nils-herrmann/clean_scirepeval huggingface dataset:
Multiscale roughness analysis of engineering surfaces: A comparison of methods for the investigation of functional correlations
Year: 2016

This study investigates the correlations between the topography of different damaged rough surfaces and process conditions. Several surfaces are measured and compared to determine if they can be discriminated. The analysis is performed by using Gaussian Filtering, Wavelet Transform and a more recent approach named Discrete Modal Decomposition. Standardized 3D roughness parameters are computed for each multiscale method, filter (e.g. high pass, low pass and band pass) and available scale. The relevance (i.e. the ability to discriminate surface topographies corresponding to different process conditions) is then investigated using a statistical analysis based on the MesRugTM expert system. The results indicate clear differences between the multiscale methods and show that the Wavelet approach is useful when characterizing localized surface defects while Gaussian Filtering is more appropriate for highly periodic morphological structures. For more complex topographies, this study also clearly shows that the Discrete Modal Decomposition exhibits compelling abilities that fall between those of the Gaussian and Wavelet approaches; this method is clearly more relevant than the Gaussian method in the case of localized defects and less relevant in the case of highly periodical structures and fractal surfaces 1 f a spectrum) This can be explained by the modulated frequency/amplitude descriptors generated via the modal basis.
Quantum process capability
Venue: Scientific Reports
Year: 2019

Physical processes in the quantum regime possess non classical properties of quantum mechanics. However, methods for quantitatively identifying such processes are still lacking. Accordingly, in this study, we develop a framework for characterizing and quantifying the ability of processes to cause quantum mechanical effects on physical systems. We start by introducing a new concept, referred to as quantum process capability, to evaluate the effects of an experimental process upon a prescribed quantum specification. Various methods are then introduced for measuring such a capability. It is shown that the methods are adapted to quantum process tomography for implementation of process capability measure and applicable to all physical processes that can be described using the general theory of quantum operations. The utility of the proposed framework is demonstrated through several examples, including processes of entanglement, coherence, and superposition. The formalism proposed in this study provides a generic approach for the identification of dynamical processes in quantum mechanics and facilitates the general classification of quantum information processing.
Improved metaheuristics for the two dimensional strip packing problem
Venue: Appl. Soft Comput.
Year: 2020

Given a fixed set of rectangular items and a single rectangular object of fixed width and unlimited height, the two dimensional strip packing problem consists of packing all the items into the object in a non overlapping manner, such that the resulting packing height is a minimum. Two improved strip packing metaheuristics are proposed in this paper. The first algorithm is a hybrid approach in which the method of simulated annealing is combined with a heuristic construction algorithm, while the second algorithm involves application of the method of simulated annealing directly in the space of completely defined packing layouts, without an encoding of solutions. These two algorithms are compared with a representative sample of metaheuristics from the literature in terms of solution quality achieved in the context of a large set of 1 718 benchmark instances, clustered into four sets of test problems, each with differing characteristics. It is found that the new algorithms indeed compare favourably with, and in some cases outperform, existing strip packing metaheuristics in the literature.
Video Description with Spatial Temporal Attention
Venue: ACM Multimedia
Year: 2017

Temporal attention has been widely used in video description to adaptively focus on important frames. However, most existing methods based on temporal attention suffer from the problems of recognition error and detail missing, because only coarse frame level global features are employed. Inspired by recent successful work in image description using spatial attention, we propose a spatial temporal attention (STAT) method to address such problems. In particular, first, we take advantage of object level local features to address the problem of detail missing. Second, the STAT method further selects relevant local features by spatial attention and then attend to important frames by temporal attention to recognize related semantics. The proposed two stage attention mechanism can recognize the salient objects more precisely with high recall and automatically focus on the most relevant spatial temporal segments given the sentence context. Extensive experiments on two well known benchmarks suggest that STAT method outperforms the state of the art methods on MSVD with BLEU4 score 0.511, and achieves superior BLEU4 score 0.374 on MSR VTT 10K. Compared to the method without local features, the relative improvements derived from our STAT method are 10.1% and 0.8% respectively on two benchmarks. Compared to the method using only temporal attention, the relative improvements derived from our STAT method are 18.3% and 9.0% respectively on two benchmarks.
Multifactorial intervention has a significant effect on diabetic kidney disease in patients with type 2 diabetes.
Venue: Kidney international
Year: 2020

To evaluate the effect of multifactorial intervention on the onset and progression of diabetic kidney disease in the patients with type 2 diabetes, we analyzed the effects of intensified multifactorial intervention (intensive therapy treatment targets; HbA1c under 6.2% blood pressure under 120/75 mmHg, low density lipoprotein cholesterol under 80 mg/dL) comparing with step wise intensification of medications and life style modifications of guideline based standard care (conventional therapy treatment targets: HbA1c under 6.9% blood pressure under 130/80 mmHg, low density lipoprotein cholesterol under 120 mg/dL) on diabetic kidney disease. A total of 2540 eligible patients in the Japan Diabetes Optimal 101 Integrated Treatment for three major risk factors of coronary diseases cohort (J DOIT3) were randomly assigned to intensive therapy (1269) and conventional therapy (1271) and treated for a median of 8.5 years. The prespecified kidney outcome measure was a composite of progression from normoalbuminuria to microalbuminuria or progression from normoalbuminuria to macroalbuminuria or progression from microalbuminuria to macroalbuminuria, serum creatinine levels elevated by two fold or more compared to baseline, or kidney failure. Primary analysis was carried out on the intention to treat population. Changes in the estimated glomerular filtration rate and albuminuria were also analyzed. A total of 438 kidney events occurred (181 in the intensive therapy group and 257 in the conventional therapy group) Intensive therapy was associated with a significant 32% reduction in kidney events compared to conventional therapy and was associated with a change in HbA1c at one year from study initiation. Thus, prespecified analysis shows that intensified multifactorial intervention significantly reduced the onset and progression of diabetic kidney disease compared to currently recommended care.
Deep Reinforcement Learning for Image Hashing
Venue: IEEE Transactions on Multimedia
Year: 2020

Deep hashing methods have received much attention recently, which achieve promising results by taking advantage of the strong representation power of deep networks. However, most existing deep hashing methods learn a whole set of hashing functions independently, while ignore the correlations between different hashing functions that can promote the retrieval accuracy greatly. Inspired by the sequential decision ability of deep reinforcement learning, we propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH) Our proposed DRLIH approach models the hashing learning problem as a sequential decision process, which learns each hashing function by correcting the errors imposed by previous ones and promotes retrieval accuracy. To the best of our knowledge, this is the first work to address hashing problem from deep reinforcement learning perspective. The main contributions of our proposed DRLIH approach can be summarized as follows: (1) We propose a deep reinforcement learning hashing network. In the proposed network, we utilize recurrent neural network (RNN) as agents to model the hashing functions, which take actions of projecting images into binary codes sequentially, so that the current hashing function learning can take previous hashing functions' error into account. (2) We propose a sequential learning strategy based on proposed DRLIH. We define the state as a tuple of internal features of RNN's hidden layers and image features, which can reflect history decisions made by the agents. We also propose an action group method to enhance the correlation of hash functions in the same group. Experiments on three widely used datasets demonstrate the effectiveness of our proposed DRLIH approach.
Multiple Intelligences based Creative Curriculum: The Best Practice
Year: 2020

The purpose of this research is: 1) to develop the model and produce the assessment of creative curriculum based learning program multiple intelligences (MI) 2) to know the characteristics and impacts of developed product models. Research using multi years by method R D (Research and Development) with two phases; First phase: 1) Preliminary survey stage, 2) definition stage, 3) design phase, 4) trial stage, and 5) development stage; The second phase: 1) the instrument design stage through the Forum Group Discussion, 2) the product trial phase of 100 children in Sleman Regency, 3) wide scale implementation of 200 children in Yogyakarta Province, 4) the evaluation phase with construct analysis and achievement of research subjects' performance, 5 the stage of measuring the effectiveness of the product with user perception. The subject comprises 200 children of early childhood and 20 kindergarten teachers in 10 kindergartens in the Yogyakarta province in Indonesia, by the approach of Reflective Measurement Theory (RMT) The results showed that: 1) the MI based creative curriculum assessment model was developed to meet valid, reliable and conformity criteria of an empirical data model, 2) The implementation of the assessment model had fulfilled the requirements worthy of using three criteria aspect; 1) The results of the assessment using creative instruments based on multiple intelligences on children get "very good" results, 2) the readiness of the teacher in learning is included in the "good" category; 3) teacher performance appraisal shows the "very good" category, and 4) the benefits of the products developed are in the "very good" category. It was concluded that the developed product had tested empirically and practically so that it was useful in learning in early childhood.
Implementasi Etnosains dalam Pembelajaran IPA di SD Muhammadiyah Alam Surya Mentari Surakarta
Venue: SEJ (Science Education Journal)
Year: 2019

The swift flow of globalization has led to increasingly eroded Indonesian local culturalvalues. This has caused a shift in neglected cultural values and local wisdom. Therefore,an approach is needed, namely ethnoscience in the learning process. This studypurposed to determine whether The Elementary School of Muhammadiyah Alam SuryaMentari has applied ethnoscience in science learning and describe the implementationof ethnoscience based on science learning in The Elementary School of MuhammadiyahAlam Surya Mentari.The method used was descriptive qualitative research design. Theresults of the study showed that the planning of science learning based on ethnoscienceapproach in This School was still unplanned, but the school had unconsciously implementedethnoscience approach. The implementation of science based on ethnosciencelearning was by integrating between the material with the environment, culture, and socialin the environment. Evaluation of the implementation of ethnoscience based on sciencelearning included cognitive, affective, and psychomotor evaluation in accordance withevaluation standards in the 2013 curriculum.
Tourism and Economic Growth: A Meta regression Analysis
Venue: Journal of Travel Research
Year: 2019

Numerous studies have focused on delineating the relationship between tourism and economic growth. In this article, we present the results of a rigorous meta regression analysis based on 545 estimates drawn from 113 studies that empirically tested the tourism led growth hypothesis (TLGH) The results suggest the presence of publication bias in the literature on this topic, where the majority of studies report positive and statistically significant estimates. Findings provide support for the TLGH, but they also suggest that the estimates are sensitive to a number of factors that are related to country data, specification, and estimation characteristics, and time span. Such sensitivities suggest that greater emphasis should be placed on reporting estimates of the relationship between tourism and economic growth across a variety of methodological characteristics and specification and estimation choices. The implications of the results for theory development are also discussed.
Firefly algorithm with neighborhood attraction
Venue: Inf. Sci.
Year: 2017

Firefly algorithm (FA) is a new optimization technique based on swarm intelligence. It simulates the social behavior of fireflies. The search pattern of FA is determined by the attractions among fireflies, whereby a less bright firefly moves toward a brighter firefly. In FA, each firefly can be attracted by all other brighter fireflies in the population. However, too many attractions may result in oscillations during the search process and high computational time complexity. To overcome these problems, we propose a new FA variant called FA with neighborhood attraction (NaFA) In NaFA, each firefly is attracted by other brighter fireflies selected from a predefined neighborhood rather than those from the entire population. Experiments are conducted using several well known benchmark functions. The results show that the proposed strategy can efficiently improve the accuracy of solutions and reduce the computational time complexity.
Foreign Direct Investment, Trade, and Economic Growth in MENA Countries: Empirical Analysis Using ARDL Bounds Testing Approach
Year: 2019

This article proposed to analyze the interrelationship between foreign direct investment (FDI) international trade, and economic growth for 15 selected Middle Eastern and North African countries over the period 1999 2012 using autoregressive distributed lag test as an approach to examine the cointegration and the vector error correction model. Our findings show that there exists a long run unidirectional relationship running from FDI to economic growth in MENA countries. We also found that FDI can generate positive spillover externalities for the previously mentioned countries. This belief was also confirmed for the host countries. The results obtained about these countries could be compared to those of other developing countries, considered to be experienced in attracting FDI and trade liberalization. These empirical insights are of a particular interest to policymakers as they help build sound economic policies to ensure a sustainable development.
Overview of chitin metabolism enzymes in Manduca sexta: Identification, domain organization, phylogenetic analysis and gene expression.
Venue: Insect biochemistry and molecular biology
Year: 2015

Chitin is one of the most abundant biomaterials in nature. The biosynthesis and degradation of chitin in insects are complex and dynamically regulated to cope with insect growth and development. Chitin metabolism in insects is known to involve numerous enzymes, including chitin synthases (synthesis of chitin) chitin deacetylases (modification of chitin by deacetylation) and chitinases (degradation of chitin by hydrolysis) In this study, we conducted a genome wide search and analysis of genes encoding these chitin metabolism enzymes in Manduca sexta. Our analysis confirmed that only two chitin synthases are present in M. sexta as in most other arthropods. Eleven chitin deacetylases (encoded by nine genes) were identified, with at least one representative in each of the five phylogenetic groups that have been described for chitin deacetylases to date. Eleven genes encoding for family 18 chitinases (GH18) were found in the M. sexta genome. Based on the presence of conserved sequence motifs in the catalytic sequences and phylogenetic relationships, two of the M. sexta chitinases did not cluster with any of the current eight phylogenetic groups of chitinases: two new groups were created (groups IX and X) and their characteristics are described. The result of the analysis of the Lepidoptera specific chitinase h (group h) is consistent with its proposed bacterial origin. By analyzing chitinases from fourteen species that belong to seven different phylogenetic groups, we reveal that the chitinase genes appear to have evolved sequentially in the arthropod lineage to achieve the current high level of diversity observed in M. sexta. Based on the sequence conservation of the catalytic domains and on their developmental stage and tissue specific expression, we propose putative functions for each group in each category of enzymes.
Effectiveness of gamification in the engagement of students
Venue: Comput. Hum. Behav.
Year: 2016

This research aims to evaluate the effectiveness of gamification platforms as a strategy for the engagement of students from the 8th year of elementary school in Brazil. We chose two badging platforms ClassDojo and ClassBadges to be evaluated based on 7 different criteria. The main objectives were to generate involvement among individual and particular situations, increasing the interest, engagement and efficiency while performing a specific task. We observed the behavior of 61 students from an elementary public school in Brazil as part of our field research. Data were collected using observation, semi structured interviews and questionnaires. Quantitative data were statistically analyzed using a multivariate technique known as cluster analysis. The results allowed us to classify students in 4 distinct groups and showed that students who received more rewards from the teacher got significantly better average performances. Cluster analysis was used to classify student engagement.Students with low engagement can be stimulated to do this activities through gamification techniques.Engagement indicators were identified and may be useful for future research in the area.Teacher participations is absolutely necessary to create experiences that include game mechanics for students.The objectives to be achieved in the classroom should be adherent with the mechanics of games.
Potential Use of Cannabinoids for the Treatment of Pancreatic Cancer
Venue: Journal of pancreatic cancer
Year: 2019

Cannabinoid extracts may have anticancer properties, which can improve cancer treatment outcomes. The aim of this review is to determine the potentially utility of cannabinoids in the treatment of pancreatic cancer.  A literature review focused on the biological effects of cannabinoids in cancer treatment, with a focus on pancreatic cancer, was conducted. In vitro and in vivo studies that investigated the effects of cannabinoids in pancreatic cancer were identified and potential mechanisms of action were assessed.  Cannabinol receptors have been identified in pancreatic cancer with several studies showing in vitro antiproliferative and proapoptotic effects. The main active substances found in cannabis plants are cannabidiol (CBD) and tetrahydrocannabinol (THC) There effects are predominately mediated through, but not limited to cannabinoid receptor 1, cannabinoid receptor 2, and G protein coupled receptor 55 pathways. In vitro studies consistently demonstrated tumor growth inhibiting effects with CBD, THC, and synthetic derivatives. Synergistic treatment effects have been shown in two studies with the combination of CBD/synthetic cannabinoid receptor ligands and chemotherapy in xenograft and genetically modified spontaneous pancreatic cancer models. There are, however, no clinical studies to date showing treatment benefits in patients with pancreatic cancer.  Cannabinoids may be an effective adjunct for the treatment of pancreatic cancer. Data on the anticancer effectiveness of various cannabinoid formulations, treatment dosing, precise mode of action, and clinical studies are lacking.
Synthetic phenolic antioxidants to biodiesel: path toward NOx reduction of an unmodified indirect injection diesel engine
Year: 2014

Biodiesel is a green alternative fuel produced from renewable resources. The major disadvantage of biodiesel is the substantial increase in NOx emission. This study examined the effects of antioxidant treated coconut biodiesel on the performance and exhaust emission characteristics of an indirect injection diesel engine. Coconut biodiesel was produced by transesterification using potassium hydroxide as a catalyst. Two low cost synthetic antioxidants, 2(3) tert butyl 4 methoxyphenol (BHA) and 2,6 di tert butyl 4 methylphenol (BHT) were added at 2000 ppm to 20% coconut methyl ester in diesel (CB20) Tests were conducted on a 55 kW 2.5 L four cylinder diesel engine at a constant load varying speed. Results showed that the antioxidants significantly reduced NOx emission with a slight effect on brake thermal efficiency. The addition of BHA and BHT to CB20 reduced the average NOx emission by 7.78% and 3.84% and the average brake specific fuel consumption by 1.77% and 1.46% respectively. The antioxidant addition increased CO, HC, and smoke opacity, but the extent of increase was still below the diesel level. Thus, the addition of antioxidants presents a promising option for NOx reduction.
Ultra Wideband (UWB) Bandpass Filter using Optimum Short Circuited Technique
Year: 2016

Filters are the key components in communication system. In this paper, the description of Ultra Wide Band filter design techniques and to show the different aspects of the design technique used. This paper presents a novel ultrawideband (UWB) bandpass filter using stepped impedance stub loaded resonator and the stub loaded Multiple mode resonator. The resonators are so formed to allow its five resonant frequencies in the UWB passband, which extends from 3.1 GHz to 10.6 GHz. SISLR is found to have the advantage of providing more degrees of freedom to adjust the resonant frequencies. The MMR is constructed by loading three open stubs in a uniformimpedance resonator, i.e. one stepped impedance stub at the center and two uniform impedance stubs at the symmetrical side locations. The simulated results showing good wideband filtering performance with sharp rejection skirts outside the passband.