nils-herrmann/clean_scirepeval huggingface dataset:
Biodegradation of agrowastes by lignocellulolytic activity of an oyster mushroom, Pleurotus sapidus

Lignocellulosic biomasses derived from dedicated crops and agro industrial waste materials are promising renewable resources for the production of fuels and other value added bio products. The production of ligninolytic and cellulolytic enzymes from different lignocellulosic agricultural wastes by Pleurotus sapidus WC 529 was investigated in the current study The production pattern of the enzymes was examined during the growth of the organism for a period of 10 days, and the enzyme activities were expressed in units/mL. Cultivation in the solid state culture of banana stalk (BS) gave rise to higher levels of laccase, manganese peroxidase (MnP) and lignin peroxidase (LiP) activities compared to the cellulolytic enzymes. Response surface methodology (RSM) was adopted to optimise the culturing conditions for maximum enzyme secretion. Optimal conditions yielding the highest enzyme activities were: initial pH, 4; temperature, 35 degC; moisture level, 60 inoculum size, 4 mL, and incubation time, 120 hours. The crude lignocellulolytic enzyme extract presented potential efficiency for the delignification of different lignocellulosic substrates within 48 hours. The results suggested the feasibility of lignocellulolytic enzyme production using cost effective agro industrial residues that can be effectively used for lignin biodegradation.
ISAAC: A Convolutional Neural Network Accelerator with In Situ Analog Arithmetic in Crossbars
Venue: 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)

A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs) These algorithms typically involve a large number of multiply accumulate (dot product) operations. A recent project, DaDianNao, adopts a near data processing approach, where a specialized neural functional unit performs all the digital arithmetic operations and receives input weights from adjacent eDRAM banks. This work explores an in situ processing approach, where memristor crossbar arrays not only store input weights, but are also used to perform dot product operations in an analog manner. While the use of crossbar memory as an analog dot product engine is well known, no prior work has designed or characterized a full fledged accelerator based on crossbars. In particular, our work makes the following contributions: (i) We design a pipelined architecture, with some crossbars dedicated for each neural network layer, and eDRAM buffers that aggregate data between pipeline stages. (ii) We define new data encoding techniques that are amenable to analog computations and that can reduce the high overheads of analog to digital conversion (ADC) (iii) We define the many supporting digital components required in an analog CNN accelerator and carry out a design space exploration to identify the best balance of memristor storage/compute, ADCs, and eDRAM storage on a chip. On a suite of CNN and DNN workloads, the proposed ISAAC architecture yields improvements of 14.8x, 5.5x, and 7.5x in throughput, energy, and computational density (respectively) relative to the state of the art DaDianNao architecture.
Accurate feature detection for out of focus camera calibration.
Venue: Applied optics

For conventional camera calibration methods, well focused images are necessary to detect features accurately. However, this requirement causes practical inconveniences to image acquisition for long and short distance photogrammetry. In this study, three active phase shift circular grating (PCG) arrays are used as calibration patterns. The PCGs' centers are regarded as feature points that can be accurately extracted by ellipse fitting of 2p phase points even though patterns are substantially blurred. In the experiments, Gaussian filters are utilized to blur pattern images, and different standard deviations are set for different fuzzy degrees. Pattern images with different defocusing degrees are also captured. The period and number of PCGs and noise are considered. Experimental results indicate that our method is accurate, reliable, and insensitive to image defocusing.
Method for out of focus camera calibration.
Venue: Applied optics

State of the art camera calibration methods assume that the camera is at least nearly in focus and thus fail if the camera is substantially defocused. This paper presents a method which enables the accurate calibration of an out of focus camera. Specifically, the proposed method uses a digital display (e.g. liquid crystal display monitor) to generate fringe patterns that encode feature points into the carrier phase; these feature points can be accurately recovered, even if the fringe patterns are substantially blurred (i.e. the camera is substantially defocused) Experiments demonstrated that the proposed method can accurately calibrate a camera regardless of the amount of defocusing: the focal length difference is approximately 0.2% when the camera is focused compared to when the camera is substantially defocused.
Scaled prioritized aggregation operators and their applications to decision making
Venue: Soft Comput.

As an extension of the prioritized aggregation operators by Yager ( 2008) this paper uses the priority labels to express the prioritized relationship between criteria and presents some scaled prioritized aggregation operators, including the scaled prioritized score operator and the scaled prioritized averaging operator. Moreover, we consider the priority under uncertain environment and develop the uncertain prioritized aggregation operators, including the uncertain prioritized scoring operator and the uncertain prioritized averaging operator. We investigate the properties of these operators and build the models to derive the weights by maximizing square deviations from a possible range to distinguish the candidate alternatives mostly. Furthermore, approaches to multi attribute decision making based on the proposed operators are given, which have benefits over the TOPSIS method (Behzadian, Expert Syst Appl 39:13051 13069, 2012) and the methods based on the OWA operator (Zhou and Chen, Fuzzy Sets Syst 168:18 34, 2011) when prioritized relationship between criteria is considered. Finally, examples are illustrated to show the feasibility and validity of the new approaches to the application of decision making.
The Golden Circle

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Chinese sentence based lexical similarity measure for artificial intelligence chatbot
Venue: 2016 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

Artificial intelligence chatbots are computer programs that make interactions via auditory or textual information between human and machine using natural language processing techniques, most of which work on the basis of pattern matching. Typically, a chatbot recognizes the audio from human and translates it to text, and then matches the text with sentences stored in advance in the database by means of measuring similarity. So far, English chatbots have done much better than Chinese ones, mainly for the reason that processing sentences in Chinese requires more sophisticated techniques than that in English. Considering the low efficiency and accuracy of the existing Chinese sentence similarity measure methods, we propose a novel method oriented for lexical similarity measure of Chinese sentences by means of Chinese sentence segmentation and weighted word matching. Based on this method, a Chinese AI chatbot is developed and tested under a variety of settings. Experimental results show that our proposed method can achieve satisfactory performance in various cases.
Utilization and efficiency of ground granulated blast furnace slag on concrete properties A review

Ground granulated blast furnace slag (GGBFS) is a byproduct from the blast furnaces of iron and it is a very beneficial in the mortar and concrete production. The present paper reviews the literature related to the utilization and the efficiency of GGBFS on the properties of mortar/concrete. Firstly, general information about GGBFS production, reaction mechanism and heat of hydration are presented. Then, workability, setting time, bleeding, rheological properties, slump loss, segregation resistance and early age cracking potential and finishability are addressed among the fresh concrete properties. Strength and rate of strength gain, modulus of elasticity, creep, shrinkage, influence of curing on performance of GGBFS, permeability, resistance to freeze/thaw cycles, carbonation resistance, deicing salt scaling, alkali silica reaction and sulfate attack are among reviewed hardened concrete properties.
Simultaneous removal of Ni(II) As(III) and Sb(III) from spiked mine effluent with metakaolin and blast furnace slag geopolymers.
Venue: Journal of environmental management

The mining industry is a major contributor of various toxic metals and metalloids to the aquatic environment. Efficient and economical water treatment methods are therefore of paramount importance. The application of natural or low cost sorbents has attracted a great deal of interest due to the simplicity of its process and its potential effectiveness. Geopolymers represent an emerging group of sorbents. In this study, blast furnace slag and metakaolin geopolymers and their raw materials were tested for simultaneous removal of Ni(II) As(III) and Sb(III) from spiked mine effluent. Blast furnace slag geopolymer proved to be the most efficient of the studied materials: the experimental maximum sorption capacities for Ni, As and, Sb were 3.74 mg/g, 0.52 mg/g, and 0.34 mg/g, respectively. Although the capacities were relatively low due to the difficult water matrix, 90 100% removal of Ni, As, and Sb was achieved when the dose of sorbent was increased appropriately. Removal kinetics fitted well with the pseudo second order model. Our results indicate that geopolymer technology could offer a simple and effective way to turn blast furnace slag to an effective sorbent with a specific utilization prospect in the mining industry.
Recycling and utilization assessment of waste fired clay bricks (Grog) with granulated blast furnace slag for geopolymer production

In the present study, the alkali activation process potential of the industrial by product called waste fired clay bricks (Grog) as well as the effects of the addition of another industrial by product known as granulated blast furnace slag (S) on the properties of the final products has been studied. Granulated blast furnace slag has been employed as 20% 40% 60% and 80% replacement of waste fired clay bricks (Grog) in the production of geopolymers. At the same time, the effects of curing time on the properties of geopolymers were investigated. The study proved a successful method for the feasibility of recycling and valorization process of two industrial by products and converts them into valuable geopolymer products. These potential recycling processes can generate significant benefits for environmental sector and many economic impacts to the construction materials sector by using it as alternative raw material resources for the production of the geopolymer.
Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks

The objective of this research was to use state of the art artificial neural network approach to estimate the extent and effect of fluctuations in the chemical composition of stainless steel at tapping of an electric arc furnace, and thus scrap and alloy weights in the charge material mix, on the specific electrical energy consumption. Such an estimation would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. The multilayer perceptron architecture 5 5 1 with hyperbolic tangent function in the hidden layer and linear function in the output layer was used as an optimal neural network model. The model was built, tested and validated based on experimental melts of the electric arc furnace at a melt shop in Italy. The proposed model was presented as an adequate one based on the coefficient of determination (R2) which was above 0.9 as well as other error parameters calculated. The highest effect on the electrical energy consumption has carbon content.
The setting time of a clay slag geopolymer matrix: the influence of blast furnace slag addition and the mixing method

Blast furnace slag from the old iron production was transformed into solids using the geopolymer technology. This type of additive was not only encapsulated in inorganic polymer netting, but it was also chemically bonded into the forming three dimensional (3D) net. Consequently, blast furnace slag influences the setting time of the geopolymer mixture, and so does the mixing method. The obtained inorganic polycondensed materials were very hard and compact; by modifying the clay/slag ratio, it was possible to change the setting time easily. This phenomenon could be exploited for quick emergency repairs or, conversely, to prolong the workability and extend the setting period if necessary.
Sustainability of Cash Waqf Development in Indonesia: A Quintuple Helix Perspective

The objective of this paper is to analyze the sustainability of cash waqf (endowment) development in Indonesia from a quintuple perspective. Waqf can be in the form of productive assets (either current or fixed) or non productive fixed assets. The method of this research uses qualitative research based on a literature review and in depth interview with some top management of cash waqf management. Quintuple helix analysis is used to see relations among aspects which support waqf development. The result shows that cash waqf needs support from many aspects in order to maintain its sustainability. Knowledge creation and innovation from related aspects can strengthen the sustainable development of cash waqf in any form. Educational, economic and political systems are identified as important aspects which support sustainability
Proposing a decision making process for the development of sustainable oil and gas resources using the petroleum fund: A case study of the East Natuna gas field

The East Natuna gas field in Indonesia is planned to be developed to meet the increasing natural gas demand of Indonesia. This research investigates the sustainable development of the field using a multi criteria decision analysis method (MCDA) namely the preference ranking organisation method for enrichment of evaluations (PROMETHEE) For this purpose a PROMETHEE model is employed to compare and appraise four alternative of petroleum funds against technical, economic, environmental and socio political criteria. Those alternatives are determined by allocating 0% 10% 15% and 25% of the government's share of the gas field exploitation as petroleum funds. The results show that the adoption of a petroleum fund for the sustainable field development is both relevant and feasible. The bigger the petroleum fund, the better the merits. Employing 25% of the government's revenue towards this fund is found to be the most preferable choice, since it benefited from technology, environment and social aspects. However, the downside of this alternative lies in the economic aspects, such as the reduction of government revenues and potential increase in government debt as well as government resistance. As this study demonstrates, PROMETHEE can provide a relevant tool to assist decision makers to formulate policies aiming at a sustainable petroleum resources development.
Extracting Conformational Ensembles of Small Molecules from Molecular Dynamics Simulations: Ampicillin as a Test Case
Venue: Comput.

The accurate and exhaustive description of the conformational ensemble sampled by small molecules in solution, possibly at different physiological conditions, is of primary interest in many fields of medicinal chemistry and computational biology. Recently, we have built an on line database of compounds with antimicrobial properties, where we provide all atom force field parameters and a set of molecular properties, including representative structures extracted from cluster analysis over ms long molecular dynamics (MD) trajectories. In the present work, we used a medium sized antibiotic from our sample, namely ampicillin, to assess the quality of the conformational ensemble. To this aim, we compared the conformational landscape extracted from previous unbiased MD simulations to those obtained by means of Replica Exchange MD (REMD) and those originating from three freely available conformer generation tools widely adopted in computer aided drug design. In addition, for different charge/protonation states of ampicillin, we made available force field parameters and static/dynamic properties derived from both Density Functional Theory and MD calculations. For the specific system investigated here, we found that: (i) the conformational statistics extracted from plain MD simulations is consistent with that obtained from REMD simulations; (ii) overall, our MD based approach performs slightly better than any of the conformer generator tools if one takes into account both the diversity of the generated conformational set and the ability to reproduce experimentally determined structures.
Energy technology roll out for climate change mitigation: A multi model study for Latin America

In this paper we investigate opportunities for energy technology deployment under climate change mitigation efforts in Latin America. Through several carbon tax and CO2 abatement scenarios until 2050 we analyze what resources and technologies, notably for electricity generation, could be cost optimal in the energy sector to significantly reduce CO2 emissions in the region. By way of sensitivity test we perform a cross model comparison study and inspect whether robust conclusions can be drawn across results from different models as well as different types of models (general versus partial equilibrium) Given the abundance of biomass resources in Latin America, they play a large role in energy supply in all scenarios we inspect. This is especially true for stringent climate policy scenarios, for instance because the use of biomass in power plants in combination with CCS can yield negative CO2 emissions. We find that hydropower, which today contributes about 800 TWh to overall power production in Latin America, could be significantly expanded to meet the climate policies we investigate, typically by about 50% but potentially by as much as 75% According to all models, electricity generation increases exponentially with a two to three fold expansion between 2010 and 2050. We find that in our climate policy scenarios renewable energy overall expands typically at double digit growth rates annually, but there is substantial spread in model results for specific options such as wind and solar power: the climate policies that we simulate raise wind power in 2050 on average to half the production level that hydropower provides today, while they raise solar power to either a substantially higher or a much lower level than hydropower supplies at present, depending on which model is used. Also for CCS we observe large diversity in model outcomes, which reflects the uncertainties with regard to its future implementation potential as a result of the challenges this CO2 abatement technology experiences. The extent to which different mitigation options can be used in practice varies greatly between countries within Latin America, depending on factors such as resource potentials, economic performance, environmental impacts, and availability of technical expertise. We provide concise assessments of possible deployment opportunities for some low carbon energy options, for the region at large and with occasional country level detail in specific cases.
Elevational turnover in the composition of leaf miners and their interactions with host plants in Australian subtropical rainforest

Leaf miners are specialist herbivorous insects that are potentially vulnerable to environmental change because of their dependency on particular host plants. Little, however, is known about how climate affects the distribution of leaf miner communities and their interactions with host plants. Elevational gradients are useful tools for understanding how ecological communities respond to local clines in climate. Given that plant communities are known to undergo elevational turnover in response to changes in climatic conditions, we expect that leaf miner species will also change with elevation. We repeatedly hand collected leaf miners along three elevational gradients in subtropical rainforest in eastern Australia. Individual leaf miners were counted and identified to species, and their host plants were recorded. We tested if leaf miner species richness and the number of unique interactions among leaf miner and host plant species were affected by elevation. We also tested if the composition of leaf miner species and the composition of interactions between leaf miners and host plants showed a relationship with elevation. The rarefied number of unique leaf miner host plant interactions significantly decreased with elevation, with a slight peak at approx. 700 m a.s.l. while neither rarefied or observed species richness (species density) of leaf miners nor observed numbers of unique interactions (interaction density) were significantly affected by elevation. The composition of leaf miner species and the composition of leaf miner host plant interactions (occurrence of pairwise interactions) were significantly related to elevation. Elevational turnover in leaf miner species composition indicated that different species varied in their response to changes in biotic and/or abiotic conditions imposed by increasing elevation. Through our analyses, we identified four leaf miner species that may be locally vulnerable to climate change, as a result of their restricted elevational distribution and level of host specificity.
Exploring the Relationships among Transformational Leadership, Organizational Culture and Product Innovation using PLS SEM

Product innovation refers to new products development and improvement of existing products. The importance of product innovation proliferated as the market grows and becomes more diversified. Among SMEs, product innovation is one of the most critical determinants for their survival, growth, and competitiveness. Nonetheless, product innovation among SMEs is still low, owning to various issues and challenges. This study attempts to focus the effect of transformational leadership and organizational culture on product innovation among 36 SMEs in Johor. A SEM PLS was used to validate the measurement model and develop the path modeling among variables studied. It is found that both transformational leadership and organizational culture are significantly related to product innovation. This finding substantiates previous findings and lends supports to the important roles of both constructs in SME's development.