nils-herrmann/clean_scirepeval huggingface dataset:
Graphene oxide--MnO2 nanocomposites for supercapacitors.

A composite of graphene oxide supported by needle-like MnO(2) nanocrystals (GO-MnO(2) nanocomposites) has been fabricated through a simple soft chemical route in a water-isopropyl alcohol system. The formation mechanism of these intriguing nanocomposites investigated by transmission electron microscopy and Raman and ultraviolet-visible absorption spectroscopy is proposed as intercalation and adsorption of manganese ions onto the GO sheets, followed by the nucleation and growth of the crystal species in a double solvent system via dissolution-crystallization and oriented attachment mechanisms, which in turn results in the exfoliation of GO sheets. Interestingly, it was found that the electrochemical performance of as-prepared nanocomposites could be enhanced by the chemical interaction between GO and MnO(2). This method provides a facile and straightforward approach to deposit MnO(2) nanoparticles onto the graphene oxide sheets (single layer of graphite oxide) and may be readily extended to the preparation of other classes of hybrids based on GO sheets for technological applications.
Soft erythrocyte-based bacterial microswimmers for cargo delivery

Bacteria-propelled biohybrid microswimmers have recently shown to be able to actively transport and deliver cargos encapsulated into their synthetic constructs to specific regions locally. However, usage of synthetic materials as cargo carriers can result in inferior performance in load-carrying efficiency, biocompatibility, and biodegradability, impeding clinical translation of biohybrid microswimmers. Here, we report construction and external guidance of bacteria-driven microswimmers using red blood cells (RBCs; erythrocytes) as autologous cargo carriers for active and guided drug delivery. Multifunctional biohybrid microswimmers were fabricated by attachment of RBCs [loaded with anticancer doxorubicin drug molecules and superparamagnetic iron oxide nanoparticles (SPIONs)] to bioengineered motile bacteria, Escherichia coli MG1655, via biotin-avidin-biotin binding complex. Autonomous and on-board propulsion of biohybrid microswimmers was provided by bacteria, and their external magnetic guidance was enabled by SPIONs loaded into the RBCs. Furthermore, bacteria-driven RBC microswimmers displayed preserved deformability and attachment stability even after squeezing in microchannels smaller than their sizes, as in the case of bare RBCs. In addition, an on-demand light-activated hyperthermia termination switch was engineered for RBC microswimmers to control bacteria population after operations. RBCs, as biological and autologous cargo carriers in the biohybrid microswimmers, offer notable advantages in stability, deformability, biocompatibility, and biodegradability over synthetic cargo-carrier materials. The biohybrid microswimmer design presented here transforms RBCs from passive cargo carriers into active and guidable cargo carriers toward targeted drug and other cargo delivery applications in medicine.
Lactate: Friend or Foe.

Lactic acid has played an important role in the traditional theory of muscle fatigue and limitation of endurance exercise performance. It has been called a waste product of anaerobic metabolism and has been believed to be responsible for the uncomfortable "burn" of intense exercise and directly responsible for the metabolic acidosis of exercise, leading to decreased muscle contractility and ultimately cessation of exercise. Although this premise has been commonly taught, it is not supported by the scientific literature and has led to a great deal of confusion among the sports medicine and exercise science communities. This review will provide the sports medicine clinician with an understanding of contemporary lactate theories, including lactate's role in energy production, its contributions to metabolic acidosis, and its function as an energy substrate for a variety of tissues. Lactate threshold concepts will also be discussed, including a practical approach to understanding prediction of performance and monitoring of training progress based on these parameters.
Modeling User Purchase Preference Based on Implicit Feedback*

In this paper we propose a new user purchase preference model based on their implicit feedback behavior. We analyze user behavior data to seek their purchase preference signals. We find that if a user has more purchase preference on a certain item he would tend to browse it for more times. It gives us an important inspiration that, not only purchasing behavior but also other types of implicit feedback like browsing behavior, can indicate user purchase preference. We further find that user purchase preference signals also exist in the browsing behavior of item categories. Therefore, when we want to predict user purchase preference for certain items, we can integrate these behavior types into our user preference model by converting such preference signals into numerical values. We evaluate our model on a real-world dataset from a shopping site in China. Results further validate that user purchase preference model in our paper can capture more and accurate user purchase preference information from implicit feedback and greatly improves the performance of user purchase prediction.
Medication-related osteonecrosis of the jaw: Clinical and practical guidelines

Medication-related osteonecrosis of the jaw (MRONJ) is a severe adverse drug reaction, consisting of progressive bone destruction in the maxillofacial region of patients. ONJ can be caused by two pharmacological agents: Antiresorptive (including bisphosphonates (BPs) and receptor activator of nuclear factor kappa-B ligand inhibitors) and antiangiogenic. MRONJ pathophysiology is not completely elucidated. There are several suggested hypothesis that could explain its unique localization to the jaws: Inflammation or infection, microtrauma, altered bone remodeling or over suppression of bone resorption, angiogenesis inhibition, soft tissue BPs toxicity, peculiar biofilm of the oral cavity, terminal vascularization of the mandible, suppression of immunity, or Vitamin D deficiency. Dental screening and adequate treatment are fundamental to reduce the risk of osteonecrosis in patients under antiresorptive or antiangiogenic therapy, or before initiating the administration. The treatment of MRONJ is generally difficult and the optimal therapy strategy is still to be established. For this reason, prevention is even more important. It is suggested that a multidisciplinary team approach including a dentist, an oncologist, and a maxillofacial surgeon to evaluate and decide the best therapy for the patient. The choice between a conservative treatment and surgery is not easy, and it should be made on a case by case basis. However, the initial approach should be as conservative as possible. The most important goals of treatment for patients with established MRONJ are primarily the control of infection, bone necrosis progression, and pain. The aim of this paper is to represent the current knowledge about MRONJ, its preventive measures and management strategies.
Automated antenna impedance adjustment for Near Field Communication (NFC)

Near Field Communication (NFC) is a very intuitive way to open a communication link, to authenticate, or to implement a payment by simply bringing two mobile personal devices closely together. NFC is based upon reliable contactless card technology and combines most prominent protocol standards in a specification driven by the NFC Forum. Devices with a NFC interface operate at 13.56 MHz via inductive loop antennas. However, these operate operated in a very different environment than contactless cards. Metal, the presence of several other antennas, and market demands for compact electronic devices are driving requirements for antennas to operate on ferrite foils, resulting in significant tolerances on antenna impedance. In NFC Reader mode, the antennas operate in a resonance circuit, making this de-tuning critical. This paper presents a prototype implementation for automated antenna impedance adjustment based upon Digitally Tunable Capacitors (DTCs). To show the benefit, the paper explains efficiency for contactless power transfer by practical measurements, comparing three scenarios-fixed impedance adjustment, matching with tolerance, and automated readjustment using a DTC.
Interval arithmetic recursive subdivision for implicit functions and constructive solid geometry

Recursive subdivision using interval arithmetic allows us to render CSG combinations of implicit function surfaces with or without anti -aliasing, Related algorithms will solve the collision detection problem for dynamic simulation, and allow us to compute mass. center of gravity, angular moments and other integral properties required for Newtonian dynamics. Our hidden surface algorithms run in ‘constant time.’ Their running times are nearly independent of the number of primitives in a scene, for scenes in which the visible details are not much smaller than the pixels. The collision detection and integration algorithms are utterly robust — collisions are never missed due 10 numerical error and we can provide guaranteed bounds on the values of integrals.
Let ’ s Make Block Coordinate Descent Go Fast !

Block coordinate descent (BCD) methods are widely-used for large-scale numerical optimization because of their cheap iteration costs, low memory requirements, amenability to parallelization, and ability to exploit problem structure. Three main algorithmic choices influence the performance of BCD methods: the block partitioning strategy, the block selection rule, and the block update rule. In this paper we explore all three of these building blocks and propose variations for each that can lead to significantly faster BCD methods. We (i) propose new greedy block-selection strategies that guarantee more progress per iteration than the Gauss-Southwell rule; (ii) explore practical issues like how to implement the new rules when using “variable” blocks; (iii) explore the use of message-passing to compute matrix or Newton updates efficiently on huge blocks for problems with a sparse dependency between variables; and (iv) consider optimal active manifold identification, which leads to bounds on the “active-set complexity” of BCD methods and leads to superlinear convergence for certain problems with sparse solutions (and in some cases finite termination at an optimal solution). We support all of our findings with numerical results for the classic machine learning problems of least squares, logistic regression, multi-class logistic regression, label propagation, and L1-regularization.
THE EFFECT OF BRAND IMAGE ON OVERALL SATISFACTION AND LOYALTY INTENTION IN THE CONTEXT OF COLOR COSMETIC

The purpose of this study is to examine the effect of brand image benefits on satisfaction and loyalty intention in the context of color cosmetic product. Five brand image benefits consisting of functional, social, symbolic, experiential and appearance enhances were investigated. A survey carried out on 97 females showed that functional and appearance enhances significantly affect loyalty intention. Four of brand image benefits: functional, social, experiential, and appearance enhances are positively related to overall satisfaction. The results also indicated that overall satisfaction does influence customers' loyalty. The results imply that marketers should focus on brand image benefits in their effort to achieve customer loyalty.
ExPregel: a new computational model for large-scale graph processing

These days, large-scale graph processing becomes more and more important. Pregel, inspired by Bulk Synchronous Parallel, is one of the highly used systems to process large-scale graph problems. In Pregel, each vertex executes a function and waits for a superstep to communicate its data to other vertices. Superstep is a very time-consuming operation, used by Pregel, to synchronize distributed computations in a cluster of computers. However, it may become a bottleneck when the number of communications increases in a graph with million vertices. Superstep works like a barrier in Pregel that increases the side effect of skew problem in distributed computing environment. ExPregel is a Pregel-like model that is designed to reduce the number of communication messages between two vertices resided on two different computational nodes. We have proven that ExPregel reduces the number of exchanged messages as well as the number of supersteps for all graph topologies. Enhancing parallelism in our new computational model is another important feature that manifolds the speed of graph analysis programs. More interestingly, ExPregel uses the same model of programming as Pregel. Our experiments on large-scale real-world graphs show that ExPregel can reduce network traffic as well as number of supersteps from 45% to 96%. Runtime speed up in the proposed model varies from 1.2× to 30×.
Convolutional Neural Networks for Crop Yield Prediction using Satellite Images

Crop yield forecasting during the growing season is useful for farming planning and management practices as well as for planning humanitarian aid in developing countries. Common approaches to yield forecast include the use of expensive manual surveys or accessible remote sensing data. Traditional remote sensing based approaches to predict crop yield consist of classical Machine Learning techniques such as Support Vector Machines and Decision Trees. More recent approaches include using deep neural network models, such as CNN and LSTM. We identify the additional gaps in the literature of existing machine learning methods as lacking of (1) standardized training protocol that specifies the optimal time frame, both in terms of years and months of each year, to be considered in the training set, (2) verified applicability to developing countries under the condition of scarce data, and (3) effective utilization of spatial features in remote sensing images. In this thesis, we first replicate the state-of-the-art approach of You et al. , in particular their CNN model for crop yield prediction. To tackle the first identified gap, we then perform control experiments to determine the best temporal training settings for soybean yield prediction. To probe the second gap, we further investigate whether this CNN model could be trained on source locations and then be transfered to new target locations and conclude that it is necessary to use source regions that have a similar or generalizable ecosystem to the target regions. This allows us to assess the transferability of CNN-based regression models to developing countries, where little training data is available. Additionally, we propose a novel 3D CNN model for crop yield prediction task that leverages the spatiotemporal features. We demonstrate that our 3D CNN outperforms all competing machine learning methods, shedding light on promising future directions in utilizing deep learning tools for crop yield prediction.
The Conchal Cartilage Butterfly Graft.

Nasal obstruction due to weakening of the nasal sidewall is a very common patient complaint. The conchal cartilage butterfly graft is a proven technique for the correction of nasal valve collapse. It allows for excellent functional results, and with experience and attention to technical detail, it may also provide excellent cosmetic results. While this procedure is most useful for restoring form and function in cases of secondary rhinoplasty following the reduction of nasal support structures, we have found it to be a very powerful and satisfying technique in primary rhinoplasty as well. This article aims to describe the butterfly graft, discuss its history, and detail the technical considerations which we have found useful.
Visualizing Amazon . com ’ s Purchase Circles

Amazon is a well-known online company that sells products such as books and music. It also tracks the purchasing patterns of a variety of groups including private corporations, government organizations, and geographic areas. Amazon defines each of these groups as a “purchase circle.” For each purchase circle, Amazon lists the bestselling items in the Books, Music, Video, DVDs, and Electronics product categories. Our objective is to create a dynamic visualization of Amazon’s purchase circles that focuses on looking at the Top 10 music titles and genres that are popular in selected U.S. cities. We present a visualization known as CityPrints, a dynamic query-based tool for producing color-coded visual representations of purchase circles data. CityPrints allows users to quickly compare popular titles in different U.S. cities, identify which music genres are popular in a given city, and rank cities according to how popular a given music genre is in that city.
Human growth hormone and human aging.

In humans, both aging and GH deficiency are associated with reduced protein synthesis, decreased lean body and bone mass, and increased percent body fat. In healthy individuals, spontaneous and stimulated GH secretion, as well as circulating IGF-I and IGFBP-3 levels, are significantly decreased with advancing age. The extent to which these age-related changes in GH and IGF-I contribute to alterations in body composition and function remains to be elucidated. GH treatment of GH-deficient adults or old men with reduced IGF-I levels with exogenous GH increases plasma IGF-I, nitrogen retention, and lean body mass, decreases percent body fat, and exerts little effect on bone mineral density. Short-term adverse effects of GH therapy have been minimized by using low-dose regimens, but it is still uncertain whether long-term GH supplementation in adult life increases the risk of metabolic abnormalities or malignancy. Administration of GHRH, which has been shown to maintain the pattern of pulsatile GH secretion in old men, may represent another possible physiological approach to therapy. It may be justifiable initially to limit use of GH to certain elderly patients such as those suffering from catabolic illnesses, malnourishment, burns, cachexia, etc. A great deal more research will be necessary to determine whether normalization of GH and IGF-I levels in healthy older persons will lead to improvements in their physical and psychological functional capacity and quality of life.
A novel cluster-based routing protocol wireless sensor networks using Spider Monkey Optimization

With rapid development, wireless sensor networks (WSNs) have been focused on improving the performance consist of energy efficiency, communication effectiveness, and system throughput. Many novel mechanisms have been implemented by adapting the social behaviors of natural creatures, such as bats, birds, ants, fish and honeybees. These systems are known as nature inspired systems or swarm intelligence in in order to provide optimization strategies, handle large-scale networks and avoid resource constraints. Spider monkey optimization (SMO) is a recent addition to the family of swarm intelligence algorithms by structuring the social foraging behavior of spider monkeys. In this paper, we aim to study the mechanism of SMO in the field of WSNs, formulating the mathematical model of the behavior patterns which cluster-based Spider Monkey Optimization (SMO-C) approach is adapted. In addition, our proposed methodology based on the Spider Monkey's behavioral structure aims to improve the traditional routing protocols in term of low-energy consumption and system quality of the network.
An Efficient Dispatcher for Large Scale GraphProcessing on OpenCL-based FPGAs

High parallel framework has been proved to be very suitable for graph processing. There are various work to optimize the implementation in FPGAs, a pipeline parallel device. The key to make use of the parallel performance of FPGAs is to process graph data in pipeline model and take advantage of on-chip memory to realize necessary locality process. This paper proposes a modularize graph processing framework, which focus on the whole executing procedure with the extremely different degree of parallelism. The framework has three contributions. First, the combination of vertex-centric and edge-centric processing framework can been adjusting in the executing procedure to accommodate top-down algorithm and bottom-up algorithm. Second, owing to the pipeline parallel and finite on-chip memory accelerator, the novel edge-block, a block consist of edges vertex, achieve optimizing the way to utilize the on-chip memory to group the edges and stream the edges in a block to realize the stream pattern to pipeline parallel processing. Third, depending to the analysis of the block structure of nature graph and the executing characteristics during graph processing, we design a novel conversion dispatcher to change processing module, to match the corresponding exchange point. Our evaluation with four graph applications on five diverse scale graph shows that .
Cross Temporal Recurrent Networks for Ranking Question Answer Pairs

Temporal gates play a significant role in modern recurrentbased neural encoders, enabling fine-grained control over recursive compositional operations over time. In recurrent models such as the long short-term memory (LSTM), temporal gates control the amount of information retained or discarded over time, not only playing an important role in influencing the learned representations but also serving as a protection against vanishing gradients. This paper explores the idea of learning temporal gates for sequence pairs (question and answer), jointly influencing the learned representations in a pairwise manner. In our approach, temporal gates are learned via 1D convolutional layers and then subsequently cross applied across question and answer for joint learning. Empirically, we show that this conceptually simple sharing of temporal gates can lead to competitive performance across multiple benchmarks. Intuitively, what our network achieves can be interpreted as learning representations of question and answer pairs that are aware of what each other is remembering or forgetting, i.e., pairwise temporal gating. Via extensive experiments, we show that our proposed model achieves state-of-the-art performance on two community-based QA datasets and competitive performance on one factoid-based QA dataset.
Binding interaction of indomethacin with human serum albumin.

The interaction between indomethacin and human serum albumin (HSA) was investigated by fluorescence quenching technique and UV-vis absorption spectroscopy. The results of fluorescence titration revealed that indomethacin, strongly quench the intrinsic fluorescence of HSA by static quenching and nonradiative energy transfer. The binding site number n and the apparent binding constant K(A), were calculated using linear and nonlinear fit to the experimental data. The distance r between donor (HSA) and acceptor (indomethacin) was obtained according to fluorescence resonance energy transfer (FRET). The study suggests that the donor and the acceptor are bound at different locations but within the quenching distance.
A finite difference scheme for fractional sub-diffusion equations on an unbounded domain using artificial boundary conditions

One-dimensional fractional anomalous sub-diffusion equations on an unbounded domain are considered in our work. Beginning with the derivation of the exact artificial boundary conditions, the original problem on an unbounded domain is converted into mainly solving an initial-boundary value problem on a finite computational domain. The main contribution of our work, as compared with the previous work, lies in the reduction of fractional differential equations on an unbounded domain by using artificial boundary conditions and construction of the corresponding finite difference scheme with the help of method of order reduction. The difficulty is the treatment of Neumann condition on the artificial boundary, which involves the time-fractional derivative operator. The stability and convergence of the scheme are proven using the discrete energy method. Two numerical examples clarify the effectiveness and accuracy of the proposed method.