nils-herrmann/clean_scirepeval huggingface dataset:
Lattice-Boltzmann Simulations of Particle-Fluid Suspensions

This paper reviews applications of the lattice-Boltzmann method to simulations of particle-fluid suspensions. We first summarize the available simulation methods for colloidal suspensions together with some of the important applications of these methods, and then describe results from lattice-gas and latticeBoltzmann simulations in more detail. The remainder of the paper is an update of previously published work, (69, 70) taking into account recent research by ourselves and other groups. We describe a lattice-Boltzmann model that can take proper account of density fluctuations in the fluid, which may be important in describing the short-time dynamics of colloidal particles. We then derive macrodynamical equations for a collision operator with separate shear and bulk viscosities, via the usual multi-time-scale expansion. A careful examination of the second-order equations shows that inclusion of an external force, such as a pressure gradient, requires terms that depend on the eigenvalues of the collision operator. Alternatively, the momentum density must be redefined to include a contribution from the external force. Next, we summarize recent innovations and give a few numerical examples to illustrate critical issues. Finally, we derive the equations for a lattice-Boltzmann model that includes transverse and longitudinal fluctuations in momentum. The model leads to a discrete version of the Green–Kubo relations for the shear and bulk viscosity, which agree with the viscosities obtained from the macro-dynamical analysis. We believe that inclusion of longitudinal fluctuations will improve the equipartition of energy in lattice-Boltzmann simulations of colloidal suspensions.
Extra robotic thumb and exoskeleton robotic fingers for patient with hand function disability

This paper presents the development of wearable robot to enhance and to assist people who lost the ability to grasp object. In the developing countries, most persons with hand disabilities still use manual and passive tools, especially for prosthetic and orthotic. We propose a wearable robot as an orthotic device namely extra robotic thumb and exoskeleton robotic fingers as new type of wearable robot for assisting person whose the thumb can not move anymore and the other four fingers has diminished fingers function. The motion of exoskeleton robotic finger has four DOF which each DOF represents the motion of flexion and extension on the fingers. For extra robotic thumb, it has two DOF to represent the motion of circumduction and flexion/extension on the thumb. The robot employs four linear actuators in exoskeleton robotic fingers and uses two micro servos in extra robotic thumb. The robot is designed based on 3D print technology. To assist person in Activity of Daily Living (ADL), five grip patterns are developed in this research. User can command the robot by using electromyography (EMG) sensor that attached on near healthy elbow. Based on the experimental results, the robot can successfully perform object grasping tasks by using the developed five grip modes.
Redefining "Learning" in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL.
An Agile BPM Project Methodology

Business Process Management (BPM) has become one of the most important management disciplines in recent years. In reality, however, many technical process improvement projects failed in the past and the expected benefits could not be established. In the meantime, the agile software development movement made massive progress in contrast to classic waterfall approaches which are still the foundational methodologies for many BPM projects. This paper investigates the combination of a traditional BPM methodology and agile software development to overcome the limitations of existing BPM methodologies. The main focus is on projects that cover the technical realization of processes based on modern Business Process Management Systems (BPMS).
A General Constant Power Generation Algorithm for Photovoltaic Systems

Photovoltaic power plants (PVPPs) typically operate by tracking the maximum power point (MPP) in order to maximize the conversion efficiency. However, with the continuous increase of installed grid-connected PVPPs, power system operators have been experiencing new challenges, such as overloading, overvoltages, and operation during grid-voltage disturbances. Consequently, constant power generation (CPG) is imposed by grid codes. An algorithm for the calculation of the photovoltaic panel voltage reference, which generates a constant power from the PVPP, is introduced in this paper. The key novelty of the proposed algorithm is its applicability for both single- and two-stage PVPPs and flexibility to move the operation point to the right or left side of the MPP. Furthermore, the execution frequency of the algorithm and voltage increments between consecutive operating points are modified based on a hysteresis band controller in order to obtain fast dynamic response under transients and low-power oscillation during steady-state operation. The performance of the proposed algorithm for both single- and two-stage PVPPs is examined on a 50-kVA simulation setup of these topologies. Moreover, experimental results on a 1-kVA PV system validate the effectiveness of the proposed algorithm under various operating conditions, demonstrating functionalities of the proposed CPG algorithm.
Identifying Key Risks in Construction Projects : Life Cycle and Stakeholder Perspectives

Managing risks in construction projects has been recognised as a very important management process in order to achieve the project objectives in terms of time, cost, quality, safety and environmental sustainability. However, until now most research has focused on some aspects of construction risk management rather than using a systematic and holistic approach to identify risks and analyse the likelihood of occurrence and impacts of these risks. This paper aims to identify and analyse the risks associated with the development of construction projects from project stakeholder and life cycle perspectives. Postal questionnaire surveys were used to collect data. Based on a comprehensive assessment of the likelihood of occurrence and their impacts on the project objectives, this paper identifies twenty major risk factors. This research found that these risks are mainly related to (in ranking) contractors, clients and designers, with few related to government bodies, subcontractors/suppliers and external issues. Among them, “tight project schedule” is recognised to influence all project objectives maximally, whereas “design variations”, “excessive approval procedures in administrative government departments”, “high performance/quality expectation”, “unsuitable construction program planning”, as well as “variations of construction program” are deemed to impact at least four aspects of project objectives. This research also found that these risks spread through the whole project life cycle and many risks occur at more than one phase, with the construction stage as the most risky phase, followed by the feasibility stage. It is concluded that clients, designers and government bodies must work cooperatively from the feasibility phase onwards to address potential risks in time, and contractors and subcontractors with robust construction and management knowledge must be employed early to make sound preparation for carrying out safe, efficient and quality construction activities.
Feed optimization for five-axis CNC machine tools with drive constraints

Real time control of five-axis machine tools requires smooth generation of feed, acceleration and jerk in CNC systems without violating the physical limits of the drives. This paper presents a feed scheduling algorithm for CNC systems to minimize the machining time for five-axis contour machining of sculptured surfaces. The variation of the feed along the five-axis tool-path is expressed in a cubic B-spline form. The velocity, acceleration and jerk limits of the five axes are considered in finding the most optimal feed along the toolpath in order to ensure smooth and linear operation of the servo drives with minimal tracking error. The time optimal feed motion is obtained by iteratively modulating the feed control points of the B-spline to maximize the feed along the tool-path without violating the programmed feed and the drives’ physical limits. Long tool-paths are handled efficiently by applying a moving window technique. The improvement in the productivity and linear operation of the five drives is demonstrated with five-axis simulations and experiments on a CNC machine tool. r 2008 Elsevier Ltd. All rights reserved.
Crawling Facebook for social network analysis purposes

We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.
Avoiding and managing the failure of conventional crowns and bridges.

The replacement of crowns and bridges is a common procedure for many dental practitioners. When correctly planned and executed, fixed prostheses will provide predictable function, aesthetics and value for money. However, when done poorly, they are more likely to fail prematurely and lead to irreversible damage to the teeth and supporting structures beneath. Sound diagnosis, assessment and technical skills are essential when dealing with failed or failing fixed restorations. These skills are essential for the 21st century dentist. This paper, with treated clinical examples, illustrates the areas of technical skill and clinical decisions needed for this type of work. It also provides advice on how the risk of premature failure can, in general, be further reduced. The article also confirms the very real risk in the UK of dento-legal problems when patients experience unexpected problems with their crowns and bridges.



This paper outlines clinical implications of failed fixed prosthodontics to the dental surgeon. It also discusses factors that we can all use to predict and reduce the risk of premature restoration failure. Restoration design, clinical execution and patient factors are the most frequent reasons for premature problems. It is worth remembering (and informing patients) that the health of the underlying supporting dental tissue is often irreversibly compromised at the time of fixed restoration failure.
On the Integration of Cloud Computing and Internet of Things

Cloud computing and Internet of Things (IoT), two very different technologies, are both already part of our life. Their massive adoption and use is expected to increase further, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and an enabler of a large number of application scenarios. In this paper we focus our attention on the integration of Cloud and IoT, which we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately: their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the CloudIoT paradigm. To bridge this gap, in this paper we review the literature about the integration of Cloud and IoT. We start analyzing and discussing the need for integrating them, the challenges deriving from such integration, and how these issues have been tackled in literature. We then describe application scenarios that have been presented in literature, as well as platforms -- both commercial and open source -- and projects implementing the CloudIoT paradigm. Finally, we identify open issues, main challenges and future directions in this promising field.
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

Existing research studies on vision and language grounding for robot navigation focus on improving model-free deep reinforcement learning (DRL) models in synthetic environments. However, model-free DRL models do not consider the dynamics in the real-world environments, and they often fail to generalize to new scenes. In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices—We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task. Our look-ahead module tightly integrates a look-ahead policy model with an environment model that predicts the next state and the reward. Experimental results suggest that our proposed method significantly outperforms the baselines and achieves the best on the real-world Room-toRoom dataset. Moreover, our scalable method is more generalizable when transferring to unseen environments.
HARMONIC MAASS FORMS , MOCK MODULAR FORMS , AND QUANTUM MODULAR FORMS

In his enigmatic death bed letter to Hardy, written in January 1920, Ramanujan introduced the notion of a mock theta function. Despite many works, very little was known about the role that these functions play within the theory of automorphic and modular forms until 2002. In that year Sander Zwegers (in his Ph.D. thesis) established that these functions are “holomorphic parts” of harmonic Maass forms. This realization has resulted in many applications in a wide variety of areas: arithmetic geometry, combinatorics, modular forms, and mathematical physics. Here we outline the general facets of the theory, and we give several applications to number theory: partitions and q-series, modular forms, singular moduli, Borcherds products, extensions of theorems of Kohnen-Zagier and Waldspurger on modular L-functions, and the work of Bruinier and Yang on Gross-Zagier formulae. Following our discussion of these works on harmonic Maass forms, we shall then study the emerging new theory of quantum modular forms. Don Zagier introduced the notion of a quantum modular form in his 2010 Clay lecture, and it turns out that a beautiful part of this theory lives at the interface of classical modular forms and harmonic Maass forms.
Sublayer-Specific Coding Dynamics during Spatial Navigation and Learning in Hippocampal Area CA1

The mammalian hippocampus is critical for spatial information processing and episodic memory. Its primary output cells, CA1 pyramidal cells (CA1 PCs), vary in genetics, morphology, connectivity, and electrophysiological properties. It is therefore possible that distinct CA1 PC subpopulations encode different features of the environment and differentially contribute to learning. To test this hypothesis, we optically monitored activity in deep and superficial CA1 PCs segregated along the radial axis of the mouse hippocampus and assessed the relationship between sublayer dynamics and learning. Superficial place maps were more stable than deep during head-fixed exploration. Deep maps, however, were preferentially stabilized during goal-oriented learning, and representation of the reward zone by deep cells predicted task performance. These findings demonstrate that superficial CA1 PCs provide a more stable map of an environment, while their counterparts in the deep sublayer provide a more flexible representation that is shaped by learning about salient features in the environment.
Chemical Structures and Bioactivities of Sulfated Polysaccharides from Marine Algae

Sulfated polysaccharides and their lower molecular weight oligosaccharide derivatives from marine macroalgae have been shown to possess a variety of biological activities. The present paper will review the recent progress in research on the structural chemistry and the bioactivities of these marine algal biomaterials. In particular, it will provide an update on the structural chemistry of the major sulfated polysaccharides synthesized by seaweeds including the galactans (e.g., agarans and carrageenans), ulvans, and fucans. It will then review the recent findings on the anticoagulant/antithrombotic, antiviral, immuno-inflammatory, antilipidemic and antioxidant activities of sulfated polysaccharides and their potential for therapeutic application.
Image Edge Detection Based On Opencv

Image processing is one of most growing research area these days and now it is very much integrated with the industrial production. Generally speaking, It is very difficult for us to distinguish the exact number of the copper core in the tiny wire, However, in order to ensure that the wire meets the requirements of production, we have to know the accurate number of copper core in the wire. Here the paper will introduce a method of image edge detection to determine the exact number of the copper core in the tiny wire based on OpenCV with rich computer vision and image processing algorithms and functions. Firstly, we use high-resolution camera to take picture of the internal structure of the wire. Secondly, we use OpenCV image processing functions to implement image preprocessing. Thirdly we use morphological opening and closing operations to segment image because of their blur image edges. Finally the exact number of copper core can be clearly distinguished through contour tracking. By using of Borland C++ Builder 6.0, experimental results show that OpenCV based image edge detection methods are simple, high code integration, and high image edge positioning accuracy.
Anatomy and nonsurgical correction of the tear trough deformity.

The development of dark circles under the eyes is one of the early signs of periorbital aging, lending a fatigued and aged appearance to the face. Loeb, in 1961, used the term "nasojugal groove" to describe the concavity at the border of the eyelid and the cheek medially. Flowers, in 1969, first named this groove the "tear trough." In the present article, the author presents a detailed description of the anatomy and nonsurgical correction of this deformity. Nonsurgical correction of the tear trough deformity with hyaluronic acid is effective and safe and is associated with high patient satisfaction. The procedure offers both an adjunct to surgery and an alternative to it in some patients. It also provides an opportunity for global midfacial volume correction. Careful patient selection and attention to technique will minimize side effects.
Injecting constraints into neural NLP models

The goal of this thesis proposal is injecting knowledge/constraints into neural models, primarily for natural language processing (NLP) tasks. While neural models have set new state of the art performance in many tasks from vision to NLP, they often fail to learn simple rules necessary for well-formed structures unless there are immense amount of training data. The thesis proposes that not all the aspects of the model have to be learned from the data itself and injecting simple knowledge/constraints into the neural models can help low-resource tasks as well as improving state-of-the-art models. The proposal focuses on the structural knowledge of the output space and injects knowledge of correct or preferred structures as an objective to the model without modification to the model structure in a model-agnostic way. The first benefit in focusing on the knowledge of output space is that it is intuitive as we can directly enforce outputs to satisfy logical/linguistic constraints. Another advantage of structural knowledge is that it often does not require labeled dataset. Focusing on deterministic constraints on the output values, this thesis proposal first applies output constraints on inference time via proposed gradient-based inference (GBI) method. In the spirit of gradient-based training, GBI enforces constraints for each input at test-time by optimizing continuous model weights until the network’s inference procedure generates an output that satisfies the constraints. The proposal shows that constraint injection on inference-time can be extended to the training time: from instance-based optimization on test time to generalization to multiple instances in training time. In training with structural constraints, the thesis proposal presents (1) structural constraint loss, (2) joint objective of structural loss and supervised loss on training set and lastly (3) joint objective on semi-supervised setting. All the loss functions show improvements and the (3) semi-supervised approach shows the largest improvement, particularly effective on low-resource setting, among them. The analysis shows that the efforts on training time and on inference time are complementary rather than exclusive: the performance is best when efforts on train-time and inference-time methods are combined. Lastly, the thesis proposes to extend the completed work to generalized spanbased models and to domain adaptation where target domain is unlabeled. Moreover, the thesis proposal promises to explore additional methodology that might bring bigger gains through constraint injection compared to currently proposed approaches. In the next sections, I provide brief description on the applications I worked with the constraints I injected per problem. November 2, 2018 DRAFT
Antioxidant activity of plant extracts containing phenolic compounds.

The antioxidative activity of a total of 92 phenolic extracts from edible and nonedible plant materials (berries, fruits, vegetables, herbs, cereals, tree materials, plant sprouts, and seeds) was examined by autoxidation of methyl linoleate. The content of total phenolics in the extracts was determined spectrometrically according to the Folin-Ciocalteu procedure and calculated as gallic acid equivalents (GAE). Among edible plant materials, remarkable high antioxidant activity and high total phenolic content (GAE > 20 mg/g) were found in berries, especially aronia and crowberry. Apple extracts (two varieties) showed also strong antioxidant activity even though the total phenolic contents were low (GAE < 12.1 mg/g). Among nonedible plant materials, high activities were found in tree materials, especially in willow bark, spruce needles, pine bark and cork, and birch phloem, and in some medicinal plants including heather, bog-rosemary, willow herb, and meadowsweet. In addition, potato peel and beetroot peel extracts showed strong antioxidant effects. To utilize these significant sources of natural antioxidants, further characterization of the phenolic composition is needed.