Miracle-dz/UniFAR-FTUs huggingface dataset:
Behind the emergence of management scholarly communities in Asia and Africa

ABSTRACT Management scholarly communities have recently emerged in Asia and Africa. What are the antecedents behind their rise? What are the processes that facilitate their acceptance by internal and external stakeholders? Drawing on the sociology of science literature, this article develops a model behind the emergence of management scholarly communities underpinned by antecedents and processes. We identify relative deprivation, motivation to excel, and availability of role models as three key antecedents, and differentiation, mobilization, and legitimation as three key processes. We offer propositions based on in-depth analysis of three management scholarly communities in Asia (Asia Academy of Management [AAOM], India Academy of Management [INDAM], and International Association for Chinese Management Research [IACMR]) and one in Africa (Africa Academy of Management [AFAM]). For the first time in the literature, we have compared and contrasted the emergence of management scholarly communities in Asia and Africa, culminating in a model that may be generalizable to other aspiring scholars endeavoring to expand such communities in other parts of the world.
BiCMOS circuits for analog Viterbi decoders

Analog Viterbi decoders are finding widespread use in class-IV partial-response disk drive applications. These analog realizations are often used because they are smaller and consume less power than their digital counterparts. However, class-IV signaling allows simplifications during Viterbi detection and thus existing analog decoders have limited applications. The purpose of this paper is to develop efficient analog circuits that can be used for general Viterbi detection. To demonstrate the feasibility of the proposed approach, the analog portions of two analog Viterbi decoders were fabricated in a 0.8 /spl mu/m BiCMOS process. With an off-chip digital path memory, operation up to 50 Mb/s is demonstrated. However, simulations indicate that with on-chip digital path memory, speeds on the order of 300 Mb/s can be achieved. The power consumption of the proposed approach is estimated to be 15 mW/state drawn from a single 5 V power supply.
Relationship Between Job Satisfaction And Turnover Intention

This study examines the level of job satisfaction and turnover intention among nurses in Karachi. The objectives of this study is to examine the impact and relationship of satisfaction with organizational policies and strategies, satisfaction with supervision, compensation levels, task clarity, and career development on turnover intention. An instrument based on five facets of job satisfaction and turnover intention was developed based on literature reviews to find out the level of job satisfaction and turnover intention. For this purpose data were collected from 400 nurses working in different hospitals of Karachi. The findings shows that satisfaction with organizational policies and strategies, satisfaction with supervision, compensation levels, task clarity, and career development all have significant negative correlation with turnover intention and overall job satisfaction was found to have a significant negative association with turnover intention.
A parallel implementation of Q-learning based on communication with cache

Q-Learning is a Reinforcement Learning method for solving sequential decision problems, where the utility of actions depends on a sequence of decisions and there exists uncertainty about the dynamics of the environment the agent is situated on. This general framework has allowed that Q-Learning and other Reinforcement Learning methods to be applied to a broad spectrum of complex real world problems such as robotics, industrial manufacturing, games and others. Despite its interesting properties, Q-learning is a very slow method that requires a long period of training for learning an acceptable policy. In order to solve or at least reduce this problem, we propose a parallel implementation model of Q-learning using a tabular representation and via a communication scheme based on cache. This model is applied to a particular problem and the results obtained with different processor configurations are reported. A brief discussion about the properties and current limitations of our approach is finally presented.
Delegated Job Design

Why do firms delegate job design decisions to workers, and what are the implications of such delegation? We develop a private-information based theory of delegation, where delegation enables high-ability workers to signal their ability by choosing difficult tasks. Such signalling provides a more efficient allocation of talent inside the firm, but at the cost that low-ability workers must be compensated to be willing to self-sort. Career concerns put a limit to the efficiency of delegation: when market observability of job content is high, the compensation needed to get low ability workers to self-sort is high, and firms limit delegation to avoid cream-skimming of the high-ability workers. We investigate implications of the theory for how misallocation of talent within firms may occur and to the design optimal incentive contracts.
Student’s Creative Thinking Skills in Mathematical Problem Posing Based on Lesson Study for Learning Community

The objective of this research is to find out the differences in terms of creative thinking ability between one class taught using implementing Mathematical problem posing based on Lesson Study for Learning Community (LSLC) and another class taught in conventional fashion. This research applied Mixed-Method study which was initiated by developing instructional instruments based on Lesson Study for Learning Community (LSLC) to train students’ creativity, collaboration, and communication. This quantitative study applied experimental method with quasi experimental design. The results of this research indicated: (1) that the developed instruments meet validity, practicality, and effectiveness criteria; (2) There is ability difference of creative thinking in mathematical problem posing between the experimental class and the control class. The application of different learning in the two sample classes has an effect on the level of students’ creative thinking skills. Mathematical problem posing based on Lesson Study for Learning Community can help students to understand the lesson.
Disclosure Dynamics and Investor Learning

This paper examines whether investor learning about profitability (i.e., the mean of earnings distribution) leads to persistence in disclosure decisions. A repeated single-period model shows that persistent investor beliefs about profitability lead to persistent disclosure decisions. Using earnings forecast data, I structurally estimate the model and perform several counterfactual analyses. I find that, when investors are assumed to know profitability, the persistence of management forecast decisions significantly declines by 17%–27%. About 24% of firms would have disclosed differently, resulting in 3.9% net change in the amount of information (i.e., posterior variance) provided to the capital market. Collectively, the results indicate the importance of learning profitability in understanding disclosure decisions and the capital market consequences of disclosures. This paper was accepted by Shiva Rajgopal, accounting.
Essential roles for the splicing regulator nSR100/SRRM4 during nervous system development

Quesnel-Vallières et al. show that loss of the vertebrate- and neural-specific Ser/Arg repeat-related protein of 100 kDa (nSR100/SRRM4) impairs development of the central and peripheral nervous systems. Accompanying these developmental defects are widespread changes in alternative splicing (AS) that primarily result in shifts to nonneural patterns for different classes of splicing events. The main component of the altered AS program comprises 3- to 27-nt neural microexons, and inclusion of a 6-nt nSR100-activated microexon in Unc13b transcripts is sufficient to rescue a neuritogenesis defect in nSR100 mutant primary neurons.
The asset allocation decision in a loss aversion world

The purpose of this paper is to derive explicit formulae for the asset allocation decision for the loss aversion utility function proposed by Kahneman and Tuversky. We show that these utility functions exhibit constant absolute risk aversion. We also give analytic results which interpret the assumptions of risk-aversion with respect to gains but risk-a! ection with respect to losses in terms of changes of the optimal investment of equity when the probability that equity outperforms cash goes up. For the Knight, Satchell and Tran (1995) family of distributions, it is straightforward to derive closed form expressions for the optimal portfolio weights in all cases. Using UK and US data, we confirmed that the values of the parameters in the loss aversion function suggested by many previous studies are compatible with the observed proportions held in equity in both the UK and the US. The distributional assumptions are not innocuous. However, whilst modelling upside and downside returns by gamma distributions leads to plausible results, modelling upside and downside by truncated normals does not.
Predicting breast cancer recurrence using effective classification and feature selection technique

Breast cancer is a major threat for middle aged women throughout the world and currently this is the second most threatening cause of cancer death in women. But early detection and prevention can significantly reduce the chances of death. An important fact regarding breast cancer prognosis is to optimize the probability of cancer recurrence. This paper aims at finding breast cancer recurrence probability using different data mining techniques. We also provide a noble approach in order to improve the accuracy of those models. Cancer patient's data were collected from Wisconsin dataset of UCI machine learning Repository. This dataset contained total 35 attributes in which we applied Naive Bayes, C4.5 Decision Tree and Support Vector Machine (SVM) classification algorithms and calculated their prediction accuracy. An efficient feature selection algorithm helped us to improve the accuracy of each model by reducing some lower ranked attributes. Not only the contributions of these attributes are very less, but their addition also misguides the classification algorithms. After a careful selection of upper ranked attributes we found a much improved accuracy rate for all three algorithms.
Impaired Social and Role Function in Ultra-High Risk for Psychosis and First-Episode Schizophrenia: Its Relations with Negative Symptoms

Objective Psychosocial dysfunction was a nettlesome problem of schizophrenia even in their prodromal phase as well as in their first-episode. In addition, its relations with psychopathology were not determined. The aim of the present study was to examine whether the social and role function impairment was found in ultra-high risk for psychosis (UHR) individuals as well as first-episode schizophrenia patients and to explore its relations with psychopathology. Methods Thirty-seven normal controls, 63 UHR participants and 28 young, first-episode schizophrenia patients were recruited. Psychosocial functioning was examined by using Global function: Social and Role scale. Psychopathologies of positive, negative and depressive symptom were also measured. Results Social and role functioning in UHR were compromised at the equivalent level of those of first-episode schizophrenia patients. Multiple linear regression analysis revealed that social and role dysfunction was associated with negative symptoms in each UHR and first-episode schizophrenia group. Conclusion These findings suggest that the significant impairment of social and role function may be appeared before the active psychosis onset at the level of extent to those of first-episode schizophrenia patients. The psychosocial intervention strategy especially targeting the negative symptoms should be developed and provided to individuals from their prepsychotic stage of schizophrenia.
Constructing and updating motor awareness: experimental studies in Anosognosia for Hemiplegia

Body awareness, the sense that our body is ours, is constructed by dynamic integration of several sensory modalities, including exteroceptive (originating outside the body), and interoceptive (originating within) and can be divided into awareness of body ownership and of motor deficits. This study examined the contribution of different, top-down and bottom-up inter-acting, cognitive and emotional factors, in the construction of body and motor awareness, by focusing on Anosognosia for Hemiplegia (AHP), a syndrome of motor awareness disruption. AHP refers to the inability of (usually) right hemisphere stroke patients to acknowledge their motor deficits. Clinical presentation of AHP varies, with some patients even displaying disruption of the sense of body ownership. Current AHP theories, based on predictive coding, suggest belief op-timisation, based on a multilevel system of top-down predictions and bottom-up feedback, is disrupted due to neurological damage, combined with prior patient’s traits. Combinations of disruptions account for the variability of AHP and affect the patient’s ability to incorporate feedback about their paralysis into their pre-existing schema of themselves, remaining fixated into delusion-like beliefs. Under the same framework, right hemisphere patients with and without anosognosia were recruited from the NHS setting, underwent neuro-psychological assessments and participated in experimental studies examin-ing this model on different levels. At the sensory level, the effect of self- and affective touch was examined, given their interoceptive properties and role affecting bodily awareness. AHP patients’ belief updating ability was tested, by examining how their performance predictions and confidence change fol-lowing motor failures. Given anosognosics’ deficits in perspective taking and proper emotion processing, their ability to update beliefs about themselves and others on cognitive and emotional level and their ability to spontaneously adopt another person’s perspective were examined. Given the elements of denial observed in AHP, presence of memory repression was examined by means of a memory test with deficit-related content.
A novel hypothesis-unbiased method for Gene Ontology enrichment based on transcriptome data

Gene Ontology (GO) classification of statistically significantly differentially expressed genes is commonly used to interpret transcriptomics data as a part of functional genomic analysis. In this approach, all significantly expressed genes contribute equally to the final GO classification regardless of their actual expression levels. Gene expression levels can significantly affect protein production and hence should be reflected in GO term enrichment. Genes with low expression levels can also participate in GO term enrichment through cumulative effects. In this report, we have introduced a new GO enrichment method that is suitable for multiple samples and time series experiments that uses a statistical outlier test to detect GO categories with special patterns of variation that can potentially identify candidate biological mechanisms. To demonstrate the value of our approach, we have performed two case studies. Whole transcriptome expression profiles of Salmonella enteritidis and Alzheimer’s disease (AD) were analysed in order to determine GO term enrichment across the entire transcriptome instead of a subset of differentially expressed genes used in traditional GO analysis. Our result highlights the key role of inflammation related functional groups in AD pathology as granulocyte colony-stimulating factor receptor binding, neuromedin U binding, and interleukin were remarkably upregulated in AD brain when all using all of the gene expression data in the transcriptome. Mitochondrial components and the molybdopterin synthase complex were identified as potential key cellular components involved in AD pathology.
Creating reference architectures: an example from avionics

ADAGE is a project to define and build a domain-specific software architecture (DSSA) environment for assisting the development of avionics software. A central concept of DSSA is the use of software system generators to implement component-based models of software synthesis in the target domain [SEI90]. In this paper, we present the ADAGE component-based model (or reference architecture) for avionics software synthesis. We explain the modeling procedures used, review our initial goals, show how component reuse is achieved, and examine what we were (and were not) able to accomplish. The contributions of our paper are the avionics reference architecture and the lessons that we learned; both may be beneficial to others in future modeling efforts.
Cetuximab intensifies the ADCC activity of adoptive NK cells in a nude mouse colorectal cancer xenograft model

Natural killer (NK) cells, discovered ~40 years ago, are believed to be the most effective cytotoxic lymphocytes to counteract cancer; however, adoptive NK cell therapy in vivo has encountered certain limitations, including a lack of specificity. The drug cetuximab can mediate antibody dependent cell mediated cytotoxicity (ADCC) activity through NK cells in vivo, and has been approved for the first-line treatment of epidermal growth factor receptor (EGFR)-positive metastatic colorectal cancer (CRC). However, the ADCC activity of adoptive NK cells, induced by cetuximab in a nude mouse CRC xenograft model, has not been previously reported. The aim of the present study was to explore the ADCC activity of cetuximab combined with adoptive NK cells in CRC xenograft models with various EGFR expressions. The nude mouse xenograft models were established by subcutaneously injecting LOVO or SW620 cells. The mice were then randomly divided into 6 groups: Phosphate-buffered saline, cetuximab, human immunoglobulin G (hIgG), NK cells, hIgG plus NK cells and cetuximab plus NK cells. The ADCC antitumor activity was evaluated in these CRC models. The results indicated that the cetuximab plus NK cells group showed the greatest tumor inhibition effect compared with the NK cells group in LOVO xenograft tumor models with positive EGFR expression. However, the combination of cetuximab and NK cells did not show a stronger tumor inhibitory effect against the SW620 xenograft tumor models compared with the efficiency of NK cells. In conclusion, cetuximab could intensify the ADCC antitumor activity of adoptive NK cells towards CRC with an increased EGFR expression. The combination of cetuximab and NK cells may be a potential immunotherapy for metastatic CRC patients with positive EGFR expression.
A Deeper Look at Technology Adoption: Moderation and Technology Selection

This study proposes a model of factors that may be driving previously observed gender differences in acceptance decisionmaking. In particular, a significant effect is found for generalized computer self-efficacy, over and above that of psychological gender role identified in prior related research. However, this study departs from prior work in technology adoption by employing actual choice between competing technologies for an identified need as the criterion variable, which is argued to represent a more realistic context for the application of this research stream.
A Competitive Modular Connectionist Architecture

We describe a multi-network, or modular, connectionist architecture that captures that fact that many tasks have structure at a level of granularity intermediate to that assumed by local and global function approximation schemes. The main innovation of the architecture is that it combines associative and competitive learning in order to learn task decompositions. A task decomposition is discovered by forcing the networks comprising the architecture to compete to learn the training patterns. As a result of the competition, different networks learn different training patterns and, thus, learn to partition the input space. The performance of the architecture on a "what" and "where" vision task and on a multi-payload robotics task are presented.
Minority Perception of Police Legitimacy in Finland: The Patterns and Predictors

Across the world, minorities are often perceived as social problems in policy formulations and processes in many societies. This perspective gets support from the notion that minorities engage in deviant and criminal behaviour. However, in Finland fewer scholarly attentions have explored the extent of minority attitudes and experiences as they relate to social problems in societies. The aim of this study is to explore procedural justice and its understanding thereof by minorities and racial groups in Finland in complying with police orders. Data for this study was collected between April 2013 and July 2015 from (N = 650) respondents from three major cities (Helsinki, Tampere and Turku) using minority experiences of racial profiling, encounters and unfair policing as variables that increase minority distrust in procedural justice in Finland. The result suggests that ethnicity influences minority views of procedural justice in the country. The implication of this finding is that there is a need for more exploration of the relationship between the police and minorities in Finland.
Deficiency in the Ubiquitin Conjugating Enzyme UBE2A in Alzheimer’s Disease (AD) is Linked to Deficits in a Natural Circular miRNA-7 Sponge (circRNA; ciRS-7)

Our understanding of the highly specialized functions for small non-coding single-stranded RNA (ssRNA) in the transcriptome of the human central nervous system (CNS) continues to evolve. Circular RNAs (circRNAs), a recently discovered class of ssRNA enriched in the brain and retina, are extremely stable and intrinsically resilient to degradation by exonuclease. Conventional methods of ssRNA, microRNA (miRNA), or messenger RNA (mRNA) detection and quantitation requiring free ribonucleotide ends may have considerably underestimated the quantity and significance of CNS circRNA in the CNS. Highly-specific small ssRNAs, such as the ~23 nucleotide (nt) Homo sapien microRNA-7 (hsa-miRNA-7; chr 9q21.32), are not only abundant in the human limbic system but are, in addition, associated with a ~1400 nt circRNA for miRNA-7 (ciRS-7) in the same anatomical region. Structurally, ciRS-7 contains about ~70 tandem anti-miRNA-7 sequences and acts as an endogenous, anti-complementary miRNA-7 “sponge” that attracts, binds, and, hence, quenches, natural miRNA-7 functions. Using a combination of DNA and miRNA array technologies, enhanced LED-Northern and Western blot hybridization, and the magnesium-dependent exoribonuclease and circRNA-sensitive probe RNaseR, here we provide evidence of a significantly misregulated ciRS-7-miRNA-7-UBE2A circuit in sporadic Alzheimer’s disease (AD) neocortex (Brodmann A22) and hippocampal CA1. Deficits in ciRS-7-mediated “sponging events”, resulting in excess ambient miRNA-7 appear to drive the selective down-regulation in the expression of miRNA-7-sensitive mRNA targets, such as that encoding the ubiquitin conjugating enzyme E2A (UBE2A; chr Xq24). UBE2A, which normally serves as a central effector in the ubiquitin-26S proteasome system, coordinates the clearance of amyloid peptides via proteolysis, is known to be depleted in sporadic AD brain and, hence, contributes to amyloid accumulation and the formation of senile plaque deposits. Dysfunction of circRNA-miRNA-mRNA regulatory systems appears to represent another important layer of epigenetic control over pathogenic gene expression programs in the human CNS that are targeted by the sporadic AD process.