Miracle-dz/UniFAR-FTUs huggingface dataset:
An Appraisal of Engineering Students’ Critical Thinking and Science Process Skills using Problem-Based Learning Approach

Problem-Based Learning approach (PBLA) promotes motivation and maximizes the potential of students through their acquired science process skills (SPS) and critical thinking skills (CTS). Most of the researches about PBLA focus solely on the SPS or CTS of the students. The study examined the effect of PBLA on both skills of the first-year engineering students in chemistry laboratory subject. Through convenience sampling, the study was carried out on one intact class of Bachelor of Science in Electronics Communication Engineering students enrolled at a state university in Manila during the 2nd semester of SY 2015-2016. Students were motivated through PBL laboratory activities and were facilitated to design their own procedures. Quantitative data was obtained using the Science Process Skills Test and Critical Thinking Appraisal. The results revealed that majority of the students’ SPS was enhanced and developed. There was significant progress on their CTS after exposure to PBLA. Likewise, qualitative data was obtained based on the Students Experience Survey piloted to them. Findings show that some of the attributes of the PBL were demonstrated and, the negative and positive experiences of students towards the PBLA. This study recommends the implementation of PBLA in teaching and learning to improve students’ manifestation of SPS and CTS. Future studies are recommended to put more emphasis on qualitative analysis to identify other intervening variables that influence students’ engagement in PBLA and the rate of development of their SPS and CTS.
Using Internet Search Trends and Historical Trading Data for Predicting Stock Markets by the Least Squares Support Vector Regression Model

Historical trading data, which are inevitably associated with the framework of causality both financially and theoretically, were widely used to predict stock market values. With the popularity of social networking and Internet search tools, information collection ways have been diversified. Instead of only theoretical causality in forecasting, the importance of data relations has raised. Thus, the aim of this study was to investigate performances of forecasting stock markets by data from Google Trends, historical trading data (HTD), and hybrid data. The keywords employed for Google Trends are collected from three different ways including users' definitions (GTU), trending searches of Google Trends (GTTS), and tweets (GTT) correspondingly. The hybrid data include Internet search trends from Google Trends and historical trading data. In addition, the correlation-based feature selection (CFS) technique is used to select independent variables, and one-step ahead policy is adopted by the least squares support vector regression (LSSVR) for predicting stock markets. Numerical experiments indicate that using hybrid data can provide more accurate forecasting results than using single historical trading data or data from Google Trends. Thus, using hybrid data of Internet search trends and historical trading data by LSSVR models is a promising alternative for forecasting stock markets.
Understanding and Detecting Evolution-Induced Compatibility Issues in Android Apps

The frequent release of Android OS and its various versions bring many compatibility issues to Android Apps. This paper studies and addresses such evolution-induced compatibility problems. We conduct an extensive empirical study over 11 different Android versions and 4,936 Android Apps. Our study shows that there are drastic API changes between adjacent Android versions, with averagely 140.8 new types, 1,505.6 new methods, and 979.2 new fields being introduced in each release. However, the Android Support Library (provided by the Android OS) only supports less than 23% of the newly added methods, with much less support for new types and fields. As a result, 91.84% of Android Apps write additional code to support different OS versions. Furthermore, 88.65% of the supporting codes share a common pattern, which directly compares variable android.os. Build.VERSION. SDK_INT with a constant version number, to use an API of particular versions. Based on our findings, we develop a new tool called IctApiFinder, to detect incompatible API usages in Android applications. IctApiFinder effectively computes the OS versions on which an API may be invoked, using an inter-procedural data-flow analysis frame-work. It detects numerous incompatible API usages in 361 out of 1,425 Apps. Compared to Android Lint, IctApiFinder is sound and able to reduce the false positives by 82.1%. We have reported the issues to 13 Apps developers. At present, 5 of them have already been confirmed by the original developers and 3 of them have already been fixed.
A Review of Measuring the Cognitive Workload of Electronic Health Records

The To Err Is Human report stated that 98 000 patients die yearly because of medical errors, and that medication errors kill more people than workplace injuries. The inadequate design and utilization of the electronic health record have been identified as major contributing factors to medical errors. Increased cognitive workload of clinicians has consistently been linked to the occurrence of medical errors. The purpose of this article was to synthesize the current state of the science on measuring clinicians' cognitive workload associated with using electronic health records in order to inform evidence-based guidelines. The major considerations identified in the literature involve the use of psychometric instruments, using efficiency as a proxy for cognitive workload, and eye tracking. The National Aeronautics and Space Administration Task Load Index was the most used psychometric instrument, but reliability measures were not reported. It is important to evaluate reliability of psychometric instruments because the consistency of the instrument can change when administered to different populations. Efficiency is an observable measure defined by the total time to complete a task and the total number of physical interactions with the user interface. Efficiency can allow the use of statistical modeling, but it does not directly evaluate the mental activity associated with using an electronic health record interface. Eye tracking has been used extensively in the literature to measure cognitive workload via changes in pupil size related to mental activity, but it is not often used to measure the cognitive workload associated with using the electronic health record. Eye tracking is very useful for continuous monitoring of cognitive workload.
Traffic-aware reconfigurable architecture for fault-tolerant 2D mesh NoCs

With the aggressive scaling of the VLSI technology, Networks-on-Chip (NoCs) are becoming more susceptible to faults. Therefore, designing reliable and efficient NoCs is of significant importance. The rerouting approach which is employed in most of the fault-tolerant methods causes the network performance to degrade considerably due to taking longer paths and creating hotspots around the faults. Moreover, they cannot adapt to the dynamic traffic distribution in the network. Considering the increasing demands for real-time systems, the necessity for designing reconfigurable and robust NoCs is even more pronounced. In this paper, a dynamically reconfigurable technique is proposed to address fault-tolerance and minimal routing in mesh NoCs. To accomplish this goal, the router architecture is modified to enable the frequently communicating nodes to bypass the faulty router and communicate through shorter paths. Thus, not only the rerouting is minimized, the connectivity of the network is maintained in the vicinity of faults. The experimental results validate the performance and reliability of the proposed technique with a small hardware overhead.
High dietary energy content increases inflammatory markers after lipopolysaccharide challenge in meat ducks

&NA; This study was conducted to investigate the effect of dietary energy concentration on growth performance, serum biochemical indices, and the mRNA expression of inflammatory cytokines in the liver of meat ducks challenged with lipopolysaccharide (LPS). A total of 600 1‐d‐old Cherry Valley meat ducks were randomly assigned to 10 treatments with six replicate cages of 10 birds/pen. A 5 × 2 factorial arrangement in a complete randomized design experiment was used to study the effect of five concentrations of dietary metabolizable energy (ME, 2,800, 2,900, 3,000, 3,100, and 3,200 kcal/kg) with or without a challenge with LPS in meat ducks. All experimental ducks were intraperitoneally injected with either 0.5 mg/kg body weight (BW) LPS or an equivalent amount of sterile saline at 15, 17, and 19 d of age. The results showed that LPS challenge significantly decreased (P < 0.05) BW (d 21), body weight gain (d 15‐21) and average daily feed intake (ADFI; d 15‐21), and markedly increased (P < 0.05) relative spleen weight, serum total protein concentration, and the mRNA expression of interleukin 6 (IL‐6), interleukin 10, transforming growth factor &bgr; (TGF‐&bgr;), and Avian Beta Defensin 10 (AVBD‐10) in the liver of meat ducks. Ducks fed 3,200 kcal/kg ME diet had the lowest (P < 0.05) ADFI than those fed with the other diets. Additionally, there were significantly LPS by diet interactions on IL‐6, interferon‐&ggr;, TGF‐&bgr;, AVBD‐10, and inducible nitric oxide synthase mRNA expression (P < 0.05), wherein a more pronounced liver inflammatory response was observed in birds fed high‐energy diets (3,100 and 3,200 kcal/kg ME) than birds fed low‐energy diet (2,800 and 2,900 kcal/kg ME). The results indicated that 3,100 and 3,200 kcal/kg ME of diets increased hepatic inflammatory markers induced by LPS challenge in ducks but did not influence performance responsiveness during this challenge.
Power splitting–based energy‐harvesting protocol for wireless‐powered communication networks with a bidirectional relay

In this paper, we apply the power splitting–based energy‐harvesting protocol to enhance the transmission between a wireless access point and a mobile user via a helping relay. The mobile user exploits the energy supplied by the access point and forwarded by the relay to transmit its own data back to the access point, again with the helping of the relay. Here, the effect of various system parameters, including power‐splitting factor and the power‐to‐noise ratio on the system performance, is rigorously studied, with closed‐form expressions for the outage probability and system throughput as the results. Furthermore, we figure out the optimal power‐splitting ratio at which the information throughput from the user to the AP is maximized, subject to the constraint on the transmitting power at the access point. All above analytical results are also supported by Monte Carlo simulation.
Automatic semantic mapping between query terms and controlled vocabulary through using WordNet and Wikipedia

Query log analysis can provide valuable information for improving information retrieval performance. This paper reports findings from a query log mining project, in which query terms falling in the very long tail of low to zero similarity (with the controlled vocabulary) scores were analyzed by using similarity algorithms. The query log data was collected from the Gateway to Educational Materials (GEM). The limited number of terms in the GEM controlled vocabulary was a major source for the long tail of low or zero similarity scores for the query terms. To mitigate this limitation, we employed a strategy that involved using the general-purpose (domain-independent) ontology WordNet and community-created Wikipedia as the bridge to establish semantic relatedness between GEM controlled vocabulary (as well as new concept classes identified by human experts) and user query terms. The two sources, WordNet and Wikipedia, were complementary in mapping different types of query terms. A combination of both sources achieved a modest rate of mapping accuracy. The paper discussed the implications of the findings for automatic semantic analysis and vocabulary development and validation.
Genetic modifiers of the BRD4-NUT dependency of NUT midline carcinoma uncovers a synergism between BETis and CDK4/6is

Using CRISPR and ORF expression screens, Liao et al. systematically examined the ability of cancer drivers to mediate resistance of NUT midline carcinoma (NMC) to bromodomain and extraterminal domain inhibitors (BETis) and uncovered six general classes/pathways mediating resistance.
Building an integrated model of information behavior through information journals

Information behavior models generally focus on one of many aspects of information behavior, either information finding, conceptualized as information seeking, information foraging or information sense-making, information organizing and information using. This ongoing study is developing an integrated model of information behavior. The research design involves a 2-week-long daily information journal self-maintained by the participants, combined with two interviews, one before, and one after the journal-keeping period. The data from the study will be analyzed using grounded theory to identify when the participants engage in the various behaviors that have already been observed, identified, and defined in previous models, in order to generate useful sequential data and an integrated model.
Clonal evolution analysis of paired anaplastic and well‐differentiated thyroid carcinomas reveals shared common ancestor

Foci of papillary or follicular thyroid carcinoma are frequently noted in thyroidectomy specimens of anaplastic thyroid carcinoma (ATC). However, whether ATCs evolve from these co‐existing well‐differentiated thyroid carcinomas (WDTCs) has not been well‐understood. To investigate the progression of ATC in patients with co‐existing WDTCs, five ATC tumors with co‐existing WDTCs and matching normal tissues were whole‐exome sequenced. After mapping the somatic alteration landscape, evolutionary lineages were constructed by sub‐clone analysis. Though each tumor harbored at least some unique private mutations, all five ATCs demonstrated numerous overlapping mutations with matched WDTCs. Clonal analysis further demonstrated that each ATC/WDTC pair shared a common ancestor, with some pairs diverging early in their evolution and others in which the ATC seems to arise directly from a sub‐clone of the WDTC. Though the precise lineal relationship remains ambiguous, based on the genetic relationship, our study clearly suggests a shared origin of ATC and WDTC.
MeCP2 isoform e1 mutant mice recapitulate motor and metabolic phenotypes of Rett syndrome

Mutations in the X-linked gene MECP2 cause the majority of Rett syndrome (RTT) cases. Two differentially spliced isoforms of exons 1 and 2 (MeCP2-e1 and MeCP2-e2) contribute to the diverse functions of MeCP2, but only mutations in exon 1, not exon 2, are observed in RTT. We previously described an isoform-specific MeCP2-e1 deficient male mouse model of a human RTT mutation that lacks MeCP2-e1 while preserving expression of MeCP2-e2. However, RTT patients are heterozygous females that exhibit delayed and progressive symptom onset beginning in late infancy, including neurologic as well as metabolic, immune, respiratory, and gastrointestinal phenotypes. Consequently, we conducted a longitudinal assessment of symptom development in MeCP2-e1 mutant females and males. A delayed and progressive onset of motor impairments was observed in both female and male MeCP2-e1 mutant mice, including hind limb clasping and motor deficits in gait and balance. Because these motor impairments were significantly impacted by age-dependent increases in body weight, we also investigated metabolic phenotypes at an early stage of disease progression. Both male and female MeCP2-e1 mutants exhibited significantly increased body fat compared to sex-matched wild-type littermates prior to weight differences. Mecp2e1-/y males exhibited significant metabolic phenotypes of hypoactivity, decreased energy expenditure, increased respiratory exchange ratio (RER), but decreased food intake compared to wildtype. Untargeted analysis of lipid metabolites demonstrated a distinguishable profile in MeCP2-e1 female mutant liver characterized by increased triglycerides. Together these results demonstrate that MeCP2-e1 mutation in mice of both sexes recapitulate early and progressive metabolic and motor phenotypes of human RTT.
Caspase-11 auto-proteolysis is crucial for noncanonical inflammasome activation

Intracellular LPS sensing by caspase-4/5/11 triggers proteolytic activation of pore-forming gasdermin D (GSDMD), leading to pyroptotic cell death in Gram-negative bacteria-infected cells. Involvement of caspase-4/5/11 and GSDMD in inflammatory responses, such as lethal sepsis, makes them highly desirable drug targets. Using knock-in (KI) mouse strains, we herein provide genetic evidence to show that caspase-11 auto-cleavage at the inter-subunit linker is essential for optimal catalytic activity and subsequent proteolytic cleavage of GSDMD. Macrophages from caspase-11–processing dead KI mice (Casp11Prc D285A/D285A) exhibit defective caspase-11 auto-processing and phenocopy Casp11−/− and caspase-11 enzymatically dead KI (Casp11Enz C254A/C254A) macrophages in attenuating responses to cytoplasmic LPS or Gram-negative bacteria infection. GsdmdD276A/D276A KI macrophages also fail to cleave GSDMD and are hypo-responsive to inflammasome stimuli, confirming that the GSDMD Asp276 residue is a nonredundant and indispensable site for proteolytic activation of GSDMD. Our data highlight the role of caspase-11 self-cleavage as a critical regulatory step for GSDMD processing and response against Gram-negative bacteria.
Spectral Learning Algorithm Reveals Propagation Capability of Complex Networks

In network science and the data mining field, a long-lasting and significant task is to predict the propagation capability of nodes in a complex network. Recently, an increasing number of unsupervised learning algorithms, such as the prominent PageRank (PR) and LeaderRank (LR), have been developed to address this issue. However, in degree uncorrelated networks, this paper finds that PR and LR are actually proportional to in-degree of nodes. As a result, the two algorithms fail to accurately predict the nodes’ propagation capability. To overcome the arising drawback, this paper proposes a new iterative algorithm called SpectralRank (SR), in which the nodes’ propagation capability is assumed to be proportional to the amount of its neighbors after adding a ground node to the network. Moreover, a weighted SR algorithm is also proposed to further involve a priori information of a node itself. A probabilistic framework is established, which is provided as the theoretical foundation of the proposed algorithms. Simulations of the susceptible-infected-removed model on 32 networks, including directed, undirected, and binary ones, reveal the advantages of the SR-family methods (i.e., weighted and unweighted SR) over PR and LR. When compared with other 11 well-known algorithms, the indices in the SR-family always outperform the others. Therefore, the proposed measures provide new insights on the prediction of the nodes’ propagation capability and have great implications in the control of spreading behaviors in complex networks.
Reducing Interactive Refactoring Effort via Clustering-Based Multi-objective Search

Refactoring is nowadays widely adopted in the industry because bad design decisions can be very costly and extremely risky. On the one hand, automated refactoring does not always lead to the desired design. On the other hand, manual refactoring is error-prone, time-consuming and not practical for radical changes. Thus, recent research trends in the field focused on integrating developers feedback into automated refactoring recommendations because developers understand the problem domain intuitively and may have a clear target design in mind. However, this interactive process can be repetitive, expensive, and tedious since developers must evaluate recommended refactorings, and adapt them to the targeted design especially in large systems where the number of possible strategies can grow exponentially. In this paper, we propose an interactive approach combining the use of multi-objective and unsupervised learning to reduce the developer's interaction effort when refactoring systems. We generate, first, using multi-objective search different possible refactoring strategies by finding a trade-off between several conflicting quality attributes. Then, an unsupervised learning algorithm clusters the different trade-off solutions, called the Pareto front, to guide the developers in selecting their region of interests and reduce the number of refactoring options to explore. The feedback from the developer, both at the cluster and solution levels, are used to automatically generate constraints to reduce the search space in the next iterations and focus on the region of developer preferences. We selected 14 active developers to manually evaluate the effectiveness our tool on 5 open source projects and one industrial system. The results show that the participants found their desired refactorings faster and more accurate than the current state of the art.
NFTsim: Theory and Simulation of Multiscale Neural Field Dynamics

A user ready, portable, documented software package, NFTsim, is presented to facilitate numerical simulations of a wide range of brain systems using continuum neural field modeling. NFTsim enables users to simulate key aspects of brain activity at multiple scales. At the microscopic scale, it incorporates characteristics of local interactions between cells, neurotransmitter effects, synaptodendritic delays and feedbacks. At the mesoscopic scale, it incorporates information about medium to large scale axonal ranges of fibers, which are essential to model dissipative wave transmission and to produce synchronous oscillations and associated cross-correlation patterns as observed in local field potential recordings of active tissue. At the scale of the whole brain, NFTsim allows for the inclusion of long range pathways, such as thalamocortical projections, when generating macroscopic activity fields. The multiscale nature of the neural activity produced by NFTsim has the potential to enable the modeling of resulting quantities measurable via various neuroimaging techniques. In this work, we give a comprehensive description of the design and implementation of the software. Due to its modularity and flexibility, NFTsim enables the systematic study of an unlimited number of neural systems with multiple neural populations under a unified framework and allows for direct comparison with analytic and experimental predictions. The code is written in C++ and bundled with Matlab routines for a rapid quantitative analysis and visualization of the outputs. The output of NFTsim is stored in plain text file enabling users to select from a broad range of tools for offline analysis. This software enables a wide and convenient use of powerful physiologically-based neural field approaches to brain modeling. NFTsim is distributed under the Apache 2.0 license.