nils-herrmann/clean_scirepeval huggingface dataset:
Magnetic resonance imaging of multifunctional pluronic stabilized iron-oxide nanoparticles in tumor-bearing mice.

We are investigating the magnetic resonance imaging characteristics of magnetic nanoparticles (MNPs) that consist of an iron-oxide magnetic core coated with oleic acid (OA), then stabilized with a pluronic or tetronic block copolymer. Since pluronics and tetronics vary structurally, and also in the ratio of hydrophobic (poly[propylene oxide]) and hydrophilic (poly[ethylene oxide]) segments in the polymer chain and in molecular weight, it was hypothesized that their anchoring to the OA coating around the magnetic core could significantly influence the physical properties of MNPs, their interactions with biological environment following intravenous administration, and ability to localize to tumors. The amount of block copolymer associated with MNPs was seen to depend upon their molecular structures and influence the characteristics of MNPs. Pluronic F127-modified MNPs demonstrated sustained and enhanced contrast in the whole tumor, whereas that of Feridex IV was transient and confined to the tumor periphery. In conclusion, our pluronic F127-coated MNPs, which can also be loaded with anticancer agents for drug delivery, can be developed as an effective cancer theranostic agent, i.e. an agent with combined drug delivery and imaging properties.
A Game Theoretical Method for Cost-Benefit Analysis of Malware Dissemination Prevention

A Game Theoretical Method for Cost-Benefit Analysis of Malware Dissemination Prevention Theodoros Spyridopoulos, Konstantinos Maraslis, Alexios Mylonas, Theo Tryfonas & George Oikonomou To cite this article: Theodoros Spyridopoulos, Konstantinos Maraslis, Alexios Mylonas, Theo Tryfonas & George Oikonomou (2015): A Game Theoretical Method for Cost-Benefit Analysis of Malware Dissemination Prevention, Information Security Journal: A Global Perspective,  To link to this article: http://dx.doi.org/10.1080/19393555.2015.1092186
DESIGN OF ROBUST COMMAND TO LINE-OF-SIGHT GUIDANCE LAW : A FUZZY ADAPTIVE APPROACH

In this paper, the design of command to line-of-sight (CLOS) missile guidance law is addressed. Taking a three dimensional guidance model, the tracking control problem is formulated. To solve the target tracking problem, the feedback linearization controller is first designed. Although such control scheme possesses the simplicity property, but it presents the acceptable performance only in the absence of perturbations. In order to ensure the robustness properties against model uncertainties, a fuzzy adaptive algorithm is proposed with two parts including a fuzzy (Mamdani) system, whose rules are constructed based on missile guidance, and a so-called rule modifier to compensate the fuzzy rules, using the negative gradient method. Compared with some previous works, such control strategy provides a faster time response without large control efforts. The performance of feedback linearization controller is also compared with that of fuzzy adaptive strategy via various simulations.
A study of material effects for the panel level package (PLP) technology

The wafer level package (WLP) is a cost-effective solution for electronic package, and it has been increasingly applied during recent years. In this study, a new packaging technology which retains the advantages of WLP, the panel level package (PLP) technology, is proposed to further obtain the capability of signals fan-out for the fine-pitched integrated circuit (IC). In the PLP, the filler material is selected to fill the trench around the chip and provide a smooth surface for the redistribution lines. Therefore, the solder bumps could be located on both the filler and the chip surface, and the pitch of the chip side is fanned-out. In our previous research, it was found that the lifetime of solder joints in PLP can easily pass 3,500 cycles. The outstanding performance is explained by the application of a soft filler and a lamination material. However, it is also learned that the deformation of the lamination material during thermal loading may affect the reliability of the adjacent metal trace. In this study, the material effects of the proposed PLP technology are investigated and discussed through finite element analysis (FEA). A factorial analysis with three levels and three factors (the chip carrier, the lamination, and the filler material) is performed to obtain sensitivity information. Based on the results, the suggested combinations of packaging material in the PLP are provided. The reliability of the metal trace can be effectively improved by means of wisely applying materials in the PLP, and therefore, the PLP technology is expected to have a high potential for various applications in the near future.
Text clustering using enhanced PLSA with word correlation

As a popular topic model, Probabilistic Latent Semantic Analysis (PLSA) has been widely applied in text clustering due to its reliability and practicability. While independence assumption contributes to its practicability, it loses the rich local information between words, which in some cases will result in incoherent topics. In this paper, we propose an enhanced PLSA model embedded with word correlation (EWPLSA) for text clustering. The new model can utilize a wide range of pairwise semantic constraints between words to get more meaningful topics. We study how to consider context information of all the words, trained with the global semantic distribution in a global manner. The relationship between similar words will be amplified in a global perspective in the next iteration. However, it will also be restricted by other words because all word contexts are considered. In this way, instead of generating independently, the topic assignment of each word is affected by other correlative words. In addition, we initialize several important parameters before the first iteration of the EM algorithm according to the local correlation information between words, leading to the reduction of iteration number. Experiments on two datasets have demonstrated the effectiveness of our method.
GREENHOUSE GAS EMISSIONS FROM ENERGY SYSTEMS : COMPARISON AND OVERVIEW

The paper provides an overview and comparison of Greenhouse Gas Emissions associated with fossil, nuclear and renewable energy systems. In this context both the direct technology-specific emissions and the contributions from full energy chains within the Life Cycle Assessment framework are considered. Examples illustrating the differences between countries and regional electricity mixes are also provided. Core results presented here are based on the work performed at PSI, and by partners within the Swiss Centre for Life-Cycle Inventories.
Multi-Objective Model Selection via Racing

Model selection is a core aspect in machine learning and is, occasionally, multi-objective in nature. For instance, hyper-parameter selection in a multi-task learning context is of multi-objective nature, since all the tasks' objectives must be optimized simultaneously. In this paper, a novel multi-objective racing algorithm (RA), namely S-Race, is put forward. Given an ensemble of models, our task is to reliably identify Pareto optimal models evaluated against multiple objectives, while minimizing the total computational cost. As a RA, S-Race attempts to eliminate non-promising models with confidence as early as possible, so as to concentrate computational resources on promising ones. Simultaneously, it addresses the problem of multi-objective model selection (MOMS) in the sense of Pareto optimality. In S-Race, the nonparametric sign test is utilized for pair-wise dominance relationship identification. Moreover, a discrete Holm's step-down procedure is adopted to control the family-wise error rate of the set of hypotheses made simultaneously. The significance level assigned to each family is adjusted adaptively during the race. In order to illustrate its merits, S-Race is applied on three MOMS problems: (1) selecting support vector machines for classification; (2) tuning the parameters of artificial bee colony algorithms for numerical optimization; and (3) constructing optimal hybrid recommendation systems for movie recommendation. The experimental results confirm that S-Race is an efficient and effective MOMS algorithm compared to a brute-force approach.
Fetal MRI: An approach to practice: A review

MRI has been increasingly used for detailed visualization of the fetus in utero as well as pregnancy structures. Yet, the familiarity of radiologists and clinicians with fetal MRI is still limited. This article provides a practical approach to fetal MR imaging. Fetal MRI is an interactive scanning of the moving fetus owed to the use of fast sequences. Single-shot fast spin-echo (SSFSE) T2-weighted imaging is a standard sequence. T1-weighted sequences are primarily used to demonstrate fat, calcification and hemorrhage. Balanced steady-state free-precession (SSFP), are beneficial in demonstrating fetal structures as the heart and vessels. Diffusion weighted imaging (DWI), MR spectroscopy (MRS), and diffusion tensor imaging (DTI) have potential applications in fetal imaging. Knowing the developing fetal MR anatomy is essential to detect abnormalities. MR evaluation of the developing fetal brain should include recognition of the multilayered-appearance of the cerebral parenchyma, knowledge of the timing of sulci appearance, myelination and changes in ventricular size. With advanced gestation, fetal organs as lungs and kidneys show significant changes in volume and T2-signal. Through a systematic approach, the normal anatomy of the developing fetus is shown to contrast with a wide spectrum of fetal disorders. The abnormalities displayed are graded in severity from simple common lesions to more complex rare cases. Complete fetal MRI is fulfilled by careful evaluation of the placenta, umbilical cord and amniotic cavity. Accurate interpretation of fetal MRI can provide valuable information that helps prenatal counseling, facilitate management decisions, guide therapy, and support research studies.
Forecasting of ozone concentration in smart city using deep learning

Clean air is one of the most important needs for the well-being of human being health. In smart cities, timely and precise air pollution levels knowledge is vital for the successful setup of smart pollution systems. Recently, pollution and weather data in smart city have been bursting, and we have truly got into the era of big data. Ozone is considered as one of the most air pollutants with hurtful impact to human health. Existing methods used to predict the level of ozone uses shallow pollution prediction models and are still unsatisfactory in their accuracy to be used in many real-world applications. In order to increase the accuracy of prediction models we come up with the concept of using deep architecture models tested on big pollution and weather data. In this paper, a new deep learning-based ozone level prediction model is proposed, which considers the pollution and weather correlations integrally. This deep learning model is used to learn ozone level features, and it is trained using a grid search technique. A deep architecture model is utilized to represent ozone level features for prediction. Moreover, experiments demonstrate that the proposed method for ozone level prediction has superior performance. The outcome of this study can be helpful in predicting the ozone level pollution in Aarhus city as a model of smart cities for improving accuracy of ozone forecasting tools.
Dysautonomia in the joint hypermobility syndrome.

Extraarticular manifestations of the joint hypermobility syndrome may include the peripheral nervous system. The purpose of this study was to investigate autonomic function in patients with this syndrome.



Forty-eight patients with the joint hypermobility syndrome who fulfilled the 1998 Brighton criteria and 30 healthy control subjects answered a clinical questionnaire designed to evaluate the frequency of complaints related to the autonomic nervous system. Next, 27 patients and 21 controls underwent autonomic evaluation: orthostatic testing, cardiovascular vagal and sympathetic functions, catecholamine levels, and adrenoreceptor responsiveness.



Symptoms related to the autonomic nervous system, such as syncope and presyncope, palpitations, chest discomfort, fatigue, and heat intolerance, were significantly more common among patients. Orthostatic hypotension, postural orthostatic tachycardia syndrome, and uncategorized orthostatic intolerance were found in 78% (21/27) of patients compared with in 10% (2/21) of controls. Patients with the syndrome had a greater mean (+/- SD) drop in systolic blood pressure during hyperventilation than did controls (-11 +/- 7 mm Hg vs. -5 +/- 5 mm Hg, P = 0.02) and a greater increase in systolic blood pressure after a cold pressor test (19 +/- 10 mm Hg vs. 11 +/- 13 mm Hg, P = 0.06). Patients with the syndrome also had evidence of alpha-adrenergic (as assessed by administration of phenylephrine) and beta-adrenergic hyperresponsiveness (as assessed by administration of isoproterenol).



The autonomic nervous system-related symptoms of the patients have a pathophysiological basis, which suggests that dysautonomia is an extraarticular manifestation in the joint hypermobility syndrome.
Generative design in minecraft (GDMC): settlement generation competition

This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map---ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.
Utilization of healthcare services in postpartum women in the Philippines who delivered at home and the effects on their health: a cross-sectional analytical study

A low ratio of utilization of healthcare services in postpartum women may contribute to maternal deaths during the postpartum period. The maternal mortality ratio is high in the Philippines. The aim of this study was to examine the current utilization of healthcare services and the effects on the health of women in the Philippines who delivered at home.



This was a cross-sectional analytical study, based on a self-administrated questionnaire, conducted from March 2015 to February 2016 in Muntinlupa, Philippines. Sixty-three postpartum women who delivered at home or at a facility were enrolled for this study. A questionnaire containing questions regarding characteristics, utilization of healthcare services, and abnormal symptoms during postpartum period was administered. To analyze the questionnaire data, the sample was divided into delivery at home and delivery at a facility. Chi-square test, Fisher's exact test, and Mann-Whitney U test were used.



There were significant differences in the type of birth attendant, area of residence, monthly income, and maternal and child health book usage between women who delivered at home and those who delivered at a facility (P<0.01). There was significant difference in the utilization of antenatal checkup (P<0.01) during pregnancy, whilst there was no significant difference in utilization of healthcare services during the postpartum period. Women who delivered at home were more likely to experience feeling of irritated eyes and headaches, and continuous abdominal pain (P<0.05).



Financial and environmental barriers might hinder the utilization of healthcare services by women who deliver at home in the Philippines. Low utilization of healthcare services in women who deliver at home might result in more frequent abnormal symptoms during postpartum.
Evaluation of hazardous chemicals in edible insects and insect-based food intended for human consumption.

Due to the rapid increase in world population, the waste of food and resources, and non-sustainable food production practices, the use of alternative food sources is currently strongly promoted. In this perspective, insects may represent a valuable alternative to main animal food sources due to their nutritional value and sustainable production. However, edible insects may be perceived as an unappealing food source and are indeed rarely consumed in developed countries. The food safety of edible insects can thus contribute to the process of acceptance of insects as an alternative food source, changing the perception of developed countries regarding entomophagy. In the present study, the levels of organic contaminants (i.e. flame retardants, PCBs, DDT, dioxin compounds, pesticides) and metals (As, Cd, Co, Cr, Cu, Ni, Pb, Sn, Zn) were investigated in composite samples of several species of edible insects (greater wax moth, migratory locust, mealworm beetle, buffalo worm) and four insect-based food items currently commercialized in Belgium. The organic chemical mass fractions were relatively low (PCBs: 27-2065 pg/g ww; OCPs: 46-368 pg/g ww; BFRs: up to 36 pg/g ww; PFRs 783-23800 pg/g ww; dioxin compounds: up to 0.25 pg WHO-TEQ/g ww) and were generally lower than those measured in common animal products. The untargeted screening analysis revealed the presence of vinyltoluene, tributylphosphate (present in 75% of the samples), and pirimiphos-methyl (identified in 50% of the samples). The levels of Cu and Zn in insects were similar to those measured in meat and fish in other studies, whereas As, Co, Cr, Pb, Sn levels were relatively low in all samples (<0.03 mg/kg ww). Our results support the possibility to consume these insect species with no additional hazards in comparison to the more commonly consumed animal products.
Obstacle detection with ultrasonic sensors and signal analysis metrics

One of the basic tasks for autonomous flight with aerial vehicles (drones) is the detection of obstacles within its flight environment. As the technology develops and becomes more robust, drones will become part of the toolkit to aid maintenance repair and operation (MRO) and ground personnel at airports. Currently laser technology is the primary means for obstacle detection as it provides high resolution and long range. The high intensity laser beam can result in temporary blindness for pilots when the beam targets the windscreen of aircraft on the ground or on final approach within the vicinity of the airport. An alternative is ultrasonic sensor technology, but this suffers from poor angular resolution. In this paper we present a solution using time-of-flight (TOF) data from ultrasonic sensors. This system uses a single commercial 40 kHz combined transmitter/ receiver which returns the distance to the nearest obstacle in its field of view, +/30 degrees given the speed of sound in air at ambient temperature. Two sonar receivers located either side of the transmitter / receiver are mounted on a horizontal rotating shaft. Rotation of this shaft allows for separate sonar observations at regular intervals which cover the field of view of the transmitter / receiver. To reduce the sampling frequency an envelope detector is used prior to the analogue-digital-conversion for each of the sonar channels. A scalar Kalman filter for each channel reduces the effects of signal noise by providing real time filtering (Drongelen, 2017a). Four signal metrics are used to determine the location of the obstacle in the sensors field of view: 1. Maximum (Peak) frequency 2. Cross correlation of raw data and PSD 3. Power Spectral Density 4. Energy Spectral Density Results obtained in an actual indoor environment are presented to support the validity of the proposed algorithm.
Assessing readiness for business process reengineering

The purpose of this paper is to propose how to minimize the risks of implementing business process reengineering (BPR) by measuring readiness. For this purpose, the paper proposes an assessment approach for readiness in BPR efforts based on the critical success and failure factors.  A relevant literature review, which investigates success and failure indicators in BPR efforts is carried out and a new categorized list of indicators are proposed. This is a base for conducting a survey to measure the BPR readiness, which has been run in two companies and compared based on a diamond model.  In this research, readiness indicators are determined based on critical success and failure factors. The readiness indicators include six categories. The first five categories, egalitarian leadership, collaborative working environment, top management commitment, supportive management, and use of information technology are positive indicators. The sixth category, resistance to change has a negative role. This paper reports survey results indicating BPR readiness in two Iranian companies. After comparing the position of the two cases, the paper offers several guidelines for amplifying the success points and decreasing failure points and hence, increasing the rate of success.  High-failure rate of BPR has been introduced as a main barrier in reengineering processes. In addition, it makes a fear, which in turn can be a failure factor. This paper tries to fill the gap in the literature on decreasing risk in BPR projects by introducing a BPR readiness assessment approach. In addition, the proposed questionnaire is generic and can be utilized in a facilitated manner.
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

Object detection – the computer vision task dealing with detecting instances of objects of a certain class (e.g ., ’car’, ’plane’, etc.) in images – attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms.
Blue or red: which intravascular laser light has more effects in diabetic patients?

The effects of intravascular laser irradiation of blood (ILIB), with 405 and 632.8 nm on serum blood sugar (BS) level, were comparatively studied. Twenty-four diabetic type 2 patients received 14 sessions of ILIB with blue and red lights. BS was measured before and after therapy. Serum BS decreased highly significant after ILIB with both red and blue lights (p < 0.0001), but we did not find significant difference between red and blue lights. The ILIB effect would be of benefit in the clinical treatment of diabetic type 2 patients, irrespective of lasers (blue or red lights) that are used.
Fast, Exact, Linear Booleans

We present a new system for robustly performing Boolean operations on linear, 3D polyhedra. Our system is exact, meaning that all internal numeric predicates are exactly decided in the sense of exact geometric computation. Our BSP-tree based system is 16-28× faster at performing iterative computations than CGAL’s Nef Polyhedra based system, the current best practice in robust Boolean operations, while being only twice as slow as the non-robust modeler Maya. Meanwhile, we achieve a much smaller substrate of geometric subroutines than previous work, comprised of only 4 predicates, a convex polygon constructor, and a convex polygon splitting routine. The use of a BSP-tree based Boolean algorithm atop this substrate allows us to explicitly handle all geometric degeneracies without treating a large number of cases.