Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of heuristic evaluation in usability evaluation of e-learning applications in higher education

The Internet, World Wide Web (WWW) and e-learning are contributing to new forms of teaching and learning. Such environments should be designed and evaluated in effective ways, considering both usability- and pedagogical issues. The selection of usability evaluation methods (UEMs) is influenced by the cost of a methods and its effectiveness in addressing users’ issues. The issue of usability is vital in e-learning, where students cannot begin to learn unless they can first use the application. Heuristic evaluation (HE) remains the most widely-used usability evaluation method. This paper describes meta-evaluation research that investigated an HE of a web-based learning (WBL) application. The evaluations were based on a synthesised framework of criteria, related to usability and learning within WBL environments. HE was found to be effective in terms of the number and nature of problems identified in the target application by a complementary team of experienced experts. The findings correspond closely with those of a survey among learners.
Reading Fluency Assessment and Instruction: What, Why, and How?

This article explains the elements of reading fluency and ways to assess and teach them. Fluent reading has three elements: accurate reading of connected text, at a conversational rate with appropriate prosody. Word reading accuracy refers to the ability to recognize or decode words correctly. Reading rate refers to both word-level automaticity and speed in reading text. Prosodic features are variations in pitch, stress patterns, and duration that contribute to expressive reading of a text. To assess reading fluency, including all its aspects, teachers listen to students read aloud. Students' accuracy can be measured by listening to oral reading and counting the number of errors per 100 words or a running record. Measuring reading rate includes both word-reading automaticity and speed in reading connected text using tests of sight-word knowledge and timed readings. A student's reading prosody can be measured using a checklist while listening to the student. To provide instruction in rate and accuracy, variations on the repeated readings technique are useful. To develop prosody, readers can listen to fluent models and engage in activities focused on expression and meaning. Opportunities to develop all areas of reading fluency are important for all readers, but especially for those who struggle.
The Impact of Different Physical Parameterizations and Their Interactions on Cold Season QPF in the American River Basin

Abstract The most significant precipitation events in California occur during the winter and are often related to synoptic-scale storms from the Pacific Ocean. Because of the terrain characteristics and the fact that the urban and infrastructural expansion is concentrated in lower elevation areas of the California Central Valley, a high risk of flooding is usually associated with these events. In the present study, the area of interest was the American River basin (ARB). The main focus of the present study was to investigate methods for Quantitative Precipitation Forecast (QPF) improvement by estimating the impact that various microphysical schemes, planetary boundary layer (PBL) schemes, and initialization methods have on cold season precipitation, primarily orographically induced. For this purpose, 3-km grid spacing Weather Research and Forecasting (WRF) model simulations of four Hydrometeorological Test bed (HMT) events were used. For each event, four different microphysical schemes and two different P.
Granule-Derived Granzyme B Mediates the Vulnerability of Human Neurons to T Cell-Induced Neurotoxicity

Multiple sclerosis (MS) is considered an autoimmune disease of the CNS and is characterized by inflammatory cells infiltrating the CNS and inducing demyelination, axonal loss, and neuronal death. Recent evidence strongly suggests that axonal and neuronal degeneration underlie the progression of permanent disability in MS. In this study, we report that human neurons are selectively susceptible to the serine-protease granzyme B (GrB) isolated from cytotoxic T cell granules. In vitro, purified human GrB induced neuronal death to the same extent as the whole activated T cell population. On the contrary, activated T cells isolated from GrB knockout mice failed to induce neuronal injury. We found that following internalization through various parts of neurons, GrB accumulated in the neuronal soma. Within the cell body, GrB diffused out of endosomes possibly through a perforin-independent mechanism and induced subsequent activation of caspases and cleavage of α-tubulin. Inhibition of caspase-3, a well-known substrate for GrB, significantly reduced GrB-mediated neurotoxicity. We demonstrated that treatment of neurons with mannose-6-phosphate prevented GrB entry and inhibited GrB-mediated neuronal death, suggesting mannose-6-phosphate receptor-dependent endocytosis. Together, our data unveil a novel mechanism by which GrB induces selective neuronal injury and suggest potential new targets for the treatment of inflammatory-mediated neurodegeneration in diseases such as MS.
Finite-Time Convergent Recurrent Neural Network With a Hard-Limiting Activation Function for Constrained Optimization With Piecewise-Linear Objective Functions

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.
Adiponectin-11377CG Gene Polymorphism and Type 2 Diabetes Mellitus in the Chinese Population: A Meta-Analysis of 6425 Subjects

Background Although adiponectin −11377CG gene polymorphism is implied to be associated with increased type 2 diabetes mellitus (T2DM) risk, results of individual studies are inconsistent. Objective and Methods A meta-analysis consisting of 12 individual studies, including a total of 6425 participants, was carried out in order to investigate the association of adiponectin −11377CG gene polymorphism with T2DM. The pooled odds ratio (OR) and its corresponding confidence interval (CI) at 95% were assessed through the random- or fixed- effect model. Results A significant relationship was observed between adiponectin −11377CG gene polymorphism and T2DM under allelic (OR: 1.150, 95% CI: 1.060 to 1.250, P = 0.001), recessive (OR: 1.450, 95% CI: 1.180–1.770, P = 0.0004), dominant (OR: 1.071, 95% CI: 1.013–1.131, P = 0.015), additive (OR: 1.280, 95% CI: 1.090–1.510, P = 0.002), and homozygous genetic models (OR: 1.620, 95% CI: 1.310–1.990, P<0.00001). No significant association was found between them under the heterozygous genetic model (OR: 1.640, 95% CI: 0.850–3.170, P = 0.140). Conclusions Adiponectin −11377CG gene polymorphism was significantly associated with T2DM risk susceptibility. G allele carriers are predisposed to T2DM risk.
The market's reaction to discount changes: what's behind the announcement effect?

Market interest rates respond to discount rate changes. What is the reason for this response. This paper investigates several competing hypotheses of why markets respond to discount rate changes. Evidence that the response is invariant to changes in the Federal Reserve's operating procedure suggests that it is purely an "announcement effect. " Contrary to common belief the evidence suggests that the does not depend critically on whether the discount rate change is unanticipated, because all discount rate changes appear to be largely unanticipated. Additional evidence suggests that, despite the fact that there have been instances when discount rate was used to signal a change in policy, e.g., the one percentage point increase in the discount rate in October 1979, generally speaking, discount rate changes do not appear to have "signaled" a change in monetary policy. This suggests that the common assertion that market interpret discount rate changes as a signal of a change in Fed policy is incorrect. It appears that changes expectations about monetary policy is not the only reason--and perhaps not the most important reason--for the market's reaction to changes in the discount rate.
On Critical Discourse Analysis

Critical discourse analysis(CDA) aims at making transparent the connections between language,power and ideology in natural speech situations from the angle of linguistics,sociology and psychology,and also studies how these factors influence each other.The main theoretical origin of CDA is Western Marxism and it employs Halliday's systemic functional analysis as the analytical tool.
Competitive Advantage Of Online Services And Complementarity Of IT Service Dimensions In Brokerage Firms

In financial service industry, firm's dependency on IT to deliver quality services has increased recent years. This study empirically examines the linkage between service dimensions of which quality depend on IT, and competitive advantage of firms. Consistent with the resource based theory, our result suggests that valuable, rare, and costly to imitate service dimensions make competitive advantage and induce better financial performance while other dimensions which are easy to imitate cannot be a source of competitive advantage. Additionally, we show the competitive advantage derived from the complementary relationship among service dimensions. The existence of complementary relationships among service dimensions is verified. This implies that, even though some dimension cannot be a source of competitive advantage alone, these are still relevant to total service quality management for financial performance.
Talking while Computing in Groups: The Not-So-Private Functions of Computational Private Speech in Mathematical Discussions

Students often voice computations during group discussions of mathematics problems. Yet, this type of private speech has received little attention from mathematics educators or researchers. In this article use excerpts from middle school students' group mathematical discussions to illustrate and describe “computational private speech. ” We analyze four examples of computational private speech using lenses from neo-Vygotskian psychology, sociolinguistics, and distributed cognition. Our analyses of computational private speech challenge the individualistic developmental assumptions of some neo-Vygotskian theories of private speech, and we show how this form of private speech can serve socio-cognitive functions during group mathematical discussions.
Differential extrageniculostriate and amygdala responses to presentation of emotional faces in a cortically blind field.

Patient G.Y. is able to discriminate emotional facial expressions presented in his blind (right) hemifield despite an extensive lesion of the corresponding (left) striate cortex. One proposal is that this residual ability (affective "blindsight") depends on a subcortical visual pathway comprising the superior colliculus, posterior (extrageniculate) thalamus and amygdala. Here we report differential amygdala responses in G.Y. to presentation of fearful and fear-conditioned faces in his blind (right) hemifield. These amygdala responses exhibited condition-dependent covariation with neural activity in the posterior thalamus and superior colliculus. Our results provide further evidence that an extrageniculostriate (colliculo-thalamo-amygdala) neural pathway can process fear-related stimuli independently of both the striate cortex and normal phenomenal visual awareness.
Are Incentive Contracts Rigged by Powerful CEOs?

We argue that powerful CEOs induce their boards to shift the weight on performance measures towards the better performing measures, thereby rigging the incentive part of their pay. The intuition is developed in a simple model in which some powerful CEOs exploit superior information and lack of transparency in compensation contracts to extract rents. The model delivers an explicit structural form for the rigging of CEO incentive pay along with testable implications that rigging is expected to (1) increase with CEO power; (2) increase with CEO human capital intensity and uncertainty about a firm's future prospects; and (3) negatively impact firm performance. Using measures of CEO power and board independence on a large panel of firms in the U.S., we find support for these predictions. Rigging accounts for 10%-30% of the sensitivity of compensation to performance measures and is increasing in CEO human capital and volatility of a firm's future prospects. Moreover, the portion of incentive pay that is predicted by power is associated with negative subsequent future stock performance of the order of 1% and operating performance of 5% per year. Overall, the results provide evidence against the agency substitution theory and support instead the entrenchment skimming theory.
Low-Complexity Interleaver Design for Turbo Codes

A low-complexity method of interleaver design, sub-vector interleaving, for both parallel and serially concatenated convolutional codes (PCCCs and SCCCs, respectively) is presented here. Since the method is low-complexity, it is uniquely suitable for designing long interleavers. Sub-vector interleaving is based on a dynamical system representation of the constituent encoders employed by PCCCs and SCCCs. Simultaneous trellis termination can be achieved with a single tail sequence using sub-vector interleaving for both PCCCs and SCCCs. In the case of PCCCs, the error floor can be lowered by sub-vector interleaving which allows for an increase in the weight of the free distance codeword and the elimination of the lowest weight codewords generated by weight-2 terminating input sequences that determine the error floor at low signal-to-noise ratios (SNRs). In the case of SCCCs, sub-vector interleaving lowers the error floor by increasing the weight of the free distance codewords. Interleaver gain can also be increased for SCCCs by interleaving the lowest weight codewords from the outer into non-terminating input sequences to the inner encoder. Sub-vector constrained S-random interleaving, a method for incorporating S-random interleaving into sub-vector interleavers, is also proposed. Simulations show that short interleavers incorporating S-random interleaving into sub-vector interleavers perform as well as or better than those designed by the best and most complex methods for designing short interleavers. A method for randomly generating sub-vector constrained S-random interleavers that maximizes the spreading factor, S, is also examined. The convergence of the turbo decoding algorithm to maximum-likelihood decisions on the decoded input sequence is required to demonstrate the improvement in BER performance caused by the use of sub-vector interleavers. Convergence to maximum-likelihood decisions by the decoder do not always occur in the regions where it is feasible to generate the statistically significant numbers of error events required to approximate the BER performance for a particular coding scheme employing a sub-vector interleaver. Therefore, a technique for classifying error events by the mode of convergence of the decoder is used to illuminate the effect of the sub-vector interleaver at SNRs where it is possible to simulate the BER performance of the coding scheme.
Influence of Affective Factors on SLA

Second Language Acquisition is regulated by many factors,among which individual emotion plays a more important role than cognition itself.This article mainly discusses the relationship between SLA and the five factors——anxiety,inhibition,introversion or extroversion,self-esteem and motivation,which affect the process of SLA.The author aims to discover how individual emotional factors influence the process of SLA as well as how to adopt corresponding measures in allusion to some passive effects,overcome and even avoid the negative effect,and thereby gain a content achievement.
A Phenylbutenoid Dimer, cis-3-(3′,4′-Dimethoxyphenyl)-4-[(E)-3′′′,4′′′-Dimethoxystyryl] Cyclohex-1-ene, Exhibits Apoptogenic Properties in T-Acute Lymphoblastic Leukemia Cells via Induction of p53-Independent Mitochondrial Signalling Pathway

The current study was designed to evaluate the in vitro cytotoxicity effect of a phenylbutenoid dimer, cis-3-(3′,4′-dimethoxyphenyl)-4-[(E)-3‴,4‴-dimethoxystyryl]cyclohex-1-ene (ZC-B11) isolated from the rhizome of Zingiber cassumunar on various cancer cell line, and normal human blood mononuclear cells, and to further investigate the involvement of apoptosis-related proteins that leads, to the probable pathway in which apoptosis is triggered. Cytotoxicity test using MTT assay showed selective inhibition of ZC-B11 towards T-acute lymphoblastic leukemia cells, CEMss, with an IC50 value of 7.11 ± 0.240 μg/mL, which did not reveal cytotoxic effects towards normal human blood mononuclear cells (IC50 > 50 μg/mL). Morphology assessments demonstrated distinctive morphological changes corresponding to a typical apoptosis. ZC-B11 also arrested cell cycle progression at S phase and causes DNA fragmentation in CEMss cells. Decline of mitochondrial membrane potential was also determined qualitatively. In the apoptosis-related protein determination, ZC-B11 was found to significantly upregulate Bax, caspase 3/7, caspase 9, cytochrome c, and SMAC and downregulate Bcl-2, HSP70, and XIAP, but did not affect caspase 8, p53, and BID. These results demonstrated for the first time the apoptogenic property of ZC-B11 on CEMss cell line, leading to the programmed cell death via intrinsic mitochondrial pathway of apoptosis induction.
Diagnosis and preoperative predictive value of serum HE4 concentrations for optimal debulking in epithelial ovarian cancer

The aim of this study was to evaluate serum human epididymis protein 4 (HE4) concentrations for the diagnosis and preoperative prediction of optimal debulking in epithelial ovarian cancer. The concentrations of serum HE4 and CA125 in 180 epithelial ovarian cancer patients, 40 benign ovarian tumor patients and 40 healthy female subjects were determined using enzyme-linked immunosorbent assays (ELISAs). The value of determining the serum HE4 concentrations for the diagnosis and preoperative prediction of optimal debulking in epithelial ovarian cancer was also analyzed. The concentration of serum HE4 was 355.2±221.29 pmol/l in ovarian cancer, 43.86±20.87 pmol/l in benign ovarian tumors and 30.22±9.64 pmol/l in healthy individuals, respectively. The serum HE4 levels of patients with ovarian cancer were significantly higher compared with those in the other two groups (P<0.01), although there were no statistically significant differences (P>0.05) between the benign ovarian tumors and healthy individuals. The maximum diagnostic value was identified at an HE4 serum concentration of 67.52 pmol/l and the sensitivity and specificity were 84 and 96%, respectively. The area under the ROC curve was 0.944 (95% CI, 0.912–0.976; P<0.001) and the κ value of the diagnosis of epithelial ovarian cancer according to HE4 was 0.814 (P=0.000). The demarcation criterion was 600 pmol/l, where a value >600 mol/l indicates a lower possibility of optimal debulking. HE4 predicted that the sensitivity of the incomplete cytoreductive surgery was 77% and specificity was 32%. The concentration of serum HE4 is a useful marker for diagnosis and preoperative prediction for the ideal tumor cytoreductive surgery in epithelial ovarian cancer.
Green Supply Chain Management Practices in Textile and Apparel Industries : Literature Review

Textile industry plays an important role in the economy of the country like India and it accounts for around one third of total export. From the design board to the tumble dryer, textile manufacturing has a huge environmental footprint. It pollutes as much as 200 tons of water per ton of fabric, uses a suite of harmful chemicals, and consumes tremendous amounts of energy for steam and hot water needed in dyeing and finishing processes. The green supply chain management is a sort of modern management mode which could c o mp r e he n s iv e l y c o n s id e r t he environmental influence and resource utilization efficiency in the whole supply chain. The main objective of this study is to implement green supply chain management in the textile and apparel enterprises The Trend of developing a green supply chain is gaining immense popularity now, and businesses in textile industry are focusing more on improving their supply chain visibility, efficiency and cost. Green supply -chain management (GSCM) is gaining increasing interest among researchers and practitioners of operations and supply chain management. The growing importance of GSCM is driven mainly by the escalating deterioration of the environment, e.g. diminishing raw material resources, overflowing waste sites and increasing levels of pollution. The study on Green Supply Chain Management Practices In textile and Apparel industries conducted by a number of researchers has been gathered in order to analyze the previous findings in accordance to the present scenario.
Quantization based Fast Inner Product Search

We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS). Each database vector is quantized in multiple subspaces via a set of codebooks, learned directly by minimizing the inner product quantization error. Then, the inner product of a query to a database vector is approximated as the sum of inner products with the subspace quantizers. Different from recently proposed LSH approaches to MIPS, the database vectors and queries do not need to be augmented in a higher dimensional feature space. We also provide a theoretical analysis of the proposed approach, consisting of the concentration results under mild assumptions. Furthermore, if a small sample of example queries is given at the training time, we propose a modified codebook learning procedure which further improves the accuracy. Experimental results on a variety of datasets including those arising from deep neural networks show that the proposed approach significantly outperforms the existing state-of-the-art.