Miracle-dz/UniFAR-FTUs huggingface dataset:
PSYCHOPATHY AND COOPERATION: PERSONALITY TRAITS AS PREDICTORS OF COOPERATION AND SUCCESS IN A PRISONER'S DILEMMA

Psychopaths are often violent, aggressive, and manipulative. They pose a serious threat to our social structures, and it is essential that we better understand the nature and origins of psychopathy. The present study examined the relationship between certain personality traits highly correlated with self-report measures of psychopathy (Payne,2004) and the strategies individuals use when playing a prisoner's dilemma. The purpose of this study was to identify relationships between the levels of cooperation used and success in a prisoner's dilemma with both broad personality domains and lower-level facets of the Five-Factor Model, as well as to explore news ways to conceptualize the psychopathic personality, and the possibility that it could be an adaptive and viable life strategy.A prisoner's dilemma is a non-zero sum game where participants face a hypothetical friend in a decision making scenario. Participants receive points based on choice of cooperating or competing with a friend on a given trial. The goal is to earn the most number of points possible. The M5 Questionnaire, the Levenson Self-Report Psychopathy Scale, and the prisoner's dilemma were administered to students enrolled in introductory psychology courses. Each measure was computerized and was presented as part of a three-fold program. Pearson correlations were conducted at the domain and facet level to analyze the relationship between personality characteristics and overall average choice scores and overall average point totals in a prisoner's dilemma. There were few significant relationships between the M5 psychopathy profile and cooperation level, but it was observed that a cooperative style of play is not any more effective than a competitive style. Finally, there were a number of significant correlations found between the M5 and the LSRP replicating previous research.
Topology Control for Building a Large-Scale and Energy-Efficient Internet of Things

The IoT is one of the most significant recent innovations in information technologies, which has attracted extensive attention from both industry and academia due to its potential impact on various networking-related fields. The rapid development in IoT technologies has resulted in widespread application of this emerging networking paradigm in various disciplines. On the other hand, IoT faces some challenges that must be fully dealt with, among which scalability and energy efficiency are two critical issues. Topology control offers a promising approach to designing a large-scale and energy-efficient IoT. In this article, we first present a taxonomy of currently available topology control algorithms, and then propose a systematic approach to topology construction in IoT to achieve scalability and energy efficiency. We intend to provide researchers and practitioners with a big picture of the current research status in this area, and hope that this article may motivate more researchers to participate in largescale IoT design, development, and deployment.
Multiple View Point Cloud Registration Based on 3 D Lines

A point cloud registration method based on 3D lines extraction from 3D data to register point cloud with obvious edges is proposed in this paper. Firstly, the line feature point cloud (LFPC), which is corresponding to the objects' edges, is extracted from the measured 3D data by using surface curvature as a measure. Then, through applying the 3D Hough transformation on LFPC, the line directions and positions are extracted respectively. Thus, the 3D lines corresponding to the edges in the two point clouds to be registered are obtained. Finally, a 3D lines matching and registration algorithm is proposed to accomplish the registration. Furthermore, the experiment is conducted to testify the feasibility of proposed method. The contribution of this paper is to propose a novel 3D Hough based lines extraction algorithm as well as a novel 3D line matching and registration algorithm, which can also be used in other 3D point cloud processing.
Evaluation of amplitude-stabilized optical pulse trains from rational harmonically mode-locked fiber ring lasers

This paper investigates rational harmonically mode-locked fiber ring lasers generating amplitude-stabilized fourth-order optical pulse trains at 13.91 GHz using a modulation signal frequency of 3.477 GHz. Pulse amplitude stabilization in terms of both amplitude equality and low amplitude noise is realized by using the nonlinear characteristics of a Mach-Zehnder intensity modulator in conjunction with intracavity optical filtering. The generated optical pulse trains are investigated for their amplitude equalization, amplitude noise, supermode noise suppression, and pulse timing jitter. It is shown that the pulses remained close to transform-limited over an operating wavelength range of 1535-1565 nm.
Regulation of proliferating cell nuclear antigen ubiquitination in mammalian cells

After exposure to DNA-damaging agents that block the progress of the replication fork, monoubiquitination of proliferating cell nuclear antigen (PCNA) mediates the switch from replicative to translesion synthesis DNA polymerases. We show that in human cells, PCNA is monoubiquitinated in response to methyl methanesulfonate and mitomycin C, as well as UV light, albeit with different kinetics, but not in response to bleomycin or camptothecin. Cyclobutane pyrimidine dimers are responsible for most of the PCNA ubiquitination events after UV-irradiation. Failure to ubiquitinate PCNA results in substantial sensitivity to UV and methyl methanesulfonate, but not to camptothecin or bleomycin. PCNA ubiquitination depends on Replication Protein A (RPA), but is independent of ATR-mediated checkpoint activation. After UV-irradiation, there is a temporal correlation between the disappearance of the deubiquitinating enzyme USP1 and the presence of PCNA ubiquitination, but this correlation was not found after chemical mutagen treatment. By using cells expressing photolyases, we are able to remove the UV lesions, and we show that PCNA ubiquitination persists for many hours after the damage has been removed. We present a model of translesion synthesis behind the replication fork to explain the persistence of ubiquitinated PCNA.
Using Logistic Trust for Event Learning and Misbehaviour Detection

The advancement in communication technologies has enabled ad hoc networks to collect large volumes of information. This information is vulnerable to various types of attacks amongst which false information dissemination and on-off attacks offer biggest threats to the networks. As the data in ad hoc networks depends on the events, it is necessary for any detection mechanism to first determine the true events. Then the information about these events can be used to judge the behavior of the senders. Therefore, in this work, the correct event is first learned using information from different sources including the observations of the receiver itself. This information is later used to learn the behavior of the senders. The learned behavior combined with the opinions of the neighbors about the sender allows the detection of malicious and honest nodes. In this work, a logistic trust model is used to combine the observed behavior and opinions. It is observed that logistic trust results in a high accuracy of over 99% and a low error of less than 1% even when the events are changing rapidly. It is also shown that the scheme can detect malicious majority and identify true events with high probability.
The Digital Journey: Reflected Learnings and Emerging Challenges

This paper identifies and examines the four stages of marketing's digitalization journey to date and the points of interest along the way. The metaphor of a journey provides a narrative through which to understand the complex changes that are occurring within the digitalization of marketing. The digital journey has no final destination yet, but it is possible to identify an origin and chart its route thus far. In reflecting on this journey, insights emerge, which pose ongoing challenges for businesses and for the marketing discipline. The digitalization of marketing provides new options for routes to markets, communication, brand building, relationship development, trialling pricing, product development, sourcing insights, as well as a platform for innovation. But, only if businesses embark on this journey, and then keep up with the pace once en route. The implications for business stemming from marketing's digital journey are detailed along with avenues for future research, to develop further understanding of digitalization. The theoretical contributions made by this paper include both a novel mapping of the complex trajectory of marketing's digitalization through a visualization and an articulation of the main four gaps in current research and practice knowledge within marketing. The gaps include the need for a strategic view, the issue of technical silos, the changing conceptions of time, and the tension between empowered consumers and their reliance on pre-filtered information. This paper provides a critical appraisal of the digital journey so far, resulting in a distilled conceptualization and route map, which should help guide future researchers.
Growth Factors, Stem Cells and Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is the chronic lung disease of prematurity mainly affecting preterm infants that are born at 24–28 weeks of gestation. Surfactant therapy, antenatal steroids and incremental improvements in perinatal care have modified the pattern of injury and allowed survival of ever more immature infants, but there is still no specific treatment for BPD. As a consequence, this disorder remains the most common complication of extreme prematurity. Arrested alveolar growth and disrupted vasculogenesis, the histological hallmarks of BPD, may persist beyond childhood and lead to chronic lung diseases in adults. Recent advances in our understanding of stem cells and their potential to repair damaged organs offer the possibility for cell-based treatment for intractable diseases. This review summarizes basic concepts of stem cell biology and discusses the recent advances and challenges of stem cell-based therapies for lung diseases, with a particular focus on BPD.
The value of histoquantitative measurements in prognostic assessment of renal adenocarcinoma

In a series of 135 patients with renal‐cell carcinomas (followed up for a mean of 9.5 years), a variety of clinical and histological factors were analyzed in relation to morphometric measurements of the nuclear parameters in the primary tumours to establish their value as prognostic factors. Clinical, histological and morphometric factors were significantly interrelated in that the metastatic high‐grade tumours had larger nuclei, larger variation in nuclear size and shape, and were also rapidly proliferating. In a univariate analysis, the most important clinical predictor of recurrence‐free survival (RFS) was T category, followed by combined nuclear grade, N category, nuclear grade, tumour size, sex and M category. The most important quantitative predictor of RFS was the mean area of the 10 largest nuclei (NAIO), the mean of the longest nuclear axis (Dmax), SD of nuclear area (SDNA), the volume‐corrected mitotic index (M/V index), inflammatory‐cell reaction, SD of nuclear perimetry (SDPE), and the mean of nuclear area (NA). M category, T category, combined nuclear grade, nuclear grade and N category were significantly related to patient survival. Of the quantitative variables, M/V index, Dmax and NA were significant predictors of survival in a univariate analysis. Females had longer RFS than men, and density of tumour‐infiltrating lymphocytes (TIL) referred to an increased risk of recurrent tumour in both sexes. In a multivariate analysis, the RFS was independently predicted by the clinical stage, female sex and mitotic frequency/mm2, while nuclear parameters or nuclear grading had no independent prognostic value. The extent of the primary tumour was the single most important determinant of survival, followed by the proliferation rate of the tumour. In local TI‐2NOMO tumours, mitotic frequency/mm2 was the only independent prognostic factor for RFS. The clinical stage, mitotic frequency/mm2, nuclear grade and density of TIL were independent predictors in Cox's analysis. In these local tumours, mitotic frequency/mm2 of neoplastic epithelium was the only independent prognostic factor. The results indicate that although an accurate prognostic evaluation of renal‐cell carcinomas can be based on subjective nuclear grading and histoquanti‐tative measurements of nuclear parameters, the simple assessment of mitotic frequency alone supplies most of the prognostic data, particularly in local tumours.
Perfusion CT Imaging of Brain Tumors: An Overview

SUMMARY: Perfusion imaging of brain tumors has been performed by using various tracer and nontracer modalities and can provide additional physiologic and hemodynamic information, which is not available with routine morphologic imaging. Tumor vascular perfusion parameters obtained by using CT or MR perfusion have been used for tumor grading, prognosis, and treatment response in addition to differentiating treatment/radiation effects and non-neoplastic lesions from neoplasms. This article is an overview of the utility of PCT for assessment of brain tumors and describes the technique, its advantages, and limitations.
Betweenness Centrality Approximations for an Internet Deployed P2P Reputation System

In the Barter cast reputation mechanism of the BitTorrent-based P2P client Tribler, peers compute local, subjective reputations of other peers by applying a flow-based algorithm to a locally maintained Barter cast graph with peers as nodes and bandwidth contributions as edges. We have previously shown that the computed reputations are more accurate when a peer takes the node with the highest Betweenness Centrality (BC) in its local Barter cast graph as the initial point in this algorithm rather than itself. BC is a powerful metric for identifying central nodes in complex network analysis, but its computation in large and dynamic networks is costly, %Unfortunately, BC computation in large and dynamic networks is costly, and previously proposed approximation methods are only designed for static networks. In this paper, first we assess the stability of the nodes with the highest BC values in growing synthetic random and scale-free, and Barter cast graphs. Next, we evaluate three BC approximation methods proposed in the literature in terms of their ability to identify the top-most central nodes. We show that these approximations are efficient and highly accurate in scale-free and Barter cast graphs, but less so in random graphs. Finally, we integrate the three BC approximations into Barter cast, and we evaluate the quality of the reputations they yield.
Super-Resolution Image with Estimated High Frequency Compensated algorithm

In this paper, we propose an Estimated High Frequency Compensated (EHFC) algorithm for super resolution images. It is based on Iterative Back Projection (IBP) method combined with compensated high frequency models according to different applications. The proposed algorithm not only improves the quality of enlarged images produced by zero-order, bilinear, or bicubic interpolation methods, but also accelerates the convergence speed of IBP. In experiments with general tested images, EHFC method can increase the speed by 1 ∼ 6.5 times and gets 0.4 ∼ 0.7 dB PSNR gain. In text image tests, EHFC method can increase 1.5 ∼ 6.5 times in speed and 1.2 ∼ 8.3 dB improvement in PSNR.
Detection of Antibiotic Resistance and Resistance Genes in Enterococci Isolated from Sucuk, a Traditional Turkish Dry-Fermented Sausage

The aim of this study was to isolate enterococci in Sucuk, a traditional Turkish dry-fermented sausage and to analyze isolates for their biodiversity, antibiotic resistance patterns and the presence of some antibiotic resistance genes. A total of 60 enterococci strains were isolated from 20 sucuk samples manufactured without using a starter culture and they were identified as E. faecium (73.3%), E. faecalis (11.7%), E. hirae (8.3%), E. durans (3.3%), E. mundtii (1.7%) and E. thailandicus (1.7%). Most of the strains were found resistant to rifampin (51.67%) followed by ciprofloxacin (38.33%), nitrofurantoin (33.33%) and erythromycin (21.67%). All strains were found susceptible to ampicillin. Only E. faecium FYE4 and FYE60 strains displayed susceptibility to all antibiotics. Other strains showed different resistance patterns to antibiotics. E. faecalis was found more resistant to antibiotics than other species. Most of the strains (61.7%) displayed resistance from between two and eight antibiotics. The ermB, ermC, gyrA, tetM, tetL and vanA genes were detected in some strains. A lack of correlation between genotypic and phenotypic analysis for some strains was detected. The results of this study indicated that Sucuk manufactured without using a starter culture is a reservoir of multiple antibiotic resistant enterococci. Consequently, Sucuk is a potential reservoir for the transmission of antibiotic resistance genes from animals to humans.
Reorganization of Functional Connectivity of the Language Network in Patients with Brain Gliomas

BACKGROUND AND PURPOSE: fcMRI measures spontaneous and synchronous fluctuations of BOLD signal between spatially remote brain regions. The present study investigated potential LN fcMRI modifications induced by left hemisphere brain gliomas. MATERIALS AND METHODS: We retrospectively evaluated fcMRI in 39 right-handed patients with a left hemisphere brain glioma and 13 healthy controls. Patients and controls performed a verb-generation task to identify individual BOLD activity in the left IFG (Broca area); the active region was used as seed to create whole-brain background connectivity maps and to identify the LN (including bilateral regions of the IFG, STS, and TPJ) following regression of task-evoked activity. We assessed differences between patients and controls in the pattern of functional connectivity of the LN, as well as potential effects of tumor position, histopathology, and volume. RESULTS: Global fcMRI of the LN was significantly reduced in patients with tumor compared with controls. Specifically, fcMRI was significantly reduced within seed regions of the affected hemisphere (left intrahemispheric fcMRI) and between the TPJ of the 2 hemispheres. In patients, the left TPJ node showed the greatest decrease of functional connectivity within the LN. CONCLUSIONS: The presence of a brain tumor in the left hemisphere significantly reduced the degree of fcMRI between language-related brain regions. The pattern of fcMRI was influenced by tumor position but was not restricted to the area immediately surrounding the tumor because the connectivity between remote and contralateral areas was also affected.
ANALISIS PENGARUH IMPLEMENTASI TEKNOLOGI INFORMASI TERHADAP KUALITAS LAYANAN PADA SEKTOR PENYEDIA JASA LOGISTIK DENGAN KINERJA RANTAI PASOK SEBAGAI VARIABEL MEDIATOR

Untuk mempermudah menjalankan proses bisnis, salah satu solusi yang sering dipilih oleh perusahaan adalah melakukan implementasi teknologi informasi (TI). Tidak terkecuali pada perusahaan penyedia jasa logistik. Perusahaan penyedia jasa logistik melakukan implementasi TI untuk dapat meningkatkan kualitas pelayanan perusahaan, selain itu implementasi TI juga diharapkan bisa memberi pengaruh pada proses manajemen rantai pasok. Namun, pada kenyataannya, implementasi TI pada perusahaan tidak menjamin suatu perusahaan bisa meningkatkan kualitas pelayan dan kinerja rantai pasok perusahaan tersebut. Pada penelitian ini akan dilakukan analisis berapa besar pengaruh implementasiTI terhadap kualitas layanan dengan variabel mediatorkinerja rantai pasok.Dalam analisis ini akan dikembangkan model kausal dan pembuktian secara empiris dengan menggunakan metode pemodelan persamaan struktural atau Structural Equation Modeling(SEM). Dengan adanya penelitian ini diharapkan akan diketahui sejauh mana pengaruh implementasiTI terhadap kinerja rantai pasok pada perusahaan penyedia jasa logistik. Sehingga perusahaan bisa menentukan kebijakan perusahan terhadap implementasi TI pada rantai pasok perusahaan.
Integrative Consensus: A Systematic Approach to Integrating Comprehensive Assessment Data for Young Children With Behavior Problems

Comprehensive assessments that include parents and teachers are essential when assessing young children vulnerable to emotional and behavioral problems given the multiple systems and contexts that influence and support optimal development (U. Bronfenbrenner & P. A. Morris, 2006; M. J. Guralnick, 2011). However, more data complicate clinical and educational decision making given the challenge of integrating comprehensive data. We report on initial efforts to develop and apply Integrative Consensus procedures designed to synthesize comprehensive assessment data using developmentally informed guidelines. Mother–teacher dyads (N = 295) reported on disruptive behavior in a sample of 295 low-income 3- to 5-year-olds; one-third referred for disruptive behaviors, one-third nonreferred with behavioral concerns, and one-third nonreferred. Two clinicians trained in Integrative Consensus procedures independently applied the framework, with findings highlighting that children identified as disruptive by Integrative Consensus ratings plus mother or teacher ratings significantly predicted behavior problems and impaired social skills. Children identified as disruptive via Integrative Consensus were 4 times more likely to be rated as impaired by their mother at follow-up than by mother or teacher report. Reliability estimates were high (&kgr; = 0.84), suggesting that the method has promise for identifying young children with behavior problems while systematically integrating comprehensive data.
Multi Reservoir Support Vector Echo State Machine for Multivariate Time Series Prediction

Chaotic time series prediction has received considerable attention in the last few years. Although many studies have been conducted in the field, there is little attention focused on multivariate time series prediction. Considering this problem, a multi reservoir support vector echo state machine(MRSVESM) based on multi kernel learning and echo state networks is proposed in this paper. The single reservoir approach may be ineffective on multivariate time series prediction, as it is not able to character multi time scale dynamics. The MRSVESM use multi different time scale reservoirs to present the dynamics of multivariate time series and replaced the "kernel trick" with "reservoir trick", that is, performed multi kernel learning in the high dimension "reservoir" state space. Two simulation examples, prediction of Lorenz chaotic time series and prediction of sunspots and the Yellow River annual runoff time series are conducted to demonstrate the effectiveness of the proposed method.
Marketing strategy: taxonomy and frameworks

Purpose – The purpose of this paper is to present taxonomy of marketing strategy concepts and integrative frameworks that differentiate and integrate its formulation and implementation processes.Design/methodology/approach – The paper is conceptual based on a review of academic literature on marketing strategy chronicled in major marketing journals January 1990‐April 2006. We present selected references classified by key marketing strategy topics for further pursuit by interested readers. Also, the paper reflects our experience and views based on practices chronicled in corporate case studies and trade journals.Findings – The literature casts marketing strategy formulation and implementation in the context of strategic planning and marketing strategy process models. The focus of the strategic planning model is on achieving corporate financial objectives through the implementation of product, pricing, promotion, and place (distribution) programs. The focus of the marketing strategy process model is on the .