Miracle-dz/UniFAR-FTUs huggingface dataset:
Organizational Ambidexterity, Performance and Knowledge Management: Empirical Evidence from the Energy and Pharmaceutical Sectors

Purpose: Several types of organizational ambidexterity proposed in the relevant literature were quantitatively analyzed in the paper. Considered ambidexterity strategies was justified by different types of knowledge management that may be applied in each case for their successful implementation. Knowledge Management is considered an institutional system which is in place in almost all companies but used differently. Yet effective knowledge management is a major driver of sustainable company development, it’s among the essential ingredients for effective use of companies knowledge and competencies as well as for strategy development and competitive intelligence. Design/methodology/approach: Empirical analysis of different ambidexterity strategies for the energy and pharmaceuticals sectors was provided. Data envelopment analysis (DEA) method was applied to estimate organizational ambidexterity using innovation performance measures. The DEA score based on entrepreneurial intensity input and short term and long term performance acted as a proxy for organizational ambidexterity. Sustainability and product ambidexterity were also considered as the key factors of organizational ambidexterity. Results: Estimations strongly associate organizational ambidexterity with efficiency of the company in both sectors examined here. Positive relation between performance and organizational ambidexterity for energy sector were discovered. At the same time orientation towards sustainability disrupts performance of pharmaceutical companies. The analysis provided in the paper provides indication for coupling knowledge management and organizational ambidexterity. Originality/value: The new approach for measurement of organizational ambidexterity using DEA is proposed in the paper. Different strategies including product ambidexterity and sustainability are estimated and their performances are compared. Knowledge management practices are used to justify the choice of ambidexterity strategies.
Localizing subcategories for finite group schemes

Let k be a field of positive characteristic p. An affine group scheme G over k is a representable functor from the category of commutative k-algebras to groups. The coordinate algebra, denoted by k[G], is a commutative Hopf k-algebra. An affine group scheme G is finite if the coordinate algebra is finite dimensional over k. In that case we define the group algebra kG to the the linear dual of k[G]. Hence, kG is a finite dimensional cocommutative Hopf algebra. Likewise, starting with any finite dimensional cocommutative Hopf algebra, its linear dual is a coordinate algebra of a finite group scheme. One therefore has an equivalence of categories: { finite group schemes } ∼ { finite dimensional cocommutative Hopf algebras }
Review of the earthworm biodiversity of Turkey and its neighbouring countries ( Clitellata , Megadrili )

This paper presents the combined list of earthworms from Turkey and its neighbouring countries: Syria, Iran, Georgia, Armenia, Azerbaijan, Cyprus, Greece and Bulgaria. Turkey has one of the richest earthworm biodiversities among the Eurasian countries. Its interesting geographic position on the conjunction of three continents (Asia, Europe and Africa) and the close proximity of three different biodiversity hotspots (Caucasus, Mediterranean and Iran-Anatolia) are the primary influence on the large number species as well as the different climatic conditions and major biogeographical barriers in the country. Till now 83 species and subspecies has been recorded and the earthworms of Turkey is characterized by the highest rate of endemism in the region (30 taxa = 36.14%). Taking into account the whole dataset (autochtonous + peregrine species) Turkish earthworms shows strong similarity to the fauna of Georgia (34 taxa = 40.96%) and Greece (32 taxa = 38.55%).
Chapter 4 Complexity and Approximation in Reoptimization

In this chapter the following model is considered: We assume that an instance I of a computationally hard optimization problem has been solved and that we know the optimum solution of such an instance. Then a new instance I ′ is proposed, obtained by means of a slight perturbation of instance I . How can we exploit the knowledge we have on the solution of instance I to compute an (approximate) solution of instance I ′ in an efficient way? This computation model is called reoptimization and is of practical interest in various circumstances. In this chapter we first discuss what kind of performance we can expect for specific classes of problems and then we present some classical optimization problems (i.e. Max Knapsack, Min Steiner Tree, Scheduling) in which this approach has been fruitfully applied. Subsequently, we address vehicle routing
Public Debt and Economic Growth : Is There a Change in Threshold Effect ?

My paper investigates the relationship between public debt and GDP growth for European countries and the heterogeneous debt threshold effect before and after the financial crisis, as well as between countries with high and lower credit rating, by using a large cross sectional panel data and two estimation methods. My empirical results suggest that the short run impact of debt on GDP growth is positive and statistically significant but decrease to negative and lose some significance at public debt and GDP ratio of 109%. After the financial crisis, either the threshold effect vanishes or the threshold values increase based different cohort and method. Furthermore, countries with higher credit rating have higher threshold value and more significant threshold effect.
FINITE ELEMENT MODELING OF A 1000 WATT WIND GENERATOR USING ANSYS

Harvesting the wind energy has existed since ancient civilization such as sailing ships. Nowadays, wind energy is harvested to generate an electrical energy using a wind generator. In order to ensure safety and durability of the wind generator, the performance and the characteristic of the generator needs to be investigated. Unfortunately, the purchased wind generator does not come with a detail drawing and electrical properties that are required for modeling purposes in the Finite Element Analysis (FEA) software. An image processing techniques is proposed to acquire the dimension of the generator internal parts. In order to generate a quality mesh, an adaptive meshing technique is used in the magnetostatic solver before it is imported into the transient solver in Ansys Maxwell. The proposed method to acquire the dimension produces error approximately 5 percent. Furthermore, using adaptive meshing technique proposed in this study, simulation results are in good agreement with experimental results with approximately 3 percent of error.
THE EFFECTS OF STAFF EDUCATION ON VAP IN THE ICU 2

Background: Mechanically ventilated patients in the ICU are at a high risk for ventilatorassociated pneumonia (VAP), and the rate of this hospital-acquired infection (HAI) is problematic. Nursing interventions utilize evidence-based practice (EBP) VAP prevention guidelines to decrease the incidence. The purpose of this paper is to examine the effects of staff education on the reduction of VAP in the critical care setting. Methods: Literature search was performed on two databases, CINAHL and PubMed. Search results were narrowed based on type of research article, relevance, and publication date, and eventually three studies were chosen. Results: In two of the studies, VAP incidence was shown to have decreased after staff education and one study had insignificant results, although there was a trend towards decreased VAP incidence. Length of stay significantly decreased post-intervention in two studies. Nurse adherence/concordance significantly increased in all studies. Conclusions: These findings show that staff education may decrease VAP incidence in ICUs, but further research needs to be performed to confirm these findings, as well as to determine the best form of education. Nurses are the primary individuals to prevent mechanically ventilated patients from acquiring VAP, and thus the next step would be focusing on how to improve longterm nurse adherence to preventative interventions and how to implement them into bedside
Enabling the Energy Performance Improvement of Entire Industries and Measuring Success

Improving the energy efficiency of the industrial sector is a key strategy for reducing carbon dioxide emissions that contribute to global climate change. The U.S. Environmental Protection Agency (EPA) pursues this strategy through the banner of the ENERGY STAR program where entire industrial sectors are engaged in a voluntary process to improve their energy performance over time. This is accomplished by providing energy management guidance that can be applied universally, specific tools that address the knowledge needs of individual companies, recognition opportunities that motivate action such as ENERGY STAR plant certification, and a collective learning experience for the industry. One of the unique tools offered by ENERGY STAR is a statistical benchmarking model embodied in the ENERGY STAR Energy Performance Indicator (EPI). The EPI scores the energy efficiency of individual manufacturing sites or “plants”. The score generated by the EPI also provides the basis for which EPA awards ENERGY STAR certification to manufacturing plants. The EPIs for auto assembly and cement plants were recently updated using new baseline data. Through the analysis conducted for updating the baselines used in these EPIs, it was observed that the Cement and Auto industries have achieved reductions of 13% and 12%, respectively, in energy use with similar CO2 savings.
CVCER: Robot to Learn Basics of Computer Vision and Cryptography

This paper describes the design of CVCER (Computer Vision and Cryptography Educational Robot) - the working model of the mobile robot used as a training stand for students. We describe the hardware and software of the robot. Hardware part of the robot can be used to teach the basics of robot architecture and software part can be used to teach the basics of computer vision and cryptography. The main goal of the project was to create a low cost, re-configurable and re-programmable robotic platform that could be used to teach the basics of the subjects described above and to test the algorithms written by students having internships in our institute and partner university.
Extending Conditional Preference Networks to Handle Changes

Conditional Preference Networks (CP-nets) are a compact and natural model to represent conditional qualitative preferences. In CP-nets, the set of variables is fixed in advance. That is, the set of alternatives available during the decision process are always the same no matter how long the process is. In many configuration and interactive problems, it is expected that some variables are subject to be included or excluded during the configuration process due to users showing interest or boredom on some aspects of the problem. Representing and reasoning with such changes is important to the success of the application and therefore, it is important to have a model capable of dynamically including or excluding variables. In this work, we introduce active CP-nets (aCP-nets) as an extension of CP-nets where variable participation is governed by a set of activation requirements. In particular, we introduce an activation status to the CP-net variables and analyze two possible semantics of aCP-nets along with their consistency requirements. Keywords—AI; changes; CP-nets; preferences; decision making; product configuration
TP 53 Polymorphism of Exon 4 at Codon 72 in Cutaneous Squamous Cell Carcinoma and Benign Epithelial Lesions of Renal Transplant Recipients and Immunocompetent Individuals : Lack of Correlation with Human Papillomavirus Status 1

A common polymorphism at codon 72 of exon 4 encoding either arginine or proline has been shown to confer a susceptibility to the development of skin tumor in renal transplant recipients. Moreover, this polymorphism may affect proteolytic degradation of p53 promoted by E6 protein from mucosal human papillomaviruses and represent a risk factor for human-papillomavirus-induced carcinogenesis. In this study, we analyzed the human papillomavirus presence and the TP53 allele distribution in cutaneous squamous cell carcinoma of renal transplant recipients and immunocompetent patients. Fifty-three squamous cell carcinomas from 40 renal transplant recipients, 50 benign epithelial skin lesions from 50 renal transplant recipients with no history of skin cancer, 51 squamous cell carcinomas from immunocompetent patients, and 29 blood samples from immunocompetent individuals without skin cancer were investigated. Human papillomavirus DNA was detected using polymerase chain reaction performed with two pairs of primers (MY09± MY11 and FAP59±FAP64). TP53 allele distribution was studied by denaturing gradient gel electrophoresis assay, followed by sequencing analysis. Human papillomavirus DNA was detected in 64% of squamous cell carcinoma and 79% of benign epithelial lesions from renal transplant recipients (NS) and only in 37% of squamous cell carcinoma from immunocompetent patients (p < 0.05). Mucosal oncogenic human papillomavirus types were predominant in squamous cell carcinoma from both renal transplant recipients and immunocompetent patients. Rate of arginine homozygosity in squamous cell carcinoma from renal transplant recipients was signi®cantly higher (83%) than in immunocompetent patients with or without squamous cell carcinoma (60% and 59%, respectively). Our results suggest that TP53 arginine/arginine genotype could represent a potential risk factor for the development of squamous cell carcinoma in renal transplant recipients compared to immunocompetent patients. No association between TP53 arginine/arginine genotype and human papillomavirus status could be determined, however.
Introduction to a Framework of E-commercial Recommendation Algorithms

Recommendation algorithm is very important for e-commercial websites when it can recommend online customers favorite products, which results out an increase in sale revenue. I propose the framework of e-commercial recommendation algorithms. This is a middleware framework or “operating system” for e-commercial recommendation software, which support scientists and software developers build up their own recommendation algorithms based on this framework with low cost, high achievement and fast speed.
A Survey on Temporal Reasoning for Temporal Information Extraction from Text

Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.
Agile optimization for routing unmanned aerial vehicles under uncertainty

The use of unmanned aerial vehicles (UAVs) is gaining popularity in contexts such as smart cities, city logistics, humanitarian logistics, natural disasters, or military operations. One optimization challenge directly related to the use of UAVs is the so-called team orienteering problem (TOP). In a TOP, each customer can either be visited only once by a single vehicle or not visited at all. Visiting each customer has associated a predefined reward, and the driving ranges of vehicles are typically limited by the duration of electric batteries. Due to the latter constraint, it is usual that not all customers can be visited. The main goal is then to find a set of open tours that maximizes the total collected reward without exceeding the fleet capacity nor the driving range limitation. In this paper, we consider the stochastic version of the problem, in which travel times are modeled as random variables following theoretical probability distributions. To solve this stochastic version of the TOP, a simheuristic algorithm combining a biased-randomized heuristic with simulation techniques is proposed. One of the main goals of our approach is to provide an ‘agile’ optimization methodology, i.e., one lightweight algorithm that can be easily implemented in real-life scenarios under uncertainty and, at the same time, can provide solutions in real-time (just a few seconds or even less).
Entangled Blockchains in Land Registry Management

Land is an immovable property, and to prove the ownership of the land. Ideally, one must provide a legal document that should conclusively prove it. But, land record in India is a loosely defined term. Here, one is presumed to be the owner, until proven otherwise. Land record is a generic term that could mean a record of rights, Khasra, Shajra, etc. Moreover, different documents are maintained by different departments, and any one of them could be used to stake a claim on the land. This leads to numerous disputes, and it is evident from the number of cases, which are pending in Indian courts. In this paper, we aim to solve this problem of coordination between the various departments, and multiplicity of land records, via entanglement of blockchains .
Intrusion Detection System based on the SDN Network, Bloom Filter and Machine Learning

The scale and frequency of sophisticated attacks through denial of distributed service (DDoS) are still growing. The urgency is required because with the new emerging paradigms of the Internet of Things (IoT) and Cloud Computing, billions of unsecured connected objects will be available. This document deals with the detection, and correction of DDoS attacks based on real-time behavioral analysis of traffic. This method is based on Software Defined Network (SDN) technologies, Bloom filter and automatic behaviour learning. Indeed, distributed denial of service attacks (DDoS) are difficult to detect in real time. In particular, it concerns the distinction between legitimate and illegitimate packages. Our approach outlines a supervised classification method based on Machine Learning that identifies malicious and normal packets. Thus, we design and implement Defined (IDS) with a great precision. The results of the evaluation suggest that our proposal is timely and detects several abnormal DDoS-based cyber-attack behaviours.
Simulation study on automotive EMB system based on self-tuning fuzzy PID control

To enhance stability of automobile brake, and shorten brake distance, study is made on anti-lock control and its control theory based on electronic mechanical brake system (EMB system). Models related to EMB system was built according to dynamics analysis for automobile braking system, and simulation was made to those models under Anti-Lock Braking System (ABS System). Then, self-tuning fuzzy PID controller was put forward to improve fuzzy control and PID control. Comparison was made between braking mode that without ABS control and self-tuning fizzy PID. Finally, pavement simulation was made in order to verify the adaptiveness of self-tuning fuzzy PID controller. The results showed that models related to EMB system are effective and have met the national standard and code for braking system. Automobile braking performance was improved because fuzzy control and PID control was improved by self-tuning fuzzy PID control. And it is concluded that self-tuning fuzzy PID controller is good at identify pavement types which meets the control requirement and expectations.
Summary of Research on Face Detection and Tracking Technology

Face detection and tracking is a hot topic in the field of computer vision. This paper introduces the problem in detail. Firstly, the difficulty of face detection and face tracking is analyzed; Secondly, the wide application of face detection and tracking technology in video surveillance, human-computer interaction and information security is introduced; Finally, the research status of face detection is analyzed. On this basis, the research status of long-term and long-term human face tracking in any environment is analyzed step by step, and the TLD tracking framework is introduced in detail.
Aqueous Extract of Sorindeia Juglandifolia Leaves Protects Methotrexate-Induced Liver and Kidney Damage in Rat

Introduction: Liver and kidney affection is a life-threatening disease caused by factors including drug-based treatment. Treatment based on methotrexate could result in liver and kidney damages. The study evaluates the preventive effects of Sorindeia juglandifolia leaves on methotrexate-induced liver and kidney impairment in rat. Methods: Healthy rats divided into 6 groups daily received distilled water, methotrexate (20 mg/kg), sub-cutaneous injection of L-carnitin (500 mg/kg) and methotrexate and the plant extract doses of 150, 250 and 350 mg/kg and methotrexate for 10 days. During treatment, body weight was recorded. At the end of the treatment, animals were sacrificed; venous blood were collected for haematological and biochemical analysis. Liver and kidney were collected for oxidative markers and histological examination. Results: The consecutive treatment of animals with plant extract and methotrexate showed a significant prevention of the body weight decrease and enhancement of the relative weight of liver and kidney. Sorindeia. juglandifolia extract also protected from the significant increase in transaminase activities, bilirubin and protein level, hypercholesterolemia, atherogenic index, and in the kidney from hypercreatininemia and the increase in serum urea level. The extract prevented the decrease of sodium level and glomerular filtration. Plant extract improved reactive oxygen species detoxification agents and protected from the histological disorganization of the liver and kidney tissues, observed in the MTX control. Conclusion: Sorindeia juglandifolia leaves extract expressed hepatorenal protective properties and could be useful to prevent liver and kidney damage induce by methotrexate.
tweeDe – A Universal Dependencies treebank for German tweets

We introduce the ﬁrst German treebank for Twitter microtext, annotated within the framework of Universal Dependencies. The new treebank includes over 12,000 tokens from over 500 tweets, independently annotated by two human coders. In the paper, we describe the data selection and annotation process and present baseline parsing results for the new testsuite
HEAVY MODELS , LIGHT MODELS AND PROXY MODELS A WORKING PAPER The Proxy Model Working Party :

The use of proxy models within the insurance sector has grown considerably in recent years, particularly in the area of capital management. This growth has been largely driven by the increased demands of a changing regulatory and risk management landscape set against the inability of traditional modelling techniques to keep up. This paper takes a look at some of the types of proxy model available to practitioners, suggesting a basic framework for “replicating formula” type proxies into which many current proxy models fit. Within this framework, and drawing heavily on recurring themes of complexity, accuracy and, in particular, use of the model, the options available in the design and implementation of a model are discussed as well as the potential impact of the choices made. Finally, four specific proxy models are discussed in greater detail, two of which are the subject of a case study. This leads to a key result concerning the distinction between risk scenario accuracy and risk distribution accuracy the key driver for risk capital estimation.