nils-herrmann/clean_scirepeval huggingface dataset:
Las personas con trastorno de juego en Internet tienen deficiencias neurocognitivas y disfunciones sociocognitivas similares a los pacientes con dependencia de la metanfetamina
Year: 2020

Cognitive dysfunction may be a critical aspect of addictive disorders. This study aimed to examine whether individuals with Internet gaming disorder (IGD) have similar neurocognitive dysfunctions and social cognitive impairments as methamphetamine dependence (MD) patients. The participants included 30 individuals with IGD, 30 MD patients and 30 normal controls (NCs) All participants completed the digit span task, Iowa gambling task (IGT) WCST and the Interpersonal Perception Task 15 (IPT 15) The results showed that the IGD and MD groups had lower forwards and backwards scores, choices of advantageous minus disadvantageous decks, mean amount of money earned, number of categories completed, percentage of conceptual level responses, and IPT 15 total and factor scores compared with the NC group. Forwards and backwards scores, number of categories completed, percentage of conceptual level responses, choices from advantageous minus disadvantageous decks and mean amount of money earned were lower in the IGD group than in the MD group. The number of cards chosen from decks A, B, C, and D, total response errors, perseverative errors and failure to maintain set were higher in the IGD and MD groups than in NCs. Total response errors, perseverative errors and failure to maintain set were higher in the IGD than the MD group. The results revealed that neurocognitive deficits and social cognitive dysfunction in IGD are similar to those in MD. From a cognitive perspective, these results supported IGD as an addictive spectrum disorder and might lead to a better assessment of therapeutic efficacy.
Quality and Safety Education for Nurses.
Venue: Nursing outlook
Year: 2007

Quality and Safety Education for Nurses (QSEN) addresses the challenge of preparing nurses with the competencies necessary to continuously improve the quality and safety of the health care systems in which they work. The QSEN faculty members adapted the Institute of Medicine(1) competencies for nursing (patient centered care, teamwork and collaboration, evidence based practice, quality improvement, safety, and informatics) proposing definitions that could describe essential features of what it means to be a competent and respected nurse. Using the competency definitions, the authors propose statements of the knowledge, skills, and attitudes (KSAs) for each competency that should be developed during pre licensure nursing education. Quality and Safety Education for Nurses (QSEN) faculty and advisory board members invite the profession to comment on the competencies and their definitions and on whether the KSAs for pre licensure education are appropriate goals for students preparing for basic practice as a registered nurse.
Research on the Application of Deep Learning Target Detection of Engineering Vehicles in the Patrol and Inspection for Military Optical Cable Lines by UAV
Venue: 2018 11th International Symposium on Computational Intelligence and Design (ISCID)
Year: 2018

The faulty free operation of the optical cable lines is the key to the stable operation of the military optical cable communication network. Because the traditional manual hiking inspection and maintenance method for optical cable lines is not efficient, time consuming and laborious, it is proposed to use the Unmanned Aerial Vehicle (UAV) to inspect the military optical cable lines. The deep learning Faster R CNN target detection algorithm is applied to the identification of hidden dangers of the lines in the aerial image captured by the UAV. The hidden danger refers to the excavation damage of the engineering vehicle. Based on the Vehicle Detection in Aerial Imagery (VEDAI) dataset, the engineering vehicle aerial dataset was produced. Through simulation training, the target detection of excavators, bulldozers and other engineering vehicles was realized in the aerial image. The detection accuracy is better than traditional machine learning target detection algorithms such as Deformable Part Model (DPM) and Histogram of Oriented Gradient Local Binary Pattern Support Vector Machine (HOG+LBP+SVM) This research can provide some reference for the maintenance and inspection work of military optical cable lines by UAV.
A Model Driven Development Approach Using AADL and Code Generation to Develop Modular Distributed Electronic Travel Aid Devices
Venue: IHSI
Year: 2019

Electronic Travel Aids (ETAs) are devices that support people who are blind or visual impaired in tasks as obstacle detection/avoidance or navigation. ETAs consist of sensing, processing and presentation components. Many research projects develop new kinds of ETAs, introducing new presentation or sensing components. An optimal ETA should be a distributed system where each component is a communicating node. In this work we propose an MDD workflow to develop distributed ETAs. This workflow uses AADL to describe the system, its components and their communication. This supports an easy exchange of a component in its system model. So, research projects, for new presentation devices or presentation components, only have to change the node they are working on instead of developing a whole new system. Automatic code generation assures that the newly developed ETA component fits into the system.
Home Economics Education: A Subject for Moral and Character Building in Junior High School Students in Indonesia
Year: 2020

Home economics, as an education subject, became the main option for junior high school students in Indonesia before the year 1960. The material is aimed at fostering morale and strengthening the character of learners. However, in 1997, home economics was replaced by a creating handcraft subject. This study aims at forming an academic argument for the reinstatement of home economics in Junior High School's 2013 curriculum. Additionally, this study looks at the feasibility of the justification for ten values in home economics as book material for Junior high school. This case study involved 103 home economics teachers in Junior High School in Central Java. The data was collected using questionnaire and forum group discussion. Research results show the following responses from teachers for the reinstatement of home economics as a subject: 14% (14 teachers) "strongly agree" and 71% (73 teachers) "agree" and 15% (16 teachers) "neutral" The followings are the responses from the teachers concerning the inclusion ten values in home economics as book material for Junior high school: 33% (34 teachers) rate "very feasible" and 57% (58 people teacher) "feasible" and 10% (11 persons) "somewhat feasible" The inclusion of ten values is appropriate to foster morale and character education.
Mapping an Invasive Plant Spartina alterniflora by Combining an Ensemble One Class Classification Algorithm with a Phenological NDVI Time Series Analysis Approach in Middle Coast of Jiangsu, China
Venue: Remote. Sens.
Year: 2020

Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost effective management strategies for conserving native biodiversity. Traditional remote sensing based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e. single scene analysis (SSA) and NDVI TSA in the core zones of Yancheng National Natural Reserve (YNNR) Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI TSA, the EOCC algorithm presented more balanced sensitivity specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e. senescence stage of November) The achieved results (overall accuracy (OA) Kappa, and true skill statistic (TSS) were 92.92% 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94% 0.815, and 0.825 for transferability to the non training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.
Estimating Treatment Heterogeneity of International Monetary Fund Programs on Child Poverty with Generalized Random Forest
Year: 2019

A flourishing group of scholars of family sociology study how macroeconomic shockwaves propagate via households dynamics and landing a blow on children's living conditions; simultaneously, scholars of political economy unravel impacts of such shockwaves on population outcomes. Since these two strands of literature have evolved independently, little is know about the relative importance of societal and family features moderating this impact on children's material living conditions. In this article, we synthesize insights from these two strands by examining the effect of economic austerity following International Monetary Fund programs a type of economic shock on child poverty across a sample representative of about half the world's population of mainly the Global South. This article addresses the following fundamental sociological questions: to what extent do the pathways of economic austerity propagate through families' living conditions and societies' structural and political characteristics. To capture these multiple non linear heterogeneous relationships between macro and micro traits, we deploy machine learning in the service of policy evaluation. First, our analysis identifies an adverse average treatment effect (ATE) following the implementation of IMF programs on children's probability of falling into poverty: 0.14, 95% CI 0.03 0.24. Second, our algorithms identify substantial impact heterogeneity distributed about this ATE. Macro constellation moderate about half of the impact variation on children, and families' capabilities moderate the other half of this variation. We named this finding the 50 50 impact moderation rule of thumb. Our algorithm identified family wealth closely followed by governments' education spending as the critical moderating factors. IMF program affects children residing in the middle of the social stratification more than compared to their peers residing in both the top and bottom of this stratification; for those children residing in societies that have selected into IMF programs and have historically spent most on education, are at a higher risk of falling into poverty. These findings identify the value of combining family sociology and political economy perspectives. Scholars will likely cross fertilize this research further by testing this 50 50 rule of thumb to other types of economic shocks.
SlowITe, a Novel Denial of Service Attack Affecting MQTT
Venue: Sensors
Year: 2020

Security of the Internet of Things is a crucial topic, due to the criticality of the networks and the sensitivity of exchanged data. In this paper, we target the Message Queue Telemetry Transport (MQTT) protocol used in IoT environments for communication between IoT devices. We exploit a specific weakness of MQTT which was identified during our research, allowing the client to configure the behavior of the server. In order to validate the possibility to exploit such vulnerability, we propose SlowITe, a novel low rate denial of service attack aimed to target MQTT through low rate techniques. We validate SlowITe against real MQTT services, considering both plain text and encrypted communications and comparing the effects of the threat when targeting different daemons. Results show that the attack is successful and it is able to exploit the identified vulnerability to lead a DoS on the victim with limited attack resources.
Optimization of Microwave Hydrothermal Carbonization Conditions of Hydrochar for Ammonium Adsorption
Venue: 2020 International Conference and Utility Exhibition on Energy, Environment and Climate Change (ICUE)
Year: 2020

This study aimed to optimize microwave hydrothermal carbonization (MHTC) conditions of rice husk hydrochar for maximum ammonium adsorption capacity. Processing parameters including hydrothermal temperature (150 200degC) residence time (20 60 min) and water to biomass ratio (5 15 mL g 1) were optimized based on the Box Behnken design. The ANOVA results indicated that only hydrothermal carbonization temperature showed a significant effect on the ammonium adsorption capacity of rice husk hydrochar. The optimum MHTC conditions were the water to biomass ratio of 10 mL g 1under 200degC for 60 min. Hydrochar produced under the optimum condition had the ammonium adsorption capacity of \mathbf{0.89}\pm \mathbf{0.4\ mg} NH4+ N g 1. Findings from this study suggested that hydrochar derived from rice husk through MHTC has the potential to be an alternative adsorbent for preventing ammonium run off from the agricultural area.
Assessment on rack intake flowrate uniformity of data center with cold aisle containment configuration
Year: 2020

The present paper investigates the rack intake flowrate uniformity in a simulated container data center subject to cold aisle containment (CAC) configuration. Based on a novel flowrate uniformity index (FUI) in prior study, a strong flowrate mal distribution at the rack inlets is observed by examining the standard deviation of FUI (sFUI) When raising the supplied volumetric flowrate from 2.52 m3 s 1 to 3.40 m3 s 1, the outlet temperature of each cabinet is remarkably reduced. Meanwhile, applying a gradient fan speed for cabinets can reduce the sFUI by 19.5 34.0% However, the mal distribution for the top and bottom cabinets is still prominent. In essence, adding air supply flowrate or changing rack intake layout show limited improvements and may even have potentially deleterious effects. To tailor the mal distribution problem, a novel configuration featuring the concept of Coanda effect is proposed. A triangular deflector is implemented to guide the cold air sweeping through fan trays in front of the data rack. The sFUI can be effectively reduced by about 51.3 62.0% compared to the original design, showing a significant improvement on the intake flowrate distribution for all racks.
Microgrid Energy Storage Design for Reliability and Cost Performances
Venue: 2020 IEEE Power Energy Society General Meeting (PESGM)
Year: 2020

Improving reliability of a microgrid (MG) by incorporating Battery Energy Storage (BES) can be a cost effective solution, hence, it should be properly assessed. A Comprehensive Microgrid Energy Storage (CMES) solution that improves both reliability and cost performance of a MG is proposed in this paper. The solution is implemented in a test MG system and the corresponding reliability, cost analysis results and potential electricity market participation of BES are presented. The results show the effectiveness of CMES solution for microgrid applications.
Capsule Networks Towards Object Detection Capsule Object Detector (COD)
Venue: International Journal of Computer Sciences and Engineering
Year: 2019

Although Convolutional Neural Networks performed better in object detection, CNNs does not care about spatial relationships existing in an image. In this paper, we try describe "capsule network based object detection" model COD based on the VGG16 model (as a base network) which presents a substantial result in many sections of object detection over Convolution Neural Network based model by achieving the problem of spatial relationships. We used matrix capsules and dynamic EM routing to classify object from different viewpoints. The whole model is grounded on "dynamic routing between capsules" which is suggested by Geoffrey E Hinton. Both proposed theories use capsules that maps feature properties of an object as information for detecting that object which is extracted by capsules and Dynamic routing groups the capsules of lower level into parent level capsules by an iterative dynamic routing process. We train and test our model on Pascal VOC 2007 and dataset. We implement this in python using Keras (Tensorflow as backend) and train our model in Google cloud compute engine. COD achieves an accuracy of 67.3 mAP on Pascal VOC 2007 dataset and performing a comparable performance with Fast R CNN.  Object Detection, CNN, Capsule Networks, VGG16
Obesity promotes resistance to anti VEGF therapy in breast cancer by up regulating IL 6 and potentially FGF 2
Venue: Science Translational Medicine
Year: 2018

Targeting IL 6 and potentially FGF 2 overcomes resistance to anti VEGF therapy in breast cancer. Tailored treatment for cancer in obesity Antiangiogenic therapy with inhibitors of the vascular endothelial growth factor (VEGF) has not proven effective in patients with many tumor types, including breast cancer. Meanwhile, obesity is a well known risk factor in many cancer types, and once again, this includes breast cancer. Now, Incio et al. link these two observations, having discovered a mechanism by which obesity promotes resistance to VEGF inhibitor therapy through increased interleukin 6 and possibly also fibroblast growth factor 2 in the tumor microenvironment. The authors target these pathways in mouse models of cancer with and without obesity and demonstrate that the deleterious effects of obesity on VEGF inhibition can be overcome through the appropriate combination therapy. Anti vascular endothelial growth factor (VEGF) therapy has failed to improve survival in patients with breast cancer (BC) Potential mechanisms of resistance to anti VEGF therapy include the up regulation of alternative angiogenic and proinflammatory factors. Obesity is associated with hypoxic adipose tissues, including those in the breast, resulting in increased production of some of the aforementioned factors. Hence, we hypothesized that obesity could contribute to anti VEGF therapy's lack of efficacy. We found that BC patients with obesity harbored increased systemic concentrations of interleukin 6 (IL 6) and/or fibroblast growth factor 2 (FGF 2) and their tumor vasculature was less sensitive to anti VEGF treatment. Mouse models revealed that obesity impairs the effects of anti VEGF on angiogenesis, tumor growth, and metastasis. In one murine BC model, obesity was associated with increased IL 6 production from adipocytes and myeloid cells within tumors. IL 6 blockade abrogated the obesity induced resistance to anti VEGF therapy in primary and metastatic sites by directly affecting tumor cell proliferation, normalizing tumor vasculature, alleviating hypoxia, and reducing immunosuppression. Similarly, in a second mouse model, where obesity was associated with increased FGF 2, normalization of FGF 2 expression by metformin or specific FGF receptor inhibition decreased vessel density and restored tumor sensitivity to anti VEGF therapy in obese mice. Collectively, our data indicate that obesity fuels BC resistance to anti VEGF therapy via the production of inflammatory and angiogenic factors.
The Analysis of Value for Money (VfM) for Public Private Partnership Infrastructure: A Public Sector Comparator Approach
Year: 2016

Countries around the world have welcomed Public Private Partnerships (PPPs) as an alternative to finance infrastructure. The Thai government is also increasingly aware of the importance of infrastructure and the need to tap private resources to help finance strategic infrastructure projects. However, in choosing PPP schemes over traditional project procurement, it should be more about efficiency and value for money (VfM) in delivery of the project rather than the reason of fiscal constraints. Accordingly, the detailed, quantitative analysis of the value for money of PPP projects is vital for the success of the projects from the public sector's point of view. One of several methods used by many governments in determining whether the private proposals offer better VfM to the public sector is a Public Sector Comparator (PSC) Essentially, the PSC estimates the hypothetical risk adjusted cost if a project were to be financed, owned and implemented by government. In Thailand, as the new PPP law was signed into law in 2013, it is required by law for the responsible agency to compare the public's total cost between traditional public procurement and PPP models, but, as to how the cost of the project will be compared, there is no definitive guideline on this. Therefore, this paper is to study how the PSC can be used for the evaluation of PPP infrastructure projects in Thailand. The main processes of the PSC will be reviewed, drawing from international experience. Example of how the PSC can be applied in real practice will also be presented. The results of the study showed that PPP project involved higher project life cycle cost to the government. However, in terms of risk, PPP project helped substantially minimized the cost risk.
Potential ecological risk of metal pollution in lead smelter contaminated agricultural soils in Khulna, Bangladesh
Venue: Environmental Monitoring and Assessment
Year: 2019

Lead smelters are important source of metal pollution. This study assessed ecological risks of three heavy metals (Pb, As, and Zn) in agricultural soils surrounding five Pb smelters from Khulna district in Bangladesh. A total of 81 surface soil samples collected within 500 m radius of the smelters were analyzed using an atomic absorption spectrophotometer (AAS) Concentrations of Pb, As, and Zn ranged within 6 3902, 1.8 9.6, and 45.4 563 mg/kg, respectively. About half of the Pb samples 51% exceeded soil quality standard target value (85 mg/kg) and the concentrations gradually decreased with horizontal distance from the smelter. The value of pollution index (PI) measured for Pb, As, and Zn varied respectively in the range of 0 195, 0.6 3.2, and 0.67 8.28, with mean values of 11.7, 1.9, and 3.92. The value of integrated pollution index (IPI) calculated for these metals remained between 0.58 and 66.2 with a mean of 5.7, and that designates 96% of the sampled soils as moderately or highly contaminated. Potential ecological risk (PER) calculated for the metals indicate that all the samples were within low to moderate risk, and the descending order of PER of the metals was Pb As Zn.