nils-herrmann/clean_scirepeval huggingface dataset:
An enterovirus from a captive primate in China
Venue: SpringerPlus

BackgroundEnteroviruses (EVs) are a genetically and antigenically diverse group of viruses infecting humans and a variety of animals including non human primates (NHPs) The present study was to investigate EVs in the fecal samples from captive NHPs in zoos in China using classic RT PCR and viral metagenomics methods.FindingsAn EV strain was detected in a fecal sample collected from a captive NHP of a zoo in eastern China. The complete genome of this EV strain (named Sev nj1) was determined and characterized. Sequence analysis indicated Sev nj1 shared the highest sequence identity (75.6 with an EV J strain, Poo 1, based on the complete genome. Phylogenetic analysis showed Sev nj1 clustered with the other EV J strains, forming a separate clade.DiscussionAccording to the genetic distance based criteria, Sev nj1 belonged to a new type within the species EV J. This is the first study detecting EV J from a NHP in China, which will be helpful for the future epidemiology study of EVs in NHPs.
Hospital resilience informed decision making: uncertainties and interdependencies

Hospitals are at the heart of the healthcare service and emergency planning. Disruptions in hospitals may inflict large consequences to health and safety of a community, especially in emergencies. Since hospitals' services are essential for effective emergency and disaster response, breakdowns (or outages/interruption) may cause direct and/or indirect impact in the form of delayed or hampered response. Therefore, hospital resilience is negatively affected by uncertainties and interdependencies induced threats and/or failures. The dependency/interdependency and complexity of the infrastructures, which provide services to hospitals, are numerous. This increases the risk of interdependency induced failures to hospital services and the impacts can get propagated to people and society that use the hospital services. In addition to that, hospital operational uncertainties are inevitable and might also cause failure/interruption to hospital services. Hence, there is a need to identify and map these uncertainties and interdependencies for more resilience informed decisions on hospitals' operation in case of emergencies. In response to this need, this paper aims to introduce an integrated resilience based framework in which hospital decision making process is improved through a collaborative environment to cope with uncertainties and interdependencies induced failures. Methodologically, a comprehensive literature review has been adopted to identify the external and internal interdependencies of a typical hospital and map them through system dynamic approach. In addition to this, the authors have theoretically assessed whether different digital construction technologies, such as Building Information Modelling technologies, could be used in order to identify, visualise and map the operational uncertainties. The study concludes that the idea behind this integrated operational framework could provide resilience based strategies which actively contributes improving current practices in hospital organizations and also to introduce protocols (Business Continuity Plans) that help stakeholders to produce effective strategies in the case of disasters.
Combining K Means and K Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units
Venue: Appl. Soft Comput.

Data clustering is related to the split of a set of objects into smaller groups with common features. Several optimization techniques have been proposed to increase the performance of clustering algorithms. Swarm Intelligence (SI) algorithms are concerned with optimization problems and they have been successfully applied to different domains. In this work, a Swarm Clustering Algorithm (SCA) is proposed based on the standard K Means and on K Harmonic Means (KHM) clustering algorithms, which are used as fitness functions for a SI algorithm: Fish School Search (FSS) The motivation is to exploit the search capability of SI algorithms and to avoid the major limitation of falling into locally optimal values of the K Means algorithm. Because of the inherent parallel nature of the SI algorithms, since the fitness function can be evaluated for each individual in an isolated manner, we have developed the parallel implementation on GPU of the SCAs, comparing the performances with their serial implementation. The interest behind proposing SCA is to verify the ability of FSS algorithm to deal with the clustering task and to study the difference of performance of FSS SCA implemented on CPU and on GPU. Experiments with 13 benchmark datasets have shown similar or slightly better quality of the results compared to standard K Means algorithm and Particle Swarm Algorithm (PSO) algorithm. There results of using FSS for clustering are promising.
Big Data Driven Service Composition Using Parallel Clustered Particle Swarm Optimization in Mobile Environment
Venue: IEEE Transactions on Services Computing

The proliferation of mobile computing and smartphone technologies has resulted in an increasing number and range of services from myriad service providers. These mobile service providers support numerous emerging services with differing quality metrics but similar functionality. Facilitating an automated service workflow requires fast selection and composition of services from the services pool. The mobile environment is ambient and dynamic in nature, requiring more efficient techniques to deliver the required service composition promptly to users. Selecting the optimum required services in a minimal time from the numerous sets of dynamic services is a challenge. This work addresses the challenge as an optimization problem. An algorithm is developed by combining particle swarm optimization and k means clustering. It runs in parallel using MapReduce in the Hadoop platform. By using parallel processing, the optimum service composition is obtained in significantly less time than alternative algorithms. This is essential for handling large amounts of heterogeneous data and services from various sources in the mobile environment. The suitability of this proposed approach for big data driven service composition is validated through modeling and simulation.
The middle of the specified node set of shortest path algorithm
Venue: 2016 IEEE 13th International Conference on Signal Processing (ICSP)

The vast majority of researches about the shortest path algorithm, nowadays, focus just on the case starting from the beginning point and ending at the ending point. If additional condition that the shortest path must go through some given nodes of which number is uncertain must be met, then most of the existing classic algorithms are not applicable. A general method based on the Virus Co Evolutionary Genetic algorithm and greedy algorithm is presented to solve this kind of problem. The time complexity of the algorithm is given by theoretical analysis and the effectiveness of the algorithm is verified by programming calculation. Furthermore, this algorithm in HUAWEI Code Craft 2016 makes 17th score in preliminary to the semi finals.
Interval Type 2 Fuzzy Set in Fuzzy Shortest Path Problem

The shortest path problem (SPP) is one of the most important combinatorial optimization problems in graph theory due to its various applications. The uncertainty existing in the real world problems makes it difficult to determine the arc lengths exactly. The fuzzy set is one of the popular tools to represent and handle uncertainty in information due to incompleteness or inexactness. In most cases, the SPP in fuzzy graph, called the fuzzy shortest path problem (FSPP) uses type 1 fuzzy set (T1FS) as arc length. Uncertainty in the evaluation of membership degrees due to inexactness of human perception is not considered in T1FS. An interval type 2 fuzzy set (IT2FS) is able to tackle this uncertainty. In this paper, we use IT2FSs to represent the arc lengths of a fuzzy graph for FSPP. We call this problem an interval type 2 fuzzy shortest path problem (IT2FSPP) We describe the utility of IT2FSs as arc lengths and its application in different real world shortest path problems. Here, we propose an algorithm for IT2FSPP. In the proposed algorithm, we incorporate the uncertainty in Dijkstra's algorithm for SPP using IT2FS as arc length. The path algebra corresponding to the proposed algorithm and the generalized algorithm based on the path algebra are also presented here. Numerical examples are used to illustrate the effectiveness of the proposed approach.
DSMR: A Parallel Algorithm for Single Source Shortest Path Problem
Venue: ICS

The Single Source Shortest Path (SSSP) problem consists in finding the shortest paths from a vertex (the source vertex) to all other vertices in a graph. SSSP has numerous applications. For some algorithms and applications, it is useful to solve the SSSP problem in parallel. This is the case of Betweenness Centrality which solves the SSSP problem for multiple source vertices in large graphs. In this paper, we introduce the Dijkstra Strip Mined Relaxation (DSMR) algorithm, an efficient parallel SSSP algorithm for shared and distributed memory systems. We also introduce a set of preprocessing optimization techniques that significantly reduce the communication overhead without increasing the total amount of work dramatically. Our results show that, DSMR is faster than the best previous algorithm, parallel D Stepping, by up to 7.38x.
The Canadian context for evidence based conservation and environmental management
Venue: Environmental Evidence

Canada has strong institutional capacity for science based decision making related to natural resource conservation and environmental management. Yet, the concept of using systematic reviews (conducted in accordance with established guidelines) to support evidence based conservation and environmental management in Canada is in its infancy. Here we discuss the Canadian context for implementing more rigorous evidence based approaches using systematic reviews. Of particular relevance to Canada is its vast size, broad diversity of ecosystems and heavy economic reliance on natural resources that vary widely in the type and scale of their environmental effects. These factors result in a wide variety of environmental monitoring needs over an extensive area that pose challenges to the scientific community charged with overseeing wise use of the environment. In addition, there are diverse and engaged user groups (e.g. hunters, trappers, fishers, bird watchers, foresters) and indigenous peoples that have constitutional rights to their natural resources. Traditional environmental knowledge is a complementary source of evidence in the Canadian environmental impact assessment process and therefore must be a part of evidence synthesis. Systematic reviews are not intended to replace local field studies, but rather have the opportunity to draw upon a broader suite of evidence that can be interfaced with local perspectives. The existing institutional structures in Canada could easily incorporate systematic reviews into their science advice and decision making frameworks but to date, there are few examples of where this has occurred. Drawing on the expertise of a growing global collaboration for environmental evidence synthesis, Canadian institutions (federal, provincial and NGO) are poised to more broadly incorporate systematic reviews once their benefits are fully realized and the capacity to undertake such systematic reviews is fully developed. Systematic reviews offer a consolidated view of the available scientific literature on a given question. The results may offer significant value when working with stakeholders and decision makers contributing other sources of information to the question. For example, mechanisms to capture and integrate scientific knowledge with stakeholder and traditional knowledge may benefit from the scientific sources being filtered, interpreted and summarized for discussion. In other cases, where timeframes for decision making preclude formal systematic reviews, opportunities for more rapid evidence synthesis methods will be needed before the concept will be fully embraced.
Anthocyanin composition, antioxidant efficiency, and a amylase inhibitor activity of different Hungarian sour cherry varieties (Prunus cerasus L.
Venue: Food chemistry

Five Hungarian sour cherry cultivars were studied to determine their anthocyanin contents and their possible inhibitory properties. The water and methanol soluble antioxidant capacities were separately assessed by photoluminescence showing values ranged from 3.4mgmg( 1) to 15.4mgmg( 1) respectively. The "VN1" variety (selected from "Csengodi csokros" showed the highest antioxidant capacity. The anthocyanin content, measured by pH differential method or isolated by solid phase extraction, was the highest also in "VN1" Correlation was found between the anthocyanin content and the high antioxidant capacity. The main anthocyanin components were cyanidin 3 O rutinoside and cyanidin 3 O glucoside. The presence of malvidin 3,5 O diglycoside was verified by MALDI TOF MS. Sour cherry extracts and selected anthocyanins inhibited the human salivary alpha amylase catalyzed hydrolysis competitively. The lowest IC50 value, 55mgmL( 1) or 80mM, was measured for malvidin 3,5 O diglycoside, for which possible binding modes within the alpha amylase active site could be investigated in silico using molecular docking and molecular dynamics.
Workers with disabilities in sheltered employment centres: a training needs analysis

The work presented is part of a study that the research group CIFO (Research Team in Training for the Labour Market, Autonomous University of Barcelona, Barcelona) has carried out, relating to the training needs analysis and basic competences and skills in the environment of sheltered employment centres (SECs) in Catalonia (this study was undertaken in collaboration with the Coordination of Workshops for People with Mental Disabilities in Catalonia) The analysis and assessment of needs is useful from the perspective of training, as a starting point for planning adapted to the reality of disabled workers in SECs. To do this, various elements such as skills, knowledge and attitudes of individual and social nature have been considered from a systemic approach. By placing ourselves in clearly defined contexts (SECs) we bear in mind the characteristics of workers, the features of the job context, the types of activities performed, the skills related to work activities carried out, the definition of competence profiles required on the basis of the essential skills and the real training needs, through the characteristics of workers and those skills related to the labour activities that are performed as well as the needs for training perceived by workers. Identifying training needs in relation to basic skills and job requirements allows us to meet the challenge, as a trend that supports a competence basis for any worker, of raising general actions that may govern and underlie the training proposals, yet without undermining the specific competences. The importance acquired by what workers themselves have considered about their positioning is understood from the standpoint of its validity, so as to assume conclusions through triangulation processes.
Preparation of Questionnaire for Training Needs Analysis of Construction Project

Training needs analysis (TNA) determines the gap between 'what is' and 'what should be' situations. Construction industry is very important for progress, development and economic growth of a country and therefore the task of analyzing training needs in construction projects is very important aspect in the present scenario of Indian construction industry. Most of the construction projects are running behind the schedule and training imparted to employees is not based on systematic analysis. In this study a model questionnaire has been prepared which is unique because questions related to training needs analysis as well as causes of delay have been included. The data so obtained will be easy for analysis and the results of hypothesis testing will be more reliable. Models developed as the result of TNA will be more realistic as in addition to questions related to TNA, it takes into consideration the most serious problem of construction work i.e. delay which is responsible for time and cost overrun. KeywordsTraining needs analysis, Construction project, Ethical issues, Relative Importance Index (R.I.I. Spearman's rank correlation, 5point Likert scale, Variables, Anova in dependent t tests.
Training As the Indicator of Female Entrepreneurship Development, and Training Needs Analysis

The paper elaborates the strategic need to support entrepreneurship and women entrepreneurs in particular, highlighting the need for an organized approach towards development of the professional and managerial capacities of small and medium enterprises (SMEs) in Serbia, with a special focus on women owned enterprises. First part of the paper deals with the women entrepreneurship (WE) importance rationale, following the presentation of the newest EU policies regarding women entrepreneurship development based on Small Business Act (SBA) SBA is a set of ten policy principles which form the framework for policies explicitly aimed at the support of SME's, both at individual (national) levels as well as in the EU. Among the 10 SBA principles, indicators of measuring female entreprenurship (WE indicators) are involved in the first one: Create an environment in which entrepreneurs and family businesses can thrive and entrepreneurship is rewarded (SBA, 2008) Education and training of WE, as one of the four indicators, is elaborated further on. Therefore, the methodology of the training needs assessment (TNA)is elaborated, as well as the analysis of the conducted TNA survey results. Special focus is given to business performance, information and networking and human resources and training.
Binding Cooperativity Matters: A GM1 Like Ganglioside Cholera Toxin B Subunit Binding Study Using a Nanocube Based Lipid Bilayer Array
Venue: PloS one

Protein glycan recognition is often mediated by multivalent binding. These multivalent bindings can be further complicated by cooperative interactions between glycans and individual glycan binding subunits. Here we have demonstrated a nanocube based lipid bilayer array capable of quantitatively elucidating binding dissociation constants, maximum binding capacity, and binding cooperativity in a high throughput format. Taking cholera toxin B subunit (CTB) as a model cooperativity system, we studied both GM1 and GM1 like gangliosides binding to CTB. We confirmed the previously observed CTB GM1 positive cooperativity. Surprisingly, we demonstrated fucosyl GM1 has approximately 7 times higher CTB binding capacity than GM1. In order to explain this phenomenon, we hypothesized that the reduced binding cooperativity of fucosyl GM1 caused the increased binding capacity. This was unintuitive, as GM1 exhibited higher binding avidity (16 times lower dissociation constant) We confirmed the hypothesis using a theoretical stepwise binding model of CTB. Moreover, by taking a mixture of fucosyl GM1 and GM2, we observed the mild binding avidity fucosyl GM1 activated GM2 receptors enhancing the binding capacity of the lipid bilayer surface. This was unexpected as GM2 receptors have negligible binding avidity in pure GM2 bilayers. These unexpected discoveries demonstrate the importance of binding cooperativity in multivalent binding mechanisms. Thus, quantitative analysis of multivalent protein glycan interactions in heterogeneous glycan systems is of critical importance. Our user friendly, robust, and high throughput nanocube based lipid bilayer array offers an attractive method for dissecting these complex mechanisms.
Proximity vital sign sensor using self oscillating mixer
Venue: 2016 URSI Asia Pacific Radio Science Conference (URSI AP RASC)

This paper presents a design of proximity vital sign sensor using a self oscillating mixer (SOM) at 2.45 GHz. The proposed system consists of a planar resonator that determines the oscillation frequency and a transistor that functions not only as an oscillator but also as a mixer. Note that the supply voltage of the transistor is in pinch off region to maximize non linearity. The input impedance of the resonator is varied due to the distance variation between the human body and the resonator. This variation of input impedance changes the oscillation frequency of the proposed sensor. Then, this frequency deviation is self mixed in the transistor. It can be detected at the emitter in a form of DC voltage, because the proposed system is a kind of direct conversion receiver. So, if we track the emitter voltage, we can obtain the vital sign of the human subject. The measurement results confirm that the proposed sensor can detect the vital sign at 20 mm away from a subject.
Summary and Statistical Analysis of the First AIAA Sonic Boom Prediction Workshop

uid dynamics solutions are gathered from nineteen participants representing three countries for the two required cases, an axisymmetric body and simple delta wing body. Structured multiblock, unstructured mixed element, unstructured tetrahedral, overset, and Cartesian cut cell methods are used by the participants. Participants provided signatures computed on participant generated and solution adapted grids. Signatures are also provided for a series of uniformly rened workshop provided grids. These submissions are propagated to the ground and noise measures are computed. This allows the grid convergence of a noise measure and a validation metric (dierence norm between computed and wind tunnel measured near eld signatures) to be studied for the rst time. A statistical analysis is also presented for these measures. An optional conguration includes fuselage, wing, tail, ow through nacelles, and blade sting. This full conguration exhibits more variation in eleven submissions than the sixty submissions provided for each required case. Recommendations are provided for potential improvements to the analysis methods and a possible subsequent workshop.
On the Security and Performance of Proof of Work Blockchains
Venue: IACR Cryptol. ePrint Arch.

Proof of Work (PoW) powered blockchains currently account for more than 90% of the total market capitalization of existing digital cryptocurrencies. Although the security provisions of Bitcoin have been thoroughly analysed, the security guarantees of variant (forked) PoW blockchains (which were instantiated with different parameters) have not received much attention in the literature. This opens the question whether existing security analysis of Bitcoin's PoW applies to other implementations which have been instantiated with different consensus and/or network parameters. In this paper, we introduce a novel quantitative framework to analyse the security and performance implications of various consensus and network parameters of PoW blockchains. Based on our framework, we devise optimal adversarial strategies for double spending and selfish mining while taking into account real world constraints such as network propagation, different block sizes, block generation intervals, information propagation mechanism, and the impact of eclipse attacks. Our framework therefore allows us to capture existing PoW based deployments as well as PoW blockchain variants that are instantiated with different parameters, and to objectively compare the tradeoffs between their performance and security provisions.
Incentive strategies for an optimal recovery program in a closed loop supply chain
Venue: Eur. J. Oper. Res.

We consider a dynamic closed loop supply chain made up of one manufacturer and one retailer, with both players investing in a product recovery program to increase the rate of return of previously purchased products. End of use product returns have two impacts. First, they lead to a decrease in the production cost, as manufacturing with used parts is cheaper than using virgin materials. Second, returns boost sales through replacement items.