nils-herrmann/clean_scirepeval huggingface dataset:
Utilization Of The Visceral Organsof Catfish (Pangasius Hypoptalmus) Added To Banana Peel (Musa Acuminata Balbisiana) To Produceliquid Organic Fertilizer
Year: 2017

This study aimed to obtain liquid organic fertilizer produced from the visceral organs of catfish added to banana skin for enriching the macro elements N, P, and K.The method used in this study was the experimental. Parameters used were the value of pH, nitrogen, phosphorus and potassium. The experimentwas composed as completely randomized design (CRD) and the treatment was addition of banana peel which varied into 4 different amoun, namely: without the banana peel (K0) banana peel 100 g (K1) banana peel200 g (K2) banana peel 300 g (K3) and then fermented for 13 days. The results showed that the bestliquid organic fertilizer was produced by the treatment of addition of banana peels 300 g (K3) The characteristics of the fertilizer were pH value of 6.97, the content of nitrogen was 3.02% phosphoruswas 1.55% and potassium was 3.15% The pH value, the content of nitrogen and potassium were met to the standard, while the content of phosphoruswas not met the standards of Agriculture No. 70 Permenten SR.140 10/2011.
Behavioural Finance: Heuristics in Investment Decisions
Year: 2017

Decision making from investment point of view is basically defined as a rational choice among alternatives or as the conscious selection of a course of action from available alternatives. Investors are also assumed to be a rational creature. Process of decision making in individuals is subject to various psychological changes, which therefore result in behaviour. Prior to a decision making, individual undergoes with several cognitive and emotional illusions which may result in irrationality as argued by several authors. Decision making is required of everyone, individuals as well as administrators. Individual finds numerous models in the extensive literature on decision making with two basic assumptions based on Simon's work: First assumption that decision making is an orderly, rational process that possesses an inherent logic; and the second assumption that steps in the process follow one another in an orderly, logical, sequential flow. A decision is the result of making a judgement or reaching a conclusion. In Heuristic decision making there is a lack of emphasis on hierarchical structure; role behaviour is characterized by freedom for every individual to explore all ideas. The emotional and social tone is relatively relaxed; openness, originality and seeking of consensus are the essentials of heuristic decision making. In a nutshell it is a creative type of decision. Heuristics is one such variable which influences the decision making of investors directly. Hence it is necessary to configure the influence of behavioural finance theory in to individual investor decision making using heuristics.
Explanation of the City Brand Equity Role on Specialists' Identity and Immigration (Case of Study: Mashhad)
Year: 2017

Brand equity is one of the significant categories in the international competition field, through which we can decrease the rate of immigration from cities and protect urban human resources. The purpose of this text is to find an organized and a coherent answer in regards to reasons of reduction of immigration of specialists who are post graduated in Mashhad and their sense of belonging to their own city as well. There are numbers of reasons for promotion of sense of attachment, so city identity shall be deemed as an important topic in this regard. However, branding topic in this area is what has been underestimated in former researches. Now the question is how an urban brand can leave an impression on the immigration of experts who are post graduated. Therefore, the current Manuscript presents a creative real model for explanation of the city brand role on reduction of specialists' immigration rate from Mashhad, emphasizing the role of identity as a mediator. The data being required for this research have been collected by questionnaires, through an accessible targeted non random sampling from specialists from Mashhad who are post graduated. This research is an applied surveying type in which the structural equations method is used by use of Smart PLS software for data analysis. The results show that the city brand is effective on identification and reduction of rate of immigration from Mashhad. The role of identity as a mediator in impression of city brand on reduction of rate of immigration from Mashhad has been proved in review of indirect effect of this study.
Sn doped ZnO nanocrystalline thin films with enhanced linear and nonlinear optical properties for optoelectronic applications
Year: 2017

In the current work, nanocrystalline undoped and Sn doped ZnO thin films with different doping concentrations (1, 3, 5, 7 at% have been deposited on glass substrate by low cost spin coating technique. The strong effect of Sn doping on structural, morphological, optical, nonlinear properties have been observed. X ray diffraction study revealed that all the thin films are preferentially grown along (002) plane. The crystallite size is found to be increased with increasing the concentration of Sn, similar behavior was observed by atomic force microscopy analysis. Optical study shows that the prepared thin films are highly transparent. The direct optical band gap was calculate and found to be 3.16, 3.20, 3.22, 3.34, 3.18 eV for pure and doped films respectively. The refractive index, linear susceptibility, nonlinear absorption coefficient, nonlinear susceptibility and nonlinear refractive index were calculated. Furthermore, the third order nonlinear optical properties are investigated using Z scan technique and their values are found to be 3.75x10 8 cm 2 /W, 3.76x10 3 cm/W and 0.65x10 3 esu for 7% Sn doped ZnO, respectively. There is a good correlation between theoretical and experimental third order nonlinear properties and higher values shows that the deposited films are may be applied in nonlinear optical applications.
Toxic Effects of Mercury on the Cardiovascular and Central Nervous Systems
Venue: Journal of biomedicine biotechnology
Year: 2012

Environmental contamination has exposed humans to various metal agents, including mercury. This exposure is more common than expected, and the health consequences of such exposure remain unclear. For many years, mercury was used in a wide variety of human activities, and now, exposure to this metal from both natural and artificial sources is significantly increasing. Many studies show that high exposure to mercury induces changes in the central nervous system, potentially resulting in irritability, fatigue, behavioral changes, tremors, headaches, hearing and cognitive loss, dysarthria, incoordination, hallucinations, and death. In the cardiovascular system, mercury induces hypertension in humans and animals that has wide ranging consequences, including alterations in endothelial function. The results described in this paper indicate that mercury exposure, even at low doses, affects endothelial and cardiovascular function. As a result, the reference values defining the limits for the absence of danger should be reduced.
Knowledge transfer and exchange: review and synthesis of the literature.
Venue: The Milbank quarterly
Year: 2007

Knowledge transfer and exchange (KTE) is as an interactive process involving the interchange of knowledge between research users and researcher producers. Despite many strategies for KTE, it is not clear which ones should be used in which contexts. This article is a review and synthesis of the KTE literature on health care policy. The review examined and summarized KTE's current evidence base for KTE. It found that about 20 percent of the studies reported on a real world application of a KTE strategy, and fewer had been formally evaluated. At this time there is an inadequate evidence base for doing "evidence based" KTE for health policy decision making. Either KTE must be reconceptualized, or strategies must be evaluated more rigorously to produce a richer evidence base for future activity.
Accounting for missing data in statistical analyses: multiple imputation is not always the answer
Venue: International journal of epidemiology
Year: 2019

Missing data are unavoidable in epidemiological research, potentially leading to bias and loss of precision. Multiple imputation (MI) is widely advocated as an improvement over complete case analysis (CCA) However, contrary to widespread belief, CCA is preferable to MI in some situations.  We provide guidance on choice of analysis when data are incomplete. Using causal diagrams to depict missingness mechanisms, we describe when CCA will not be biased by missing data and compare MI and CCA, with respect to bias and efficiency, in a range of missing data situations. We illustrate selection of an appropriate method in practice.  For most regression models, CCA gives unbiased results when the chance of being a complete case does not depend on the outcome after taking the covariates into consideration, which includes situations where data are missing not at random. Consequently, there are situations in which CCA analyses are unbiased while MI analyses, assuming missing at random (MAR) are biased. By contrast MI, unlike CCA, is valid for all MAR situations and has the potential to use information contained in the incomplete cases and auxiliary variables to reduce bias and/or improve precision. For this reason, MI was preferred over CCA in our real data example.  Choice of method for dealing with missing data is crucial for validity of conclusions, and should be based on careful consideration of the reasons for the missing data, missing data patterns and the availability of auxiliary information.
Design of a CMOS Broadband Transimpedance Amplifier with Floating Active Inductor
Venue: 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
Year: 2019

This work presents the design and performance of a transimpedance amplifier (TIA) implemented in a 180nm CMOS technology. The introduced TIA uses a floating active inductor (FAI) based on gyrator C structure, which is why it can increase the bandwidth while occupying a smaller chip area. And we have explained the schematic and characteristics of the gyrator C structure and FAI. In addition, the proposed TIA also uses the combination of capacitive degeneration, broadband matching network, and the regulated cascode (RGC) input stage, which turns the transimpedance amplifier (TIA) design into a fifth order low pass filter with butterworth response, to enhance the bandwidth and the gain. The TIA is implemented in 0.18 m Rohm CMOS technology and a 1.8 V supply. The transimpedance amplifier achieves a transimpedance gain of 41dBO and 3 dB frequency of 10 GHz with 0.1pF total input capacitance. Layout size of the proposed TIA is 180 mm x 118 mm.
Inductive Learning on Commonsense Knowledge Graph Completion
Venue: 2021 International Joint Conference on Neural Networks (IJCNN)
Year: 2021

Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG) where entities are composed of free form text. Existing CKG completion methods focus on transductive learning setting, where all the entities are present during training. Here, we propose the first inductive learning setting for CKG completion, where unseen entities may appear at test time. We emphasize that the inductive learning setting is crucial for CKGs, because unseen entities are frequently introduced due to the fact that CKGs are dynamic and highly sparse. We propose InductivE as the first framework targeted at the inductive CKG completion task.  first ensures the inductive learning capability by directly computing entity embeddings from raw entity attributes. Second, a graph neural network with novel densification process is proposed to further enhance unseen entity representation with neighboring structural information. Experimental results show that InductivE performs especially well on inductive scenarios where it achieves above 48% improvement over previous methods while also outperforms state of the art baselines in transductive settings.
Transferable Model for Shape Optimization subject to Physical Constraints
Venue: 2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021

The interaction of neural networks with physical equations offers a wide range of applications. We provide a method which enables a neural network to transform objects subject to given physical constraints. Therefore an U Net architecture is used to learn the underlying physical behaviour of fluid flows. The network is used to infer the solution of flow simulations, which will be shown for a wide range of generic channel flow simulations. Physical meaningful quantities can be computed on the obtained solution, e.g. the total pressure difference or the forces on the objects. A Spatial Transformer Network with thin plate splines is used for the interaction between the physical constraints and the geometric representation of the objects. Thus, a transformation from an initial to a target geometry is performed such that the object is fulfilling the given constraints. This method is fully differentiable i.e. gradient informations can be used for the transformation. This can be seen as an inverse design process. The advantage of this method over many other proposed methods is, that the physical constraints are based on the inferred flow field solution. Thus, we have a transferable model which can be applied to varying problem setups and is not limited to a given set of geometry parameters or physical quantities.
Data Driven Adaptive Optimal Control for Linear Systems With Structured Time Varying Uncertainty
Venue: IEEE Access
Year: 2019

In this paper, a data driven adaptive optimal control strategy is proposed for a class of linear systems with structured time varying uncertainty, minimizing the upper bound of a pre defined cost function while maintaining the closed loop stability. An off policy data driven reinforcement learning algorithm is presented, which uses repeatedly the online state signal on some fixed time intervals without knowing system information, yielding a guaranteed cost control (GCC) law with quadratic stability for the system. This law is further optimized through a particle swarm optimization (PSO) method, the parameters of which are adaptively adjusted by a fuzzy logic mechanism, and an optimal GCC law with the minimum upper bound of the cost function is finally obtained. The effectiveness of this strategy is verified on the dynamic model of a two degree of freedom helicopter, showing that both stability and convergence of the closed loop system are guaranteed and that the cost is minimized with much less iteration than the conventional PSO method with constant parameters.
Enriqueta Marti, el monstruo de Barcelona (1912)
Year: 2011

espanolEntre los asesinos en serie espanoles, la historia de Enriqueta Marti el Monstruo de Barcelona ocupa un lugar preferente con una biografia que parece el argumento de una pelicula de terror. EnglishAmong the Spanish serial killers, the story of Enriqueta Marti The Barcelona's Monster has held a prominently seat with a biography that seems the screenplay of a horror movie.
Bangla Handwritten Character Recognition: an overview of the state of the art classification algorithm with new dataset
Venue: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)
Year: 2019

Recognition of handwritten characters from Bangla handwritten texts is of immense importance considering the complexity of the task. Researchers have explored the task of recognizing Bangla handwritten digits, but a few numbers of published works are available for Bangla Handwritten Character Recognition (BHCR) In our paper, we present a comparative overview of classification algorithms for BHCR, which may help the researcher to decide an appropriate classification algorithm for their work. We have created a new dataset of Bangla handwritten characters from 150 volunteers at different levels. We extracted around 2500 samples of Bangla characters, which consist of Bangla Vowels only. Histogram adjustment and other image preprocessing techniques are applied in handwritten characters before their classification. We compare the performance of seven commonly used classification algorithms for BHCR in terms of Sensitivity, Miss Rate, Specificity, Precision, Fall out, F score, and Overall Accuracy. This result shows that among the seven algorithms, ANN (Artificial Neural Network) performed best. LR (Logistic Regression) performed well compared to others in terms of the standard measures like sensitivity, specificity and error rate. This comparative overview will help scientists, especially the new researchers to give a quick start with Bangla handwritten character recognition.
Microgrid management system based on a multi agent approach: An office building pilot
Year: 2020

Microgrids bring advantages to end users and to the smart grid environment. However, adequate management software, enabling bringing to the field new energy management concepts, is not available yet. Small, single tasked, software is usually proposed and tested while a clear overall system architecture for microgrid management required to take full advantage of the microgrids' potential. Previous publications usually focus on energy related problems and do not provide an efficient and viable solution for players' representation and microgrid operation. This paper proposes a complete architecture for a microgrid management system based on a multi agent approach uGIM allowing the easy implementation of different energy strategies. The uGIM agents can independently manage local resources while able to collaborate and/or compete with other agents. Designed to run in single board computers, uGIM agents are light weighted and easily deployed in buildings. To demonstrate these capabilities, the paper details and presents a microgrid deployment using uGIM solution.
Using SensorML to construct a geoprocessing e Science workflow model under a sensor web environment
Venue: Comput. Geosci.
Year: 2012

Many achievements in web based geoprocessing focus on logically chaining Open Geospatial Consortium (OGC) web services, for example using Business Process Execution Language to orchestrate web services that are interfaced through the OGC Web Processing Service. For e Science application in a sensor web environment, how to internally integrate the sensor system, observation, and processes (physical and non physical) as a geoprocessing e Science workflow model is a critical issue. The OGC Sensor Model Language offers the possibility to construct a geoprocessing e Science workflow model in the form of observation processes. We propose a construction method for a geoprocessing e Science workflow model that integrates logical and physical processes into a composite process chain for sensor observations. The three phases of geoprocessing e Science workflow creation are abstract process chain modeling, process chain instantiation, and process chain workflow execution. An experiment on chaining related sub processes for deriving the Normalized Difference Vegetation Index of Hubei Province (China) was conducted to verify the feasibility of the proposed workflow model.
Graph Based Adaptive Fusion of GNSS and VIO Under Intermittent GNSS Degraded Environment
Venue: IEEE Transactions on Instrumentation and Measurement
Year: 2021

Consistent and accurate global positioning is a crucial problem for autonomous vehicles and robots. It is especially challenging in situations with a global navigation satellite system (GNSS) being intermittently degraded. We propose an adaptive fusion system, namely, GNSS/visual inertial navigation system (GVINS) which adaptively fuses GNSS and visual inertial odometry (VIO) to achieve consistent and accurate global positioning, even in GNSS intermittently degraded scenarios. Compared with existing methods, GVINS can provide positioning under an Earth fixed geographic coordinate, rather than a local tangent plane (LTP) coordinate, as long as any GNSS measurement presents in the trajectory. To adaptively fuse VIO and GNSS, we first use an inertial measurement unit (IMU) preintegration based depth uncertainty estimation method to evaluate the accuracy of VIO. Then, in the optimization backend, we perform an innovative overparameterized, 15 degree of freedom pose graph fusion. An alternating minimization (AM) algorithm is used to efficiently solve this problem. Evaluation results on both public and custom built data sets demonstrate that GVINS outperforms state of the art fusion methods in both accuracy and stability in GNSS intermittently degraded environments.
REFLECTING ON OUR TEACHING PRACTICE TO BRING ABOUT A MORE STUDENT CENTRED APPROACH TO LEARNING AND PROMOTE "DEEPER" LEARNING BY THE STUDENTS
Year: 2011

The Dublin Institute of Technology is one of the largest multi level higher education providers in Ireland, catering for over 22000 students annually. Under the 1999 Qualifications (Education and Training) Act, DIT became an awarding body in its own right. Programme provision covers apprenticeships, short continuous professional development courses, taught undergraduate and postgraduate, research MPhil and PhDs. The Institute's traditional mission has always been focused on teaching and learning in the field of advanced technical vocational education and training (TVET) and one of its current agendas is to foster and encourage changes in teaching practice and methodology in order to enhance the student learning. The current teaching of the Carpentry and Joinery apprentices appears to be flawed, as it is shaped by the assessment system. In the written theory papers, these assessments promote a surface learning approach and the student has 'slipped into' the characteristics of this surface learning. A sole aim is to pass exams, recall given notes, passively accept notes given out and focus on the important elements only. A culture of expectancy has developed among the students where there is too much dependency on lecturer help. The current lecturer pedagogic practice shapes itself towards the assessment. We feel a change in teaching practice is required to promote learning to higher levels in each domain of learning. Our aim was to ensure that any new teaching methods employed, improved and moved our teaching to a more student centred approach, and that there was a mechanism for student fee.dback, something which the current system does not particularly allow for. Through our research we hope to be able to highlight our concerns to the relevant authority and help bring about a change in Apprentice assessment system that will stimulate the current pedagogy from its current closed state.
An energy efficient fuzzy cluster head selection algorithm for WSNs
Venue: 2018 International Workshop on Advanced Image Technology (IWAIT)
Year: 2018

In wireless sensor networks (WSNs) tiny sensor nodes are randomly deployed with limited amount of energy to transmit massive data, like images and videos. Related information can be obtained when a CMOS camera is connected to a sensor node to capture images. Thus how to improve energy efficiency and prolong network lifetime is a challenging research issue. Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the network lifetime. However, many existing algorithms select the Cluster Head (CH) randomly based on a probabilistic threshold value or only consider the remaining energy parameter. In this work, we propose a fuzzy cluster head selection algorithm which takes three parameters into consideration, namely node's remaining energy, centrality of the clusters as well as the distance between node and the mobile sink. Simulation results show that our proposed algorithm is superior to other popular algorithms in terms of network lifetime and energy consumption.