Miracle-dz/UniFAR-FTUs huggingface dataset:
Translating Sustainabilities between Green Niches and Socio-Technical Regimes

A role for green niches has risen to prominence in the environment and innovation literature. The role of idealistic enthusiasts in the creation of sustainability initiatives in niches is widely recognized. The importance of tensions in incumbent socio-technical regimes is acknowledged to provide niches with development opportunities. However, the literature currently gives insufficient consideration to the processes by which niches and regimes interact and are interdependent. This paper addresses this by considering socio-technical translations between niches and regimes. It does so by analysing niche-regime interactions in the areas of food and housing and the development of eco-housing and organic food in the UK. Three kinds of translations are identified that affect the sustainabilities practiced in niches and regimes, and which do not all flow from green niche to incumbent regime.
Contributions of Gender, Stress, and Adult Attachment Orientations

This study investigated whether gender, stressful problems common among college students, and adult attachment orientations (anxiety and avoidance) contributed to self-reported perpetration of psychological abuse in dating relationships among 127 college students. College men’s stress levels were the strongest predictor of perpetration of emotional abuse against their female romantic partners. Attachment avoidance among college men and women was associated with higher levels of emotional abuse perpetration when selfreported stress levels were high. Recommendations for research and practice are provided.
Template-Independent Web Object Extraction

There are various kinds of objects embedded in static Web pages and online Web databases. Extracting and integrating these objects from the Web is of great significance for Web data management. The existing Web information extraction (IE) techniques cannot provide satisfactory solution to the Web object extraction task since objects of the same type are distributed in diverse Web sources, whose structures are highly heterogeneous. The classic information extraction (IE) methods, which are designed for processing plain text documents, also fail to meet our requirements. In this paper, we propose a novel approach called Object-Level Information Extraction (OLIE) to extract Web objects. This approach extends a classic IE algorithm, Conditional Random Fields (CRF), by adding Web-specific information. It is essentially a combination of Web IE and classic IE. Specifically, visual information on the Web pages is used to select appropriate atomic elements for extraction and also to distinguish attributes, and structured information from external Web databases is applied to assist the extraction process. The experimental results show OLIE can significantly improve the Web object extraction accuracy.
Gait pattern classification with integrated shoes

In this paper, we aim to study and classify gait patterns among flat walking, descending stairs, and ascending stairs using inertial measurement unit (IMU) including triaxial accelerometers and gyroscopes. Six subjects were invited to gather gait data of flat walking, descending stairs, and ascending stairs wearing the shoe-integrated system with free speeds. The design of the classifier for identifying gait patterns based on continuous kinematic signals is composed of three steps. In the first step, we separate gait signals of the six sensors in the same period into gait segments which are further used as the units for pattern feature analysis. Secondly, based on discrete wavelet transform (DWT), the average sum of squares of wavelet coefficients of each segment for anteroposterior acceleration, vertical acceleration, and sagittal plane angular rate are demonstrated and selected as the common features for gait pattern classification. At the last step, the fuzzy logic based classifier is proposed according to the distribution of the common features of different gait patterns. Experimental results demonstrate the proposed methodology is efficient for classifying gait patterns during humans' daily activity.
An Incentivized Auction-Based Group-Selling Approach for Demand Response Management in V2G Systems

Vehicle-to-grid (V2G) system with efficient demand response management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at electric vehicles (EVs). An incentivized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling-based auction. In the proposed approach, a number of aggregators sit on the first-level auction responsible to communicate with a group of EVs. EVs as bidders consider quality of energy (QoE) requirements, and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate that the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.
Preferential exploration method of transfer learning for reinforcement learning in Same Transition Model

We aim to accelerate learning processes in reinforcement learning by transfer learning. Its concept is that knowledge to solve similar tasks accelerates a learning process of a target task. We have proposed that the basic transfer method based on forbidden rule set that is a set of rules which cause to immediately failure of a target task. However, the basic method works poorly for the "Same Transition Model," which has same state transition probability and different goal. In this article, we propose an effective transfer learning method in same transition model. In detail, it consists of two strategies: (1) approaching to the goal for the selected source task quickly, and (2) exploring states around the goal preferentially.
Downlink Performance Improvements in (A)synchronous GPRS Networks using Single/Multiple Antenna Interference Cancellation Techniques

The aim of this paper is to assess GPRS data throughput of GSM networks for single and multiple antenna interference cancellation (SAIC/MAIC) receivers in the terminals. This is done by using a two-step top-down simulation methodology. Both synchronous and asynchronous networks with traditional European reuses of 1/3 and 3/9 are considered. It turns out that SAIC/MAIC receivers are very beneficial also for European network planning. The results indicate a substantial gain in data throughput in the order of 20-35% for SAIC compared to terminals with a conventional receiver. A further dramatic increase in throughput up to 120% can be achieved by terminals using an MAIC receiver. In spite of the widespread view that a maximum gain will be achieved for a synchronized network, where the amount of overlap between desired signal and interference can be controlled, our results show that the performance of both network types is very similar.
A Trademark Retrieval Method Based on Support Vector Machines Self-Learning

Relevance feedback is a good method for semantic gap in image retrieval. In this paper we propose a method which uses support vector machines for conducting effective relevance feedback for trademark retrieval. The algorithm takes the test results to adjust the already trained support vector machines. We select the Tamura textures features which consistent with human vision perception and the low-level feature of image to trian support vector machines. Experimental results show that it achieves significantly higher search accuracy after just three or four rounds of relevance feedback.
Protection Principles for Future Microgrids

Realization of future low-voltage (LV) microgrids requires that all technical issues, such as power and energy balance, power quality and protection, are solved. One of the most crucial one is the protection of LV microgrid during normal and island operation. In this paper, protection issues of LV microgrids are presented and extensions to the novel LV-microgrid-protection concept has been developed based on simulations with PSCAD simulation software. Essential in the future-protection concept for LV microgrids will be the utilization of high speed, standard, e.g., IEC-61850-based communication to achieve fast, selective, and reliable operation protection.
Learning document aboutness from implicit user feedback and document structure

Capturing the "aboutness" of documents has been a key research focus throughout the history of automated textual information processing. In this work, we represent aboutness using words and phrases that best reflect the central topics of a document. We present a machine learning approach that learns to score and rank words and phrases in a document according to their relevance to the document. We use implicit user feedback available in search engine click logs to characterize the user-perceived notion of term relevance. Using a small set of manually generated training data, we show that the surrogate training data from click logs correlates well with this data, thus eliminating the need to create data for training manually which is both expensive and fundamentally difficult to obtain for such a task. Further, we use a diverse set of features in our learning model that capitalize heavily on the structural and visual properties of web documents. In our extensive experimentation, we pay particular attention to tail web pages and show that our approach trained on mainly head web pages generalizes and performs well on all kinds of documents. In several evaluation methods using manually generated summaries and term relevance judgments, our system shows 25% improvement over other aboutness solutions.
Differentiated technological regimes and changing industrial organisation: Theory and evidence from the upstream oil and gas industry

The upstream oil and gas industry has experienced two different technological regimes since it became international in the 1920s. Its incentives to innovate remained weak during the first fifty years, whereas from the 1970s onward new technical challenges forced the industry into a technological revolution, further amplified by fierce competitive pressure on hydrocarbon prices since the oil price collapse in 1986. Performing this technological acceleration has entailed deep structural changes in the relationship between international oil and gas companies in terms of an increasing propensity to cooperate, more balanced partnerships and a more selective choice of partners. These empirical results are supported by two complementary theories of inter-firm cooperation: a static one sketched in terms of resources allocation for the technological tranquillity period, and a dynamic one that deals with the creation of resources for the technological revolution period.
Centrality rankings in multiplex networks

The vertiginous increase of e-platforms for social communication has boosted the ways people use to interact each other. Micro-blogging and decentralized posts are used indistinctly for social interaction, usually by the same individuals acting simultaneously in the different platforms. Multiplex networks are the natural abstraction representation of such "layered" relationships and others, like co-authorship. Here, we re-define the betweenness centrality measure to account for the inherent structure of multiplex networks and propose an algorithm to compute it in an efficient way. To show the necessity and the advantage of the proposed definition, we analyze the obtained centralities for two real multiplex networks, a social multiplex of two layers obtained from Twitter and Instagram and a co-authorship network of four layers obtained from arXiv. Results show that the proposed definition provides more accurate results than the current approach of evaluating the classical betweenness centrality on the aggregated network, in particular for the middle ranked nodes. We also analyze the computational cost of the presented algorithm.
A Procedure to Integrate Well Test Data, Reservoir Performance History and 4-D Seismic Information into a Reservoir Description

This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Abstract This paper deals with the problem of estimating the distributions of permeability and porosity in heterogeneous and multiphase petroleum reservoirs by matching the dynamic behavior. The dynamic data is in the form of field measurements from well testing, production history, interpreted 4-D seismic information, and other data such as correlations between permeability and porosity, geostatistics in the form of a variogram model and the inference of large scale geological structure. The issue was posed as an inverse problem and solved by using nonlinear parameter estimation. The procedure developed here is capable of processing all the information simultaneously and this results in a fast and efficient method. The procedure is also able to determine the uncertainty associated with the estimated permeability and porosity fields. Examples of different parameter types that may be estimated by this approach include: (a) individual block permeabilities and porosities; (b) geological objects such as channels and faults; (c) pilot points that form the basis of a kriged distribution; and (d) seismic attenuation values from 3-D seismic images. An important conclusion of this work is that the value of each piece of information does not reside in its isolated use but in the value it adds to integrated analysis of the complete set of information. Thus data that traditionally was considered to be of low information content for reservoir characterization becomes useful and enhances the value of the data set as a whole. Introduction Devising the optimal strategy for the development of an oil or gas reservoir is an …
Increased Glucose Levels Are Associated With Episodic Memory in Nondiabetic Women

OBJECTIVE—Patients with type 2 diabetes have an increased risk of a reduction in cognitive function. We investigated the hypothesis that plasma glucose is associated with a reduction in episodic and/or semantic memory already in nondiabetic subjects. RESEARCH DESIGN AND METHODS—We linked two large population-based datasets in Sweden: the Betula study, in which a random sample from the population aged 35–85 years was investigated for cognitive function, including episodic and semantic memory; and the Västerbotten Intervention Program, a health survey with subjects aged 40, 50, and 60 years, that includes measuring of fasting and 2-h plasma glucose, along with other risk factors for diabetes and cardiovascular disease. We identified 411 (179 men and 232 women, mean age 50.6 ± 8.0 years) nondiabetic subjects, free from dementia, who had participated in the two surveys within 6 months. RESULTS—Women had better episodic (score 7.37 ± 1.42) and semantic memory (score 16.05 ± 2.76) than men (score 6.59 ± 1.29 and 15.15 ± 2.92, respectively, P < 0.001 for both). In an adjusted multivariate model, fasting plasma glucose (fPG) and 2-h plasma glucose (2hPG) were significantly negatively associated with episodic memory (fPG: B −0.198, SE 0.068, β −0.209, P = 0.004; and 2hPG: B −0.061, SE 0.031, β −0.148, P = 0.048, respectively) in women but not in men. The association was not found in relation to semantic memory. CONCLUSIONS—We conclude that an increase in plasma glucose is associated with impairment in episodic memory in women. This could be explained by a negative effect on the hippocampus caused by raised plasma glucose levels.
Towards real-time obstacle detection using a hierarchical decomposition methodology for stereo matching with a genetic algorithm

This work is concerned with the stereo matching problem for real-time obstacle detection. The correspondence problem is viewed as an optimization task where the objective is to find a solution for which the matches are as compatible as possible with respect to specific constraints. The optimization process is performed by means of a genetic algorithm with a new encoding scheme. For an effective exploitation of the genetic algorithm for real-time obstacle detection, a multilevel searching strategy is proposed in order to speed-up the stereo matching process. The multilevel searching strategy consists of matching the edges at different levels by considering their gradient magnitudes. The performance of the proposed multilevel genetic stereo matching procedure is evaluated for real-time obstacle detection in front of a moving vehicle using linear stereo vision.
An In Vivo Method to Quantify Lymphangiogenesis in Zebrafish

Background Lymphangiogenesis is a highly regulated process involved in the pathogenesis of disease. Current in vivo models to assess lymphangiogenesis are largely unphysiologic. The zebrafish is a powerful model system for studying development, due to its rapid growth and transparency during early stages of life. Identification of a network of trunk lymphatic capillaries in zebrafish provides an opportunity to quantify lymphatic growth in vivo. Methods and Results Late-phase microangiography was used to detect trunk lymphatic capillaries in zebrafish 2- and 3-days post-fertilization. Using this approach, real-time changes in lymphatic capillary development were measured in response to modulators of lymphangiogenesis. Recombinant human vascular endothelial growth factor (VEGF)-C added directly to the zebrafish aqueous environment as well as human endothelial and mouse melanoma cell transplantation resulted in increased lymphatic capillary growth, while morpholino-based knockdown of vegfc and chemical inhibitors of lymphangiogenesis added to the aqueous environment resulted in decreased lymphatic capillary growth. Conclusion Lymphatic capillaries in embryonic and larval zebrafish can be quantified using late-phase microangiography. Human activators and small molecule inhibitors of lymphangiogenesis, as well as transplanted human endothelial and mouse melanoma cells, alter lymphatic capillary development in zebrafish. The ability to rapidly quantify changes in lymphatic growth under physiologic conditions will allow for broad screening of lymphangiogenesis modulators, as well as help define cellular roles and elucidate pathways of lymphatic development.
Antibiotic-Free Selection in Biotherapeutics: Now and Forever

The continuously improving sophistication of molecular engineering techniques gives access to novel classes of bio-therapeutics and new challenges for their production in full respect of the strengthening regulations. Among these biologic agents are DNA based vaccines or gene therapy products and to a lesser extent genetically engineered live vaccines or delivery vehicles. The use of antibiotic-based selection, frequently associated with genetic manipulation of microorganism is currently undergoing a profound metamorphosis with the implementation and diversification of alternative selection means. This short review will present examples of alternatives to antibiotic selection and their context of application to highlight their ineluctable invasion of the bio-therapeutic world.
Soil Nitrogen Availability and Plant Genotype Modify the Nutrition Strategies of M. truncatula and the Associated Rhizosphere Microbial Communities

Plant and soil types are usually considered as the two main drivers of the rhizosphere microbial communities. The aim of this work was to study the effect of both N availability and plant genotype on the plant associated rhizosphere microbial communities, in relation to the nutritional strategies of the plant-microbe interactions, for six contrasted Medicago truncatula genotypes. The plants were provided with two different nutrient solutions varying in their nitrate concentrations (0 mM and 10 mM). First, the influence of both nitrogen availability and Medicago truncatula genotype on the genetic structure of the soil bacterial and fungal communities was determined by DNA fingerprint using Automated Ribosomal Intergenic Spacer Analysis (ARISA). Secondly, the different nutritional strategies of the plant-microbe interactions were evaluated using an ecophysiological framework. We observed that nitrogen availability affected rhizosphere bacterial communities only in presence of the plant. Furthermore, we showed that the influence of nitrogen availability on rhizosphere bacterial communities was dependent on the different genotypes of Medicago truncatula. Finally, the nutritional strategies of the plant varied greatly in response to a modification of nitrogen availability. A new conceptual framework was thus developed to study plant-microbe interactions. This framework led to the identification of three contrasted structural and functional adaptive responses of plant-microbe interactions to nitrogen availability.
Bismarck: the White Revolutionary

Part 3 The sorcerer's apprentice: new constellations, new conflicts the Reich and Europe a change of course at home foreign policy reorientation, domestic policy choices new paths to old objectives - the foreign policy of the 1880s the "stopgap" system - domestic policy afte 1881 the end the shadow of the past.
A Novel PET Imaging Using 64Cu-Labeled Monoclonal Antibody against Mesothelin Commonly Expressed on Cancer Cells

Mesothelin (MSLN) is a 40-kDa cell differentiation-associated glycoprotein appearing with carcinogenesis and is highly expressed in many human cancers, including the majority of pancreatic adenocarcinomas, ovarian cancers, and mesotheliomas, while its expression in normal tissue is limited to mesothelial cells lining the pleura, pericardium, and peritoneum. Clone 11-25 is a murine hybridoma secreting monoclonal antibody (mAb) against human MSLN. In this study, we applied the 11-25 mAb to in vivo imaging to detect MSLN-expressing tumors. In in vitro and ex vivo immunochemical studies, we demonstrated specificity of 11-25 mAb to membranous MSLN expressed on several pancreatic cancer cells. We showed the accumulation of Alexa Fluor 750-labeled 11-25 mAb in MSLN-expressing tumor xenografts in athymic nude mice. Then, 11-25 mAb was labeled with 64Cu via a chelating agent DOTA and was used in both in vitro cell binding assay and in vivo positron emission tomography (PET) imaging in the tumor-bearing mice. We confirmed that 64Cu-labeled 11-25 mAb highly accumulated in MSLN-expressing tumors as compared to MSLN-negative ones. The 64Cu-labeled 11-25 mAb is potentially useful as a PET probe capable of being used for wide range of tumors, rather than 18F-FDG that occasionally provides nonspecific accumulation into the inflammatory lesions.