nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of the effects of commonly used wound agents on epithelialization and neovascularization.

The primary effect sought with most topical wound therapy is antimicrobial. Topical wound agents are thought to promote normal healing by protecting the wound from infection. In this study, we examined the effect of six commonly used topical wound agents (bacitracin, sodium hypochlorite, silver nitrate, silver sulfadiazine, mafenide acetate, and povidone-iodine) on epithelialization and neovascularization in noninfected wounds. For this study, a new wound model was used in which direct visualization and quantification of wound epithelialization and neovascularization were carried out throughout the entire healing process.



We measured the effect which 500 U per g of bacitracin, 0.25 percent of sodium hypochlorite, 0.5 percent silver nitrate, 1 percent silver sulfadiazine, 8.5 percent mafenide acetate, and 10 percent povodione-iodine had on the rate of wound epithelialization and neovascularization. The agents were applied topically to 99 circular full-thickness wounds (2.25 mm diameter, 0.125 mm depth) created on the dorsum of male hairless mouse ears. This model enabled us to visualize and measure directly wound epithelialization and neovascularization repeatedly throughout healing, using intravital video microscopy and computerized digitized planimetry.



Control wounds and wounds treated with silver sulfadiazine (n = 18) and mafenide acetate (n = 14) epithelialized in 7.2 +/- 0.7, 7.1 +/- 0.3, and 7.3 +/- 0.3 days, respectively. This was significantly (p < 0.01) faster than the wounds treated with povidone-iodine (n = 10), sodium hypochlorite, (n = 8), and bacitracin (n = 13). Wounds treated with povidone-iodine epithelialized the slowest (11.8 +/- 0.55 days). Wound neovascularization was completed most rapidly in the groups treated with povidone-iodine and silver sulfadiazine (15.0 +/- 0.4 and 15.3 +/- 0.7 days, respectively). This was significantly (p < 0.05) faster than wounds treated with silver nitrate (n = 15), which neovascularized in 18.4 +/- 0.56 days. One-half of the wounds treated with sodium hypochlorite (eight of 16) did not epithelialize or neovascularize.



The various antimicrobial agents studied in our in vivo model affect wound epithelialization and neovascularization differently. These effects on these two very important aspects of healing should be taken into consideration when indicating a specific agent for treatment of different types of wounds.
Optically resonant dielectric nanostructures

Rapid progress in nanophotonics is driven by the ability of optically resonant nanostructures to enhance near-field effects controlling far-field scattering through intermodal interference. A majority of such effects are usually associated with plasmonic nanostructures. Recently, a new branch of nanophotonics has emerged that seeks to manipulate the strong, optically induced electric and magnetic Mie resonances in dielectric nanoparticles with high refractive index. In the design of optical nanoantennas and metasurfaces, dielectric nanoparticles offer the opportunity for reducing dissipative losses and achieving large resonant enhancement of both electric and magnetic fields. We review this rapidly developing field and demonstrate that the magnetic response of dielectric nanostructures can lead to novel physical effects and applications.
Emergent Sources of Prebiotics: Seaweeds and Microalgae.

In recent years, scientists have become aware that human microbiota, in general, and gut microbiota, in particular, play a major role in human health and diseases, such as obesity and diabetes, among others. A large number of evidence has come to light regarding the beneficial effects, either for the host or the gut microbiota, of some foods and food ingredients or biochemical compounds. Among these, the most promising seem to be polysaccharides (PS) or their derivatives, and they include the dietary fibers. Some of these PS can be found in seaweeds and microalgae, some being soluble fibers, such as alginates, fucoidans, carrageenans and exopolysaccharides, that are not fermented, at least not completely, by colonic microbiota. This review gives an overview of the importance of the dietary fibers, as well as the benefits of prebiotics, to human health. The potential of the PS from marine macro- and microalgae to act as prebiotics is discussed, and the different techniques to obtain oligosaccharides from PS are presented. The mechanisms of the benefits of fiber, in general, and the types and benefits of algal fibers in human health are highlighted. The findings of some recent studies that present the potential effects of prebiotics on animal models of algal biomass and their extracts, as well as oligo- and polysaccharides, are presented. In the future, the possibility of using prebiotics to modulate the microbiome, and, consequently, prevent certain human diseases is foreseen.
A Novel TRM Calibration Method for Improvement of Modelling Accuracy at mm-wave Frequency

Ahstract-A novel TRM calibration procedure aimed to improve the quality of on-wafer S-parameter measurement, especially in mm-wave frequency band, has been proposed. This procedure is based on active reverse signal injections to improve the accuracy of the raw thru s-parameter measurement. This calibration method can effectively improve the S-parameter measurement quality at mm-wave frequency and hence improve the modelling accuracy. This new optimized calibration method eliminates the need of utilizing complex and expensive loadpull system or post calibration optimization algorithms, and can be easily implemented in modelling extraction process or further implemented into other LRL/TRL based calibration algorithms. Finally, this proposed method has been tested on a real measurement system over a 16nm FinFET CMOS device to test its validity.
The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics

The emergence of big data has stimulated enormous investments into business analytics solutions, but large-scale and reliable empirical evidence about the business value of big data and analytics (BDA) remains scarce. This paper presents the results of an econometric study that analyzes the direction, sign, and magnitude of the relationship between BDA and firm performance based on objective measurements of BDA assets. Using a unique panel data set that contains detailed information about BDA solutions owned by 814 companies during the timeframe from 2008 to 2014, on the one hand, and their financial performance, on the other hand, we estimate the relationship between BDA assets and firm productivity and find that live BDA assets are associated with an average of 3 to 7 percent improvement in firm productivity. Yet, we also find substantial differences in returns from BDA when we consider the industry in which a firm operates: While firms in IT-intensive or highly competitive industries are clearly able to extract value from BDA assets, we did not detect measurable productivity improvement for firms outside these industry groups. Taken together, our findings provide robust empirical evidence for the business value of BDA, but also highlight important boundary conditions.
Toward Quantifying Vertex Similarity in Networks

Vertex similarity is a major problem in network science with a wide range of applications. In this work we provide novel perspectives on finding (dis)similar vertices within a network and across two networks with the same number of vertices (graph matching). With respect to the former problem, we propose to optimize a geometric objective which allows us to express each vertex uniquely as a convex combination of a few extreme types of vertices. Our method has the important advantage of supporting efficiently several types of queries such as “which other vertices are most similar to this vertex?” by the use of the appropriate data structures and of mining interesting patterns in the network. With respect to the latter problem (graph matching), we propose the generalized condition number –a quantity widely used in numerical analysis– κ(LG, LH) of the Laplacian matrix representations of G,H as a measure of graph similarity, where G,H are the graphs of interest. We show that this objective has a solid theoretical basis and propose a deterministic and a randomized graph alignment algorithm. We evaluate our algorithms on both synthetic and real data. We observe that our proposed methods achieve high-quality results and provide us with significant insights into the network structure.
A quantitative analysis of factors contributing electronic data interchange adoption among Bruneian SMEs: A pilot study

This study investigates fifty small and medium enterprises by using a survey approach to find out the key factors that are determinants to EDI adoption. Based upon the existing model, the study uses six factors grouped into three categories, namely organizational, environmental and technological aspects. The findings indicate that factors such as perceived benefits government support and management support are significant determinants of EDI adoption. The remaining factors like organizational culture, motivation to use EDI and task variety remain insignificant. Based upon the analysis of data, recommendations are made.
Co-hierarchical analysis of shape structures

We introduce an unsupervised co-hierarchical analysis of a set of shapes, aimed at discovering their hierarchical part structures and revealing relations between geometrically dissimilar yet functionally equivalent shape parts across the set. The core problem is that of representative co-selection. For each shape in the set, one representative hierarchy (tree) is selected from among many possible interpretations of the hierarchical structure of the shape. Collectively, the selected tree representatives maximize the within-cluster structural similarity among them. We develop an iterative algorithm for representative co-selection. At each step, a novel cluster-and-select scheme is applied to a set of candidate trees for all the shapes. The tree-to-tree distance for clustering caters to structural shape analysis by focusing on spatial arrangement of shape parts, rather than their geometric details. The final set of representative trees are unified to form a structural co-hierarchy. We demonstrate co-hierarchical analysis on families of man-made shapes exhibiting high degrees of geometric and finer-scale structural variabilities.
Platform Support for Mobile Edge Computing

Computing resources including mobile devices at the edge of a network are increasingly connected and capable of collaboratively processing what's believed to be too complex to them. Collaboration possibilities with today's feature-rich mobile devices go far beyond simple media content sharing, traditional video conferencing and cloud-based software as a services. The realization of these possibilities for mobile edge computing (MEC) requires non-trivial amounts of efforts in enabling multi-device resource sharing. The current practice of mobile collaborative application development remains largely at the application level. In this paper, we present CollaboRoid, a platform-level solution that provides a set of system services for mobile collaboration. CollaboRoid's platform-level design significantly eases the development of mobile collaborative applications promoting MEC. In particular, it abstracts the sharing of not only hardware resources, but also software resources and multimedia contents between multiple heterogeneous mobile devices. We implement CollaboRoid in the application framework layer of the Android stack and evaluate it with several collaboration scenarios on Nexus 5 and 7 devices. Our experimental results show the feasibility of the platform-level collaboration using CollaboRoid in terms of the latency and energy consumption.
Glutamic acid as anticancer agent: An overview.

The objective of the article is to highlight various roles of glutamic acid like endogenic anticancer agent, conjugates to anticancer agents, and derivatives of glutamic acid as possible anticancer agents. Besides these emphases are given especially for two endogenous derivatives of glutamic acid such as glutamine and glutamate. Glutamine is a derivative of glutamic acid and is formed in the body from glutamic acid and ammonia in an energy requiring reaction catalyzed by glutamine synthase. It also possesses anticancer activity. So the transportation and metabolism of glutamine are also discussed for better understanding the role of glutamic acid. Glutamates are the carboxylate anions and salts of glutamic acid. Here the roles of various enzymes required for the metabolism of glutamates are also discussed.
Speech2Health: A Mobile Framework for Monitoring Dietary Composition From Spoken Data

Diet and physical activity are known as important lifestyle factors in self-management and prevention of many chronic diseases. Mobile sensors such as accelerometers have been used to measure physical activity or detect eating time. In many intervention studies, however, stringent monitoring of overall dietary composition and energy intake is needed. Currently, such a monitoring relies on self-reported data by either entering text or taking an image that represents food intake. These approaches suffer from limitations such as low adherence in technology adoption and time sensitivity to the diet intake context. In order to address these limitations, we introduce development and validation of Speech2Health, a voice-based mobile nutrition monitoring system that devises speech processing, natural language processing (NLP), and text mining techniques in a unified platform to facilitate nutrition monitoring. After converting the spoken data to text, nutrition-specific data are identified within the text using an NLP-based approach that combines standard NLP with our introduced pattern mapping technique. We then develop a tiered matching algorithm to search the food name in our nutrition database and accurately compute calorie intake values. We evaluate Speech2Health using real data collected with 30 participants. Our experimental results show that Speech2Health achieves an accuracy of 92.2% in computing calorie intake. Furthermore, our user study demonstrates that Speech2Health achieves significantly higher scores on technology adoption metrics compared to text-based and image-based nutrition monitoring. Our research demonstrates that new sensor modalities such as voice can be used either standalone or as a complementary source of information to existing modalities to improve the accuracy and acceptability of mobile health technologies for dietary composition monitoring.
Big Data management in smart grid: concepts, requirements and implementation

A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. In essence, smart grids bring profound changes in the information systems that drive them: new information flows coming from the electricity grid, new players such as decentralized producers of renewable energies, new uses such as electric vehicles and connected houses and new communicating equipments such as smart meters, sensors and remote control points. All this will cause a deluge of data that the energy companies will have to face. Big Data technologies offers suitable solutions for utilities, but the decision about which Big Data technology to use is critical. In this paper, we provide an overview of data management for smart grids, summarise the added value of Big Data technologies for this kind of data, and discuss the technical requirements, the tools and the main steps to implement Big Data solutions in the smart grid context.
Taskonomy: Disentangling Task Transfer Learning

Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity. We proposes a fully computational approach for modeling the structure of space of visual tasks. This is done via finding (first and higher-order) transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D, and semantic tasks in a latent space. The product is a computational taxonomic map for task transfer learning. We study the consequences of this structure, e.g. nontrivial emerged relationships, and exploit them to reduce the demand for labeled data. We provide a set of tools for computing and probing this taxonomical structure including a solver users can employ to find supervision policies for their use cases.
Effects of Ketamine in Normal and Schizophrenic Volunteers

This study evaluates the effects of ketamine on healthy and schizophrenic volunteers (SVs) in an effort to define the detailed behavioral effects of the drug in a psychosis model. We compared the effects of ketamine on normal and SVs to establish the comparability of their responses and the extent to which normal subjects might be used experimentally as a model. Eighteen normal volunteers (NVs) and 17 SVs participated in ketamine interviews. Some (n = 7 NVs; n = 9 SVs) had four sessions with a 0.1–0.5 mg/kg of ketamine and a placebo; others (n = 11 NVs; n = 8 SVs) had two sessions with one dose of ketamine (0.3 mg/kg) and a placebo. Experienced research clinicians used the BPRS to assess any change in mental status over time and documented the specifics in a timely way. In both volunteer groups, ketamine induced a dose-related, short (<30 min) increase in psychotic symptoms. The scores of NVs increased on both the Brief Psychiatric Rating Scale (BPRS) psychosis subscale (p = .0001) and the BPRS withdrawal subscale (p = .0001), whereas SVs experienced an increase only in positive symptoms (p = .0001). Seventy percent of the patients reported an increase (i.e., exacerbation) of previously experienced positive symptoms. Normal and schizophrenic groups differed only on the BPRS withdrawal score. The magnitude of ketamine-induced changes in positive symptoms was similar, although the psychosis baseline differed, and the dose-response profiles over time were superimposable across the two populations. The similarity between ketamine-induced symptoms in SVs and their own positive symptoms suggests that ketamine provides a unique model of psychosis in human volunteers. The data suggest that the phencyclidine (PCP) model of schizophrenia maybe a more valid human psychosis/schizophrenia drug model than the amphetamine model, with a broader range of psychotic symptoms. This study indicates that NVs could be used for many informative experimental psychosis studies involving ketamine interviews.
Foreshadow: Extracting the Keys to the Intel SGX Kingdom with Transient Out-of-Order Execution

Trusted execution environments, and particularly the Software Guard eXtensions (SGX) included in recent Intel x86 processors, gained significant traction in recent years. A long track of research papers, and increasingly also realworld industry applications, take advantage of the strong hardware-enforced confidentiality and integrity guarantees provided by Intel SGX. Ultimately, enclaved execution holds the compelling potential of securely offloading sensitive computations to untrusted remote platforms. We present Foreshadow, a practical software-only microarchitectural attack that decisively dismantles the security objectives of current SGX implementations. Crucially, unlike previous SGX attacks, we do not make any assumptions on the victim enclave’s code and do not necessarily require kernel-level access. At its core, Foreshadow abuses a speculative execution bug in modern Intel processors, on top of which we develop a novel exploitation methodology to reliably leak plaintext enclave secrets from the CPU cache. We demonstrate our attacks by extracting full cryptographic keys from Intel’s vetted architectural enclaves, and validate their correctness by launching rogue production enclaves and forging arbitrary local and remote attestation responses. The extracted remote attestation keys affect millions of devices.
OF HOTEL TAJ IN THE CONTEXT OF CRM AND CUSTOMER RETENTION

To enhance profitability and guest satisfaction and loyalty, the organizations (hotels) should focus on implementing Customer Relationship Management (CRM) strategies that aim to seek, gather and store the right information, validate and share it throughout the organization . Hotel industry is a highly flourishing, lucrative and competitive market. To compete in such a market, the hotels should focus on maintaining good relations with the customers and satisfying the customers. Increasingly, the organizations are using Customer Relationship Management (CRM) to help boost sales and revenues by focusing on customer retention and customer loyalty. The present research was undertaken to study the Customer Relationship Management (CRM) practices in hotel industry. The purpose of this study was to determine the impact of Customer Relationship Management (CRM) on customer loyalty in the hotel industry. The study was conducted at the Hotel Taj Hotel, New Delhi. The objectives of the study were to determine if (CRM) has an impact on customer retention, to determine if the practice of effective CRM in organizations leads to a long or short term financial impact, to find out the extent or degree to which effective CRM leads to customer satisfaction and to assess if the services provided by the hotel meets the needs and wants of customers. It was found that most of the employees had a positive attitude towards CRM practices and the most common activities undertaken were studying the existing database of the customers and personal counseling. The benefits of CRM are increased customer satisfaction and increased customer retention.
HapTouch and the 2+1 state model: potentials of haptic feedback on touch based in-vehicle information systems

Haptic feedback on touch-sensitive displays provides significant benefits in terms of reducing error rates, increasing interaction speed and minimizing visual distraction. This particularly holds true for multitasking situations such as the interaction with mobile devices or touch-based in-vehicle systems. In this paper, we explore how the interaction with tactile touchscreens can be modeled and enriched using a 2+1 state transition model. The model expands an approach presented by Buxton. We present HapTouch -- a force-sensitive touchscreen device with haptic feedback that allows the user to explore and manipulate interactive elements using the sense of touch. We describe the results of a preliminary quantitative study to investigate the effects of tactile feedback on the driver's visual attention, driving performance and operating error rate. In particular, we focus on how active tactile feedback allows the accurate interaction with small on-screen elements during driving. Our results show significantly reduced error rates and input time when haptic feedback is given.