nils-herrmann/clean_scirepeval huggingface dataset:
Enzymatic dehairing of cattlehide with and alkaline protease isolated from Aspergillus Tamarii
Year: 2008

An enzymatic dehairing protocol based on the alkaline serine protease, isolated from the fungus Aspergillus tamarii, required 16h, and we observed concomitant grain damage. The use of sodium dodecyl sulfate (SDS) as a pretreatment to remove the lipids from the hide allowed a shortening of the dehairing time to 6 h without grain damage. We postulated that the SDS removed all of the sebaceous grease from the pores of the hair, facilitating the penetration of the enzyme through the grain layer. Using a lypophilic dye, Nile red, we showed that SDS did remove some of the grease from the grain side of the hide. By tagging the enzyme with a fluorescent label, we clearly showed, however, that the enzyme penetrated the hide only through its flesh side. The enzymatic dehairing process did not remove the fine hairs from the hide. Adding a common sharpening agent, sodium sulfide, to the liming step removed the fine hairs but also caused grain damage. Employing an auxiliary oxidative dehairing step, based on alkaline sodium percarbonate, did remove the fine hairs; under carefully controlled conditions, grain damage was not observed. Mechanical data were collected from leather prepared from enzymatically dehaired hide that had been limed, enzymatically dehaired hide that had been further oxidatively dehaired and hide that had been dehaired with sulfide and relimed. The tensile strength of the leather made from enzymatically dehaired and oxidatively treated hide and the leather prepared from the enzymatically dehaired and traditionally relimed hide was not significantly different from the control. The leather made from the enzymatically dehaired and oxidatively treated hide, however, was significantly stronger than the leather prepared from the enzymatically dehaired and traditionally relimed hide.
Developing the health workforce for universal health coverage
Venue: Bulletin of the World Health Organization
Year: 2020

Optimizing the management of the health workforce is necessary for the progressive realization of universal health coverage. Here we discuss the six main action fields in health workforce management as identified by the Human Resources for Health Action Framework: leadership; finance; policy; education; partnership; and human resources management systems. We also identify and describe examples of effective practices in the development of the health workforce, highlighting the breadth of issues that policy makers and planners should consider. Achieving success in these action fields is not possible by pursuing them in isolation. Rather, they are interlinked functions that depend on a strong capacity for effective stewardship of health workforce policy. This stewardship capacity can be best understood as a pyramid of tools and factors that encompass the individual, organizational, institutional and health system levels, with each level depending on capacity at the level below and enabling actions at the level above. We focus on action fields covered by the organizational or system wide levels that relate to health workforce development. We consider that an analysis of the policy and governance environment and of mechanisms for health workforce policy development and implementation is required, and should guide the identification of the most relevant and appropriate levels and interventions to strengthen the capacity of health workforce stewardship and leadership. Although these action fields are relevant in all countries, there are no best practices that can simply be replicated across countries and each country must design its own responses to the challenges raised by these fields.
Growth and properties of ZnO film grown on AlN buffer layer by PLD
Year: 2012

In this work, c plane AlN/c sapphire templates have been used to grow high quality ZnO films by pulsed laser deposition. The thickness of AlN buffer layer was 150nm and the ZnO thin films was investigated using photoluminescence (PL) spectroscopy, Raman measurements, high resolution x ray diffraction (HRXRD) and atomic force microscopy (AFM)
Alzheimer's Diseases Detection by Using Deep Learning Algorithms: A Mini Review
Venue: IEEE Access
Year: 2020

The accurate diagnosis of Alzheimer's disease (AD) plays an important role in patient treatment, especially at the disease's early stages, because risk awareness allows the patients to undergo preventive measures even before the occurrence of irreversible brain damage. Although many recent studies have used computers to diagnose AD, most machine detection methods are limited by congenital observations. AD can be diagnosed but not predicted at its early stages, as prediction is only applicable before the disease manifests itself. Deep Learning (DL) has become a common technique for the early diagnosis of AD. Here, we briefly review some of the important literature on AD and explore how DL can help researchers diagnose the disease at its early stages.
An energy efficient internet of things (IoT) architecture for preventive conservation of cultural heritage
Venue: Future Gener. Comput. Syst.
Year: 2018

Internet of Things (IoT) technologies can facilitate the preventive conservation of cultural heritage (CH) by enabling the management of data collected from electronic sensors. This work presents an IoT architecture for this purpose. Firstly, we discuss the requirements from the artwork standpoint, data acquisition, cloud processing and data visualization to the end user. The results presented in this work focuses on the most critical aspect of the architecture, which are the sensor nodes. We designed a solution based on LoRa and Sigfox technologies to produce the minimum impact in the artwork, achieving a lifespan of more than 10 years. The solution will be capable of scaling the processing and storage resources, deployed either in a public or on premise cloud, embedding complex predictive models. This combination of technologies can cope with different types of cultural heritage environments.
Application of Multimedia in Basic English Vocabulary Learning with the ADDIE Method
Year: 2019

English is taught at an early age in the hope that students are better prepared and confident so they can communicate fluently at the next level of education. However, the fact is there are still many obstacles in the process of learning English, including teachers facing difficulties in the teaching process because there is no appropriate multimedia to teach English in Schools, so far the teaching and learning process is still fixed on textbooks. This of course makes most students feel bored and bored even to the point of losing motivation to learn English. This study aims to develop an interesting and interactive basic level English vocabulary learning application in addition to English language learning material. This research uses the ADDIE method in making multimedia based applications. That the application of multimedia is able to develop basic English vocabulary learning to be more interesting and interactive. Nearly 80% of the 26 respondents agreed that multimedia is able to attract students 'attention and anticipate the learning process, encourage active students to understand learning and the images and animations that are presented can increase students' enthusiasm in understanding English vocabulary at the elementary level.
Diffusion weighted imaging outside the brain: Consensus statement from an ISMRM sponsored workshop
Venue: Journal of magnetic resonance imaging JMRI
Year: 2016

The significant advances in magnetic resonance imaging (MRI) hardware and software, sequence design, and postprocessing methods have made diffusion weighted imaging (DWI) an important part of body MRI protocols and have fueled extensive research on quantitative diffusion outside the brain, particularly in the oncologic setting. In this review, we summarize the most up to date information on DWI acquisition and clinical applications outside the brain, as discussed in an ISMRM sponsored symposium held in April 2015. We first introduce recent advances in acquisition, processing, and quality control; then review scientific evidence in major organ systems; and finally describe future directions.  Imaging 2016;44:521 540.
Visual Reinforcement Learning with Imagined Goals
Venue: NeurIPS
Year: 2018

For an autonomous agent to fulfill a wide range of user specified goals at test time, it must be able to learn broadly applicable and general purpose skill repertoires. Furthermore, to provide the requisite level of generality, these skills must handle raw sensory input such as images. In this paper, we propose an algorithm that acquires such general purpose skills by combining unsupervised representation learning and reinforcement learning of goal conditioned policies. Since the particular goals that might be required at test time are not known in advance, the agent performs a self supervised "practice" phase where it imagines goals and attempts to achieve them. We learn a visual representation with three distinct purposes: sampling goals for self supervised practice, providing a structured transformation of raw sensory inputs, and computing a reward signal for goal reaching. We also propose a retroactive goal relabeling scheme to further improve the sample efficiency of our method. Our off policy algorithm is efficient enough to learn policies that operate on raw image observations and goals for a real world robotic system, and substantially outperforms prior techniques.
Reference based compressed sensing: A sample complexity approach
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2016

We address the problem of reference based compressed sensing: reconstruct a sparse signal from few linear measurements using as prior information a reference signal, a signal similar to the signal we want to reconstruct. Access to reference signals arises in applications such as medical imaging, e.g. through prior images of the same patient, and compressive video, where previously reconstructed frames can be used as reference. Our goal is to use the reference signal to reduce the number of required measurements for reconstruction. We achieve this via a reweighted l1 l1 minimization scheme that updates its weights based on a sample complexity bound. The scheme is simple, intuitive and, as our experiments show, outperforms prior algorithms, including reweighted l1 minimization, l1 l1 minimization, and modified CS.
Metabolomic Approaches to Explore Chemical Diversity of Human Breast Milk, Formula Milk and Bovine Milk
Venue: International journal of molecular sciences
Year: 2016

Although many studies have been conducted on the components present in human breast milk (HM) research on the differences of chemical metabolites between HM, bovine milk (BM) and formula milk (FM) is limited. This study was to explore the chemical diversity of HM, BM and FM by metabolomic approaches. GC TOFMS and UPLC QTOFMS were applied to investigate the metabolic compositions in 30 HM samples, 20 FM samples and 20 BM samples. Metabolite profiling identified that most of the non esterified fatty acids, which reflected the hydrolysis of triglycerides, were much more abundant in HM than those in FM and BM, except for palmitic acid and stearic acid. The levels of tricarboxylic acid (TCA) intermediates were much higher in FM and BM than those in HM. Each type of milk also showed its unique composition of free amino acids and free carbohydrates. In conclusion, higher levels of non esterified saturated fatty acids with aliphatic tails <16 carbons, monounsaturated fatty acids and polyunsaturated fatty acids and lower levels of TCA intermediates are characteristic of HM, as compared with FM and BM. The content of non esterified fatty acids may reflect the hydrolysis of triglycerides in different milk types.
Late Holocene lava flow morphotypes of northern Harrat Rahat, Kingdom of Saudi Arabia: Implications for the description of continental lava fields
Year: 2014

A "lava morphotype" refers to the recognizable and distinctive characteristics of the surface morphology of a lava flow after solidification, used in a similar way to a sedimentary facies. This classification method is explored on an example volcanic field in the Kingdom of Saudi Arabia, where copious lava outpourings may represent an important transition between monogenetic and flood basalt fields. Here, young and well preserved mafic lava fields display a wide range of surface morphologies. We focussed on four post 4500 yrs. BP lava flow fields in northern Harrat Rahat <10 Ma) and propose a framework for describing systematic changes in morphotypes down flow. The morphotypes give insight into intrinsic and extrinsic parameters of emplacement, rheology and dominant flow behavior, as well as the occurrence and character of other lava structures. The Harrat Rahat lava flow fields studied extend up to 23 km from the source, and vary between 1 2 m and 12 m in thickness. Areas of the lava flow fields are between ~32 and ~61 km2, with individual flow field volumes estimated between ~0.085 and ~0.29 km3. They exhibit Shelly Slabby and Rubbly pahoehoe, Platy Cauliflower and Rubbly a'a, and Blocky morphotypes. Morphotypes reflect the intrinsic parameters of: composition, temperature, crystallinity and volatile content/vesicularity; along with external influences, such as: emission mechanism, effusion rate, topography and slope control of flow velocity. One morphotype can transition to another in individual flow units or lobes and they may dominate zones. Not all morphotypes were found in a single lava flow field. Pahoehoe morphotypes are related to the simple mechanical disaggregation of the crust, whereas a'a morphotypes are related to the transitional emergence and subsequent transitional disappearance of clinker. Blocky morphotypes result from fracturing and auto brecciation. A'a morphotypes (i.e. platy cauliflower rubbly a'a) dominate the lava flow field surfaces in northern Harrat Rahat, which suggests that core dominated flows were predominant during flow movement. Lava structures are well developed and well preserved and some may be related to some morphotypes. Down flow changes exhibit key illustrative and easy recognizable features in the lava flow fields and might provide insights into real time monitoring of future flows in this region.
Separating climate change and human contributions to variations in streamflow and its components using eight time trend methods
Year: 2019

Northwest Institute of Eco Environment and Resources, Chinese Academy of Sciences, Lanzhou, China Ministry of Education, Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing, China 3 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Hydrology Water Resources Bureau of Gansu Province, Lanzhou, China College of Resources, Sichuan Agricultural University, Chengdu, China Correspondence Ling Zhang,  Northwest Institute of Eco Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.  zhanglingky@lzb.ac.cn Funding information Strategic Priority Research Program of Chinese Academy of Sciences, Grant/Award Numbers: XDA20100104 and XDA19040504; CAS "Light of West China"
Automatic Generation of Non intrusive Updates for Third Party Libraries in Android Applications
Venue: RAID
Year: 2019

Third Party libraries, which are ubiquitous in Android apps, have exposed great security threats to end users as they rarely get timely updates from the app developers, leaving many security vulnerabilities unpatched. This issue is due to the fact that manually updating libraries can be technically nontrivial and time consuming for app developers. In this paper, we propose a technique that performs automatic generation of non intrusive updates for third party libraries in Android apps. Given an Android app with an outdated library and a newer version of the library, we automatically update the old library in a way that is guaranteed to be fully backward compatible and imposes zero impact to the library's interactions with other components. To understand the potential impact of code changes, we propose a novel Value sensitive Differential Slicing algorithm that leverages the diffing information between two versions of a library. The new slicing algorithm greatly reduces the over conservativeness of the traditional slicing while still preserving the soundness with respect to update generation. We have implemented a prototype called LIBBANDAID. We further evaluated its efficacy on 9 popular libraries with 173 security commits across 83 different versions and 100 real world open source apps. The experimental results show that LIBBANDAID can achieve a high average successful updating rate of 80.6% for security vulnerabilities and an even higher rate of 94.07% when further combined with potentially patchable vulnerabilities.
When Organizations Rule: Judicial Deference to Institutionalized Employment Structures1
Venue: American Journal of Sociology
Year: 2011

This article offers a theoretical and empirical analysis of legal endogeneity a powerful process through which institutionalized organizational structures influence judicial conceptions of compliance with antidiscrimination law. It finds that organizational structures (e.g. grievance and evaluation procedures, antiharassment policies) become symbolic indicators of rational governance and compliance with antidiscrimination laws, first within organizations, but eventually in the judicial realm as well. Lawyers and judges tend to infer nondiscrimination from the mere presence of those structures. Judges increasingly defer to organizational structures in their opinions, ultimately inferring nondiscrimination from their presence. Legal endogeneity theory is tested by analyzing a random sample of 1,024 federal employment discrimination opinions (1965 99) and is found to have increased over time. Judicial deference is most likely when plaintiffs lack clout and when the legal theories require judges to rule on unobservable organizational attributes. The authors argue that legal endogeneity weakens the impact of law when organizational structures are viewed as indicators of legal compliance even in the face of discriminatory actions.
CRaft: An Erasure coding supported Version of Raft for Reducing Storage Cost and Network Cost
Venue: FAST
Year: 2020

Consensus protocols can provide highly reliable and available distributed services. In these protocols, log entries are completely replicated to all servers. This complete entry replication causes high storage and network costs, which harms performance. Erasure coding is a common technique to reduce storage and network costs while keeping the same fault tolerance ability. If the complete entry replication in consensus protocols can be replaced with an erasure coding replication, storage and network costs can be greatly reduced. RS Paxos is the first consensus protocol to support erasure coded data, but it has much poorer availability compared to commonly used consensus protocols, like Paxos and Raft. We point out RSPaxos's liveness problem and try to solve it. Based on Raft, we present a new protocol, CRaft. Providing two different replication methods, CRaft can use erasure coding to save storage and network costs like RS Paxos, while it also keeps the same liveness as Raft. To demonstrate the benefits of our protocols, we built a key value store based on CRaft, and evaluated it. In our experiments, CRaft could save 66% of storage, reach a 250% improvement on write throughput and reduce 60.8% of write latency compared to original Raft.
A formal statistical approach to representing uncertainty in rainfall runoff modelling with focus on residual analysis and probabilistic output evaluation Distinguishing simulation and prediction
Year: 2012

While there seems to be consensus that hydrological model outputs should be accompanied with an uncertainty estimate the appropriate method for uncertainty estimation is not agreed upon and a debate is ongoing between advocators of formal statistical methods who consider errors as stochastic and GLUE advocators who consider errors as epistemic, arguing that the basis of formal statistical approaches that requires the residuals to be stationary and conform to a statistical distribution is unrealistic. In this paper we take a formal frequentist approach to parameter estimation and uncertainty evaluation of the modelled output, and we attach particular importance to inspecting the residuals of the model outputs and improving the model uncertainty description. We also introduce the probabilistic performance measures sharpness, reliability and interval skill score for model comparison and for checking the reliability of the confidence bounds. Using point rainfall and evaporation data as input and flow measurements from a sewer system for model conditioning, a state space model is formulated that accounts for three different flow contributions: wastewater from households, and fast rainfall runoff from paved areas and slow rainfall dependent infiltration inflow from unknown sources. We consider two different approaches to evaluate the model output uncertainty, the output error method that lumps all uncertainty into the observation noise term, and a method based on Stochastic Differential Equations (SDEs) that separates input and model structure uncertainty from observation uncertainty and allows updating of model states in real time. The results show that the optimal simulation (off line) model is based on the output error method whereas the optimal prediction (on line) model is based on the SDE method and the skill scoring criterion proved that significant predictive improvements of the output can be gained from updating the states continuously. In an effort to attain residual stationarity for both the output error method and the SDE method transformation of the observations were necessary but the statistical assumptions were nevertheless not 100% justified. The residual analysis showed that significant autocorrelation was present for all simulation models. We believe users of formal approaches to uncertainty evaluation within hydrology and within environmental modelling in general can benefit significantly from adopting the evaluation measures applied here, so the probabilistic performance of their models can be assessed properly.
A multi scale study of enzyme diffusion in macromolecular solutions and physical gels of pectin polysaccharides
Year: 2013

Pectin accounts for one of the major polysaccharides of the plant cell wall. Pectin methylesterases (PMEs) are enzymes that are able to alter the pectin structure and affect its gelling properties by de esterifying galacturonic acid. In this work, the diffusion of two PMEs with different origins and modes of action was characterised with a multi scale approach in different media consisting of pectin macromolecular solutions and physical gels. Fluorescence Recovery After Photobleaching (FRAP) and Fluorescent Correlation Spectroscopy (FCS) investigations showed similar diffusion behaviour for both enzymes at a microscopic scale. In contrast, their behaviours tended to differ when increasing the observation scale up to mm. Indeed, fungal PMEs diffused faster and in a larger range than plant PMEs. These results are discussed with regard to the respective modes of action, processivity and in vivo roles of the two enzymes.