Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel viewpoint in glaucoma therapeutics: enriched environment

Glaucoma is one of the world’s most frequent visual impairment causes and leads to selective damage to retinal ganglion cells and their axons. Despite glaucoma’s most accepted risk factor is increased intraocular pressure (IOP), the mechanisms behind the disease have not been fully elucidated. To date, IOP lowering remains the gold standard; however, glaucoma patients may still lose vision regardless of effective IOP management. Therefore, the exclusive IOP control apparently is not enough to stop the disease progression, and developing new resources to protect the retina and optic nerve against glaucoma is a goal of vast clinical importance. Besides pharmacological treatments, environmental conditions have been shown to prevent neurodegeneration in the central nervous system. In this review, we discuss current concepts on key pathogenic mechanisms involved in glaucoma, the effect of enriched environment on these mechanisms in different experimental models, as well as recent evidence supporting the preventive and therapeutic effect of enriched environment exposure against experimental glaucomatous damage. Finally, we postulate that stimulating vision may become a non-invasive and rehabilitative therapy that could be eventually translated to the human disease, preventing glaucoma-induced terrible sequelae resulting in permanent visual disability.
Single-phase half-bridge rectifier with power factor correction

A single-phase half-bridge switching mode rectifier is presented to draw a sinusoidal line current, to achieve power factor correction and to maintain the DC-link voltage constant. Four active switches are used in the proposed rectifier to generate a unipolar pulse width modulation (PWM) voltage waveform on the AC terminal voltage. There is no clamping diode in the proposed rectifier (as compared with the neutral point clamped (NPC) converter) so as to achieve three-level PWM operation. Two control loops are used in the proposed control scheme. In the outer control loop, a proportional integral voltage controller is used to regulate the DC-link voltage. A phase lock loop circuit is adopted to generate a sinusoidal waveform in phase with mains voltage to achieve power factor correction. In the inner control loop, a carrier-based current controller is used to track the line current command. To compensate the neutral point voltage due to the load variation, a neutral point voltage compensator is used in the control scheme. Three voltage levels are generated on the AC terminal of the adopted rectifier. Computer simulations and experimental results are presented to verify the effectiveness of the proposed control algorithm.
On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning

Plastic neural networks have the ability to adapt to new tasks. However, in a continual learning setting, the conﬁguration of parameters learned in previous tasks can severely reduce the adaptability to future tasks. In particular, we show that, when using weight decay, weights in successive layers of a deep network may become “mutually frozen”. This has a double effect: on the one hand, it makes the network updates more invariant to nuisance factors, providing a useful bias for future tasks. On the other hand, it can prevent the network from learning new tasks that require signiﬁcantly different features. In this context, we ﬁnd that the local input sensitivity of a deep model is correlated with its ability to adapt, thus leading to an intriguing trade-off between adaptability and invariance when training a deep model more than once. We then show that a simple intervention that “resets” the mutually frozen connections can improve transfer learning on a variety of visual classiﬁcation tasks. The efﬁcacy of “resetting” itself depends on the size of the target dataset and the difference of the pre-training and target domains, allowing us to achieve state-of-the-art results on some datasets.
ID-based, Proxy, Threshold Signature Scheme

—We propose the proxy threshold signature scheme with the application of elegant construction of veriﬁable delegating key in the ID-based infrastructure, and also with the bilinear pairings. The protocol satisﬁes the classical security requirements used in the proxy delegation of signing rights. The description of the system architecture and the possible application of the protocol in edge computing designs is enclosed.
The effect of dexmedetomidine combined with sevoflurane for controlled hypotension on renalfunction during shoulder arthroscopy

Objective: To investigate the effect of dexmedetomidine (DMED) and sevoflurane (SEV) for controlled hypotension (CH) on patients’ renal function during shoulder arthroscopy. Methods: One hundred patients undergoing shoulder arthroscopy were equally assigned to five groups. The patients in group A (control) received SEV, while the patients in groups B, C, D, and E received DMED and SEV (DMED 0.2, 0.4, 0.6, 0.8 μg/kg respectively). We measured the following: mean arterial pressure (MAP) and heart rate (HR) before anesthesia (T1), 10 min after anesthesia induction (T2), 5 min after initiating CH (T3), 1 h after initiating CH (T4), and 10 min after stopping CH (T5); the erythrocyte sedimentation rate (ESR), whole blood specific viscosity, creatinine, blood urea nitrogen (BUN), cystatin C, and glomerular filtration rate (GFR) at 2 h and 24 h post-operation. Results: No intergroup differences were observed in the MAP and HR at T1, T2, or T5 (all P>0.05). At T3 and T4, the two markers were reduced in the four groups (reduction level: group E>D>C>B, all P<0.05). Moreover, no intergroup differences were found in preoperative levels of creatinine, BUN, cystatin C, and GFR (all P>0.05). At 2 h and 24 h post-operation, group A had higher creatinine, BUN, cystatin C and lower GFR levels (creatinine, BUN, cystatin C levels: group A>B>C>D, group E>D, GFR level: group A<B<C<D, group E<D, all P<0.05); ESR and whole blood specific viscosity levels were similar among the groups. Conclusions: DMED combined with SEV during shoulder arthroscopy can achieve good hypotension results. DMED at a dose of 0.6 μg/kg has a minimal impact on renal function and exhibits protective effects on the kidneys.
Space variant median filters for the restoration of impulse noise corrupted images

This brief proposes a generalized framework of median based switching schemes, called multi-state median (MSM) filter. By using a simple thresholding logic, the output of the MSM filter is adaptively switched among those of a group of center weighted median (CWM) filters that have different center weights. As a result, the MSM filter is equivalent to an adaptive CWM filter with a space varying center weight which is dependent on local signal statistics. The efficacy of the proposed filter has been evaluated by extensive simulations.
EVALUATING THE IMPACT OF MULTIPLE LEADING INDICATOR IN FORECASTING NEXT DAY STOCK PRICE WITH LSTM

LSTM was the preferred choice for financial time series forecasting, whereas fundamental analysis and technical analysis were among the most favorable feature sets. Earlier studies had several suggestions to improve forecasting performance, by using features known to carry information about the future price movement, and features associated with substantial price movements: the foreign investors' trading volume. Overall trading volume and those volumes from foreign investors have been introduced as a leading indicator. However, empirical literatures which examines price-volume relationship using LSTM had not used foreign investors’ trading volume. This study evaluates the use of multiple leading indicators as input, and optimum hyperparameters configurations using LSTM to next day prediction performance. Experiments are evaluated based on 88 stocks in Indonesia stock market, ranging from Jan. 2, 2015 to Dec. 30, 2019. Financial time series forecasting using simple LSTM architecture performs as good as baseline performance with the advantage of fewer computing requirements. Optimum hyperparameters are a single hidden layer, 50 nodes, and ten days of the input window. The highest winning stocks are achieved using feature sets consisting of a lagging indicator (price) and multiple leading indicators (overall trading volume and foreign investors' trading volume). The findings indicate that multiple leading indicators contain predictability factors which can be further explored to improve financial time series forecasting. This study contributes the use of foreign investors’ which improves financial time series forecasting with LSTM.
An Empirical Analysis of the Impacts of LIBOR Changes on the Volumes of Global Seaborne Trade and the Growth of World Gross Domestic Product-Cointegration and Causal Nexuses -

Ship finance (SF), global seaborne trade (WST), world gross domestic product (WGDP) and, the London Interbank Offered Rate (LIBOR) really are the fascinating issues to the ship owners, financial institutes, bankers, banking risk managers and, maritime researchers. The remarkable developments of global shipbuilding and sea transportation are important and significantly created more productivities and businesses to the world economy today compared to last 20 century, special in the containerization, oil tankers, liquefied petroleum gas (LPG), liquefied natural gas (LNG), dry bulk carriers and others. The ship investors normally require the large amount of capital and always seek for the suitable financial sources.
Testing the Efficacy of Leadership for Empowerment and Abuse Prevention (LEAP), a Healthy Relationship Training Intervention for People with Intellectual Disability

Leadership for Empowerment and Abuse Prevention (LEAP) is an abuse prevention intervention for people with intellectual disability. The purpose of this research was to evaluate the intervention’s efficacy. Findings indicated no significant differences in scenario identification questions depicting acceptable or concerning situations. However, statistically significant improvements were noted in participants’ depth of understanding, including their ability to correctly describe why a scenario was abusive or exploitative and what to do next when confronted with unhealthy situations. Limitations and implications for practice are discussed. Plain Language Summary LEAP is a training program for people to help them have good relationships. We did research to see if LEAP helped people who came to training better tell the difference between good and bad relationships and what to do if they are in a bad relationship. We found that people did not get better at pointing out good and bad relationships, but they did get better at telling why a relationship was good or bad and what to do next if in a bad situation.
EVD 2 BSCAN: ENHANCED VORONOI DIAGRAM DENSITY BASED CLUSTERING ALGORITHM FOR DATA CLUSTERING

Clustering concern is a systematic approach for partitioning of data objects into matching clusters and typically regarded as unsupervised learning problem. The real-time multiple constraints for objects are difficult to achieve for conventional algorithms. The appropriate cluster quantity from the data attained by the distance-based clustering algorithm occurs in seldom manner autonomously. During clustering process, missing data problem is a serious issue in many applications as it has significant effects the conclusion drawn from the data. The missing values and irrelevant data are resolved by computing the mean of other data in either subject wise manner or student wise manner. In addition this work presents a Voronoi Diagram Density Based Clustering Algorithm (VD2BSCAN) by fast search and determining density peaks. The input samples’ Voronoi diagram creation is a key factor in retrieving the density information which is accomplished by the suggested VD2BSCAN. The corresponding parts of the instance space point density is influenced directly by the point cells volume. The merging of densest parts of the instance space into clusters is achieved by scanning over the input points and their Voronoi cells at a time. However, wide density variation inside the clusters is a serious concern to be considered in DBSCAN algorithm. This issue is mitigated by enhanced EVD2BSCAN algorithm by estimation of growing Density Mean (DM) for some core object, in which density of its ε-neighborhood is considered with respect to DM has its own significance. The data are collected manually from the college placement departments for the evaluation in R tool. The data are stored and fetched from Structured Query Language (SQL) database. The clustering performance metrics are employed for the analysis of the performance. The EVD2BSCAN algorithm improves the clustering accuracy significantly compared than the DBSCAN, Hierarchical Clustering (HC) analysis which is revealed by the evaluation results. The proposed algorithm outperforms well which can be utilized for various applications.
Statistical modeling of effective doses in hormetic dose-response relationships by reparameterization of a bilogistic model for inverted U-shaped curves

Hormesis is a biphasic dose (concentration)-response phenomenon characterized by low dose stimulatory and high dose inhibitory effects exerted by stressors on living organisms. Recently, there has been increased interest in the phenomenon and statistical models for its exploration in toxicological studies. The bilogistic model of Beckon et al. is one of such models for modeling biphasic dose-response relationships in toxicological studies. However, there is no explicit formula for the estimation of effective doses (EDK) with the model. In this study, a simple general approach was suggested to reparameterize the model, leading to a range of mathematical models for determination of effective doses at both stimulatory and inhibitory ranges in inverted U-shaped hormetic dose-response relationships. The reparameterized models were tested on experimental data from three different in vitro experimental systems obtained from literature and our experiment. They were successfully applied to test for significance of hormesis and estimate effective doses and their statistical properties. In addition, reparameterization of the model for a particular effective dose (EDK) did not affect estimation of other parameters (such as x1, x2, β1, β2 and M). The reparameterized models provided useful tools for adequate exploration of the tested hormetic dose-response relationships. The extended models could hopefully be versatile in characterization of variable hormetic dose response relationships in many toxicological disciplines.
DESIGN AND EVALUATION OF QUALITY ASSURANCE-BASED INTERACTIVE MACHINE LEARNING MODEL

Some complex task can be difficult to fully understand or represent or there may even be few data available about them thereby making it difficult to solve with classical machine learning approaches. Hence, Interactive Machine Learning (iML), an approach where humans are used to complement machines. However, existing interactive machine learning approaches have not sufficiently considered quality assurance of the human feedback in the interaction cycle to guaranty improved performance of the model. Interactive machine learning systems take on data for updates without checking the quality of the update data. This is capable of misleading a machine learning model if wrong, noisy or malicious data are used to update the model. Therefore, this paper proposes a quality assurance-based interactive machine learning model that is able to evaluate the quality of the feedback obtained from the human in the iterative feedback loop before passing such feedback to the learning model to update its knowledge about the problem. Existing literatures and concepts on interactive machine learning were reviewed. Also explored are areas where interactive machine learning approach has resulted in faster, less expensive model training process than the classical Machine Learning, especially when applied on rare and complex problems. Questionnaire method was used to conduct a survey that evaluates the usability and understandability of the proposed quality assurance-based interactive machine learning model using the Cognitive Walkthrough Strategy. The result gave a rating of 4.2 out of 5 for the usability of proposed model. This approach will increase the acceptability of the interactive machine learning model and the credibility of its predictions.
Sustainable human resource strategy

Sustainable human resource (HR) strategies could help companies manage their human resources HRs to achieve “net optimality” of companies’ objectives as well as a stable community. Using a theoretical model, an attempt is made to explain the “externality” of retrenchment and highlight the need for sustainable HR strategies to achieve the “unitary economics” of human life. Further, the sustainable benefit of HR conservation and HR recuperation in understanding the HR asset, and how this can be used in reducing the externality effect of retrenchment and downsizing strategies on the social and emotional web of the community is discussed.
Developing an empirical model for spillover and emergence: Orsay virus host range in Caenorhabditis

A lack of tractable experimental systems in which to test hypotheses about the ecological and evolutionary drivers of disease spillover and emergence has limited our understanding of these processes. Here we introduce a promising system: Caenorhabditis hosts and Orsay virus, a positive-sense single-stranded RNA virus that naturally infects C. elegans. We assayed the susceptibility of species across the Caenorhabditis tree and found 21 of 84 wild strains belonging to 14 of 44 species to be susceptible to Orsay virus. Confirming patterns documented in other systems, we detected effects of host phylogeny on susceptibility. We then tested whether susceptible strains were capable of transmitting Orsay virus by transplanting exposed hosts and determining whether they transmitted infection to conspecifics during serial passage. We found no evidence of transmission in 10 strains (virus undetectable after passaging), evidence of low-level transmission in 5 strains (virus lost between passage 1 and 5), and evidence of sustained transmission in 6 strains (including all 3 experimental C. elegans strains). Transmission was associated with host phylogeny and with viral amplification in exposed populations. Variation in Orsay virus susceptibility and transmission among Caenorhabditis species suggests that the system could be powerful for studying spillover and emergence.
Analysis of the CO2 influence in the gas mixture for WAAM using CMT version of the GMAW process

Abstract: A researching hype upon the Wire and Arc Additive Manufacturing (WAAM) seen in the last decade, has led to a notable increase of the knowledge in how parts built by WAAM are affected by the electrical and thermal parameters, and deposition strategies. In this innovative manufacturing route, the selection of the welding conditions seems to be even more critical than in usual joining and facing applications. The deposition planning in WAAM is highly influenced by these conditions, for example, the feedstock characteristics (composition, form, size), the electric arc (power and density) and metal transfer conditions. A factor that highly influences these parameters is the shielding gas mixture choice. The Gas Metal Arc Welding (GMAW) shielding gas mixtures are mostly based in Argon with parts of Carbon dioxide due to its relatively lower cost. However, the increase of the Carbon dioxide content can negatively influence the stability of the metal transfer and increase the presence of undesirable oxides between the layers. Although, the influence of Carbon dioxide in the formation of these defects during the manufacture of parts via WAAM using the Cold Metal Transfer (CMT), an advanced variant of the GMAW process, is not yet fully described and explained in the literature. To fulfill this gap, this work focuses on the analyses of two types of industrial gas mixtures: 92% Argon + 8% CO2, and 75% Argon + 25% CO2. As feedstock, 1.2 mm ER70S6 filler wire was deposited over 12.7 mm thickness ASTM A36 carbon steel plates. The welding process used in this study was the Fronius CMT, due to its highly usage among WAAM manufacturing and research groups. The influence of CO2 content on the electrical, thermal, and geometrical characteristics of single welding beads, single bead walls and pads was qualitatively and quantitatively analyzed. The results showed a correlation with the increase of CO2 content and the increase in the silicon oxide formation and incidence of pores and lack of fusion between layers. The thermal analysis showed that the thermal profile did not change within the range of 8% to 25% of CO2, even the processes showing a slight difference in electrical power. Furthermore, the geometric profile showed no significant difference between specimens. Therefore, to attenuate inclusions of silicon oxide and to diminish the lack of fusion incidence the mixture with 25 % of carbon dioxide is the most adequate, however this does not discredit the usage of 8% of carbon dioxide in the gas mixture, but it shows the need for the implementation of a more accurate overlapping parametrization.
Logistic Regression Analysis on Social Networking Advertisement

: In most of the Machine learning models tables and charts need to be plot for result analysis and predictions to be made. The result analysis is a fundamental need for any company reuired to analyse their objective of their product development. In the present work Logistic regression model is developed to illustrate social networking advertisement dataset. With this fundamental Logistic Regression Model implementation one can predict whether a user is willing to purchase their product or not. In the present paper Logistic Regression Model is designed in python to predict by evaluating accuracy.
STRONTIUM ZINC FERRITE BY USING HYDROTHERMAL TECHNIQUE

Conceptual Sr-Zn ferrite nanoparticles of compound equation SrxZn(1-x)Fe2O4 (x=0.5, 0.7, 0.9) are combined by utilizing aqueous procedure at a temperature of 7000C for 3 hours and pound for a fine powder. The basic investigation through X-ray beam diffraction (XRD) shows the development of spinel cubic structure with a normal glasslike size in the scope of 8.6nm to 13.6nm. The d-dividing and grid boundary increments by expanding the doping grouping of Strontium (Sr). The expansion in cross section boundary and the decline in molecule size with the increment in Strontium substance might be connected to an expected rearranging of Fe 3+ and Zn 2+ particles inside octahedral and tetrahedral destinations. The morphology and the porosity is decide by Scanning Electron Microscopy (SEM). A diminishing pattern in the particles size has likewise been watched, which affirms that the precious stone development is being impeded because of the presence of Strontium.