nils-herrmann/clean_scirepeval huggingface dataset:
Photothermally driven fast responding photo-actuators fabricated with comb-type hydrogels and magnetite nanoparticles

To overcome the slow kinetics of the volume phase transition of stimuli-responsive hydrogels as platforms for soft actuators, thermally responsive comb-type hydrogels were prepared using synthesized poly(N-isopropylacrylamide) macromonomers bearing graft chains. Fast responding light-responsive hydrogels were fabricated by combining a comb-type hydrogel matrix with photothermal magnetite nanoparticles (MNP). The MNPs dispersed in the matrix provide heat to stimulate the volume change of the hydrogel matrix by converting absorbed visible light to thermal energy. In this process, the comb-type hydrogel matrix exhibited a rapid response due to the free, mobile grafted chains. The comb-type hydrogel exhibited significantly enhanced light-induced volume shrinkage and rapid recovery. The comb-type hydrogels containing MNP were successfully used to fabricate a bilayer-type photo-actuator with fast bending motion.
Enabling scalability-sensitive speculative parallelization for FSM computations

Finite state machines (FSMs) are the backbone of many applications, but are difficult to parallelize due to their inherent dependencies. Speculative FSM parallelization has shown promise on multicore machines with up to eight cores. However, as hardware parallelism grows (e.g., Xeon Phi has up to 288 logical cores), a fundamental question raises: How does the speculative FSM parallelization scale as the number of cores increases? Without answering this question, existing methods for speculative FSM parallelization simply choose to use all available cores, which might not only waste computing resources, but also result in suboptimal performance.
 In this work, we conduct a systematic scalability analysis for speculative FSM parallelization. Unlike many other parallelizations which can be modeled by the classic Amdahl's law or its simple extensions, speculative FSM parallelization scales unconventionally due to the non-deterministic nature of speculation and the cost variations of misspeculation. To address these challenges, this work introduces a spectrum of scalability models that are customized to the properties of specific FSMs and the underlying architecture. The models, for the first time, precisely capture the scalability of speculative parallelization for different FSM computations, and clearly show the existence of a "sweet spot" in terms of the number of cores employed by the speculative FSM parallelization to achieve the optimal performance. To make the scalability models practical, we develop S3, a <u>s</u>calability-<u>s</u>ensitive <u>s</u>peculation framework for FSM parallelization. For any given FSM, S3 can automatically characterize its properties and analyze its scalability, hence guide speculative parallelization towards the optimal performance and more efficient use of computing resources. Evaluations on different FSMs and architectures confirm the accuracy of the proposed models and show that S3 achieves significant speedup (up to 5X) and energy savings (up to 77%).
Design and implementation of a push-pull phase-shifted bi-directional inverter with a dsPIC controller

This paper presents design and implementation of a bi-directional inverter, including a high frequency transformer, a push-pull switch configuration at the dc side, a cycloconverter at the ac side, and a dsPIC controller. The dc/ac conversion is achieved with a phase-shifted control strategy. In addition, this topology also can achieve an ac/dc conversion with the PFC function. In this circuit, the dsPIC realizes almost all functions, including generation of PWM signals, A/D conversion, phase shift, circuit protection, and PFC during ac/dc conversion. The proposed bi-directional inverter can reduce weight, size and volume significantly as compared to a conventional low frequency transformer approach. Experimental results obtained from a prototype with a dc side voltage of 48V, an ac side voltage of 110Vrms, and power rating of 500W have verified its feasibility.
Effects of diet cycling during training on weight loss and resting energy expenditure: a preliminary analysis

Long-term dieting has been reported to reduce resting energy expenditure (REE) leading to weight regain once the diet has been curtailed. Diets are also difficult to follow for a significant length of time. The purpose of this preliminary proof of concept study was to examine the effects of short-term intermittent dieting during exercise training on REE and weight loss in overweight women.
Challenges of Agile Development and Implementation in a Developing Country: A Zambia Case Study

Douglas Kunda Mulungushi University, School of Science, Engineering and Technology, Kabwe, Zambia  Agile development is a software development process that advocates adaptive planning, early delivery, evolutionary development and continuous betterment and supports rapid and flexible response to change. The purpose of Agile development is minimize project failure through customer interactions and responding to change. However, Agile development is vulnerable to failure because of a number of factors and these factors can be categorized under four dimensions, namely; organizational, people, process and technical. This paper reports the result of a study aimed at identifying factors that influence success and/or failure of Agile development in a developing country, Zambia. A multiple case study approach and grounded theory approach was used for this case study. The study shows that there are challenges that are unique to developing countries and therefore measures should be developed to address these unique problems when implementing Agile projects in developing countries.
Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps

Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown objects. In this work we improve on one of the most promising approaches, the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset [15].We propose a new architecture for the GQ-CNN and describe practical improvements that increase the model validation accuracy from 92.2% to 95.8% and from 85.9% to 88.0% on respectively image-wise and object-wise training and validation splits.
Squalene Emulsions for Parenteral Vaccine and Drug Delivery

Squalene is a linear triterpene that is extensively utilized as a principal component of parenteral emulsions for drug and vaccine delivery. In this review, the chemical structure and sources of squalene are presented. Moreover, the physicochemical and biological properties of squalene-containing emulsions are evaluated in the context of parenteral formulations. Historical and current parenteral emulsion products containing squalene or squalane are discussed. The safety of squalene-based products is also addressed. Finally, analytical techniques for characterization of squalene emulsions are examined.
Approximate Area-to-Point Regression Kriging for Fast Hyperspectral Image Sharpening

Area-to-point regression kriging (ATPRK) is an advanced image fusion approach in remote sensing In this paper, ATPRK is considered for sharpening hyperspectral images (HSIs), based on the availability of a fine spatial resolution panchromatic or multispectral image. ATPRK can be used straightforwardly to sharpen each coarse hyperspectral band in turn. This scheme, however, is computationally expensive due to the large number of bands in HSIs, and this problem is exacerbated for multiscene or multitemporal analysis. Thus, we extend ATPRK for fast HSI sharpening with a new approach, called approximate ATPRK (AATPRK), which transforms the original HSI to a new feature space and image fusion is performed for only the first few components before back transformation. Experiments on two HSIs show that AATPRK greatly expedites ATPRK, but inherits the advantages of ATPRK, including maintaining a very similar performance in sharpening (both ATPRK and AATPRK can produce more accurate results than seven benchmark methods) and precisely conserving the spectral properties of coarse HSIs.
Serial and superficial suction for steatopygia (Hottentot Bustle)

Steatopygia, from the Greek “steato” meaning “fat” and pygia meaning “buttocks,” is defined as excessive fat of the buttocks, usually seen in women and sometimes called Hottentot Bustle because it was commonly seen in the Hottentot people of southern Africa. This affliction is characterized by protrusion and excessive fatness of just the buttocks region. Our patient was not particularly overweight (166 lb) and had a reasonably thin waist and legs. However, her buttocks were so protruding as to appear to have been from a completely different person. She wore only full skirts and could never wear jeans or pants of any kind. By using both superficial and deep serial suction techniques we were able to remove 12.5 L of subcutaneous fat over a two-year period without hospitalization or exogenous transfusion. The superficial technique improved skin retraction.
Evaluation of cursive and non-cursive scripts using recurrent neural networks

Character recognition has been widely used since its inception in applications involved processing of scanned or camera-captured documents. There exist multiple scripts in which the languages are written. The scripts could broadly be divided into cursive and non-cursive scripts. The recurrent neural networks have been proved to obtain state-of-the-art results for optical character recognition. We present a thorough investigation of the performance of recurrent neural network (RNN) for cursive and non-cursive scripts. We employ bidirectional long short-term memory (BLSTM) networks, which is a variant of the standard RNN. The output layer of the architecture used to carry out our investigation is a special layer called connectionist temporal classification (CTC) which does the sequence alignment. The CTC layer takes as an input the activations of LSTM and aligns the target labels with the inputs. The results were obtained at the character level for both cursive Urdu and non-cursive English scripts are significant and suggest that the BLSTM technique is potentially more useful than the existing OCR algorithms.
Defining key drivers of online impulse purchasing: A perspective of both impulse shoppers and system users

With the proliferation of e-commerce, a large part of online shopping is attributed to impulse buying. Hence, there is a particular necessity to understand impulse buying in the online context. Impulse shoppers incline to feel unable to control their tendencies and behaviors from various stimuli. Specifically, online consumers are both the impulse shoppers and the system users of websites in the purchase process. Impulse shoppers concern individual traits and system users cover the attributes of online stores. Online impulse buying therefore entails two key drivers, technology use and trust belief, and the mediator of flow experience. Grounding on flow experience, technology-use features, and trust belief, this study
The ADRENALINE testbed: An SDN/NFV packet/optical transport network and edge/core cloud platform for end-to-end 5G and IoT services

The fifth generation of mobile networks (5G) and the internet of Things (IoT) impose very stringent requirements to the optical transport networks. On the one hand, high flexibility, ultra-low latency and high capacity, in order to support the forecasted 1000x growth in mobile data traffic with latencies below millisecond. On the other hand, massive edge and core cloud infrastructure integrated with the transport network to dynamically deploy NFV, MEC, and IoT analytics. This paper presents ADRENALINTE testbed, an SDN/NFV packet/optical transport network and edge/core cloud platform for end-to-end 5G and IoT services.
An efficient UWB FSS for electromagnetic shielding

An efficient Ultra-Wideband (UWB) Frequency Selective Surface (FSS) is presented to mitigate the potential harmful effects of Electromagnetic Interference (EMI) caused by the radiations emitted by radio devices. The proposed design consists of circular and square elements printed on the opposite surfaces of FR4 substrate of 3.2 mm thickness. It ensures better angular stability by up to 600, bandwidth has been significantly enhanced by up to 16. 21 GHz to provide effective shielding against X-, Ka- and K-bands. While signal attenuation has also been improved remarkably in the desired band compared to the results presented in the latest research. Theoretical results are presented for TE and TM polarization for normal and oblique angles of incidence.
Differential interference contrast x-ray microscopy with twin zone plates.

X-ray imaging in differential interference contrast (DIC) with submicrometer optical resolution was performed by using a twin zone plate (TZP) setup generating focal spots closely spaced within the TZP spatial resolution of 160 nm. Optical path differences introduced by the sample are recorded by a CCD camera in a standard full-field imaging and by an aperture photodiode in a standard scanning transmission x-ray microscope. Applying this x-ray DIC technique, we demonstrate for both the full-field imaging and scanning x-ray microscope methods a drastic increase in image contrast (approximately 20x) for a low-absorbing specimen, similar to the Nomarski DIC method for visible-light microscopy.
Neural connectivity in Internet gaming disorder and alcohol use disorder: A resting-state EEG coherence study

The present study compared neural connectivity and the level of phasic synchronization between neural populations in patients with Internet gaming disorder (IGD), patients with alcohol use disorder (AUD), and healthy controls (HCs) using resting-state electroencephalography (EEG) coherence analyses. For this study, 92 adult males were categorized into three groups: IGD (n = 30), AUD (n = 30), and HC (n = 32). The IGD group exhibited increased intrahemispheric gamma (30–40 Hz) coherence compared to the AUD and HC groups regardless of psychological features (e.g., depression, anxiety, and impulsivity) and right fronto-central gamma coherence positively predicted the scores of the Internet addiction test in all groups. In contrast, the AUD group showed marginal tendency of increased intrahemispheric theta (4–8 Hz) coherence relative to the HC group and this was dependent on the psychological features. The present findings indicate that patients with IGD and AUD exhibit different neurophysiological patterns of brain connectivity and that an increase in the fast phasic synchrony of gamma coherence might be a core neurophysiological feature of IGD.
Marrow-inspired matrix cues rapidly affect early fate decisions of hematopoietic stem and progenitor cells

Hematopoiesis is the physiological process where hematopoietic stem cells (HSCs) continuously generate the body’s complement of blood and immune cells within unique regions of the bone marrow termed niches. Although previous investigations have revealed gradients in cellular and extracellular matrix (ECM) content across the marrow, and matrix elasticity and ligand type are believed to be strong regulators of stem cell fate, the impact of biophysical signals on HSC response is poorly understood. Using marrow-inspired ECM ligand–coated polyacrylamide substrates that present defined stiffness and matrix ligand cues, we demonstrate that the interplay between integrin engagement and myosin II activation processes affects the morphology, proliferation, and myeloid lineage specification of primary murine HSCs within 24 hours ex vivo. Notably, the impact of discrete biophysical signals on HSC fate decisions appears to be correlated to known microenvironmental transitions across the marrow. The combination of fibronectin and marrow matrix-associated stiffness was sufficient to maintain hematopoietic progenitor populations, whereas collagen and laminin enhanced proliferation and myeloid differentiation, respectively. Inhibiting myosin II–mediated contraction or adhesion to fibronectin via specific integrins (α5β1 and ανβ3) selectively abrogated the impact of the matrix environment on HSC fate decisions. Together, these findings indicate that adhesive interactions and matrix biophysical properties are critical design considerations in the development of biomaterials to direct HSC behavior in vitro.
Significance of Reduced Features for Subcellular Bioimage Classification

High-throughput screening (HTS) system has the capability to produce thousands of images containing the millions of cells. An expert could categorize each cell’s phenotype using visual inspection under a microscope. In fact, this manual approach is inefficient because image acquisition systems can produce massive amounts of cell image data per hour. Therefore, we propose an automated and efficient machine-learning model for phenotype detection from HTS system. Our goal is to find the most distinctive features (using feature selection and reduction), which will provide the best phenotype classification both in terms of accuracy and validation time from the feature pool. First, we used minimum redundancy and maximum relevance (MRMR) to select the most discriminant features and evaluate their corresponding impact on the model performance with a support vector machine (SVM) classifier. Second, we used principal component analysis (PCA) to reduce our feature to the most relevant feature list. The main difference is that MRMR does not transform the original features, unlike PCA. Later, we calculated an overall classification accuracy of original features (i.e., 1025 features) and compared with feature selection and reduction accuracies (∼30 features). The feature selection method gives the highest accuracy than reduction and original features. We validated and evaluated our model against well-known benchmark problem (i.e. Hela dataset) with a classification accuracy of 92.70% and validation time in 0.41 seconds.
Performance Evaluation of Darshan 3.0.0 on the Cray XC30

Darshan is a lightweight I/O characterization tool used to gather and summarize salient I/O workload statistics from HPC applications. Darshan was designed to minimize any possible perturbations of an application’s performance, leading it to be enabled by default on a number of production HPC systems. For each file accessed by a given application, Darshan records the count and types of I/O operations performed, histograms of access sizes, cumulative timers on the amount of time spent doing I/O, and other statistical data. This type of data has proved invaluable in understanding and improving the I/O performance of HPC applications. Darshan 3.0.0 is the new modularized version of the traditional Darshan library and file format, allowing users to easily add more in-depth I/O characterization data to Darshan logs. In this work we perform an empirical evaluation of Darshan 3.0.0 to ensure that it continues to meet performance expectations for broad deployment. In particular, we evaluate the imposed overhead on instrumented I/O operations, time taken to shut down and generate corresponding Darshan log files, and resultant log file sizes for different workloads. These performance results are compared to results of Darshan 2.3.0 on the Edison XC30 system at NERSC to determine whether the new version is lightweight enough to run full-time on production HPC systems. Our evaluation shows that Darshan has limited impact on application I/O performance and can fully generate a corresponding log file for most application workloads in under two seconds.
Design-Time Process Optimization through Optimization Patterns and Process Model Matching

The goal of process design is the construction of a process model that is a priori optimal w.r.t. the goal(s) of the business owning the process. Process design is therefore a major factor in determining the process performance and ultimately the success of a business. Despite this importance, the designed process is often less than optimal. This is due to two major challenges: First, since the design is an a priori ability, no actual execution data is available to provide the foundations for design decisions. Second, since modeling decision support is typically basic at best, the quality of the design largely depends on the ability of business analysts to make the ”right” design choices. To address these challenges, we present in this paper our deep Business Optimization Platform that enables (semi-) automated process optimization during process design based on actual execution data. Our platform achieves this task by matching new processes to existing processes stored in a repository based on similarity metrics and by using a set of formalized best-practice process optimization patterns.
Reliability assessment of distribution system using fuzzy logic for modelling of transformer and line uncertainties

Reliability assessment of distribution system, based on historical data and probabilistic methods, leads to an unreliable estimation of reliability indices since the data for the distribution components are usually inaccurate or unavailable. Fuzzy logic is an efficient method to deal with the uncertainty in reliability inputs. In this paper, the ENS index along with other commonly used indices in reliability assessment are evaluated for the distribution system using fuzzy logic. Accordingly, the influential variables on the failure rate and outage duration time of the distribution components, which are natural or human-made, are explained using proposed fuzzy membership functions. The reliability indices are calculated and compared for different cases of the system operations by simulation on the IEEE RBTS Bus 2. The results of simulation show how utilities can significantly improve the reliability of their distribution system by considering the risk of the influential variables.
A systematic review of shockwave therapies in soft tissue conditions: focusing on the evidence.

'Shock wave' therapies are now extensively used in the treatment of musculoskeletal injuries. This systematic review summarises the evidence base for the use of these modalities.



A thorough search of the literature was performed to identify studies of adequate quality to assess the evidence base for shockwave therapies on pain in specific soft tissue injuries. Both focused extracorporeal shockwave therapy (F-ESWT) and radial pulse therapy (RPT) were examined.



23 appropriate studies were identified. There is evidence for the benefit of F-ESWT and of RPT in a number of soft tissue musculoskeletal conditions, and evidence that both treatment modalities are safe. There is evidence that F-ESWT is effective in the treatment of plantar fasciitis, calcific tendinitis, and that RPT is effective in plantar fasciitis. Where benefit is seen in F-ESWT, it appears to be dose dependent, with greater success seen with higher dose regimes. There is low level evidence for lack of benefit of low-dose F-ESWT and RPT in non-calcific rotator cuff disease and mixed evidence in lateral epicondylitis.