Miracle-dz/UniFAR-FTUs huggingface dataset:
Content trends in sustainable business education: an analysis of introductory courses in the USA

Purpose This study aims to identify the content in introductory business sustainability courses in the USA to determine the most frequently assigned reading material and its sustainability orientation. Design/methodology/approach In total, 81 introductory sustainable business course syllabi reading lists were analyzed from 51 US colleges and universities. The study utilized frequency counts for authors and readings and R analysis of key words to classify readings along the sustainability spectrum. Findings The study reveals the most frequently assigned authors and readings in US sustainable business courses (by program type) and places them along the sustainability spectrum from weak to strong. In total, 55 per cent of the top readings assigned in the sample advocate a weak sustainability paradigm, and 29 per cent of the top readings advocate a strong sustainability paradigm. Research limitations/implications This study focused on reading lists of introductory courses in the USA; cases, videos and supplemental materials were excluded, and the study does not analyze non-US courses. Practical implications The findings of this study can inform instructors of the most commonly assigned authors and readings and identify readings that align with weak sustainability and strong sustainability. Instructors are now able to select sustainable business readings consistent with peers and which advance a weak or strong sustainability orientation. Originality/value This is the first research to identify the most commonly assigned authors and readings to aid in course planning. This is also the first research to guide instructors in identifying which readings represent weak versus strong sustainability.
Analysis of Electronic Health Record Implementation and Usage in Texas Acute Care Hospitals

Despite a consensus that the use of health information technology should lead to more efficient, safer, and higher-quality care, there are no reliable estimates of the prevalence of adoption and physician usage of electronic health records (EHRs). Data from the American Hospital Association was examined for the presence of specific electronic-recorded functionalities. We also examined the differences in implementation and usage of EHRs to specific hospital characteristics.
Connectivity Practices and Activity of Greek Political Blogs

This paper uses Social Network Analysis indexes to study Greek political blogs. The indexes describe bloggers’ community recommendations, centrality and bloggers’ attempt to form spheres of influence. Five Social Network Analysis indexes are used: incoming links, normalized betweenness, outgoing links, number of 1-cliques a blog belongs to, and size of blog’s ego-network. By recording 127 Greek political blogs, the paper finds that there are two distinct blog performance properties regarding connectivity: Only a few blogs serve as authority blogs having many incoming links and centrality, while a few others try to expand their influence territory by having many outgoing links and forming larger 1-cliques and ego-networks. Next, the paper associates the proposed indexes with blogs’ and users’ community activity. Authority blogs present high blog activity and users’ community activity, as well. These are recorded by large numbers of posts and comments to the blog posts, respectively. It is shown that blogs, which strive to expand their network by using many outgoing links are more likely to link to the authority blogs. Content analysis reveals that authority blogs provide news and information and promote discussion to a much higher degree compared to the overall Greek political blogosphere.
Stable roommates and geometric spanners

In this paper we devise a new geometric spanner based on a generalization of the known Stable Roommates algorithm. This spanner is on the \path" between the Yao graph and Yao-Yao graph, and as such it can be computed in a distributed manner and has a bounded degree as the Yao-Yao graph, while on the other hand, it has the same stretch factor bound as the Yao graph.
Emergence of Norms in a Society of Heterogeneous Agents Influenced by the Rules of Cellular Automata Techniques

This paper deals with study Emergence of Norms in a Society of Heterogeneous Agents Influenced by the Rules of Cellular Automata Techniques.To study the phenomenon of emergence of social norms, we have assumed that the interactions between the agents are private, i.e.; not observable to the other agents not involved in the interactions. We consider a population of agents, where, in each interaction each agent is paired with another agent selected randomly from its neighborhood or from the population in a nonuniform manner. Each agent is learning concurrently over repeated interactions with selected opponents from the society. An agent learns a policy to play the game from repeated interactions with multiple agents. We are particularly interested in finding out if the entire population learns to converge to a consistent norm when multiple action combinations yield the same optimal payoff. In addition to this, we also would like to explore the effects of heterogeneous populations where different agents may be using cellular automata techniques.
Individual and occupational determinants of low back pain according to various definitions of low back pain

OBJECTIVES To test associations between non-specific low back pain and several risk factors when definitions of low back pain vary. DESIGN/SETTING/PARTICIPANTS A cross sectional study was set up in 1991, 725 workers from four occupational sectors answered a self administrated questionnaire including the Nordic questionnaire and questions about intensity of pain and individual and occupational factors. MAIN RESULTS Prevalence of low back pain varied from 8% to 45% according to the definition used. Psychosomatic problems, bending or carrying loads were often associated to low back pain, whereas other risk factors were related to some specific dimensions of the disorder. CONCLUSIONS Risk factors of low back pain vary with the definition. This could explain inconsistencies found in literature reviews. To be able to compare data, it seems important to be precise what definition is used and to use comparable questionnaires.
Multiple Imputation for Incomplete Data With Semicontinuous Variables

We consider the application of multiple imputation to data containing not only partially missing categorical and continuous variables, but also partially missing 'semicontinuous' variables (variables that take on a single discrete value with positive probability but are otherwise continuously distributed). As an imputation model for data sets of this type, we introduce an extension of the standard general location model proposed by Olkin and Tate; our extension, the blocked general location model, provides a robust and general strategy for handling partially observed semicontinuous variables. In particular, we incorporate a two-level model for the semicontinuous variables into the general location model. The first level models the probability that the semicontinuous variable takes on its point mass value, and the second level models the distribution of the variable given that it is not at its point mass. In addition, we introduce EM and data augmentation algorithms for the blocked general location model with missing data; these can be used to generate imputations under the proposed model and have been implemented in publicly available software. We illustrate our model and computational methods via a simulation study and an analysis of a survey of Massachusetts Megabucks Lottery winners.
Increased Binding Activity at an Antioxidant-Responsive Element in the Metallothionein-1 Promoter and Rapid Induction of Metallothionein-1 and -2 in Response to Cerebral Ischemia and Reperfusion

Metallothioneins (MTs) are cysteine-rich metal-binding proteins that are potentially involved in zinc homeostasis and free radical scavenging. The expression pattern of MT-1 and the binding activity of various MT-1 promoter elements were investigated after mild focal cerebral ischemia in the rat. Transient focal ischemia was induced by occluding both common carotid arteries and the right middle cerebral artery for 30 min. By the use of real-time quantitative PCR, a 10-fold increase in MT-1 and -2 mRNA levels was found in the cortex 24 hr after reperfusion. In situhybridization and immunocytochemistry showed a rapid increase in MT-1 and -2 mRNA and MT protein in endothelial cells of microvessels at 6 hr after reperfusion, followed by an increased expression in astrocytes of the infarcted cortex at 24 hr after reperfusion. The early increase in MT expression preceded an increase in cerebral edema measured with T2-weighted magnetic resonance imaging. Gel shift assays were performed on nuclear extracts prepared from cortices before and at 6 and 24 hr after reperfusion. Increased binding activity was found at an antioxidant/electrophilic response element (ARE) sequence in the MT-1 promoter at 6 hr with a lower and variable binding activity at 24 hr after reperfusion. Constitutive binding activity was found for Sp1 and a metal response element in the MT-1 promoter that did not increase after ischemia and reperfusion. This study suggests a role of ARE-binding proteins in inducing cerebral MT-1 expression and implicates MT-1 as one of the early detoxifying genes in an endogenous defense response to cerebral ischemia and reperfusion.
INEX 2012 Benchmark a Semantic Space for Tweets Contextualization

In this paper, we present a method of tweet contextualization by using a semantic space to extend the tweet vocabulary. This method is evaluated on the tweet contextualization benchmark. Contextualiza- tion is build with the sentences from English Wikipedia. The context is obtained by querying a baseline system of summary. The query is made with words from a semantic space that is estimated via a latent dirichlet allocation (LDA) algorithm. Our experiment demonstrate the eectiveness of the proposal.
Non Adaptive Second-Order Generalized Integrator for Identification of a Biased Sinusoidal Signal

This note presents a new algorithm that is designed to identify the frequency, magnitude, phase and offset of a biased sinusoidal signal. The structure of the algorithm includes an orthogonal system generator based on a second-order generalized integrator. The proposed strategy has the advantages of a fast and accurate signal reconstruction capability and a good rejection to noise.
Computational Models for Chinese Selectional Preferences Induction

Selectional preference (SP) is an important kind of semantic knowledge. It can be used in various natural language processing tasks, including metaphor computing, lexicon building, syntactic structure disambiguation, word sense disambiguation, semantic role labeling, anaphora resolution, etc. This paper presents and compares two computational models for Chinese SP induction, a HowNet-based Selectional Association model (SA) and a Latent Dirichlet Allocation model (LDA). Pseudo-disambiguation experiments show that LDA model outperforms SA model in both coverage and accuracy. However, LDA model needs to improve the interpretability of its induced SPs compared with SA model.
Fax clustering algorithm based on adaptive ant colony optimize

The fax clustering is that the same or similar faxes will be clustered into one group in mass faxes. In order to improve accuracy of clustering, a fax clustering algorithm based on adaptive ant colony optimization was proposed in this paper. The algorithm simulates ant feeding theory, and improves the coefficient of pheromone updating, and avoids falling into local optimum. The experimental results show that the algorithm has good robustness. The effect of clustering is improved obviously.
Energy efficient and congestion aware routing algorithms for Wireless Sensor Networks connected as Hypercube

Wireless Sensor Networks (WSN) have been noticed and researched nowadays. These new type of networks have unique characteristics. WSNs have different limitations such as computational power, storage capacity, energy source and etc. The main constraint of wireless sensor networks is energy. The energy source of sensor nodes is limited and not rechargeable. Therefore, optimizing consumption of energy is essential for WSNs protocols. Hypercube topology is very popular in distributed environments. In last few years, many different topology specific routing algorithms have been designed for wireless sensor networks, but to the best of our knowledge, none of them consider hypercube as their topology. In this paper, different routing algorithms are proposed for WSN with hypercube topology. The performance of all proposed routing algorithms is evaluated, and the best algorithm which can provide more fairness is introduced. Simulation results which are represented in last section of this paper are the base of our conclusions. The main goal of this paper is to find a fair energy aware routing algorithm for wireless sensor network connected as hypercube.
Fusion framework for moving-object classification

Perceiving the environment is a fundamental task for Advance Driver Assistant Systems. While simultaneous localization and mapping represents the static part of the environment, detection and tracking of moving objects aims at identifying the dynamic part. Knowing the class of the moving objects surrounding the vehicle is a very useful information to correctly reason, decide and act according to each class of object, e.g. car, truck, pedestrian, bike, etc. Active and passive sensors provide useful information to classify certain kind of objects, but perform poorly for others. In this paper we present a generic fusion framework based on Dempster-Shafer theory to represent and combine evidence from several sources. We apply the proposed method to the problem of moving object classification. The method combines information from several lists of moving objects provided by different sensor-based object detectors. The fusion approach includes uncertainty from the reliability of the sensors and their precision to classify specific types of objects. The proposed approach takes into account the instantaneous information at current time and combines it with fused information from previous times. Several experiments were conducted in highway and urban scenarios using a vehicle demonstrator from the interactIVe European project. The obtained results show improvements in the combined classification compared with individual class hypothesis from the individual detector modules.
Generating power-hungry test programs for power-aware validation of pipelined processors

As CMOS technology scaled to nanometer regimes (100nm and below) power dissipation and power density have become major design constraints. The power consumed by active devices is converted into heat, which in turn increases the substrate temperature. Working at high temperatures may affect several figures of merit (e.g., frequency and leakage power), as well as the reliability of the entire system. Therefore, considering power consumption during test and design validation procedures has become a testing due for modern SoCs. While a huge range of techniques focus on low-power test, we consider the other side of the problem: how to maximize the power absorbed by a processor core (while still remaining into legal operations) in order to test the robustness, and/or validate the functionality of the surrounding components, and the core itself, under high power operating conditions. In this paper, we first demonstrate the actual difficulty of assembling power-hungry test programs on pipelined processors. Second, we propose an automated methodology, based on an automatic optimizer, that allows a push-bottom generation of high-power consuming programs under user-defined constraints. The proposed flow is validated using an open-source pipelined processor mapped into an industrial 65nm technology
Strict SLAs for Operational Business Intelligence

Today, SLAs for SaaS business applications usually lack stringent service level objectives and significant penalties. Moreover, Operational Business Intelligence features of modern business applications, like analytic dashboards, result in mixed workloads which make it even more difficult to predict execution times accurately due to resource contention. In contrast to the traditional three-tier architecture, an architecture for SaaS business applications should combine application and database layer to allow for processing business transactions and queries according to a queuing approach which enables strict SLAs with stringent response time and throughput guarantees. With stricter SLAs it would be easier to compare different cloud offerings with on-premise solutions and thus cloud computing could become more attractive for potential customers.
Common structural motif identification in genomic sequences

Identification of structural and sequence motifs in genomic sequences is gaining much attention now a days. Ribonucleic acid or RNA is one of the important biomolecule whose secondary structure defines its functionality. Soft computing techniques like genetic programming have been used for motif identification. In this paper, we propose a method for identifying common structural motifs in a set of RNA sequences by particle swarm optimization technique. The common motif identification finds its application in drug design. To prove the correctness of our approach, we tested it on data sets previously used to find common structural motif using genetic programming approaches. Several experiments were conducted using Signal Recognition Particle (SRP), Ferritin Iron Response Element (IRE) and microRNA sequences. Our results are comparable to the latest genetic programming approach. Some of the results indicate that our method out performs to most of the genetic programming approaches.
Random matrices in Magnetic Resonance signal processing

Many important problems in the processing of Magnetic Resonance data can be reduced to well known ill-posed inverse problems. MR relaxometry, spectroscopy and MR image formation problems can be formulated respectively as the numerical inversion of Laplace transform, the modal analysis problem and the truncated trigonometric moment problem. A unified framework to efficiently solve these problems is provided by considering random Pade’ approximants to the Z-transform s(z) of the measured signal. It turns out that the singularities of s(z) on the complex plane are the key quantities to make inference. Their location can be estimated from the logarithmic potential of the condensed density of the Pade’ poles or, equivalently, of the eigenvalues of a random pencil of Hankel matrices. Several alternatives to compute these quantities are discussed and compared by numerical examples.
Performance analysis of an application-level cooperative control protocol

This paper proposes the modeling and the performance analysis of a high-level control protocol - COCOP, which enables cooperative groups of clients to control a shared server delivering time-dependant data services. Several synchronous multimedia systems such as media on-demand, web casting, and networks of real/virtual sensors, can beneficially exploit COCOP to furnish cooperative control sessions. The protocol can be mapped onto a multicast transport support based either on IP-multicast or on an application level multicast infrastructure. An event-driven simulation framework is purposely customized and exploited to analyze the protocol performance and thus the dynamics of a cooperative control session over simple, yet representative topologies.
A metabolic link to skeletal muscle wasting and regeneration

Due to its essential role in movement, insulating the internal organs, generating heat to maintain core body temperature, and acting as a major energy storage depot, any impairment to skeletal muscle structure and function may lead to an increase in both morbidity and mortality. In the context of skeletal muscle, altered metabolism is directly associated with numerous pathologies and disorders, including diabetes, and obesity, while many skeletal muscle pathologies have secondary changes in metabolism, including cancer cachexia, sarcopenia and the muscular dystrophies. Furthermore, the importance of cellular metabolism in the regulation of skeletal muscle stem cells is beginning to receive significant attention. Thus, it is clear that skeletal muscle metabolism is intricately linked to the regulation of skeletal muscle mass and regeneration. The aim of this review is to discuss some of the recent findings linking a change in metabolism to changes in skeletal muscle mass, as well as describing some of the recent studies in developmental, cancer and stem-cell biology that have identified a role for cellular metabolism in the regulation of stem cell function, a process termed “metabolic reprogramming. ”
Institutional Knowledge at Singapore Management University Research Collection Lee Kong Chian School Of Business Lee Kong Chian School of Business 2014 Mindfulness in Organizations

This chapter discusses the practice of mindfulness in organizations. In the first section we describe the growing interest in mindfulness training among organizations and discuss possible reasons for this development. We then review work on the definition and concept of mindfulness as they have been developed in psychology and organizational scholarship. In the second section, we discuss different forms of mindfulness practice in organizations, including Mindfulness-Based Stress Reduction (MBSR) as the most prominent. The third section reviews empirical evidence on the effects of mindfulness on work-related outcomes and processes such as employee performance, employee wellbeing, leadership, and ethical decision making. We then discuss in more detail a recently developed self-administered mindfulness training program as it contains some unique and interesting features relevant to mindfulness intervention studies. In the fifth section, we present the results from qualitative interviews we conducted with participants of a corporate mindfulness training program. We conclude that the study and application of mindfulness in the workplace offers many promising directions; however, much more research is needed to create a basis of evidence for successful mindfulness training programs. Furthermore, a deeper understanding of the (intended and unintended) consequences, mediating mechanisms, moderating factors, and boundary conditions of mindfulness would benefit organizational scholarship.
University of Birmingham Physical simulation for monocular 3D model based tracking

—The problem of model-based object tracking in three dimensions is addressed. Most previous work on tracking assumes simple motion models, and consequently tracking typically fails in a variety of situations. Our insight is that incorporating physics models of object behaviour improves tracking performance in these cases. In particular it allows us to handle tracking in the face of rigid body interactions where there is also occlusion and fast object motion. We show how to incorporate rigid body physics simulation into a particle ﬁlter. We present two methods for this based on pose and force noise. The improvements are tested on four videos of a robot pushing an object, and results indicate that our approach performs considerably better than a plain particle ﬁlter tracker, with the force noise method producing the best results over the range of test videos.