Miracle-dz/UniFAR-FTUs huggingface dataset:
Production, Manufacturing and Logistics Managing inventories in a two-echelon dual-channel supply chain

We present a two-echelon dual-channel inventory model in which stocks are kept in both a manufacturer warehouse (upper echelon) and a retail store (lower echelon), and the product is available in two supply channels: a traditional retail store and an Internet-enabled direct channel. The system receives stochastic demand from two customer segments: those who prefer the traditional retail store and those who prefer the Internet-based direct channel. The demand of retail customers is met with the on-hand inventory from the bottom echelon while the demand in the Internet-enabled channel is fulfilled through direct delivery with the on-hand inventory from the upper echelon. When a stockout occurs in either channel, customers will search and shift to the other channel with a known probability. A one-for-one inventory control policy is applied. In order to develop operational measures of supply chain flexibility, we define a cost structure which captures two different operational cost factors: inventory holding costs and lost sales costs. We discuss several insights that are evident from the parametric analysis of the model. We also examine the performance of two other possible channel distribution strategies: retail-only and direct-only strategies. Computational outcomes indicate that the dual-channel strategy outperforms the other two channel strategies in most cases, and the cost reductions realized by the flexibility of the dual-channel system may be significant under some circumstances. 2003 Elsevier B.V. All rights reserved.
Study of histopathological changes in thyroid gland in buffaloes

Present Study is observarionf Histopathological changes of Thyroid Gland In Buffaloes. Tissue samples i.e.thyroid glands were collected from the 300 buffaloes slaughtered at Municipal Slaughter House, Balapur Akola. Thyroid glands were cut in to small pieces for further histopathological processing. They were dehydrated in ascending (50%, 70, 95, 100%) order of alcohol, cleared in xylene and embedded in paraffin and sections of 4-6 micro diameter were obtained and stained with haematoxylin and eosin stain. The animals slaughtered were certified as non-productive. During present study mean, standard deviation and standard error were calculated as per the standard procedure. Microscopically 76.96% glands were normal, incidence of colloid goitre was recorded in 11.72% thyroid, parenchymatous goitre in 1.89% thyroid, increased interfollicular spaces and fibrotic condition in 9.66% glands. Haemorrhages and congestion was observed in 2.75% thyroid glands. Conclusion of this study is colloid goitre due to distention and enlargement of number of follicles, congestion in the thyroid gland occurs as the part of more general syndrome and Interfollicular hemorrhages occurs due to distribution of RBCs, escaped from blood vessels.
Should we introduce a dislike button for academic articles?

There is a mutual resemblance between the behavior of users of the Stack Exchange and the dynamics of the citations accumulation process in the scientific community, which enabled us to tackle the outwardly intractable problem of assessing the impact of introducing “negative” citations. Although the most frequent reason to cite an article is to highlight the connection between the 2 publications, researchers sometimes mention an earlier work to cast a negative light. While computing citation‐based scores, for instance, the h‐index, information about the reason why an article was mentioned is neglected. Therefore, it can be questioned whether these indices describe scientific achievements accurately. In this article we shed insight into the problem of “negative” citations, analyzing data from Stack Exchange and, to draw more universal conclusions, we derive an approximation of citations scores. Here we show that the quantified influence of introducing negative citations is of lesser importance and that they could be used as an indicator of where the attention of the scientific community is allocated.
Stabilized Backward Diffusion for Partial Volume Correction

This paper proposes a novel algorithm for correcting the Partial Volume Effect in Positron Emission Tomography (PET) images, using registered Computed Tomography (CT) data to enhance the blurred PET image. The algorithm is based on a forward-and-backward anisotropic heat equation solver that deblurs the PET image along CT gradients. A forward diffusion force is also utilized to stabilize the process where necessary. The algorithm retains average original PET intensity and avoids the introduction of negative PET values, a crucial property in clinical uses. A GPU implementation of our work is outlined and we present the reconstruction results on measured PET/CT data.
Multi-objective Hybrid Differential Evolution Algorithm Based on Pareto Optimal Solution Migration

By using differential evolution algorithm (DE) to solve multi-objective optimization problems, Pareto optimal solution migration based differential evolution for multi-objective optimization (PSDEMO) is proposed. The elitist strategy is adopted in the algorithm. Pareto non-dominated solutions found in the evolution operation are archived dynamically with the evolution process, and all the non-dominated solutions in the archive are applied to migration operation after mutation and crossover operations of DE are finished. Compared with standard DE, simulation results show the PSDEMO not only helps to improve quantity of the Pareto non-dominated solutions, but also helps to enhance quality of the Pareto non-dominated solutions, it also has good balance keeping ability between diversity and convergence.
Relationship between genomic distance‐based regression and kernel machine regression for multi‐marker association testing

To detect genetic association with common and complex diseases, two powerful yet quite different multimarker association tests have been proposed, genomic distance‐based regression (GDBR) (Wessel and Schork [2006] Am J Hum Genet 79:821–833) and kernel machine regression (KMR) (Kwee et al. [2008] Am J Hum Genet 82:386–397; Wu et al. [2010] Am J Hum Genet 86:929–942). GDBR is based on relating a multimarker similarity metric for a group of subjects to variation in their trait values, while KMR is based on nonparametric estimates of the effects of the multiple markers on the trait through a kernel function or kernel matrix. Since the two approaches are both powerful and general, but appear quite different, it is important to know their specific relationships. In this report, we show that, under the condition that there is no other covariate, there is a striking correspondence between the two approaches for a quantitative or a binary trait: if the same positive semi‐definite matrix is used as the centered similarity matrix in GDBR and as the kernel matrix in KMR, the F‐test statistic in GDBR and the score test statistic in KMR are equal (up to some ignorable constants). The result is based on the connections of both methods to linear or logistic (random‐effects) regression models. Genet. Epidemiol 35: 211‐216, 2011 © 2011 Wiley‐Liss, Inc.
Design Procedure for Class E Switching Circuits Allowing Implicit Circuit Equations

This paper presents novel design procedures for class E switching circuits allowing implicit circuit equations. Because of the allowance, circuit simulators can be used in the proposed design procedures. Moreover, the proposed design procedures also allow any conditions considered until now. The proposed design algorithms are implemented by using PSpice and OPTIMUS. This paper shows the design examples of two kinds of class E switching circuits. In particular, the design example of the class E oscillator shows the benefit of the proposed design procedure eminently, i.e., it is unnecessary to make an equivalent model of the semiconductor devices for the design. These design examples show the validity and effectiveness of the proposed design procedures.
Shigella IpaH0722 E3 Ubiquitin Ligase Effector Targets TRAF2 to Inhibit PKC–NF-κB Activity in Invaded Epithelial Cells

NF-κB plays a central role in modulating innate immune responses to bacterial infections. Therefore, many bacterial pathogens deploy multiple mechanisms to counteract NF-κB activation. The invasion of and subsequent replication of Shigella within epithelial cells is recognized by various pathogen recognition receptors as pathogen-associated molecular patterns. These receptors trigger innate defense mechanisms via the activation of the NF-κB signaling pathway. Here, we show the inhibition of the NF-κB activation by the delivery of the IpaH E3 ubiquitin ligase family member IpaH0722 using Shigella's type III secretion system. IpaH0722 dampens the acute inflammatory response by preferentially inhibiting the PKC-mediated activation of NF-κB by ubiquitinating TRAF2, a molecule downstream of PKC, and by promoting its proteasome-dependent degradation.
Social Network Analysis of Human Mobility and Implications for DTN Performance Analysis and Mobility Modeling

Social Network Analysis (SNA) has emerged as a promising method for designing data dissemination algorithms over Delay Tolerant Networks (DTN). These algorithms try to identify and exploit macroscopic regular relationships between nodes. Despite initial encouraging results, the type and complexity of the alleged underlying social structure has not been sufficiently studied or quantified. In this paper, we perform a systematic study and comparison of 4 mobility traces and 3 state-of-the-art synthetic models with respect to social properties. We represent each model as a weighted contact graph and study community structure, graph spectrum, interand intra-community weight distributions, etc. We also discuss the implications for synthetic mobility models. Finally, to underline the importance of these contact graph properties, we (i) show that the delay of distributed estimation depends on the second largest eigenvalue of the normalized weighted contact graph, and (ii) express the performance of various (random and SNA-based) DTN routing schemes as a function of the volume of cuts between communities.
ALK-Activating Homologous Mutations in LTK Induce Cellular Transformation

Leukocyte tyrosine kinase (LTK) is a receptor tyrosine kinase reported to be overexpressed in human leukemia. Though much regarding the function of LTK remains unknown, it shares a high degree of similarity with anaplastic lymphoma kinase (ALK), which is found mutated in human cancer. In order to determine if LTK has transforming potential, we created two LTK mutants, F568L and R669Q, that correspond to two well-characterized activating mutations of ALK (F1174L and R1275Q). LTK-F568L, but not wildtype LTK or LTK-R669Q, transformed hematopoietic cells to cytokine independence. LTK-F568L exhibited a stronger ability to induce loss of contact inhibition and anchorage-independent growth of epithelial cells compared to LTK-R669Q, while wildtype LTK was non-transforming in the same cells. Likewise, LTK-F568L induced greater neurite outgrowth of PC12 cells than R669Q, while wildtype LTK could not. Correlating with transforming activity, LTK-F568L displayed significantly enhanced tyrosine phosphorylation compared to wildtype LTK and LTK-R668Q and induced activation of various signaling proteins including Shc, ERK and the JAK/STAT pathway. Expression of wildtype LTK or LTK-R669Q generally led to weaker activation of signaling proteins than expression of LTK-F568L, or no activation at all. Thus, mutating LTK at residue F568, and to a lesser extent at R669, activates the receptor tyrosine kinase, inducing cell signaling that results in transforming properties. These studies suggest that aberrant activation of LTK may contribute to neoplastic cell growth.
OASIS: Online Active Semi-Supervised Learning

We consider a learning setting of importance to large scale machine learning: potentially unlimited data arrives sequentially, but only a small fraction of it is labeled. The learner cannot store the data; it should learn from both labeled and unlabeled data, and it may also request labels for some of the unlabeled items. This setting is frequently encountered in real-world applications and has the characteristics of online, semi-supervised, and active learning. Yet previous learning models fail to consider these characteristics jointly. We present OASIS, a Bayesian model for this learning setting. The main contributions of the model include the novel integration of a semi-supervised likelihood function, a sequential Monte Carlo scheme for efficient online Bayesian updating, and a posterior-reduction criterion for active learning. Encouraging results on both synthetic and real-world optical character recognition data demonstrate the synergy of these characteristics in OASIS.
A LONGITUDINAL STUDY OF ELEMENTARY SCHOOL CLIMATE AND ACHIEVEMENT: TESTING A PROTECTIVE RESILIENCE MODEL

This study sought to fill a gap in resilience and school climate theory. Research has found that the protective model of resilience allows resources and assets to act as moderators to protect individuals from risk. The protective model thus provides a way to understand how the school environment can protect youth from individual level risk. School climate, providing a holistic measure of the school environment may act as a resource to protect youth from risk on academic achievement. This dissertation first investigated how school climate should be defined in a longitudinal study and then hypothesized that student, parent, and personnel perceptions of positive school climate will protect youth from individual risk on grades and test scores. The study relied on elementary student data provided from a large urban school district in the southeast of the United States of America. Findings showed that school climate perceptions stay consistent over a three-year span and that the relationship of student risk on test scores or grades was not conditional on student, parent and faculty reported school climate. Other findings, limitations and applications are discussed. INDEX WORDS: resilience, school climate, student achievement A LONGITUDINAL STUDY OF ELEMENTARY SCHOOL CLIMATE AND ACHIEVEMENT: TESTING A PROTECTIVE RESILIENCE MODEL
Performance improvements for calculations of third party risk around airports

During the past two decades, NLR has been performing research for third party risk around airports, and a calculation model and methodology for different types of airports have been developed. The NLR third party risk model is used to evaluate the risk for people living and working around an airport. The design of new or changed air routes and runway infrastructure at airports require that third party risk studies are conducted to determine the impact for the airport surroundings. Due to the increase of traffic, the availability of improved individual flight track data, and the need for detailed calculations on a denser grid, the time needed for a risk calculation has increased significantly. It becomes challenging when more scenarios are involved and hence more risk calculations are needed in the decision making with respect to airport development and land use planning. In this paper, the authors present the research of using parallel programming hardware to improve the performance of the calculation model, and in particular the use of graphics cards (GPUs) to carry out massive parallel operations. Because of different phases in the calculations, and the relation between adjacent grid cells, the translation of this model into a parallel implementation is not straightforward. Results show that a calculation for Schiphol airport, based on the traffic for a calendar year and a dense grid that took a week to complete in the original implementation, will finish well within one hour in the improved parallel implementation, with identical results. Other findings of the research show that the dedicated focus on improved performance has also helped finding and solving performance issues in the original model implementation.
The Research on Security Technology for Low-Performance Iot Sensor Node

Background/Objectives: IoT developmental background: IoT, which is the key technology in the fourth industrial revolution, utilizes the Internet. Particularly, the growth of convergence products that utilize it has been constant with the demands of IoT services using the network of the Internet.Methods/Statistical analysis: IoT network consists of products equipped with various sensors to communicate; sensor nodes are made up of low volume memory, low performance CPU, and battery when they are used in the network. There has been the demand of secure transmission of information measured by a sensor node to the IoT platform. We conduct a study on how we can improve security in the IoT environment.Findings: Generally, sensor nodes are applied with basic security provided by IoT communication protocol rather than their own encryption. Therefore, sensor nodes are vulnerable in terms of security and the IoT platform that utilizes information collected by them would process distorted information.In order to draw a strategy to prevent security breach, we analyze security threat and the type of attacks.Improvements/Applications: We suggest a countermeasure to deal with security threat of sensor nodes and situations in which sensor nodes are vulnerable in IoT environment. To secure integrity of communication and transaction between a sensor node and an IoT platform in the future, the application of block chain technology into the IoT environment is necessary.
Drama Temelli Dijital Hikâye Anlatıcılığı Programının 6 Yaş Çocuklarının Bazı Sosyal Becerilerinin Gelişimine Etkisi

Bu arastirma, ana sinifina devam eden alti yas cocuklarinin, bazi sosyal becerilerine drama temelli dijital hikâye anlaticiligi programinin etkisini belirlemek amaciyla yapilan yari deneysel bir calismadir. Arastirma 2016 – 2017 egitim ogretim yilinin birinci yariyilinda Aydin ili Efeler ilcesindeki resmi bir anaokulunda yapilmistir. Arastirma bir deney ve bir kontrol grubunda bulunan toplam 48 cocuk uzerinde gerceklestirilmistir. On hafta suren arastirmada deney grubuna drama temelli dijital hikâye anlaticiligi programinda yer alan etkinlikler uygulanirken, kontrol grubunda okul oncesi programinda yer alan etkinlikler kendi ogretmenleri tarafindan uygulanmistir. Olcme araci olarak deney ve kontrol grubuna “Sosyal Becerileri Degerlendirme Olcegi” (Avcioglu, 2007) ontest – sontest olarak uygulanmistir. Ayrica deneysel mudahale programinin oncesinde, cocuklar hakkinda bilgi edinmek amaciyla “Kisisel Bilgi Formu” kullanilmistir. Arastirmada veri toplama araclarindan elde edilen verilerin cozumleri SPSS (23.0) paket programindan yararlanilarak gerceklestirilmistir. Gruplarin Sosyal Becerileri Degerlendirme Olceginden aldiklari puanlar arasinda farklilasma olup olmadigini belirlemek icin kovaryans analizi kullanilmistir. Analizlerde anlamlilik duzeyi p<. 05 olarak alinmistir. Sonuc olarak Sosyal Beceriler Degerlendirme Olceginden elde edilen sontest puanlari acisindan deney ve kontrol gruplari arasinda deney grubu lehine anlamli fark bulunmustur. Olcegin dokuz alt boyutundan “Kizginlik Davranislarini Kontrol Etme ve Degisikliklere Uyum Saglama Becerileri”, “Akran Baskisi ile Basa Cikma Becerileri”, “Sozel Aciklama Becerileri”, “Dinleme Becerileri”, “Amac Olusturma Becerileri” ve “Gorevleri Tamamlama Becerileri” alt boyutlarinda deney grubu lehine anlamli fark bulunmustur. “Kisiler Arasi Beceriler”, “Kendini Kontrol Etme Becerileri” ve “Sonuclari Kabul Etme Becerileri” alt boyutlarinda ise deney ve kontrol grubu sontest puanlari acisindan anlamli bir fark bulunamamistir.
Multi-view terrain classification using panoramic imagery and LIDAR

The focus of this work is addressing the challenges of performing object recognition in real world scenes as captured by a commercial, state-of-the-art, surveying vehicle equipped with a 360° panoramic camera in conjunction with a 3D laser scanner (LIDAR). Even with state-of-the-art surveying equipment, there is colour saturation and very dark regions in images, as well as some degree of time-varying misalignment between the point cloud data and imagery due to, for instance, imperfect tracking of sensor pose. Moreover, there are frequent occlusions due to both static and moving objects. These issues are inherently difficult to avoid and therefore need to be dealt with in a more robust fashion. This is where the contribution of the paper is; that is, the development of a consensus method that can intelligently incorporate feature responses from multiple views and reject those that are not very descriptive. It is shown that the overall performance in a ten class problem is increased from 70.5% for a simple 2D-3D classification system, to 77.5%. Subsequently, an enhanced CRF which has become robust using the misclassifications of training data and equipped with the probabilities of the adjacent points, was applied to the system and further improved its performance to 82.9%. The experiments were performed on a challenging dataset captured both in summer and winter.
Maturity of Judgment and Perceptual Deterrence

Recent deterrence literature has found that the degree to which sanction threats are perceived to influence subsequent offending differs within individuals and between individuals over time. This study examines whether three psychosocial aspects (temperance, perspective, responsibility) relevant to the maturity of judgment predict within-individual and between-individual differences in levels of perceptual deterrence. Random effects regression models with fixed effects (hybrid models) are used to estimate the impact of maturity of judgment on the perceived risks, costs, and benefits of crime among a sample of serious juvenile offenders from the Pathways to Desistance study over 7 years of development. The results support both within-person effects and between-person effects. More mature judgment ability is generally associated with the perception of greater risks, heavier costs of punishment, and fewer rewards of crime. The rate of change in perceptual deterrence by maturity of judgment varies between individuals. Implications of the findings are discussed.
Dynamic Distribution of an Entity-based Simulation

We consider simulations constituted by numerous entities which evolve and interact dynamically. To be able to represent a significant problem as an ecosystem, the modelization must be distributed. We are in front of a problem where the processes must obtain resources in a dynamically changing environment and be designed to collaborate despite of a variety of asynchronous and unpredictable changes. In such a context, applications are computational ecosystems analogous to biological ecosystems and human economies. This analogy suggests to tackle the problem as a complex system, in particular for the emergent aspects and the self-organization. Thus we detect organizations by an ant algorithm which allows to take into account the dynamic side, load balancing of the processes and minimization of the impact of the communications between them. This has been tested successfully on a modelization of a very simple aquatic ecosystem where organizations appear, evolve and disappear.
SVM-based Learning for Multiple Model Estimation

This paper presents new constructive learning methodology for multiple model estimation. Under multiple model formulation, training data are generated by several (unknown) statistical models, so existing learning methods (for classification or regression) based on a single model formulation are no longer applicable. We describe general framework for multiple model estimation using SVM methodology. The proposed constructive methodology is analyzed in detail for regression formulation. We also present several empirical examples for multiple-model regression formulation. These empirical results illustrate advantages of the proposed multiple model estimation approach.