Miracle-dz/UniFAR-FTUs huggingface dataset:
SEDIMENT MINING: A SUSTAINABLE STRATEGY FOR CONTAMINATED SEDIMENTS

The main dilemma of contaminated sediments has been the proper management with reduced environmental footprints. Furthermore, by considering the fact that global warming and climate change may complicate the choice of management options, finding appropriate solutions become extremely critical. In the present work, mining of contaminated sediments to recover valuable constituents such as metals and nutrients is proposed as sustainable strategy, both through enhancing resilience of ecosystem and remediation. Contaminated sediments in the Oskarshamn harbor, southeast of Sweden were collected and analyzed through a modified sequential extraction in order to evaluate the feasibility of metals recovery. The results have shown that among different metals present in the sediments, Cu and Pb can be initially considered as economically feasible to recover. The shifting in the concept of dredging and further remediation of contaminated sediments towards sediment mining and recover of valuable metals can be considered in the near future as a sustainable strategy to tackle contaminated harbor/ports areas. However, it must be highlighted that short and long-term environmental impacts related to such activities should be addressed.
COVID-19 Pandemic Simulation Studies on the Transmissibility

be critical for the response to the COVID-19 outbreak. In order to assess their threat to humans, we explored to infer the potential hosts of coronaviruses using a dual-model approach with discriminant model achieved high accuracies in leave-one-out cross-validation of training data consisting of standard representative coronaviruses [34-37]. Predictions on chosen additional coronaviruses precisely conformed to conclusions or speculations by other researchers. The novel coronavirus disease 19 (COVID-19) is rapidly spreading with a rising death toll and transmission rate reported in high income countries rather than in low income countries. The overburdened healthcare systems and poor disease surveillance systems in resource-limited settings may struggle to cope with this COVID-19 outbreak and this calls for a tailored strategic response for these settings. Here, we recommend blockchain Abstract COVID-19 was identified as the causative virus of pneumonia based on unknown etiology. COVID-19 has multiple characteristics distinct from other infectious diseases, including high infectivity during incubation, time delay between real dynamics and daily numbers of confirmed cases, and the intervention effects of quarantine and control measures. Public health concerns are being paid globally on how many people are infected and suspected reach to pandemics. Therefore, it is urgent to develop a mathematical model to estimate the transmissibility and dynamic of the transmission of the virus.
Scrum Methodology

: The agile methods, such as Scrum and Extreme Programming (XP), have been a topic of much discussion in the software community over the last few years. While these have gained importance in the industry because of their approach on the issues of human agility and return on investment, usually within a context of small-to-medium size projects with significant requirements volatility, the ones who do not support these methods have expressed serious concerns about the effectiveness of the methods. Scrum attempts to build the work in short iterations where each iteration consists of short time boxes. This paper reviews several papers on Scrum, its framework including its artifacts and the ceremonies which are involved. This paper gives an insight of the Scrum Methodology to any beginner.
Hilbert-Style Axiomatizations of Disjunctive and Implicative Finitely-Valued Logics with Equality Determinant

Here, we develop a unversal method of [eﬀective] constructing a [ﬁnite] Hilbert-style axiomatization of the logic of a given ﬁnite disjunctive/implicative matrix with equality determinant (in particular, any/implicative four-valued expansion of Belnap’s logic) [and ﬁnitely many connectives].
Real‐time dense mapping for online processing and navigation

Autonomous robots require accurate localizations and dense mappings for motion planning. We consider the navigation scenario where the dense representation of the robot surrounding must be immediately available, and require that the system is capable of an instantaneous map correction if a loop closure is detected by the localization module. To satisfy the real‐time processing requirement of online robotics applications, our presented system bounds the algorithmic complexity of the localization pipeline by restricting the number of variables to be optimized at each time instant. A dense map representation along with a local dense map reconstruction strategy is also proposed. Despite the limits that are imposed by the real‐time requirement and planning safety, the mapping quality of our method is comparable to other competitive methods. For implementations, we additionally introduce a few engineering considerations, such as the system architecture, the variable initialization, the memory management, the image processing, and so forth, to improve the system performance. Extensive experimental validations of our presented system are performed on the KITTI and NewCollege datasets, and through an online experiment around the Hong Kong University of Science and Technology (HKUST) university campus. We release our implementation as open‐source robot operating system (ROS) packages for the benefit of the community.
Contribution of zooplankton nutrient recycling and effects on phytoplankton size structure in a hypereutrophic reservoir

Consumer nutrient recycling influences aquatic ecosystem functioning by altering the movement and transformation of nutrients. In hypereutrophic reservoirs, zooplankton nutrient recycling has been considered negligible due to high concentrations of available nutrients. A comparative analysis ( Moody and Wilkinson, 2019) found that zooplankton communities in hypereutrophic lakes are dominated by nitrogen (N)-rich species, which the authors hypothesized would increase phosphorus (P) availability through excretion. However, zooplankton nutrient recycling likely varies over the course of a growing season due to changes in biomass, community composition and grazing pressure on phytoplankton. We quantified zooplankton, phytoplankton and nutrient concentration dynamics during the summer of 2019 in a temperate, hypereutrophic reservoir. We found that the estimated contribution of zooplankton excretion to the dissolved nutrient pool on a given day was equivalent to a substantial proportion (21–39%) of the dissolved inorganic P standing stock in early summer when P concentrations were low and limiting phytoplankton growth. Further, we found evidence that zooplankton affected phytoplankton size distributions through selective grazing of smaller phytoplankton cells likely affecting nutrient uptake and storage by phytoplankton. Overall, our results demonstrate zooplankton excretion in hypereutrophic reservoirs likely helped drive springtime phytoplankton dynamics through nutrient recycling while grazing influenced phytoplankton size distributions.
Education, Distance to Frontier and Economic Growth A Critical Review of Empirical Analyses

We discuss a potential problem inherent in an empirical analysis of higher education and subsequent technological growth. By following the results of existing literature, we assume that tertiary education would play a more prominent role in advanced economies, and we empirically investigate this hypothesis empirically, specifically by using an interaction term on tertiary education attainment and proximity to the technological frontier. We however find that the results diverge from existing literature if we use different timelines and different samples in the estimation. In particular, by including dummy variables in the regression, we find that the proclaimed positive relation between tertiary education and technology growth does not hold in the periods in which countries experience rapid economic growth. Consequently, we claim that a reconsideration of the model is necessary if the validity is to be universally maintained.
Identiﬁcation-robust analysis of DSGE and structural macroeconomic models Journal of Monetary Economics

Full- and limited-information identiﬁcation-robust methods are proposed for structural systems, notably DSGE models, which are valid whether identiﬁcation is weak or strong, theory-intrinsic or data-speciﬁc. The proposed methods are applied to a standard New Keynesian system for the U.S. Single- and multi-equation estimation and ﬁt are also compared. When a unique rational-expectation stable equilibrium is imposed, the model is rejected. In contrast, limited-information inference produces informative results regarding forward-looking behavior in the NKPC and precise conclusions on feedback coefﬁcients in the reaction function, which cannot be reached via single-equation methods.
Feasibility Study of Plasma Actuator for Flow Separation Control

The plasma actuator, or dielectric barrier discharged (DBD) actuator, is a flow control technique which comprises three simple components, namely, an exposed electrode, a dielectric layer, and a covered electrode. By providing sufficient applied voltage, the air will locally ionize. In the presence of the electric field, the ionized air induces thrust in the surrounding air, thus jetting the flow in the stream-wise direction, and momentum will be generated in the ambient air, which forms the basis for flow separation control strategy. This project presents the findings of experimental tests and numerical simulations on the operation of plasma actuator under several input conditions and geometries in order to investigate the feasibility of plasma actuator to control the flow separation. An experiment was conducted to visualize the formation of plasma on 0.15 mm thick of Kapton dielectric actuator under an applied voltage of 5 to 10 kVp-p. The results demonstrate that, by increasing the input voltage, the generation of plasma also increases. Moreover, numerical analysis of plasma actuator under various applied voltages, dielectric materials, dielectric thicknesses, and covered electrode widths were computed using COMSOL Multiphysics software. Based on the findings, the trend of the results produced is nearly similar to those of previous researchers. However, further studies are needed to investigate the impact of gas temperature, pressure and flow velocity on the discharge of a plasma actuator.
Feeling Uncertain

An appropriate understanding of a machine’s competences may be critical for safe use. Sharing measures of real-time function reliability could help users to adjust their reliance on machine capabilities. We designed a vibrotactile interface that communicates spatiotemporal information about surrounding events and further encodes a representation of spatial uncertainty. We evaluated this interface in a driving simulator experiment with varying levels of machine confidence linked to a simulated degradation of sensor signal quality and varying levels of human confidence induced through a degradation of visual feedback. A comparison between variants of the system indicated positive performance effects of providing uncertain information compared to a more conservative solution that only provided information above a specific confidence level. Subjective reports revealed a positive acceptance of uncertainty signaling in low-visibility conditions, comparable to acceptance ratings of a fully confident machine that accurately signaled the precise location of events.
THE STUDY OF CORPORATE BANKRUPTCY PREDICTION MODELS: UNIVARIATE ANALYSIS AND LOGISTIC REGRESSION

Corporate failure has become a major academic research over the last fifty years. During this time, various failure prediction models and bankruptcy theories were developed. From the initial univariate analysis to market-based models of the twenty-first century, the accuracy of the prediction models are improved continually and their link with corporate governance is attracting more attention. However, the emergence of new prediction methods does not mean the decline of traditional empirical analysis. To inspect the relationship between accounting ratios and bankruptcy risk, this paper investigates the efficacy of univariate analysis and logit model to predict bankruptcy problems with a sample of 52 failed and 518 non-failed companies over the period from 2004 to 2008. Further, comparing with the model incorporated with corporate governance information, including number of directors, female percentage, board average age, average tenure and outside directors percentage, I find corporate governance can enhance the accuracy of the prediction model. After combining these corporate governance variables, the accuracy of the prediction model has been improved. Based on the findings of this study, I explore the evidence of factors contributed to the bankruptcy of companies during the financial crisis.
Risperidone-induced inactivation and clozapine-induced re-activation of rat cortical astrocyte 5-HT 7 receptors: evidence for in situ GPCR homodimer protomer cross-talk

: We have previously reported novel drug-induced inactivation and re-activation of h5-HT 7 receptors in a recombinant cell line. In order to explain these novel observations, a homodimer structure displaying protomer-protomer crosstalk was proposed. In order to determine if these novel observations and interpretations are due to an artifactual GPCR mechanism unique to the recombinant cell line, we explored the properties of r5-HT 7 receptors expressed by cortical astrocytes in primary culture. As in the recombinant cell line, risperidone, 9-OH-risperidone, methiothepin and bromocryptine were found to potently inactivate r5-HT 7 receptors. As in the recombinant cell line, exposure of risperidone-inactivated astrocyte r5-HT 7 receptors to competitive antagonists resulted in the re-activation of r5-HT 7 receptors. The potencies of the re-activating drugs closely correlated with their affinities for h5-HT 7 receptors. These results indicate the novel inactivating and re-activating property of drugs is not due to an artifact of the recombinant cell line expressing h5-HT 7 receptors but is an intrinsic property of 5-HT 7 receptors in vitro and ex vivo . This evidence suggests that a native (non-mutated) GPCR, in its native membrane environment (cortical astrocyte primary culture), can function as a homodimer with protomer-protomer crosstalk. Homodimers may be a common GPCR structure. The experimental design employed in our studies can be utilized to explore the properties of other GPCRs in their native forms in recombinant cells, primary cultures expressing the endogenous GPCRs, and possibly in vivo . The homodimer structure and protomer-protomer crosstalk offer new avenues of research into receptor dysfunction in disease states and the development of novel drugs. cAMP accumulation (re-activation concentration-response curves): Astrocytes were lifted in 1 ml of diluted trypsin/EDTA (1:3 in PBS) followed by addition of 5 ml/dish HBSS buffer. Astrocytes were centrifuged at 330×g for 3 minutes, and the pellet was resuspended in fresh HBSS buffer. Cells were added to tubes containing HBSS or 7nM risperidone. Cell suspensions were incubated for 30 min at 37ºC, followed by the addition of varying concentrations of a non-inactivating antagonist. Cell suspensions were incubated for 90 min at 37ºC and thoroughly washed 3 times: tubes were centrifuged at 330×g for 3 min, and pellet was resuspended in fresh HBSS buffer and incubated for 10 min. Following the final wash, cells were resuspended in stimulation buffer. Cells were counted, added to a 96-well opaque plate containing stimulation buffer in the absence or presence of 10µM 5-HT incubated for 30 min, followed by addition of Detection Mix. TR-FRET was measured and data analyzed as above.
Real Time Password Strength Analysis on a Web Application Using Multiple Machine Learning Approaches

— Passwords, being the most common mechanism for authentication for its easy implementation, pave a way for attackers to break into accounts by guessing passwords. This happens due to predictable patterns that humans usually set these passwords to like dictionary words, known phrases, names of people and places, keyboard patterns, etc. Many password cracking tools have been made to guess passwords either online or offline that mostly results in cracking such accounts with weak passwords or common password patterns. The proposed model provides a common and efficient way to defend against these online and offline attacks by forcing the users to choose a strong password by implementing multiple machine learning algorithms such as Decision Tree (DT), Naïve Bayes (NB), Linear Regression (LR), Random Forest (RF), and Neural Network (NN) on a web application over real time. This results in logging in into user’s account only if the password strength from all algorithms happens to be strong. However, while testing the models over the test set the best results were evaluated by Decision Tree with an accuracy of 99% and the lowest by Naïve Bayes with an accuracy of 87%. Using Burp Suite software we performed three types of password cracking attacks like Brute Force Attack, Dictionary Attack, and Reverse Brute Force Attack on our web application. We made 250 accounts wherein 150 accounts contain strong passwords and other left 100 accounts contain weak passwords. The strong passwords that were set for 150 accounts were not cracked with any of these three types of attacks whereas 86 passwords out of all 100 weak passwords were cracked.
The Journal of Rheumatology A Multidimensional Model of Fatigue in Patients with Rheumatoid Arthritis

Objective. To evaluate a multidimensional model testing disease activity, mood disturbance, and poor sleep quality as determinants of fatigue in patients with rheumatoid arthritis (RA). Method. The data of 106 participants were drawn from baseline of a randomized comparative efficacy trial of psychosocial interventions for RA. Sets of reliable and valid measures were used to represent model constructs. Structural equation modeling was used to test the direct effects of disease activity, mood disturbance, and poor sleep quality on fatigue, as well as the indirect effects of disease activity as mediated by mood disturbance and poor sleep quality. Results. The final model fit the data well, and the specified predictors explained 62% of the variance in fatigue. Higher levels of disease activity, mood disturbance, and poor sleep quality had direct effects on fatigue. Disease activity was indirectly related to fatigue through its effects on mood disturbance, which in turn was related to poor sleep quality. Mood disturbance also indirectly influenced fatigue through poor sleep quality. Conclusion. Our findings confirmed the importance of a multidimensional framework in evaluating the contribution of disease activity, mood disturbance, and sleep quality to fatigue in RA using a structural equation approach. Mood disturbance and poor sleep quality played major roles in explaining fatigue along with patient-reported disease activity. (J Rheumatol First Release June 1 2012; doi:10.3899/ jrheum.111068)
Newton–Cartan (super)gravity as a non-relativistic limit

We define a procedure that, starting from a relativistic theory of supergravity, leads to a consistent, non-relativistic version thereof. As a first application we use this limiting procedure to show how the Newton–Cartan formulation of non-relativistic gravity can be obtained from general relativity. Then we apply it in a supersymmetric case and derive a novel, non-relativistic, off-shell formulation of three-dimensional Newton–Cartan supergravity.
Prognostic value of myocardial perfusion imaging with CZT SPECT camera in patients suspected for coronary artery disease

The prognostic value of myocardial perfusion imaging (MPI) with the cadmium zinc telluride (CZT) single photon emission computed tomography (SPECT) camera is not well established. Therefore, the aim of the current study was to evaluate the prognostic value of MPI performed with a CZT SPECT camera in a large cohort of patients suspected for coronary artery disease (CAD). Methods: 4057 consecutive symptomatic stable patients without a history of CAD underwent CZT SPECT MPI. During a median follow-up of 2.4 years (25 th -75 th percentile:1.7-3.4), patients were monitored for primary (nonfatal myocardial infarction and cardiac mortality) and secondary outcomes (late revascularization (>90 days after scanning) and primary outcome). Results: Patients with normal perfusion demonstrated low annual event rates (primary outcome: 0.2%, secondary outcome: 0.6 %). Annual event rates increased with the extent of abnormality of myocardial perfusion. In patients with small ischemic perfusion defects, annual event rates were 0.7% and 2.8% for the primary and secondary outcome, respectively. In patients with moderate or large ischemic perfusion defects these event rates were 1.2% and 4.3%, respectively. After multivariate analysis, the risk for events were significantly associated with the extent of ischemia (hazard ratio for small ischemic defects: 2.2, 95%CI 0.9-5.9 and 4.6, 95%CI 2.8-7.6, hazard ratio for moderate or large ischemic defects: 4.0, 95%CI 1.5-10.5 and 12.1, 95%CI 7.2-20.2 for primary and secondary outcomes, respectively). Conclusion: Our findings show that MPI acquired with CZT SPECT camera provides excellent prognostic information, with low event rates in patients with normal myocardial perfusion. In patients with abnormal SPECT MPI, the extent of abnormality is independently associated with an increased risk of events. for primary (nonfatal myocardial infarction and cardiac mortality) and secondary outcomes (late revascularization (>90 days after scanning) and primary outcome). Early elective revascularizations within 90 days after imaging were excluded from the survival analysis to eliminate events driven by imaging findings.
LYMPHOID NEOPLASIA HIF-1 a regulates the interaction of chronic lymphocytic leukemia cells with the tumor microenvironment

hypoxia and are often activated in solid and hematologic malignancies due to intratumoral hypoxia and emerging new layers of regulation. We found that in chronic lymphocytic leukemia (CLL), HIF-1 a is a novel regulator of the interaction of CLL cells with protective leukemia microenvironments and, in turn, is regulated by this interaction in a positive feedback loop that promotes leukemia survival and propagation. Through unbiasedmicroarrayanalysis,wefoundthatinCLLcells,HIF-1 a regulatestheexpression of important chemokine receptors and cell adhesion molecules that control the interaction of leukemic cells with bone marrow and spleen microenvironments. Inactivation of HIF-1 a impairs chemotaxis and cell adhesion to stroma, reduces bone marrow and spleen colonization in xenograft and allograft CLL mouse models, and prolongs survival in mice. Of interest, we found that in CLL cells, HIF-1 a is transcriptionally regulatedaftercoculturewithstromalcells.Furthermore,HIF-1 a messengerRNAlevelsvarysignificantlywithinCLLpatientsandcorrelate with the expression of HIF-1 a target genes, including CXCR4, thus further emphasizing the relevance of HIF-1 a expression to CLL pathogenesis. ( Blood . 2016;127(16):1987-1997) Histopathology and immunohistochemistry Mice tissues were xed 4% formalin, paraf n embedded, 5- Immunohistochemistry performed CD31 antibody (Thermo Scien-ti ﬁ edpathologistevaluatedhistologicsections.Imagesweretaken with a Zeiss Axioskop 40 microscope equipped with a Zeiss AxioCam MRc digital camera. Statistics measure cance.Forsurvivalexperiments,Kaplan-Meiercurveswereanalyzedwith