Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions of Presence in 3-D, Haptic-enabled, Virtual Reality Instruction

— This exploratory case study evaluated perceived presence of students and teachers after learning science topics on a 3-dimensional, haptic-enabled, virtual reality system. Twenty students and teachers completed four, one hour sessions with the instructional technology exploring biology and physical science content. Participants were interviewed and surveyed to evaluate their perception of presence via four constructs: control, sensory, distraction, and realism. Additional questions assessed changes in participants’ acquired skills from sessions. Results indicated the survey had high reliability for all constructs. Differences between students’ and teachers’ responses showed students perceived their virtual experience as more realistic than teachers. Students more than teachers reported improved gains science process skills (analyzing data, reporting results, recognizing error) and understandings of the nature of science (tentativeness and sociocultural context of science). When asked how this technology was regarded as a viable instructional option for learning science, students ranked the virtual technology higher than teachers as more interesting and increasing their understanding. Results showed students ranked zSpace as the most preferred instructional option and teachers ranked hands-on activities with materials as their most preferred instructional option.
Human Emotion and Machine Emotion - Studies of Emotion in AI

— Artificial Intelligence (AI) is undoubtedly a hot word in the field of contemporary art in recent years. The consciousness, intuition and emotion of AI have attracted the attention of artists in particular, and many art works have explored this topic. Does AI have the emotional characteristics of humans? Is the emotion of AI equal to the emotion that defines human beings? This paper attempts to step out of the anthropocentric perspective, examine the boundary and relationship between human emotion and machine emotion, and inject a new theoretical perspective into the AI artistic practice. This paper first discusses what emotion is in the biological sense, analyzes whether a machine can have emotion from both positive and negative aspects, and puts forward the definition of "machine emotion". This paper also reviews some theories and practices related to emotion in the AI field. The conclusion of this paper is that although artificial intelligence cannot possess human emotions, it can possess “machine emotions” beyond the narrow sense of human emotions, and the construction of emotional mechanisms inside AI may become a new research direction in this field.
Motivated Strategies for Learning and Students’ Achievement in Biological Science using Self-Regulated Learning

One of the important decisions a teacher has to make is to choose specific teaching method that would best achieve lesson objectives and ensure effective learning to take place among students. The use of active strategies among college students is said to promote higher achievement among students. One of these methods is self-regulated learning. This research aimed to investigate the effectiveness of self-regulating learning(SRL) method in relation to student’s motivation and use of learning strategies and students’ achievement in Biological Science. The study used a pretest-posttest design. One intact class was used with 39 students. Teacher-made achievement test, course teaching manual, course reader and Motivated Strategies for Learning Questionnaire (MSLQ) by Pintrich et al. (1991) were used in the study. Results revealed that SRL method increased students’ achievement in Biological Science. Results on MSLQ revealed no significant differences between the mean scores of students. MSLQ Motivation and Learning Strategy scales showed no significant relationships with achievement score. SRL method sustained students’ motivation and use of self-regulated learning strategies towards learning Biological Science. Findings of the study may provide implications on the use of active strategies that could effectively teach Biological Science among college students.
SPDE based modeling of large space-time data sets

Increasingly larger data sets of processes in space and time ask for statistical models and methods that can cope with such data. We show that solutions of stochastic advectiondiusion partial dierential equations (SPDEs) provide a exible model class for spatiotemporal processes which is computationally feasible also for large data sets. The solution of the SPDE has in general a nonseparable covariance structure. Furthermore, its parameters can be physically interpreted as explicitly modeling phenomena such as transport and diusion that occur in many natural processes in diverse elds ranging from environmental sciences to ecology. In order to obtain computationally ecient statistical algorithms we use spectral methods to solve the SPDE. This has the advantage that approximation errors do not accumulate over time, and that in the spectral space the computational cost grows linearly with the dimension, the total computational costs of Bayesian or frequentist inference being dominated by the fast Fourier transform. The proposed model is applied to postprocessing of precipitation forecasts from a numerical weather prediction model for northern Switzerland. In contrast to the raw forecasts from the numerical model, the postprocessed forecasts are calibrated and quantify prediction uncertainty. Moreover, they outperform the raw forecasts.
An Analysis of a Bike-Sharing System from a Business Model Perspective *

Goal: Product-service systems are innovative business models that aim to contribute to sustainability issues. Bike-sharing system is a classic example of this kind of business model. This work covers an analysis of a bike-sharing system implemented in the South of Brazil from a business model perspective by focusing on the value network. Design / Methodology / Approach: Data was collected based on interviews with the executives of a focal company and information retrieved from other sources, e.g., company internal reports, PSS design documents, etc.). A research protocol was developed based on the literature to identify a set of variables to be interpreted. A semi-structured questionnaire was developed for interviews, which was recorded for later content transcription and analysis. Results: The business model value network and mechanisms of value proposition, configuration, delivery, and value capture are described. This business model is based on a diverse value network and should be developed and updated on co-creation mechanisms to deliver more sustainable results. Limitation of the investigation: It is a single study conducted in a medium-sized town in the South of Brazil that implemented a bike-sharing system. In addition, the analyis was considered in the “problem identification phase”. Practical implications: The bike-sharing business model analysis may improve the business model towards increasing the inhabitants' quality of life as well as city image. Originality / Value: The study was conducted in a developing country, analyzing a PSS business model from a sustainable operations perspective, which is not common in the current literature.
Network Intrusion Detection using Supervised Machine Learning

To secure a network from intrusion and for the confidentiality of any facts, an Intrusion Detection system performs a important position. the primary goal is to acquire an correct performance of an NIDS device which adepts in detection of diverse sorts of attack in the network. on this paper, we've explored the performance of an Network Intrusion Detection System (NIDS) which could hit upon numerous sorts of attacks inside the network the use of Deep Reinforcement Learning Algorithm of rules (DRL). we've got exploited Deep Q community set of rules that's a cost-primarily based Reinforcement Learning knowledge of set of rules method used in detection of network intrusions. moreover, we have analysed the accuracy of our model in evaluation with unique sorts of attacks. on this paper, we illustrated the comparison of our NIDSDQN version to a previous version designed in different tactics like J48, artificial neural network, random Forest , support vector system. Our aim is to hit upon distinctive varieties of attacks without depending at the past revel in and at its first strive. We used information set for minimising the false alarm rate. preceding work turned into primarily based on a benchmark dataset which includes KDD-99, NSL-KDD, which shares the equal attributes for all models. we've worked on eighty five attributes which aided as an effective way in detection of various forms of attacks. The Deep Q community-Intrusion Detection device (DQN-IDS) version improves the accuracy and performance an IDS and affords a brand new means as a research approach for intrusion detection.
and multi-feature fusion for automatic modulation classification

Automatic modulation classi ﬁ cation (AMC) lies at the core of cognitive radio and spectrum sensing. In this Letter, the authors propose a novel convolutional neural network (CNN)-based AMC method with multi-feature fusion. First, the modulation signals are transformed into two image representations of cyclic spectra (CS) and constellation diagram (CD), respectively. Then, a two-branch CNN model is developed, a gradient decent strategy is adopted and a multi-feature fusion technique is exploited to integrate the features learned from CS and CD. The proposed method is computationally ef ﬁ cient, bene ﬁ ted from its simple neural network. Experimental results show that the proposed method can achieve identical or better results with much reduced learned parameters and training time, compared with the state-of-the-art deep learning-based methods.
Src-independent ERK signaling through the rat (cid:2) 3 isoform of Na/K-ATPase

Z. Src-independent ERK signaling through the rat (cid:2) 3 isoform of Na/K-ATPase. Am J Physiol Cell Physiol 312: C222–C232, 2017. 2016. —The Na/K-ATPase (cid:2) 1 polypeptide supports both ion-pumping and signaling functions. The Na/K-ATPase (cid:2) 3 polypeptide differs from (cid:2) 1 in both its primary structure and its tissue distribution. The expression of (cid:2) 3 seems particularly important in neurons, and recent clinical evidence supports a unique role of this isoform in normal brain function. The nature of this speciﬁc role of (cid:2) 3 has remained elusive, because the ubiquitous presence of (cid:2) 1 has hindered efforts to characterize (cid:2) 3-speciﬁc functions in mammalian cell systems. Using Na/K-ATPase (cid:2) 1 knockdown pig kidney cells (PY-17), we generated the ﬁrst stable mammalian cell line expressing a ouabain-resistant form of rat Na/K-ATPase (cid:2) 3 in the absence of endogenous pig (cid:2) 1 detectable by Western blotting. In these cells, Na/K-ATPase (cid:2) 3 formed a functional ion-pumping enzyme and rescued the expression of Na/K-ATPase (cid:3) 1 and caveolin-1 to levels comparable with those observed in PY-17 cells rescued with a rat Na/K-ATPase (cid:2) 1 (AAC-19). The (cid:2) 3-containing enzymes had lower Na (cid:4) afﬁnity and lower ouabain-sensitive transport activity than their (cid:2) 1-containing counter-parts under basal conditions, but showed a greater capacity to be activated when intracellular Na (cid:4) was increased. In contrast to Na/ K-ATPase (cid:2) 1, (cid:2) 3 could not regulate Src. Upon exposure to ouabain, Src activation did not occur, yet ERK was activated through Src-independent pathways involving PI3K and PKC. Hence, (cid:2) 3 expression confers signaling and pumping properties that are clearly distinct from that of cells expressing Na/K-ATPase (cid:2) 1. Calbiochem. Expression vectors, gene transfer, and selection. Rat
Unemployment Beneﬁts and Unemployment in the Great Recession: The Role of Macro Eﬀects ∗

We exploit policy discontinuity at U.S. state borders to identify the eﬀects of unemployment insurance policies on unemployment. We ﬁnd large eﬀects of unemployment beneﬁt extensions on unemployment. In fact, the estimates imply that most of the persistent increase in unemployment during the Great Recession can be accounted for by the unprecedented extensions of unemployment beneﬁt eligibility. In contrast to the existing literature that mainly focused on estimating the eﬀects of beneﬁt duration on job search and acceptance strategies of the unemployed – the micro eﬀect – we focus on measuring the general equilibrium macro eﬀect that operates primarily through the response of the job creation to unemployment beneﬁt extensions. We ﬁnd that it is the latter eﬀect that is very important quantitatively.
Health Issues and Heavy Metals

Several health hazards have been associated with heavy metals for a long time. The risk is continuously increasing though emissions have declined in developed countries over the last century. It is because of the fact that the heavy metals are very difficult to recycle and the new therapies used for treatment of many neurogenetic disorders involve the intake of heavy metals. Cigarette smoking whether active or passive is also an important source of exposure to these metals. The process of biomagnifications has given fatal diseases to many people where the pregnant women and the foetus are at maximum risk. They sometimes act as a pseudo element of the body while at certain times they interfere with the basic metabolic processes. Various measures have been taken by different countries at policy level and public level to control, prevent and treat metal toxicity occurring at various levels, such as occupational exposure, accidents and environmental factors. However, metal toxicity depends upon the amount of absorbed dose, the route of exposure and duration of exposure, i.e. acute or chronic. This can lead to various disorders and can also result in excessive damage due to oxidative stress induced by free radical formation. This review gives details about some heavy metals and their toxicity mechanisms, along with their health effects. Synthesized in liver and kidney majorly, exists in four main isoforms MT1, MT2, MT3, and MT4. In 2001, outcome of a scientific
Institutional Knowledge at Singapore Management University Institutional Knowledge at Singapore Management University Accounting quality, stock price delay and future stock returns Accounting quality, stock price delay and future stock returns

are associated with signiﬁcantly higher price delay. Results are robust to using AQ as the sole measure of accounting quality. Results are also robust to measuring accounting quality by the FOG index of Li 2008, which is a measure of the readability of qualitative (non–ﬁnancial statement) information in annual reports. We ﬁnd that high delay ﬁrms have signiﬁcantly higher future stock returns of about 5 percent annually relative to low delay ﬁrms. We also ﬁnd that poor accounting quality in particular is associated with a statistically signiﬁcant annual return premium of about 7.7 percent in ﬁrms with the highest non–accounting-associated delay. These results suggest several opportunities for future research. One opportunity is to examine whether poor accounting quality is associated with a delay in bond prices. Another opportunity, along the lines of Verrecchia 1980, is to examine the types of news that are associated with greater stock price delay.
Computational evaluation of certain flavonoids against poly (ADP-Ribose) Polymerase-1 using in silico docking studies

Poly (ADP-Ribose) Polymerase-1 ( PARP-1) inhibitors may represent a class of chemotherapeutic mediators at cancers with defective DNA-damage repair. The objective of the present study is to examine the inhibitory affinity potential of the certain commercially available flavonoids such as acacatechin, catechin, daidzein, galangin, and kaempferol against PARP-1 enzyme using in silico docking studies. The in silico docking studies were performed using AutoDock 4.2. Olaparib, a known PARP-1 inhibitor was used as standard. In the docking studies, the conformational site analysis and the docking parameters were evaluated for the flavonoids against the PARP-1. The selected flavonoids showed excellent theoretical pharmacokinetic properties and no violations were described against Lipinski’s Rule of 5. Further the flavonoids were subjected to molecular docking analyses. The docking energy and other parameters demonstrated that the flavonoids are highly conserved in nature. The top ranking molecule catechin has a minimum energy score of -6.55 kcal/mol. The amino acid residues responsible for the PARP-1 inhibition of the catechin were found to be Lys 15, Ser 108, and Gly 109. This compound is thus a good leading point for further development of strong inhibitors against PARP-1 enzyme, which might be attributed to the presence of benzopyran ring in its structure. Hence, further examination is required to develop novel chemical entities for the prevention and management of ovarian and breast cancer.
InGaAs/InP DHBT small‐signal model up to 325 GHz with two intrinsic base resistances

In this paper, a novel small‐signal equivalent‐circuit model of terahertz InGaAs/InP double‐heterojunction bipolar transistor (DHBT) is presented. Two intrinsic base resistances are introduced to represent the nonuniform extrinsic impedance of base‐emitter and base‐collector junctions separately. This treatment makes the high‐frequency model more close to the triple‐mesa InP DHBT structure. Systematical parasitic parameter extraction method and intrinsic element initialization of a 0.5 μm * 5μm InGaAs/InP DHBT are presented. As a high‐frequency device model, inter‐electrode parasitic capacitances are calculated by three‐dimensional electromagnetic simulation. The proposed model results in a good agreement with measured multibiased S‐parameters up to 325 GHz.
The ER chaperones BiP and Grp94 selectively associate when BiP is in the ADP

The ER chaperones BiP and Grp94 selectively associate when BiP is in the ADP conformation. ABSTRACT Hsp70 and Hsp90 chaperones are critical for protein quality control in the cytosol, while organelle-specific Hsp70/Hsp90 paralogs provide similar protection for mitochondria and the endoplasmic reticulum (ER). Hsp70/Hsp90 can operate sequentially with Hsp90 selectively associating with Hsp70 after Hsp70 is bound to a client protein. This observation has long suggested that Hsp90 could have a preference for interacting with clients at their later stages of folding. However, recent work has shown that cytosolic Hsp70/Hsp90 can directly interact even in the absence of a client, which opens up an alternative possibility that the ordered interactions of Hsp70/Hsp90 with clients could be a consequence of regulated changes in the direct interactions between Hsp70 and Hsp90. However, it is unknown how such regulation could occur mechanistically. Here we find that the ER Hsp70/Hsp90
An Exercise in R: High Frequency Covariance estimation using Malliavin-Mancino and Hayashi-Yoshida estimators

Science Department of Statistical Sciences Honours in Statistical Science An Exercise in R: High Frequency Covariance estimation using Malliavin-Mancino and Hayashi-Yoshida estimators by Patrick CHANG & Roger BUKURU We revisit and demonstrate two well-known non-parametric estimators; the Malliavin-Mancino (MM) [1], [2] and the Hayashi-Yoshida (HY) [3] estimator. Both address the issue of covariance estimation for high-frequency asynchronous time-series data. The first by embracing a Fourier perspective and the latter using averaging over discrete windows. The aim of the work here aims to provide an easily re-used practical tutorial to argue for the efficacy of the MM estimator in high-frequency finance applications. Towards this end, we conduct Monte Carlo experiments to demonstrate that the two estimators differ only under asynchronous observations, where the MM estimator has lower correlation estimates compared to the HY estimator. Unsurprisingly, we attribute this difference to the Epps effect [4]. However, as a novel application, we show the existence of the Epps effect in the top 10 stocks from the Johannesburg Stock Exchange (JSE) by various methods of aggregating Trade and Quote (TAQ) data. Specifically, by comparing calendar time based sampling with volume time sampling methods. We argue that the MM estimator is more representative of trade-time reality. The world of high-frequency finance is not a missing data problem as sampled from some underlying synchronous continuous stochastic process, but rather it is a world of truly disconnected, inter-related, discrete and asynchronous events where the relationship between events are the fundamental entities and measurables. We argue the MM estimator is a more faithful representation of the underlying data as it does not over-estimate short-term correlations in such an asynchronous event driven world.
Initial Identiﬁcation of UDP-Glucose Dehydrogenase as a Prognostic Marker in Breast Cancer Patients, Which Facilitates Epirubicin Resistance and Regulates Hyaluronan Synthesis in MDA-MB-231 Cells

: UDP-glucose-dehydrogenase (UGDH) synthesizes UDP-glucuronic acid. It is involved in epirubicin detoxiﬁcation and hyaluronan synthesis. This work aimed to evaluate the effect of UGDH knockdown on epirubicin response and hyaluronan metabolism in MDA-MB-231 breast cancer cells. Additionally, the aim was to determine UGDH as a possible prognosis marker in breast cancer. We studied UGDH expression in tumors and adjacent tissue from breast cancer patients. The prognostic value of UGDH was studied using a public Kaplan–Meier plotter. MDA-MB-231 cells were knocked-down for UGDH and treated with epirubicin. Epirubicin-accumulation and apoptosis were analyzed by ﬂow cytometry. Hyaluronan-coated matrix and metabolism were determined. Autophagic-LC3-II was studied by Western blot and confocal microscopy. Epirubicin accumulation increased and apoptosis decreased during UGDH knockdown. Hyaluronan-coated matrix increased and a positive modulation of autophagy was detected. Higher levels of UGDH were correlated with worse prognosis in triple-negative breast cancer patients that received chemotherapy. High expression of UGDH was found in tumoral tissue from HER2 - -patients. However, UGDH knockdown contributes to epirubicin resistance, which might be associated with increases in the expression, deposition and catabolism of hyaluronan. The results obtained allowed us to propose UGDH as a new prognostic marker in breast cancer, positively associated with development of epirubicin resistance and modulation of extracellular matrix. silico triple negative
The role of diagnostics and treatment — lung cancer with ALK rearrangement

Lung cancer is the most common cause of cancer-related deaths both in Poland and worldwide. Recently, the incidence of lung adenocarcinoma has been increasing and currently it accounts for about 45% of all diagnosed lung cancers. Patients diagnosed with non-squamous non-small cell lung cancer (NSCLC), especially with adenocarcinoma, cancer containing adenocarcinoma component, large cell carcinoma, as well as patients with not otherwise specified (NOS) cancer may benefit from targeted therapy if molecular tests confirm the presence of activating EGFR gene mutations, ALK , ROS1 or NTRK rearrangement, or BRAF gene mutations. The ALK gene rearrangement is a positive predictive marker of tyrosine kinase inhibitors (TKIs) effectiveness, which are more effective than standard chemotherapy in this population, are associated with improving the quality of life and also indicate a different, more tolerable toxicity profile. This study presents the diagnostic sequence and registered treatment options for patients with ALK-positive NSCLC.