Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategy as a Disciplinary Technology – Discursive Engineering in the Newspaper World 2000 / 5

Most scholars of strategy reproduce the inherent functionalist assumptions in that research by mainly locating themselves inside the mainstream orientation. Strategy is consequently often treated as an object or thing-like phenomena. In contrast, this paper shall, in line with a critical stream of research, treat strategy as discourse with certain effects on identity and organizations. Based on a critically inspired empirical study of changes in three Swedish newspaper organizations it is argued that the strategy discourse can be seen as a disciplinary technology with productive effects on identity and organizations. Much of the critical research on strategy however, tends to focus on the instrumental orientation that the strategy discourse give rise to and dismiss the distinctive disciplinary impact of strategy on an organizational level. In contrast, this paper exhibits not only the productive effects of the strategy discourse on managerial identities but also show that strategy as a disciplinary technology in practice created conditions for organizational development and transformation of power relations between higher level managers and editors in the newspaper industry. It is suggested that the strategy discourse defined new realities, new rituals, and a new professional subject who redefined the government of the newspaper companies from a single-business to a multi-business orientation. This development also redefined the status of the editorial, subordinating that to the predominantly rational reasoning of strategy.
Predictive modeling based on proportional integral derivative neural networks and quantum computation

Quantum neural networks (QNN) is a burgeoning new field built upon the combination of classical neural networks and quantum computations, which has many problems needed to solve. The predictive model of QNN is an issue that must be settled to develop QNN based on proportional integral derivative neural networks and quantum computation, which can be so called generalized quantum neural networks (GQNN). Firstly, we describe the theory of quantum computation and neural networks. Secondly, it can realize the algorithm of prediction to construct the modeling of generalized quantum neural networks for those complexity nonlinear systems. Finally, using an example explains the model of generalized quantum neural networks. The computational results shows that GQNN is more effective than conventional neural networks.
Development of a new construct of employee well-being: A qualitative study

There is a growing interest for positive psychology at organizational management. Companies have increasingly been valuing the well-being of their employees, and seeking the impact to their work performance. Nonetheless, present models are limited in explaining employee well-being and in predicting performance; therefore a broader, multi-dimensional model is needed. This study presents the qualitative part of a larger research analysis, proposing a new and multi-dimensional well-being construct to explain employee well-being and to predict individual work performance. Through a series of literature review and focus group research analysis, it has determined 20 out of 38 positive psychology and work-related elements to predict performance. These elements set the ground for a future quantitative research phase, measuring the mediating effect of employee‟s well-being on the human resources practices and work performance relationship. Based on extensive body of research on theory of well-being, first major contribution of this study is the proposal of a new well-being construct to predict employees‟ performance. Second, it offers the possibility to determine the significance of HR practices on employees‟ well-being and performance. Results, future research and implications have been discussed.
Protein kinases as switches for the function of upstream stimulatory factors: implications for tissue injury and cancer

The upstream stimulatory factors (USFs) are regulators of important cellular processes. Both USF1 and USF2 are supposed to have major roles in metabolism, tissue protection and tumor development. However, the knowledge about the mechanisms that control the function of USFs, in particular in tissue protection and cancer, is limited. Phosphorylation is a versatile tool to regulate protein functions. Thereby, phosphorylation can positively or negatively affect different aspects of transcription factor function including protein stability, protein–protein interaction, cellular localization, or DNA binding. The present review aims to summarize the current knowledge about the regulation of USFs by direct phosphorylation and the consequences for USF functions in tissue protection and cancer.
Is It Just Me?: The Different Consequences of Personal and Group Relative Deprivation

Two professors discover that their salary is significantly lower than the salaries for faculty members at institutions with less stringent job requirements. One professor immediately joins the faculty union and actively participates in rallies and strike actions. The other professor redoubles her efforts to receive grants and merit pay. Why might these professors react so differently to the same disadvantage? According to relative deprivation theory, the same disadvantage framed in different ways will lead to different reactions. If the professor views herself as a representative faculty member deprived in comparison to faculty at other institutions (group relative deprivation), she should be motivated to support collective action. However, if the professor views herself as a unique individual deprived in comparison to other individual faculty (personal relative deprivation), she should be motivated to pursue individualistic opportunities. Unfortunately, the initial promise of relative deprivation (RD) as an explanation for collective behavior has not been fulfilled. Some investigations strongly support RD models (e.g., Abrams, 1990; Pettigrew, 1978; Runciman, 1966; Vanneman & Pettigrew, 1972; Walker & Mann, 1987), but others do not (e.g., Gaskell & Smith, 1984; Thompson, 1989). In response to these inconsistencies, previous literature reviews have sought to clarify the theoretical antecedents and components of the concept (Crosby, 1976; Martin, 1986; Walker & Pettigrew, 1984), or to dismiss its value completely (e.g., Finkel & Rule, 1986; Gurney & Tierney, 1982). However, dismissing the usefulness of RD may be premature.
Robust generalized low rank approximations of matrices for video denoising

Low rank matrix algorithm has attracted widely attention since it was put forward. There are many algorithms proposed to improve it. Most methods take each sample as a column, but Robust Generalized Low Rank Approximations of Matrices(RGLRAM) treats each sample as a matrix, thus we can find the low rank approximations on a collection of matrices not just a single matrix. RGLRAM are not only robust to small Gaussian noise but also to large sparse noise. In these cases, we extend it to video approximation. The video can be treated as a collection of images. Each frame of the video is divided into blocks with equal size according to their coordinates. After the operation of each group, the reconstructed video could be got via splicing the blocks based on these coordinates. Experimental results show that method proposed in this paper is effective.
Classification of kidney lesions using Gabor wavelet texture features

In the present work, characterization of primary benign and primary malignant focal kidney lesions has been carried out using Gabor wavelet texture features. The angiomyolipomas (AMLs) i.e., primary benign kidney lesions and renal cell carcinomas (RCCs) i.e., primary malignant kidney lesions have been considered in the present study. The Gabor features have been computed from region of interest (ROIs) extracted from each image. The total dataset consists of 101ROIs (23 AML ROIs and 78 RCC ROIs) that have been extracted from within the lesions (ignoring the renal sinus part). For the classification task, support vector machine (SVM) has been used. It is observed that the proposed system yields an overall classification accuracy of 90.0 %for differential analysis between primary benign and primary malignant kidney lesions.
Perturbations of Dipole Decay Dynamics Induced by Plasmonic Nano-Antennas — A Study within the Discrete Dipole Approximation

We report a discrete dipole approximation approach to analyse the perturbations induced by silver nano-particles on the decay dynamics of a point-like emitter placed in their proximity. Due to the excitation of localized surface plasmons, metallic nano-particles behave like optical antennas and are able to convert localized fields into free-propagating optical radiation, and vice versa. Field localization and enhancement induce strong changes on the decay dynamics of dipoles located in the perturbed electromagnetic environment, and these can be faithfully quantified within the framework of classical electromagnetism in terms of total, radiative and non-radiative decay rates. The method is tested on benchmark cases, i.e., nanospheres and nano-shells, and it is then applied to analytically-unsolvable shapes such as sharp nano-cones and oxide-covered small nano-antennas. Numerical results reveal 105-order enhancements in the total decay rate of the dipole when located very near to the sharp tip, both with and without a thin Ag2O layer. Moreover, the counter-intuitive behaviour of the cone response in relation to the distance between the metal and the source of the radiation is discussed. Applications span from strong coupling studies to time-resolved fluorescence spectroscopy.
The Immunomodulatory Effects of Plant Extracts and Plant Secondary Metabolites on Chronic Neuroinflammation and Cognitive Aging: A Mechanistic and Empirical Review

Advances in healthcare have considerably improved the life expectancy of the human population over the last century and this has brought about new challenges. As we live longer the capacity for cognitive aging increases. Consequently, it has been noted that decline in cognitive performance in the elderly in domains of reasoning, problem solving skills, attention, processing speed, working memory and episodic memory is a significant societal problem. Despite the enormity of this issue there are relatively few interventions for cognitive aging. This may be due to our current state of knowledge on biological factors that underpin cognitive aging. One of the biological contributors to cognitive aging is chronic neuroinflammation. This review will provide an overview of the peripheral and central mechanisms involved in chronic neuroinflammation and how neuroinflammation may be related to age-associated cognitive decline. Plant based extracts including herbal and nutritional supplements with anti-inflammatory properties will be examined in relation to their utility in treating age-related cognitive decline. Plant based extracts in particular offer interesting pharmacological properties that may be quickly utilized to prevent cognitive aging.
Building An Integrated Formal Ontology for Semantic Interoperability in the Fishery Domain

This paper outlines a project (involving FAO, SIFAR, and CNR) aiming at building a formal ontology in the fishery domain. The ontology will support semantic interoperability among existing fishery information systems and will enhance information extraction and text marking, envisaging a fishery semantic web. The ontology is being built through the conceptual integration of existing fishery terminologies, thesauri, reference tables, and topic trees.
First Semester College Students’ Definitions of and Expectations for Engaging in Hookups

Our study was guided by a developmental lens (emerging adulthood) and the theory of symbolic interaction as we attended to the meanings and definitions that contextualize expectations for engaging in hookups while attending college. Using a directed approach to qualitative content analysis, we examined first semester college men’s and women’s (N = 253) responses to a series of open-ended questions focused on definitions of and expectations for hooking up. Six definitional variations of hooking up emerged from these responses: (a) “sex” left undefined, (b) making out, (c) a range of sexual activities, (d) “sex” defined as intercourse, (e) the “all but sex” continuum, and (f) hanging out or going on a date. Chi-square analyses indicated no proportional differences in these definitions based on participant gender. Characteristics of hookup partners were also identified in participant responses. Whereas a greater proportion of men identified physical appearance as a necessary attribute of a potential hookup partner, a greater proportion of women discussed (a) familiarity with a hookup partner and (b) hookups as occurring between noncommitted individuals. Also, a greater proportion of men than women indicated expectations to hookup while attending college. Implications for research, relationship and sexual health education, and policy are discussed.
Social behavior in bacterial foraging optimization algorithm for image registration

In this paper a strategy to enhance performances in Bacterial Foraging Optimization Algorithm (BFOA) based Image Registration (IR) is proposed. The goal of the IR optimization problem is to determine a geometric transform which maximizes the similarity to a model image when is applied to a source image. In each step of standard BFOA, the bacteria move in random directions. Using the social behavior of bacteria colony, the proposed method directs the movement towards bacteria with lower values of cost function. This method allows reducing the number of iterations required to obtain the optimal geometric transform and offers better approximations.
Concurrent realization of the multiply-by-7 Elliptic Curve Scalar Multiplication algorithm

This paper investigates the multiply-by-7 Elliptic Curve (EC) point P Scalar Multiplication algorithm for reduced computational complexity and enhanced inherent parallel property based on the Area-Time (AT2) metric. The findings were compared with those obtained when the algorithm was again realized on the Jacobian projective coordinate and the Non-Adjacent Form (NAF). The investigation revealed 27% and 8% computational complexity reductions over the Jacobian and NAF realizations. The algorithm also presents the best AT2 value.
IMPLEMENTING DISTRIBUTED SERVICES WITH MOBILE CODE: THE CASE OF THE MESSENGER ENVIRONMENT

Mobile code is being investigated by many researchers as an alternative for the implementation of more flexible distributed applications. A number of programming paradigms based on mobile code have been developed. This paper focuses on implementation of distributed services using the messenger paradigm. With the messenger approach which combines the potential of mobile code, runtime code generation and code interpretation, it is no longer necessary t o know the service interface at development time in order to implement a client that can interact appropriately with the service. The service is published with its operational inte rface. The client interprets, at run time, the service’s oper ational interface and collects from it the information requi red to interact appropriately with the service.
Handwritten Pattern Recognition Using Kohonen Neural Network Based on Pixel Character

Handwriting is the human way in communicating each other using written media. By the advancement in technology and development of science, there are a lot of changes of technology in terms of communication with computer through handwriting. Therefore, it is needed computer able to receive input in the form of handwriting data and able to recognize the handwriting input. Therefore, this research focuses on handwritten character recognition using Kohonen neural network. The purpose of this research is to find handwriting recognition algorithm which can receive handwriting input and recognize handwritten character directly inputted in computer using Kohonen neural network. This method studies the distribution of a set of patterns without any class information. The basic idea of this technique is understood from how human brain stores images/patterns that have been recognized through eyes, and then able to reveal the images/patterns back. This research has been successful in developing an application to recognize handwritten characters using Kohonen neural network method, and it has been tested. The application is personal computer based and using a canvas as input media. The recognition process consist of 3 stages layer: Input layer, Training Layer and Hidden Layer. The Kohonen neural network method on handwritten character recognition application has good similarity level of character patterns in character mapping process.
Supporting Organisational learning with Technologies for Electronic Documents

Supporting the opportunistic learning that often occurs in organizations requires other approaches than traditional document centric knowledge management techniques and intelligent learning environments. Such opportunistic learning takes place in situations where (1) the learner is engaged in tasks/activities where there are no known solutions a priori, (2) where it is not easy to evaluate learning outcomes and (3) where the learning environment is dynamic: new information, processes, people are continuously evolving. This paper discusses this type of learning and describes how technologies for electronic documents could be used to augment an existing corporate memory framework that captures lessons learnt (L2Corp [Alem 98]) with a project memory and a tool for finding people. We argue that an intelligent organisational learning system needs to capture and represent the lessons learnt, needs to capture in a project memory the documents created and the electronic discussions between people, and should provide a means for finding experts within the organization.
Transcriptomic and phenotypic analysis of paralogous spx gene function in Bacillus anthracis Sterne

Spx of Bacillus subtilis is a redox‐sensitive protein, which, under disulfide stress, interacts with RNA polymerase to activate genes required for maintaining thiol homeostasis. Spx orthologs are highly conserved among low %GC Gram‐positive bacteria, and often exist in multiple paralogous forms. In this study, we used B. anthracis Sterne, which harbors two paralogous spx genes, spxA1 and spxA2, to examine the phenotypes of spx null mutations and to identify the genes regulated by each Spx paralog. Cells devoid of spxA1 were sensitive to diamide and hydrogen peroxide, while the spxA1 spoxA2 double mutant was hypersensitive to the thiol‐specific oxidant, diamide. Bacillus anthracis Sterne strains expressing spxA1DD or spxA2DD alleles encoding protease‐resistant products were used in microarray and quantitative real‐time polymerase chain reaction (RT‐qPCR) analyses in order to uncover genes under SpxA1, SpxA2, or SpxA1/SpxA2 control. Comparison of transcriptomes identified many genes that were upregulated when either SpxA1DD or SpxA2DD was produced, but several genes were uncovered whose transcript levels increased in only one of the two SpxADD‐expression strains, suggesting that each Spx paralog governs a unique regulon. Among genes that were upregulated were those encoding orthologs of proteins that are specifically involved in maintaining intracellular thiol homeostasis or alleviating oxidative stress. Some of these genes have important roles in B. anthracis pathogenesis, and a large number of upregulated hypothetical genes have no homology outside of the B. cereus/thuringiensis group. Microarray and RT‐qPCR analyses also unveiled a regulatory link that exists between the two spx paralogous genes. The data indicate that spxA1 and spxA2 are transcriptional regulators involved in relieving disulfide stress but also control a set of genes whose products function in other cellular processes.
Lsm proteins and Hfq

The bacterial Hfq protein is a versatile modulator of RNA function and is particularly important for regulation mediated by small non-coding RNAs. Hfq is a bacterial Sm protein but bears more similarity to the eukaryotic Sm-like (Lsm) family of proteins than the prototypical Sm proteins. Hfq and Lsm proteins share the ability to chaperone RNA-RNA and RNA/protein interactions and an interesting penchant for protecting the 3′ end of a transcript from exonucleolytic decay while encouraging degradation through other pathways. Our view of Lsm function in eukaryotes has historically been informed by studies of Hfq structure and function but mutational analyses and structural studies of Lsm sub-complexes have given important insights as well. Here, we aim to compare and contrast the roles of these evolutionarily related complexes and to highlight areas for future investigation.
Regression Modeling Strategies for Microarchitectural Performance and Power Prediction

We propose regression modeling as an eﬀective approach for accurately predicting performance and power for various applications executing on any microprocessor conﬁguration in a large microarchitectural design space. This report addresses fundamental challenges in microarchitectural simulation costs via statistical modeling. Speciﬁcally, we derive and validate regression models for performance and power. Such models enable computationally eﬃcient statistical inference, requiring the simulation of only 1 in 5 million points of a joint microarchitecture-application design space while achieving error rates as low as 4.1 percent for performance and 4.3 percent for power. Although both models achieve similar accuracy, the sources of accuracy are strikingly diﬀerent. We present optimizations for a baseline regression model to obtain (1) per benchmark application-speciﬁc models designed to maximize accuracy in performance prediction and (2) regional power models leveraging only the most relevant samples from the microarchitectural design space to maximize accuracy in power prediction. Assessing model sensitivity to sample and region sizes, we ﬁnd 4,000 samples from a design space of approximately 22 billion points, are suﬃcient for both application-speciﬁc and regional modeling and prediction. Collectively, our results suggest signiﬁcant potential in accurate and eﬃcient statistical inference for microarchitectural design space exploration via regression models.
Cyclodextrins and their Pharmaceutical Applications

Cyclodextrins are formed by the action of cyclodextrin-trans-glycosidase enzyme (CTG) on the medium containing starch. Cyclodextrins are cyclic oligosaccharides containing at least six D-(+)- glucopyranose units attached by α (1-4) glucoside bonds. The three natural cyclodextrins, α , β and γ differ in their ring size and solubility.One of the striking feature of cyclodextrins is their ability to form inclusion complexes with a variety of compounds, by entrapping their molecules (guest) inside the cyclodextrin cavity, which act as a host. Cyclodextrin complexing agent are often used, in pharmaceutical formulation of oral products, to increase the bioavailability of poorly water soluble or unstable drug. The simplified review presents the evaluation of cyclodextrins drug complexes in pharmaceutical formulation. The preparation of sodium valproate phenytoin sodium/ β-cyclodextrin inclusion complex in a trial to stabilize the drug against moisture absorption and forming non-hygroscopic powders which are suitable for tablets by direct compression was reviewed. The preparation of phenytoin sodium / β-cyclodextrin inclusion complex in a trial to stabilize the drug against moisture absorption and mask its bitter taste was reviewed.The preparation of piroxicam/ β-cyclodextrin inclusion complexes is a exhibited higher dissolution rates and absorption efficiency values, than the corresponding un-complexes drug.Cyclodextrins as complexing agents are often used in pharmaceutical formulation of oral products to increase the bioavailability of poorly water soluble or unstable drugs.