behind the incivility incidents. Interestingly, we observe that there are strong signals of repetitions in incivil behavior. In particular, we find that there are a significant fraction of account holders who act as repeat offenders - attacking the targets even more than 10 times. Similarly, there are also targets who get targeted multiple times. In general, the targets are found to have higher reputation scores than the account holders.
Solid state transitions of Bi_2O_3 nanoparticles

The solid-state phase transitions of bismuth(III) oxide (Bi_2O_3) nanoparticles were investigated by complementary methods such as differential scanning calorimetry, differential thermal analysis with combined thermogravimetry and mass spectrometry, and high-temperature x-ray diffraction as compacted nanopowder. At room temperature the particles resided in the β-phase, which is usually a metastable high-temperature phase of bulk Bi_2O_3. The complementary experimental methods were linked and a nanophase (tetragonal β-phase) → bulk-phase (monoclinic α-phase) transition was identified which was preceded by crystal growth and evaporation of O and C containing species. It was also shown that the atmosphere (more precisely its absolute pressure) has an influence on the transition behavior. An interpretation was proposed that successfully explains all observations from this work and from literature: A sudden destabilization takes place around 735 K due to the loss of the stabilizing, carbonized surface. This leads to the observed transformation to the bulk-phase. But if the particles are smaller than a certain, critical size in the nanorange and are not allowed to grow, they remain in the nanophase until they melt.
OPTIMIZING REFOLDING CONDITION FOR RECOMBINANT TISSUE PLASMINOGEN ACTIVATOR

Low molecular size additives such as L-arginine and the redox compounds have been used both in the culture medium and in vitro refolding to increase recombinant proteins production. Additives increase protein refolding and yield of active proteins by suppressing aggregate formation or enhancing refolding process. In this work, a comparative study was performed on refolding of recombinant plasminogen activator (rPA) in the presence of different concentrations of denaturants and additives. Escherichia coli-expressed rPA inclusion bodies were solubilized in chaotropic denaturants and subjected to protein refolding by dilution method. The effects of various additives, the impact of pH, residual Guanidin Hydrochloride (Gn-HCl) and Dithiothreitol (DTT) on refolding process were investigated. The refolding process was assessed by determination of protein solubility and biological assay. The results of the study demonstrated that the best condition for solubilizing the rPA inclusion body was 6M guanidine hydrochloride at pH=10. In refolding step, Larginine showed increasing effect on suppression of aggregation at concentrations of 200-1000 mM. Glutathione pairs (GSH-GssG) showed refolding enhancer effect in a range of 2-20 mM. The highest refolding yield was obtained in 500 mM L-arginine and reduced/oxidized glutathione 10:1 ratio in pH 10. In conclusion, the results show that L-arginine plays an important role in the refolding of human PA, preventing the aggregation of folding intermediate, and glutathione pair is essential for the correct refolding. The results also revealed that higher solubility in the pres
AMMONIUM REMOVAL BY ION EXCHANGE: USING BIOLOGICALLY RESTORED REGENERANT

Nitrogen in the ammonia form can have deleterious effects including accelerated eu trophication of lakes, dissolved oxygen (do) depletion in receiving waters, and fish toxicity. Ammonia also reacts with chlorine used for disinfection in wastewater treatment to pro duce chlor amines. Chloramines are less effec tive disinfectants and are toxic to aquatic life in the receiving waters. Ion exchange has been demonstrated to be competitive with other methods of ammonia removal.1 However, the cost of chemical re generation of clinoptilolite remains high and represents more than 50 to 60% of the total process costs even with r? g? n? rant reuse.1 In an effort to find a more economical means
Yolk−Shell Catalyst of Single Au Nanoparticle Encapsulated within Hollow Mesoporous Silica Microspheres

Synthesis and catalysis of yolk−shell microspheres containing a single Au nanoparticle core and a mesoporous shell of hollow mesoporous silica microspheres (HMSM) are reported. This synthesis employs polystyrene-co-poly(4-vinylpyridine) microspheres as both template to fabricate the HMSM shell through sol−gel process and scaffold to immobilize the Au nanoparticle. Since the single Au nanoparticle core is supernatant within the inert HMSM shell, the yolk−shell catalyst has minimum support effect and is a promising model to explore the origin of Au catalysis. Catalyzed reduction of 4-nitrophenol with NaBH4 demonstrates size-dependent induction or activation and size-dependent activity of the Au nanoparticle core of the yolk−shell catalyst.
Deep Learning and System Identfication

Deep learning is a topic of considerable interest today. Since it deals with estimating – or learning – models, there are connections to the area of System Identification developed in the Automatic Control community. Such connections are explored and exploited in this contribution. It is stressed that common deep nets such as feedforward and cascadeforward nets are nonlinear ARX (NARX) models, and can thus be easily incorporated in System Identification code and practice. The case of LSTM nets is an example of NonLinear State-Space (NLSS) models.
Reducing dynamics

Reduction of a dynamical theory consists of two steps: (i) finding its phase portrait (collection of all trajectories), and (ii) recognizing in it a pattern that is then identified with the phase portrait of the reduced dynamical theory. The original dynamical system becomes split in the reduction into the reduced dynamics representing the recognized pattern and the reducing dynamics representing the process of its emergence. From the experimental point of view, the reducing dynamics represents the process of preparing macroscopic systems for using the reduced description.
Nonlinear Acoustic Echo Cancellation with Deep Learning

We propose a nonlinear acoustic echo cancellation system, which aims to model the echo path from the far-end signal to the near-end microphone in two parts. Inspired by the physical behavior of modern hands-free devices, we first introduce a novel neural network architecture that is specifically designed to model the nonlinear distortions these devices induce between receiving and playing the far-end signal. To account for variations between devices, we construct this network with trainable memory length and nonlinear activation functions that are not parameterized in advance, but are rather optimized during the training stage using the training data. Second, the network is succeeded by a standard adaptive linear filter that constantly tracks the echo path between the loudspeaker output and the microphone. During training, the network and filter are jointly optimized to learn the network parameters. This system requires 17 thousand parameters that consume 500 Million floating-point operations per second and 40 Kilo-bytes of memory. It also satisfies hands-free communication timing requirements on a standard neural processor, which renders it adequate for embedding on hands-free communication devices. Using 280 hours of real and synthetic data, experiments show advantageous performance compared to competing methods.
AKI-CLIF-SOFA Score in Predicting Mortality of Critically Ill Cirrhotic Patients with Acute Kidney Injury

Introduction: Acute kidney injury (AKI) is a fatal complication of Chronic Liver Disease (CLD) and is a significant cause of mortality. Prognostication is vital in this setting because evidence-based decisions on the likely outcome might assist in decision making in relation to the delivery of critical care to patient groups most likely to benefit. The study aims at examining the diagnostic accuracy of AKI-CLIF-SOFA score in predicting 30 day and 90 day mortality of critically ill cirrhotic patients with acute kidney injury and to compare the novel AKI-CLIF-SOFA score with pre-existing scores. Material and methods: This was a cross sectional – diagnostic accuracy study. The data of CLD patients admitted to the ICU with new onset renal failure were collected. The diagnostic accuracy of AKI-CLIF-SOFA score in predicting mortality was calculated and compared with the pre-existing prognostic scores – CHILD, MELD and CLIF-SOFA. Results: A total of 50 patients were included in the study. The diagnostic accuracies are 82% and 76% in predicting 30 and 90 day mortality respectively. AKI-CLIF-SOFA score was compared with the pre-existing scores. For 30 day mortality, CLIF-SOFA is found to be superior followed by AKI-CLIFSOFA, CHILD and MELD. For 90 day mortality, AKI-CLIFSOFA is found to be superior followed by CLIF-SOFA, CHILD and MELD. Conclusion: The novel score – AKI-CLIF-SOFA – has good diagnostic accuracy in predicting the mortality of critically ill Cirrhotic patients with acute kidney injury. The comparison with the already existing scores also shows superior discriminatory power of AKI-CLIF-SOFA score.
Anti-inflammatory activity of Moringa oleifera stem bark extracts against carrageenen induced rat paw edema.

The ethanolic and aqueous extracts of the stem bark of Moringa oleifera were tested to study the effects on the inflammatory reaction, using the tec hnique of carageenan induced paw edema in albino rats. Both the ethanolic and alcoholic extra ct showed significant anti-inflammatory activity comparable to the reference standard Diclo f nac sodium.
A Study on the Influence of Organizational Culture and Authentic Leadership on Job Crafting

We study confirmed the factors influencing the organizational culture(collective culture, development culture, rational culture, hierarchical culture) perceived by members of the organization and the manager's authentic leadership(self-awareness, balanced information processing, relational transparency, internalized moral perspective) on job crafting. In addition, the relationship between organizational culture and authentic leadership was empirically analyzed. In order to verify the hypothesis of the research model, the survey results of 269 parts were verified as follows using the statistical program of SPSS 24.0. First, the organizational cultures perceived by members of the organization, development culture and rational culture, showed positive (+) influence on job crafting. In other words, Hypothesis 1 established by the research model was partially adopted. Second, the group culture, development culture, and rational culture of organizational culture were statistically significant in the relationship between the hypothesis 2 organizational culture and authentic leadership. In other words, Hypothesis 2 was partially adopted. Third, the three hypotheses of authentic leadership (self-awareness, balanced information processing, relational transparency, and an internalized moral perspective) all showed positive (+) effects on job crafting. As a result of this study, it was possible to confirm the importance of the organizational culture that improves the job-crafting of the members of the organization and the strategic activation plan for authentic leadership. Therefore, the necessity of strategic human resource development for the development and application of programs to revitalize organizational culture and improve the manager's authentic leadership has emerged.
Significance of developing Multiple Big Data Analytics Platforms with Rapid Response

—This paper looks at how multiple big data platforms can be very important in achieving the best outcome when used in rapid response operations. Big data analytics and technological innovation place a strain on traditional data governance processes, creating a slew of complex issues inside corporations. However, a governed data lake demonstrates how this can be accomplished [1]. The most difficult aspect of integrating different systems is figuring out how to adjust to their unique programming capabilities to complete tasks as quickly as possible to achieve the best results. This paper introduces the latest big data analytics platforms for a rapid response which integrates large-scale data frameworks like RHadoop and SparkR to build high-performance, multi-platform, Big Data analysis for rapid data collection and R-programming analysis [1]. The goal is to improve work scheduling optimization using the big data analytics platforms and to apply the customized device selection based on ascertaining capabilities to significantly boost system performance. Furthermore, rather than running Java or Scala programs, users will issue R commands to conduct data extraction and analytics in the presented platforms [1]. As a consequence, while the configured data analytics platform selection will greatly minimize data extraction and analysis period, and as per the output index measured for different approaches, planned optimization almost certainly improves device reliability considerably.
Finding a semi-randomly hidden clique

We recall some bounds from spectral graph theory that apply to random graphs. Let p ≤ 1 2 satisfy p ≥ n n for some > 0 (we think of as fixed as n grows). Then for the adjacency matrix of a random G ∈R Gn,p graph, the following bounds hold with overwhelming probability. (The probability is over the choice of G. We abbreviate the term with overwhelming probability to w.o.p., and interpret it to mean that the probability is at least 1−O(2n), for some δ > 0. )
A Comprehensive Review of Retinal Vascular and Optical Nerve Diseases Based on Optical Coherence Tomography Angiography

The optical coherence tomography angiography (OCTA) is a noninvasive imaging technology which aims at imaging blood vessels in retina by studying decorrelation signals between multiple sequential OCT B-scans captured in the same cross section. Obtaining various vascular plexuses including deep and superficial choriocapillaris, is possible, which helps in understanding the ischemic processes that affect different retina layers. OCTA is a safe imaging modality that does not use dye. OCTA is also fast as it can capture high-resolution images in just seconds. Additionally, it is used in the assessment of structure and blood flow. OCTA provides anatomic details in addition to the vascular flow data. These details are important in understanding the tissue perfusion, specifically, in the absence of apparent morphological change. Using these anatomical details along with perfusion data, OCTA could be used in predicting several ophthalmic diseases. In this paper, we review the OCTA techniques and their ability to detect and diagnose several retinal vascular and optical nerve diseases, such as diabetic retinopathy (DR), anterior ischemic optic neuropathy (AION), age-related macular degeneration (AMD), glaucoma, retinal artery occlusion and retinal vein occlusion. Then, we discuss the main features and disadvantages of using OCTA as a retinal imaging method.
Megaverse: Simulating Embodied Agents at One Million Experiences per Second

We present Megaverse, a new 3D simulation platform for reinforcement learning and embodied AI research. The efficient design of our engine enables physics-based simulation with high-dimensional egocentric observations at more than 1,000,000 actions per second on a single 8-GPU node. Megaverse is up to 70x faster than DeepMind Lab in fully-shaded 3D scenes with interactive objects. We achieve this high simulation performance by leveraging batched simulation, thereby taking full advantage of the massive parallelism of modern GPUs. We use Megaverse to build a new benchmark that consists of several single-agent and multi-agent tasks covering a variety of cognitive challenges. We evaluate model-free RL on this benchmark to provide baselines and facilitate future research. The source code is available at https://www.megaverse. info
Assessment of coxarthritis risk with dimensionless biomechanical parameters.

PURPOSE Unfavourable distribution of contact stress over the load bearing area is considered a risk factor for early coxarthritis and it is of interest to outline respective biomechanical parameters for its prediction. The purpose of the work was to develop a transparent mathematical model which can be used to assess contact stress in the hip from imaged structures of pelvis and proximal femora, in large population studies and in clinical practice. METHODS We upgraded a previously validated three-dimensional mathematical model of the human hip in the one-legged stance HIPSTRESS by introducing parameters independent from the size of the structures in the images. We validated a new parameter - dimensionless peak stress normalized by the body weight and by the radius of the femoral head (pmaxr²/WB) on the population of 172 hips that were in the childhood subjected to the Perthes disease and exhibited increased proportion of dysplastic hips. RESULTS The dimensionless parameter pmaxr²/WB exhibited smaller number of indecisive cases of hip dysplasia predicted by the model than the previously used parameter pmax/WB (6% vs. 81%, respectively). A threshold for an increased risk of early coxarthritis development by the HIPSTRESS parameter H = pmaxr²/WB was found to be 2. CONCLUSIONS We proposed a dimensionless peak stress on the load bearing area with the border value of 2 as a decisive parameter over which hips are at risk for early development of degenerative processes and presented a method for determination of biomechanical parameters with the use of nomogram.
Assessing Data Sharing's Model Fitness Towards Open Data by using Pooled CFA

This study demonstrates the step-by-step procedure to perform Pooled Confirmatory Factor Analysis (CFA) in the measurement part of Structural Equation Modelling (SEM). CFA is crucial for the SEM measurement model to obtain the acceptable model fit before modeling the structural model. There are two techniques in CFA; individual CFA and Pooled-CFA. Usually, Pooled-CFA is done due to the high number of constructs and items. If the model is too complicated and has so many constructs and items, then it is recommended to perform Pooled-CFA to simplify the model's looks yet easy to understand. The perception of Malaysia Technical University Network (MTUN) academics on data sharing towards open data was analysed by using pooled-CFA. There are three main constructs: data sharing with its 4 sub-constructs; (technological factor, organizational factor, environmental factor, and individual factor), mediator construct (open data licenses), and open data construct was analyzed in this research. Furthermore, second-order constructs' factor loadings towards their corresponding sub-constructs were investigated. This research collected the primary data of 442 respondents using a stratified random sampling technique. This paper will explain the theoretical framework before revealing the results of Pooled-CFA on data sharing towards open data.
Internet-of-Things Enabled Manufacturing: Challenges to Machine Learning and Deep Learning

: Although many general frameworks have been proposed for Internet-of-Things (IoT) enabled systems and their potential in industrial applications, there is limited study on the properties of these industrial IoT devices. In particular, there is little study on the characteristics of the data generated from these IoT devices and what challenges they present to the machine learning and deep learning modeling techniques in industrial applications. In this work, we present our study of the data characteristics of two IoT-enabled manufacturing testbeds. One is the application of IoT vibration sensors for monitoring a pump-flow process and the other is the application of short-range IoT Wi-Fi for estimating moisture content of woodchips in a chemical pulping process. We demonstrate that the traditional data cleaning or preprocessing approaches are not good options for dealing with the veracity of IoT data. We also demonstrate that integrating domain knowledge and human learning with machine learning is the key to successful applications.
Older Adults’ Engagement in Mindfulness Practices

IJOAR: https://escipub.com/international-journal-of-aging-research/ 1 Neha Shivhare et al., IJOAR, 2021 4:82 IJOAR: https://escipub.com/international-journal-of-aging-research/ 2 Introduction There is substantial evidence to suggest that mindfulness practices and exercises positively affect older adults’ physical, emotional, and cognitive wellbeing. Research has shown that older adults experience calmness, inner joy, reduced anxiety and depression levels as a result of performing mindfulness practices [1,2,3,4,5] . However, despite these strong positive findings, there is still not much known about the level of engagement of older adults in these practices. Therefore, the present study addresses this issue by exploring the prevalence of older adults performing these practices. Benefits of mindfulness practices A meta-analysis involving 42 studies found that yoga asanas based heterogeneous interventions reduced evening cortisol, waking cortisol, ambulatory systolic blood pressure, resting heart rate, high-frequency heart rate variability, fasting blood glucose, cholesterol, and low-density lipoprotein, and improved regulation of the sympathetic nervous system and hypothalamic-pituitary-adrenal system in various populations . In another study, the Mindfulness-Based Cognitive Approach for Seniors (MBCAS) was found effective in inducing a positive and accepting approach towards life and agingrelated challenges in reasonably healthy older adults . In their retrospective analysis based study, Young and Baime (2010) also found that eight weeks of Mindfulness-Based Stress Reduction training helpful in alleviating stress and promoting vitality among 141 adults aged 65 years and older.