nils-herrmann/clean_scirepeval huggingface dataset:
An ethical framework for genetic counseling in the genomic era
Venue: Journal of genetic counseling
Year: 2019

The field of genetic counseling has grown and diversified since the profession emerged in the early 1970s. In the same period, genomic testing has become more complex, profitable, and widespread. With these developments, the scope of ethical considerations relevant to genetic counseling has expanded. In light of this, we find it helpful to revisit how ethical and relational variables are used to inform genetic counseling practice. Our specific focus is on whether, and to what extent, it is ethically acceptable for genetic counselors to make normative recommendations to patients. This article builds on prior literature that has critiqued nondirectiveness, a concept that has influenced and constrained the modern profession of genetic counseling since its origin. In it, we review scholarly efforts to move beyond nondirectiveness, which we believe privilege patient autonomy at the expense of other important values. We then argue that genetic counselors should favor a more explicit commitment to the principles of beneficence and non maleficence, as well as a broader understanding of autonomy and the relational variables that impact genetic counseling. Finally, to translate our arguments into practice, we present a framework of six considerations that genetic counselors should take into account when deciding whether it is ethically acceptable, or even desirable, to make recommendations to patients in certain areas of their work.
Effects of street landscape planting and urban public parks on dwelling environment evaluation in Japan
Year: 2012

In this paper, we use data from 12 metropolitan cities in Japan to investigate the effects of street landscape planting and urban park areas on land prices, and estimate the magnitude and saturation levels of these effects, as well as examining the current level of planting and park areas in these cities. The empirical results imply that increasing the street landscape planting ratio improves the dwelling environment, in terms of both the magnitude and saturation level of the planting ratio, while increasing the amount of urban park area does not.
Optimization of Virtual Power Plant Topology with Distributed Generation Sources
Venue: 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE)
Year: 2018

The article is devoted to the construction of the Virtual Power Plant (VPP) distribution network (DN) which unites sources of distributed generation and a centralized electrical network. Graph theory and structural topological analysis are used to determining the optimal VPP DN topology. The main idea is to assess the reliability and efficiency of VPP DN on the basis of structural topological characteristics of its graphs. The results of calculations for a low voltage VPP DN with a wind power plant and a gas piston unit are presented. The PSCAD model of VPP DN is described, which explores the dependencies of VPP DN parameters on its topologies and operating modes. A physical model VPP DN has been developed: it consists of simulators of wind and solar power plants, power storage devices, loads and power lines. On the basis of the physical model, a study of the quality of electricity in VPP DN depending on the ratio of the power of the centralized electrical network and the distributed generation sources has been carried out. Based on the results of the study, an algorithm for determining the optimal VPP DN topology has been developed.
The sustainable intensification of agriculture aEUR" challenges the Common Agricultural Policy and opportunities for Bulgaria
Year: 2014

Development of European policy in agriculture targets achieving sustainable relation agriculture environment. The sustainable agriculture intensification combines intensive agriculture with achieving high standards in environmental protection. This challenges farmers to improve radically the resources usage efficiency and to meet the high standards for the protection of biodiversity, climate, soil, water, landscape etc. Sustainable intensification suggests greener production, harmful environment effects reduction. Sustainable intensification will be determined by local conditions, current productivity and farms environmental indicators. Integration of environmental management in the Common Agricultural Policy (CAP) increases in the new programming period. It is necessary to reinforce the knowledge providing, better observations and measurement of the impact of agriculture on the environment is needed. It is also necessary to strengthen of consultancy and to support the innovative approaches in the new CAP. Improving the sustainability of agriculture in our country implies mobilization of the efforts of all farmers and implementing a new support policy.
Neural networks and learning systems for human machine interfacing
Venue: Neurocomputing
Year: 2020

With developments of the sensor and computing technologies, human machine interfaces (HMIs) are designed to meet the increasing user demands of machines and systems. This is because human effects are becoming the key issues to allow some advanced mechanical devices, such as robots and biometric systems, to perform complicate tasks intelligently in an unknown environment. An effective HMI with learning ability can process, interpret, recognize, and simulate the intention and behaviors of human beings, and then utilize intelligent algorithms to drive the machine devices. The HMIs also enable us to bring humanistic intelligence and actions in robotic devices, biometric systems and other machines through two ways interactions, such as using deep neural networks. In recent years, a growing number of researchers and studies focusing on this area have clearly demonstrated the importance of learning systems for HMIs.
Concentrating solar power technologies for solar thermal grid electricity in Nigeria: a review
Year: 2018

Grid electricity generation in Nigeria has been unstable for a long time now. With respect to her continued dependence on oil and gas and seasonal variations on water level for hydropower, immediate reliable and steady electricity generation in the country is not guaranteed. Incorporating alternative source of energy like solar is a solution. Solar Chimney technology is not yet technically and economically mature, as investment on it is still low, thereby posing more financial risks at least at the megawatt capacity. Photovoltaic and Solar Thermal technologies are technically and economically more established, but they can only ensure 24 h power supply with the aid of energy storage systems. Solar thermal value addition is on its propensity for thermal energy storage and hybridization with liquid fuels at commercial capacity. Hence, due to technology advancement and the country's high direct normal irradiation potential, concentrating solar power is the more suitable solar power technology for commercial electricity generation in Nigeria. In this paper, concentrating solar power technologies are analysed under operational, environmental and social conditions in Nigeria using data from desktop survey to determine the most suitable technology for solar thermal electric power plant. It is observed that the technical maturity of parabolic trough concentrator distinguishes it for preference to all other technologies and would exert moderate pressure on land requirement even though, it is the most expensive and water demanding technology. In terms of unit cost of electricity and water usage capabilities, parabolic dish concentrator is least expensive and uses least water but lacks proven commercial application. Solar tower is technically simpler and possesses better thermodynamic properties than the rest but its low installed capacity increases its financing risk. Hence, the trough system is the most suitable for immediate and medium term projects given its proven technology maturity and flexible financing mechanism.
Tidal volume variability and respiration rate estimation using a wearable accelerometer sensor
Year: 2014

The measurement of respiration rate and tidal volume variability are critical to the diagnosis and monitoring of a wide range of breath disorders as well as being useful broader parameters of a patient's condition. This paper presents a portable real time platform designed to support a computationally efficient human respiratory tracking system for medical applications. The proposed system is designed particularly for patients with breathing problems (e.g. respiratory complications after surgery) or sleep disorders. We introduce the use of accelerometer sensor to detect changes in the anterior posterior diameter of the chest; whereas these changes provide an accurate measurement of respiration rate as well as tidal volume variability. The complete system was comprised of wearable calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud database. The experiments are conducted with 8 subjects and the overall error in respiration rate calculation is obtained 0.2% considering SPR BTA spirometer as the reference. We also present a method for Tidal Volume variability (TVvar) estimation while validated using Pearson correlation. The mean value of the correlation coefficient between TVvar derived from the accelerometer and spirometer for all subjects and three breath patterns is 0.87 which shows a high correspondence of two signals. Furthermore, the results indicate that the accelerometer driven TVvar achieves the average MSE 1.6E 03+ 3.69E 03 compared to the reference.
DC 3 A Diagnostic Case Challenge Collection for Clinical Decision Support
Year: 2019

In clinical care, obtaining a correct diagnosis is the first step towards successful treatment and, ultimately, recovery. Depending on the complexity of the case, the diagnostic phase can be lengthy and ridden with errors and delays. Such errors have a high likelihood to cause patients severe harm or even lead to their death and are estimated to cost the U.S. healthcare system several hundred billion dollars each year. To avoid diagnostic errors, physicians increasingly rely on diagnostic decision support systems drawing from heuristics, historic cases, textbooks, clinical guidelines and scholarly biomedical literature. The evaluation of such systems, however, is often conducted in an ad hoc fashion, using non transparent methodology, and proprietary data. This paper presents DC3, a collection of 31 extremely difficult diagnostic case challenges, manually compiled and solved by clinical experts. For each case, we present a number of temporally ordered physician generated observations alongside the eventually confirmed true diagnosis. We additionally provide inferred dense relevance judgments for these cases among the PubMed collection of 27 million scholarly biomedical articles.
Adversarial android malware detection for mobile multimedia applications in IoT environments
Venue: Multimedia Tools and Applications
Year: 2020

In this paper, we propose two defense methods against adversarial attack to a malware detection system for mobile multimedia applications in IoT environments. They are Robust NN and a combination of convolutional neural network and 1 nearest neighbors(C4N) which modify training data that has been poisoned by an adversarial attack. As a result, the trained machine learning model will be accurate and if the malicious program is entered by any IoT device, the model generates necessary alerts. We provide an explanation of the used attack method and the algorithms proposed to defend against this attack. In order to evaluate the suitability of the proposed defense methods, sufficient analysis is presented, i.e. Drebin, Contagio and Genome datasets which include benign and malware Android apps are applied to perform experiments. To confirm the effectiveness of the suggested defense algorithms, this paper compared their performance with two state of the art defense algorithms used to detect adversarial samples, namely e2SAD and EAT. The experiments are performed on two types of API and Permission features from the mentioned datasets. The results confirm that accuracy rates of classification algorithms decrease to 40% after attack in some cases (related to Drebin dataset by reviewing API feature sets) Additionally, the accuracy rates increase to 94.94% and 96.03% by applying Robust NN and C4N algorithms, respectively. Therefore, they are comparable with existing cutting edge defense algorithms. Also, the adversarial attack increased the FPR to 45.81% which will be reduced to 4.84% and 4.15% using Robust NN and C4N, respectively. Consequently, the proposed methods will be robust against adversarial attacks.
Did the Affordable Care Act Decrease Veteran Enrollment in the Veterans Health Administration?
Venue: Medical care
Year: 2020

Supplemental Digital Content is available in the text.  Provisions of the Affordable Care Act (ACA) provided nonelderly individuals, including Veterans, with additional health care coverage options. This may impact enrollment for health care through the Veterans Health Administration (VHA) National enrollment data was used to: (1) compare characteristics of enrollees at 3 time points in relation to the implementation of ACA insurance provisions (2012) and (2) examine enrollment trends.  The study population included a 10% sample of Veterans under age 65 who were VHA enrollees between January 2012 and September 2015. Demographic and baseline characteristics were compared between 3 enrollment groups: pre 2012, pre ACA (2012 2013) and post ACA (2014 2015) Using an interrupted time series approach, we employed pooled logistic regression to assess trends in new VHA enrollment, overall, and by select enrollee characteristics.  A total of 429,833 enrollees were identified. Compared with pre ACA enrollees, post ACA enrollees were more likely to be older, have a service connected disability, live further away from a VHA medical center, but less likely to use primary care within 6 months. The post ACA quarterly trend in the odds of being a new enrollee was 3% lower (95% confidence interval: 0.96, 0.98) as compared with the pre ACA trend. This decline was consistent across sex, geography, (all but 1) priority group, and state Medicaid expansion subgroups.  The ACA appears to have contributed to a decline in new VHA enrollment. In addition, the profile of newer enrollees differs from that of pre ACA enrollees. The VHA must continue to monitor trends in demand in order to continue delivering high quality, efficient care.
Women in powerful conversation: collaborative autoethnography and academia
Venue: International Journal of Qualitative Studies in Education
Year: 2019

Working as women in academia may still be regarded as 'complex and fraught with myths, gross generalisations and mixed emotions' (Barakat, 2014, p. 1) In this paper, we articulate the collaborative autoethnographic process in which we have been engaged over some time and through which we have challenged generalisations, explored emotions and illuminated further our complex identities as women in academia. Sharing and making visible our collaborative autoethnographic conversations and writing to other readers is risky and exposes us to possible censure. We realise that we are susceptible to being disparaged for being self indulgent a common criticism of autoethnography yet we contend that our conversations and writing are both self and socially luminous as we connect our 'selves' with the UK higher education context. The paper's main focus is the collaborative autoethnographic process in which we have been engaged. Examples from our conversations and writing are included in order to demonstrate the power of this process and its potential and wider relevance for research.
Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation
Venue: Information Fusion
Year: 2020

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
A TEXT CLASSIFICATION APPROACH FOR EVALUATION OF DELAY CLAIMS
Year: 2018

The significant rise in the complexity and scope of construction projects led by the introduction of highly advanced building systems characterizes the current construction industry. This entails a significant increase in coordination and planning and an overall change in the management culture of all project participants. On the other hand, project stakeholders are faced with an increasing demand from project owners to implement fast track programmes to achieve an early return on investment. Consequently, claims and disputes throughout the majority of project delivery systems have surged, influenced by the project parties' inability to effectively manage the claims process. The aim of this paper is to introduce a new methodology for the automatic text classification of project delay claims documents to enhance efficiency in the management of delay claim process. The proposed model utilizes activity and Work Breakdown Structure keywords of a given delay event activity path for the training of the proposed model, which is then utilized to predict unlabeled project documents. The proposed model has been implemented on a series of delay claims events in a mega project, the implementation yielded promising results in the performance evaluation measures (precision, recall, and F1 Score) compared to similar text classification models
Home is the new gym: exergame as a potential tool to maintain adequate fitness levels also during quarantine
Year: 2020

With the coronavirus outbreak, the preventive measures include staying at home and isolation, increasing sedentary behaviours and risk for worsening of chronic diseases To explore alternative forms of home based physical activity, the study aim was to evaluate physiological (heart rate [HR] respiratory exchange ratio [RER] oxygen consumption O2] energy expenditure [EE] metabolic equivalent task [MET] and psychological (session rating of perceived exertion [sRPE] enjoyment) parameters of real (Zumba(r) class [ZC] and exergame (Zumba(r) Fitness Rush [ZFR] activities in relation to sex  Overall, 12 female (age: 24 7 0 9 years) and 8 male (age: 25 3 2 1 years) college students randomly performed 2 experimental 60 min sessions, ZC and ZFR, during which HR (expressed as percentage of individual maximal HR %HRmax] O2, RER, MET and EE were measured After each session, sRPE and Physical Activity Enjoyment Scale (PACES) were used to monitor exercise intensity and enjoyment, respectively  Main effects for sex (p 0 02) and modality (p 0 0001) emerged for %HRmax, with women showing higher values than men and ZC showing higher values than ZFR EE presented main effects for sex (p 0 0001) and modality (p 0 0002) with higher values in men and in ZC Main effect (p 0 0001) for modality emerged also for O2 and MET, with higher values in ZC regardless of sex No significant differences were observed for RER, sRPE, or PACES Conclusions Although ZC elicited higher cardiovascular and metabolic responses, ZFR, classified as a moderate to vigorous activity, could be used to maintain regular physical activity in a safe home environment during the coronavirus crisis (c) University School of Physical Education in Wroclaw
Zaburzenia przewodu pokarmowego i terapia zywieniowa dzieci z zaburzeniami ze spektrum autyzmu aktualny stan wiedzy
Year: 2014

The incidence of autism spectrum disorders is steadily growing. The ultimate cause of the disease still has not been determined. However with a high probability it is known that the pathogenesis of characterized disorders is multifactorial. One of the aspects that could have a negative impact on the patient's behavior is abnormal distribution of gluten, casein, and probably another nutrients, which is the result of epithelium of the small intestine enzymes deficiency. On this basis, so called opioid theory assumes that enzymatic deficiency leads to the formation of exorphins substances with opioid activity. With proven coexistence of increased intestinal permeability in ASD patients, improperly digested food particles may be transported to the central nervous system (cerebrospinal axis intestinal) adversely affecting the child's behavior. Reports of opioids food potential effects on the child's behavior, as well as frequently reported gastrointestinal disorders in ASD patients are the cause of the elimination diet implementation by a large group of parents and carers of children with ASD. Meanwhile, there is still no objective, randomized clinical trials, indicating elimination diet real impact for the patient with ASD. Reports regarding the effectiveness of nutritional therapy are derived mainly from therapists and parents/carers of children with autism spectrum disorders. The potential benefits, which may result from the possible modification of diet, point to the urgent need for well designed clinical studies in this field.
Constellation Design for Bit Interleaved Coded Modulation (BICM) Systems in Advanced Broadcast Standards
Venue: IEEE Transactions on Broadcasting
Year: 2017

This paper presents a generic methodology to optimize constellations based on their geometrical shaping for bit interleaved coded modulation (BICM) systems. While the method can be applicable to any wireless standard design it has been tailored to two delivery scenarios typical of broadcast systems: 1) robust multimedia delivery and 2) UHDTV quality bitrate services. The design process is based on maximizing the BICM channel capacity for a given power constraint. The major contribution of this paper is a low complexity optimization algorithm for the design of optimal constellation schemes. The proposal consists of a set of initial conditions for a particle swarm optimization algorithm, and afterward, a customized post processing procedure for further improving the constellation alphabet. According to the broadcast application cases, the sizes of the constellations proposed range from 16 to 4096 symbols. The BICM channel capacities and performance of the designed constellations are compared to conventional quadrature amplitude modulation constellations for different application scenarios. The results show a significant improvement in terms of system performance and BICM channel capacities under additive white Gaussian noise and Rayleigh independently and identically distributed channel conditions.