nils-herrmann/clean_scirepeval huggingface dataset:
Qualities of exemplary nurse leaders: perspectives of frontline nurses.

This paper reports on a study that looked at the characteristics of exemplary nurse leaders in times of change from the perspective of frontline nurses.


Large-scale changes in the health care system and their associated challenges have highlighted the need for strong leadership at the front line.


In-depth personal interviews with open-ended questions were the primary means of data collection. The study identified and explored six frontline nurses' perceptions of the qualities of nursing leaders through qualitative content analysis. This study was validated by results from the current literature.


The frontline nurses described several common characteristics of exemplary nurse leaders, including: a passion for nursing; a sense of optimism; the ability to form personal connections with their staff; excellent role modelling and mentorship; and the ability to manage crisis while guided by a set of moral principles. All of these characteristics pervade the current literature regarding frontline nurses' perspectives on nurse leaders.


This study identified characteristics of nurse leaders that allowed them to effectively assist and support frontline nurses in the clinical setting.


The findings are of significance to leaders in the health care system and in the nursing profession who are in a position to foster development of leaders to mentor and encourage frontline nurses.
“When the going gets tough, who keeps going?” Depletion sensitivity moderates the ego-depletion effect

Self-control relies on a limited resource that can get depleted, a phenomenon that has been labeled ego-depletion. We argue that individuals may differ in their sensitivity to depleting tasks, and that consequently some people deplete their self-control resource at a faster rate than others. In three studies, we assessed individual differences in depletion sensitivity, and demonstrate that depletion sensitivity moderates ego-depletion effects. The Depletion Sensitivity Scale (DSS) was employed to assess depletion sensitivity. Study 1 employs the DSS to demonstrate that individual differences in sensitivity to ego-depletion exist. Study 2 shows moderate correlations of depletion sensitivity with related self-control concepts, indicating that these scales measure conceptually distinct constructs. Study 3 demonstrates that depletion sensitivity moderates the ego-depletion effect. Specifically, participants who are sensitive to depletion performed worse on a second self-control task, indicating a stronger ego-depletion effect, compared to participants less sensitive to depletion.
Exploring the Impacts of Social Networking Sites on Academic Relations in the University

Social networking sites (SNS) affordances for persistent interaction, collective generation of knowledge, and formation of peer-based clusters for knowledge sharing render them useful for developing constructivist knowledge environments. However, notwithstanding their academic value, these environments are not necessarily insulated from the exercise of academic/ power. Despite a growing corpus of literature on SNS’s capacity to enhance social capital formation, foster trust, and connect interactants in remote locations, there is a dearth of research on how SNS potentially leverages academic /power relations in university settings. Mindful of the unsubstantiated nexus between power relations, knowledge construction, and academic appropriation of SNS, unraveling the impact of SNS on lecturer-student and student-peer power relations in the university can illuminate the understanding of this academic connection/puzzle. This work employs Critical Theory of Technology (CTT) and virtual case study method to explore the influence of SNS use on power relations of lecturers, students, and their peers in a blended (Facebookenhanced) Information Technology course at a middle-sized South African university. The findings demonstrate that academic appropriation of SNS differentially empower academics and students at different times, and students employ various forms of sophisticated authorial language to territorialise power in their interactions with lecturers and peers. Academics and instructional designers are urged to examine different forms of language employed in lecturer-student and student-peer discourses to grasp student learning needs and to foster meaningful, knowledge-rich learning environments.
MOOCs - A Review of the State-of-the-Art

Massive open online courses (MOOCs) have drastically changed the way we learn as well as how we teach. The main aim of MOOCs is to provide new opportunities to a massive number of learners to attend free online courses from anywhere all over the world. MOOCs have unique features that make it an effective technology-enhanced learning (TEL) model in higher education and beyond. The number of academic publications around MOOCs has grown rapidly in the last few years. The purpose of this paper is to compile and analyze the state-of-the-art in MOOC research that has been conducted in the past five years. A template analysis was used to map the conducted research on MOOCs into seven dimensions, namely concept, design, learning theories, case studies, business model, targets groups, and assessment. This classification schema aims at providing a comprehensive overview for readers who are interested in MOOCs to foster a common understanding of key concepts in this emerging field. The paper further suggests new challenges and opportunities for future work in the area of MOOCs that will support communication between researchers as they seek to address these challenges.
Occurrence of sialic acids in healthy humans and different disorders.

Sialic acid (SA), N-acetylated derivatives of neuraminic acid, play a central role in the biomedical functioning of humans. The normal range of total sialic acid (TSA) level in serum/plasma is 1.58-2.22 mmol L-1, the free form of SA only constituting 0.5-3 mumol L-1 and the lipid-associated (LSA) forms 10-50 mumol L-1. Notably, considerably higher amounts of free SA are found in urine than in serum/plasma (approximately 50% of the total SA). In inherited SA storage diseases such as Salla's disease, SA levels are elevated many times over, and their determination during clinical investigation is well established. Furthermore, a number of reports describe elevated SA levels in various other diseases, tentatively suggesting broader clinical utility for SA markers. Increased SA concentrations have been reported during inflammatory processes, probably resulting from increased levels of richly sialylated acute-phase glycoproteins. A connection between increased SA levels and elevated stroke and cardiovascular mortality risk has also been reported. In addition, SA levels are slightly increased in cancer, positively correlating with the degree of metastasis, as well as in alcohol abuse, diabetes, chronic renal failure and chronic glomerulonephritis. Several different mechanisms are assumed to underlie the elevated SA concentrations in these disorders. The apparent non-specificity of SA to a given disease limits the potential clinical usefulness of SA determination. In addition, some non-pathological factors, such as aging, pregnancy and smoking, may cause changes in SA concentrations. The absolute increases in SA levels are also rather small (save those in inherited SA storage disorders); this further limits the clinical potential of SA as a marker. Tentatively, SA markers might serve as adjuncts, when combined with other markers, in disease screening, disease progression follow-up, and in the monitoring of treatment response. To become clinically useful, however, the existing SA determination assays need to be considerably refined to reduce interferences, to be specific for certain SA forms, and to be more easy to use.
Learning to Select Knowledge for Response Generation in Dialog Systems

Generating informative responses in end-toend neural dialogue systems attracts a lot of attention in recent years. Various previous work leverages external knowledge and the dialogue contexts to generate such responses. Nevertheless, few has demonstrated their capability on incorporating the appropriate knowledge in response generation. Motivated by this, we propose a novel open-domain conversation generation model in this paper, which employs the posterior knowledge distribution to guide knowledge selection, therefore generating more appropriate and informative responses in conversations. To the best of our knowledge, we are the first one who utilize the posterior knowledge distribution to facilitate conversation generation. Our experiments on both automatic and human evaluation clearly verify the superior performance of our model over the state-of-the-art baselines.
Ingestion, Indexing and Retrieval of High-Velocity Multidimensional Sensor Data on a Single Node

Sources of multidimensional data are becoming more prevalent, partly due to the rise of the Internet of Things (IoT), and with that the need to ingest and analyze data streams at rates higher than before. Some industrial IoT applications require ingesting millions of records per second, while processing queries on recently ingested and historical data. Unfortunately, existing database systems suited to multidimensional data exhibit low per-node ingestion performance, and even if they can scale horizontally in distributed settings, they require large number of nodes to meet such ingest demands. For this reason, in this paper we evaluate a singlenode multidimensional data store for high-velocity sensor data. Its design centers around a two-level indexing structure, wherein the global index is an in-memory R*-tree and the local indices are serialized kd-trees. This study is confined to records with numerical indexing fields and range queries, and covers ingest throughput, query response time, and storage footprint. We show that the adopted design streamlines data ingestion and offers ingress rates two orders of magnitude higher than those of a selection of open-source database systems, namely Percona Server, SQLite, and Druid. Our prototype also reports query response times comparable to or better than those of Percona Server and Druid, and compares favorably in terms of storage footprint. In addition, we evaluate a kd-tree partitioning based scheme for grouping incoming streamed data records. Compared to a random scheme, this scheme produces less overlap between groups of streamed records, but contrary to what we expected, such reduced overlap does not translate into better query performance. By contrast, the local indices prove much more beneficial to query performance. We believe the experience reported in this paper is valuable to practitioners and researchers alike interested in building database systems for high-velocity multidimensional data.
The induction of pain: an integrative review

The highly disagreeable sensation of pain results from an extraordinarily complex and interactive series of mechanisms integrated at all levels of the neuroaxis, from the periphery, via the dorsal horn to higher cerebral structures. Pain is usually elicited by the activation of specific nociceptors ('nociceptive pain'). However, it may also result from injury to sensory fibres, or from damage to the CNS itself ('neuropathic pain'). Although acute and subchronic, nociceptive pain fulfils a warning role, chronic and/or severe nociceptive and neuropathic pain is maladaptive. Recent years have seen a progressive unravelling of the neuroanatomical circuits and cellular mechanisms underlying the induction of pain. In addition to familiar inflammatory mediators, such as prostaglandins and bradykinin, potentially-important, pronociceptive roles have been proposed for a variety of 'exotic' species, including protons, ATP, cytokines, neurotrophins (growth factors) and nitric oxide. Further, both in the periphery and in the CNS, non-neuronal glial and immunecompetent cells have been shown to play a modulatory role in the response to inflammation and injury, and in processes modifying nociception. In the dorsal horn of the spinal cord, wherein the primary processing of nociceptive information occurs, N-methyl-D-aspartate receptors are activated by glutamate released from nocisponsive afferent fibres. Their activation plays a key role in the induction of neuronal sensitization, a process underlying prolonged painful states. In addition, upon peripheral nerve injury, a reduction of inhibitory interneurone tone in the dorsal horn exacerbates sensitized states and further enhance nociception. As concerns the transfer of nociceptive information to the brain, several pathways other than the classical spinothalamic tract are of importance: for example, the postsynaptic dorsal column pathway. In discussing the roles of supraspinal structures in pain sensation, differences between its 'discriminative-sensory' and 'affective-cognitive' dimensions should be emphasized. The purpose of the present article is to provide a global account of mechanisms involved in the induction of pain. Particular attention is focused on cellular aspects and on the consequences of peripheral nerve injury. In the first part of the review, neuronal pathways for the transmission of nociceptive information from peripheral nerve terminals to the dorsal horn, and therefrom to higher centres, are outlined. This neuronal framework is then exploited for a consideration of peripheral, spinal and supraspinal mechanisms involved in the induction of pain by stimulation of peripheral nociceptors, by peripheral nerve injury and by damage to the CNS itself. Finally, a hypothesis is forwarded that neurotrophins may play an important role in central, adaptive mechanisms modulating nociception. An improved understanding of the origins of pain should facilitate the development of novel strategies for its more effective treatment.
Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication

This paper presents the findings of two studies that replicate previous work by Fred Davis on the subject of perceived usefulness, ease of use, and usage of information technology. The two studies focus on evaluating the psychometric properties of the ease of use and usefulness scales, while examining the relationship between ease of use, usefulness, and system usage. Study 1 provides a strong assessment of the convergent validity of the two scales by examining heterogeneous user groups dealing with heterogeneous implementations of messaging technology. In addition, because one might expect users to share similar perspectives about voice and electronic mail, the study also represents a strong test of discriminant validity. In this study a total of 118 respondents from 10 different organizations were surveyed for their attitudes toward two messaging technologies: voice and electronic mail. Study 2 complements the approach taken in Study 1 by focusing on the ability to demonstrate discriminant validity. Three popular software applications (WordPerfect, Lotus 1-2-3, and Harvard Graphics) were examined based on the expectation that they would all be rated highly on both scales. In this study a total of 73 users rated the three packages in terms of ease of use and usefulness. The results of the studies demonstrate reliable and valid scales for measurement of perceived ease of use and usefulness. In addition, the paper tests the relationships between ease of use, usefulness, and usage using structural equation modelling. The results of this model are consistent with previous research for Study 1, suggesting that usefulness is an important determinant of system use. For Study 2 the results are somewhat mixed, but indicate the importance of both ease of use and usefulness. Differences in conditions of usage are explored to explain these findings.
The Knowledge-Learning-Instruction ( KLI ) Framework : Toward Bridging the Science-Practice Chasm to Enhance Robust Student Learning

The volume of research on learning and instruction is enormous. Yet progress in improving educational outcomes has been slow at best. Many learning science results have not been translated into general practice and it appears that most that have been fielded have not yielded significant results in randomized control trials. Addressing the chasm between learning science and educational practice will require massive efforts from many constituencies, but one of these efforts is to develop a theoretical framework that permits a more systematic accumulation of the relevant research base.
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning

Current methods for knowledge graph (KG) representation learning focus solely on the structure of the KG and do not exploit any kind of external information, such as visual and linguistic information corresponding to the KG entities. In this paper, we propose a multimodal translation-based approach that defines the energy of a KG triple as the sum of sub-energy functions that leverage both multimodal (visual and linguistic) and structural KG representations. Next, a ranking-based loss is minimized using a simple neural network architecture. Moreover, we introduce a new large-scale dataset for multimodal KG representation learning. We compared the performance of our approach to other baselines on two standard tasks, namely knowledge graph completion and triple classification, using our as well as the WN9-IMG dataset.1 The results demonstrate that our approach outperforms all baselines on both tasks and datasets.
Image Forensics: Detecting duplication of scientific images with manipulation-invariant image similarity

Manipulation and re-use of images in scientific publications is a concerning problem that currently lacks a scalable solution. Current tools for detecting image duplication are mostly manual or semi-automated, despite the availability of an overwhelming target dataset for a learning-based approach. This paper addresses the problem of determining if, given two images, one is a manipulated version of the other by means of copy, rotation, translation, scale, perspective transform, histogram adjustment, or partial erasing. We propose a data-driven solution based on a 3-branch Siamese Convolutional Neural Network. The ConvNet model is trained to map images into a 128-dimensional space, where the Euclidean distance between duplicate images is smaller than or equal to 1, and the distance between unique images is greater than 1. Our results suggest that such an approach has the potential to improve surveillance of the published and in-peer-review literature for image manipulation.
Intellectual capital - defining key performance indicators for organizational knowledge assets

Measuring intellectual capital is on the agenda of most 21st century organisations. This paper takes a knowledge-based view of the firm and discusses the importance of measuring organizational knowledge assets. Knowledge assets underpin capabilities and core competencies of any organisation. Therefore, they play a key strategic role and need to be measured. This paper reviews the existing approaches for measuring knowledge based assets and then introduces the knowledge asset map which integrates existing approaches in order to achieve comprehensiveness. The paper then introduces the knowledge asset dashboard to clarify the important actor/infrastructure relationship, which elucidates the dynamic nature of these assets. Finally, the paper suggests to visualise the value pathways of knowledge assets before designing strategic key performance indicators which can then be used to test the assumed causal relationships. This will enable organisations to manage and report these key value drivers in today’s economy.  In the last decade management literature has paid significant attention to the role of knowledge for global competitiveness in the 21st century. It is recognised as a durable and more sustainable strategic resource to acquire and maintain competitive advantages (Barney, 1991a; Drucker, 1988; Grant, 1991a). Today’s business world is characterised by phenomena such as e-business, globalisation, higher degrees of competitiveness, fast evolution of new technology, rapidly changing client demands, as well as changing economic and political structures. In this new context companies need to develop clearly defined strategies that will give them a competitive advantage (Porter, 2001; Barney, 1991a). For this, organisations have to understand which capabilities they need in order to gain and maintain this competitive advantage (Barney, 1991a; Prahalad and Hamel, 1990). Organizational capabilities are based on knowledge. Thus, knowledge is a resource that forms the foundation of the company’s capabilities. Capabilities combine to The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at www.emeraldinsight.com/researchregister www.emeraldinsight.com/1463-7154.htm The authors would like to thank, Göran Roos, Steven Pike, Oliver Gupta, as well as the two anonymous reviewers for their valuable comments which helped us to improve this paper.
Leadership Style and Staff Retention in Organisations

Organisations in all sectors are operating in highly competitive environment which requires that these institutions retain their core employees in order to gain and retain competitive advantage. Because of globalisation and new methods of management, different organisations have experienced competition both locally and globally in terms of market and staff. The role of leaders in employee retention is critical since their leadership styles impact directly on the employees’ feelings about the organization. The paper sought to find out the influence of leadership style on staff retention in organisations. The s tudy was purely based on l i tera ture review. From the review of several empirical studies it was established that leadership style significantly influences intention to leave of staff and hence there is need to embrace a leadership style that promotes staff retention.
Convolutional Deep Belief Networks on CIFAR-10

We describe how to train a two-layer convolutional Deep Belief Network (DBN) on the 1.6 million tiny images dataset. When training a convolutional DBN, one must decide what to do with the edge pixels of teh images. As the pixels near the edge of an image contribute to the fewest convolutional lter outputs, the model may see it t to tailor its few convolutional lters to better model the edge pixels. This is undesirable becaue it usually comes at the expense of a good model for the interior parts of the image. We investigate several ways of dealing with the edge pixels when training a convolutional DBN. Using a combination of locally-connected convolutional units and globally-connected units, as well as a few tricks to reduce the e ects of over tting, we achieve state-of-the-art performance in the classi cation task of the CIFAR-10 subset of the tiny images dataset.