nils-herrmann/clean_scirepeval huggingface dataset:
PHRASEOLOGY IN AIR TRAFFIC CONTROLLER'S COMMUNICATION
Year: 2017

The research design is qualitative research design. The instruments of data collection are voice recorder to record the data, camcorder to interview then observe the subject and a camera is used to capture some photographs to show the real situation in conducting as part of field research method. The findings of the study are drawn. (1) The types of phraseology used by ATC in this study are five; they are General Phraseology (GP) Radar Control Phraseology (RCP) Area Control Phraseology (Area CP) Approach Control Phraseology (App CP) and Aerodrome Control Phraseology (Aerodrome CP) as standard phraseology, and also Non standard phraseology (2) The patterns of phraseology used by ATC in communication with pilot are commonly systemized but sometimes it depends on the situation faced by controllers, there is no certain patterns used. (3) ATC commonly use standard phraseology which has been standardized by ICAO. ATC may combine the types of phraseology based on the condition and situation needed. In another case and situation, ATC sometimes used non standard phraseology in communication with pilot, that's why phraseology is the unique language used by ATC in their daily job, interact with pilots in a communication in order to prevent collisions, organize and expedite the flow of traffic, and provide information and other support for pilots Key words: phraseology, air traffic controller, communication
Novel analgesic o conotoxins from the vermivorous cone snail Conus moncuri provide new insights into the evolution of conopeptides
Venue: Scientific Reports
Year: 2018

Cone snails are a diverse group of predatory marine invertebrates that deploy remarkably complex venoms to rapidly paralyse worm, mollusc or fish prey. o Conotoxins are neurotoxic peptides from cone snail venoms that inhibit Cav2.2 voltage gated calcium channel, demonstrating potential for pain management via intrathecal (IT) administration. Here, we isolated and characterized two novel o conotoxins, MoVIA and MoVIB from Conus moncuri, the first to be identified in vermivorous (worm hunting) cone snails. MoVIA and MoVIB potently inhibited human Cav2.2 in fluorimetric assays and rat Cav2.2 in patch clamp studies, and both potently displaced radiolabeled o conotoxin GVIA (125I GVIA) from human SH SY5Y cells and fish brain membranes (IC50 2 9 pM) Intriguingly, an arginine at position 13 in MoVIA and MoVIB replaced the functionally critical tyrosine found in piscivorous o conotoxins. To investigate its role, we synthesized MoVIB [R13Y] and MVIIA [Y13R] Interestingly, MVIIA [Y13R] completely lost Cav2.2 activity and MoVIB [R13Y] had reduced activity, indicating that Arg at position 13 was preferred in these vermivorous o conotoxins whereas tyrosine 13 is preferred in piscivorous o conotoxins. MoVIB reversed pain behavior in a rat neuropathic pain model, confirming that vermivorous cone snails are a new source of analgesic o conotoxins. Given vermivorous cone snails are ancestral to piscivorous species, our findings support the repurposing of defensive venom peptides in the evolution of piscivorous Conidae.
Learning to Compose Task Specific Tree Structures
Venue: AAAI
Year: 2018

For years, recursive neural networks (RvNNs) have been shown to be suitable for representing text into fixed length vectors and achieved good performance on several natural language processing tasks. However, the main drawback of RvNNs is that they require structured input, which makes data preparation and model implementation hard. In this paper, we propose Gumbel Tree LSTM, a novel tree structured long short term memory architecture that learns how to compose task specific tree structures only from plain text data efficiently. Our model uses Straight Through Gumbel Softmax estimator to decide the parent node among candidates dynamically and to calculate gradients of the discrete decision. We evaluate the proposed model on natural language inference and sentiment analysis, and show that our model outperforms or is at least comparable to previous models. We also find that our model converges significantly faster than other models.
Footprint Characteristics Revised for Field Scale Soil Moisture Monitoring with Cosmic Ray Neutrons
Year: 2015

Cosmic ray neutron probes are widely used to monitor environmental water content near the surface. The method averages over tens of hectares and is unrivaled in serving representative data for agriculture and hydrological models at the hectometer scale. Recent experiments, however, indicate that the sensor response to environmental heterogeneity is not fully understood. Knowledge of the support volume is a prerequisite for the proper interpretation and validation of hydrogeophysical data. In a previous study, several physical simplifications have been introduced into a neutron transport model in order to derive the characteristics of the cosmic ray probe's footprint. We utilize a refined source and energy spectrum for cosmic ray neutrons and simulate their response to a variety of environmental conditions. Results indicate that the method is particularly sensitive to soil moisture in the first tens of meters around the probe, whereas the radial weights are changing dynamically with ambient water. The footprint radius ranges from 130 to 240 m depending on air humidity, soil moisture and vegetation. The moisture dependent penetration depth of 15 to 83 cm decreases exponentially with distance to the sensor. However, the footprint circle remains almost isotropic in complex terrain with nearby rivers, roads or hill slopes. Our findings suggest that a dynamically weighted average of point measurements is essential for accurate calibration and validation. The new insights will have important impact on signal interpretation, sensor installation, data interpolation from mobile surveys, and the choice of appropriate resolutions for data assimilation into hydrological models.
Caracterizacion epidemiologica de los casos fatales con reporte positivo para Tramadol atendidos por el instituto nacional de medicina legal y ciencias forenses, sede Bogota, entre 2013 Y 2018
Year: 2020

El tramadol es un analgesico de la familia de los opioides, con efectos monoaminergicos en su mecanismo de accion. En Colombia el medicamento se encuentra regulado estableciendose su venta con formula medica; a pesar de esto se han descrito casos de consumo con fines diferentes al control del dolor, como lo es el recreativo, como lo expreso United Nations Office on Drugs and Crime, en su reporte mundial del 2018 en Africa. Por esta razon se busco caracterizar epidemiologicamente los casos fatales con reporte positivo para tramadol atendidos por el Instituto Nacional de Medicina Legal y Ciencias Forenses, Sede Bogota, entre los anos del 2013 y 2018, por medio de un estudio de corte transversal, obteniendo un total de 35 casos, correspondiendo su mayoria adultos activos laboralmente y de sexo masculino. Por otra parte, el 40,00% de los individuos tenian como causa de muerte la intoxicacion con tramadol, en alguna de sus diferentes presentaciones. Sumado a esto, el 25,71% de las muestras de laboratorio mostraban algun otro tipo de sustancia presente en el analisis toxicologico. En contraste a lo anterior, se observa la existencia de 3 casos de menores de edad quienes presentaron un con consumo accidental, debido a errores en el manejo o disposicion del medicamento.
TIMs, TAMs, and PS antibody targeting: implications for cancer immunotherapy
Venue: Cell Communication and Signaling
Year: 2020

Immunotherapy for cancer is making impressive strides at improving survival of a subset of cancer patients. To increase the breadth of patients that benefit from immunotherapy, new strategies that combat the immunosuppressive microenvironment of tumors are needed. Phosphatidylserine (PS) signaling is exploited by tumors to enhance tumor immune evasion and thus strategies to inhibit PS mediated immune suppression have potential to increase the efficacy of immunotherapy. PS is a membrane lipid that flips to the outer surface of the cell membrane during apoptosis and/or cell stress. Externalized PS can drive efferocytosis or engage PS receptors (PSRs) to promote local immune suppression. In the tumor microenvironment (TME) PS mediated immune suppression is often termed apoptotic mimicry. Monoclonal antibodies (mAbs) targeting PS or PSRs have been developed and are in preclinical and clinical testing. The TIM (T cell/transmembrane, immunoglobulin, and mucin) and TAM (Tyro3, AXL, and MerTK) family of receptors are PSRs that have been shown to drive PS mediated immune suppression in tumors. This review will highlight the development of mAbs targeting PS, TIM 3 and the TAM receptors.
A tensor based Bayesian probabilistic model for citywide personalized travel time estimation
Year: 2018

Urban travel time information is of great importance for many levels of traffic management and operation. This paper develops a tensor based Bayesian probabilistic model for citywide and personalized travel time estimation, using the large scale and sparse GPS trajectories generated by taxicabs. Combined with the knowledge learned from historical trajectories, travel times of different drivers on all road segments in some time slots are modeled with a 3 order tensor. This tensor based modeling approach incorporates both the spatial correlation between different road segments and the person specific variation between different drivers, as well as the coarse grain temporal correlation between recent and historical traffic conditions and the fine grain temporal correlation between different time slots. To account for the variability caused by the intrinsic uncertainties in urban road network, each travel time entry in the built tensor is treated as a variable following a log normal distribution. With the help of the fully Bayesian treatment, the model achieves automatic hyper parameter tuning and model complexity controlling, and therefore the problem of over fitting is prevented even when the used data is large scale and sparse. The proposed model is applied to a real case study on the citywide road network of Beijing, China, using the large scale and sparse GPS trajectories collected from over 32,670 taxicabs for a period of two months. Empirical results of extensive experiments demonstrate that the proposed model provides an effective and robust approach for urban travel time estimation and outperforms the considered competing methods.
Adoption of Hospital Information System (HIS) in Malaysian Public Hospitals
Year: 2015

Hospital Information System (HIS) is important to be adopted by the hospitals to improve their operations and services. Despite their importance, only 15.2% of Malaysian Public Hospitals implemented the system through THIS, IHIS and BHIS categories which shows low adoption level of HIS in Malaysia. This study aims to identify factors affecting the HIS adoption across different categories of HIS's hospitals. The finding showed that there are significant differences between factors affecting HIS adoption in the THIS compared to IHIS's hospitals, and THIS and BHIS's hospitals. However there is no significant difference among factors between IHIS with BHIS's hospitals.
Zero day malware detection using transferred generative adversarial networks based on deep autoencoders
Venue: Inf. Sci.
Year: 2018

Detecting malicious software (malware) is important for computer security. Among the different types of malware, zero day malware is problematic because it cannot be removed by antivirus systems. Existing malware detection mechanisms use stored malware characteristics, which hinders detecting zero day attacks where altered malware is generated to avoid detection by antivirus systems. To detect malware including zero day attacks robustly, this paper proposes a novel method called transferred deep convolutional generative adversarial network (tDCGAN) which generates fake malware and learns to distinguish it from real malware. The data generated from a random distribution are similar but not identical to the real data: it includes modified features compared with real data. The detector learns various malware features using real data and modified data generated by the tDCGAN based on a deep autoencoder (DAE) which extracts appropriate features and stabilizes the GAN training. Before training the GAN, the DAE learns malware characteristics, produces general data, and transfers this capacity for stable training of the GAN generator. The trained discriminator passes down the ability to capture malware features to the detector, using transfer learning. We show that tDCGAN achieves 95.74% average classification accuracy which is higher than that of other models and increases the learning stability. It is also the most robust against modeled zero day attacks compared to others.
Maximizing the penetration levels of hybrid renewable energy systems in rural areas with demand side management approaches in achieving SDGs
Year: 2019

Access to quality healthcare services is an integral part of sustainable development goals (SDGs) and reliable electricity access is a pre requisite for improving human lives, enhancing healthcare delivery, education as well as other developmental growth within local communities. However, many rural locations far away from the grid centres have difficulties in accessing electricity, due to distance and cost of extension of grid to such areas, and this has resulted in the inability to provide basic amenities. Rural healthcare centres located in such places are unable to deliver their intended mandate, thus hindering the realization of SDG 3. Therefore, the innovative idea of this study is increasing the penetration of renewable energy technology while minimizing the cost by integrating the users' perspective with demand side management (DSM) approaches, techno economic analysis, and environmental impact assessment of hybrid energy system configuration, taking a health institution as the case study. To meet the considered rural healthcare facility's initial average load demand of 20.58kWh/day, an optimum PV Gen battery hybrid system was selected using HOMER with cost of energy (COE) of $0.224/kWh, net present cost (NPC) of $61,917.6 and initial capital cost of $16,046.5. After the DSM measures were applied to reduce the peak and average demand, an optimum hybrid system was obtained, producing a COE of $0.166/kWh, NPC of $18,614.7 and initial capital cost of $10,070.8. The cost saving realized for the considered rural healthcare facility is $0.057/kWh, representing a 25.8% reduction from the current COE and a 70% reduction in Total NPC. On the other hand, the optimal configurations gave around a 75% reduction in CO2 emissions compare to a diesel alone configuration. The work provides novel insights which may be applicable worldwide. It has the potential to significantly advance the development of high quality and timely evidence to underpin current and future developments in the rural energy sector and contribute to the implementation of SDG3 and 7.
The dynamics of COVID 19 in the UAE based on fractional derivative modeling using Riesz wavelets simulation
Venue: Advances in difference equations
Year: 2021

The well known novel virus (COVID 19) is a new strain of coronavirus family, declared by the World Health Organization (WHO) as a dangerous epidemic. More than 3.5 million positive cases and 250 thousand deaths (up to May 5, 2020) caused by COVID 19 and has affected more than 280 countries over the world. Therefore studying the prediction of this virus spreading in further attracts a major public attention. In the Arab Emirates (UAE) up to the same date, there are 14,730 positive cases and 137 deaths according to national authorities. In this work, we study a dynamical model based on the fractional derivatives of nonlinear equations that describe the outbreak of COVID 19 according to the available infection data announced and approved by the national committee in the press. We simulate the available total cases reported based on Riesz wavelets generated by some refinable functions, namely the smoothed pseudosplines of types I and II with high vanishing moments. Based on these data, we also consider the formulation of the pandemic model using the Caputo fractional derivative. Then we numerically solve the nonlinear system that describes the dynamics of COVID 19 with given resources based on the collocation Riesz wavelet system constructed. We present graphical illustrations of the numerical solutions with parameters of the model handled under different situations. We anticipate that these results will contribute to the ongoing research to reduce the spreading of the virus and infection cases.
Sensor based Remote Quality Control Application in Automotive Components Assembly
Venue: Concurr. Eng. Res. Appl.
Year: 2010

Many global enterprises are now operating their production facilities in foreign countries in an effort to capitalize on the merits that each country provides. In such environment, where design, production, supply chains, and other business functions are geographically separated, great challenges exist in terms of how to effectively measure, control, and monitor the product quality on remotely situated production lines. In this context, this article presents a novel, holistic, network based quality control strategy, which is called EQM (short for e quality for manufacture) With EQM, remotely situated designers can access real time quality data and update quality control processes as changes in production occur. EQM also allows the quality data to be integrated within the company's information network with other related business functions. Such integration enables instant generation of corrective actions, such as automatic notification to supply chains/vendors for nonconformities. EQM relieves human operators from laborious, tedious, and error prone tasks of quality monitoring and sampling practices, hence greatly improves the overall efficiency of production operations. The proposed remote quality control strategy has been tested using the networked robotic cell, which produces automotive starter gear housing assemblies. The results vindicate the notion of EQM, manifesting many potential benefits for industry.
Hubungan Kualitas Tidur dengan Risiko Depresi Postpartum di Bidan Praktik Mandiri Rika Hardi, S.ST Kota Padang
Year: 2019

ABSTRAK Latar Belakang dan Tujuan Penelitian Ibu postpartum merupakan kelompok yang sangat rentan mengalami gangguan tidur. Gangguan tidur pada akhirnya akan menghasilkan output berupa kualitas tidur yang buruk. Kualitas tidur yang buruk dapat mengakibatkan berbagai masalah kesehatan, baik itu gangguan mood seperti depresi dan gangguan fungsi tubuh lainnya. Penelitian ini dilakukan untuk mengetahui bagaimana hubungan antara kuakitas tidur dengan risiko depresi postpartum pada ibu nifas. Metode Jenis penelitian ini adalah analitik dengan desain potong lintang. Responden penelitian adalah 41 orang wanita 2 6 minggu postpartum. Metode pengambilan sampel adalah consecutive sampling, dimana populasi yang termasuk kriteria akan menjadi sampel penelitian. Instrumen yang digunakan dalam penelitian ini adalah The Pittsburgh Sleep Quality Index (PSQI) untuk menghitung kualitas tidur responden. Kualitas tidur dinyatakan baik apabila skor PSQI5, dan dinyatakan buruk apabila skor 5. Instrumen yang digunakan untuk menghitung tingkat depresi responden adalah Edinburgh Postnatal Depression Scale (EPDS) Responden dinyatakan berisiko depresi apabila skor EPDS10, dan dinyatakan tidak berisiko apabila skor 10. Data dianalisis menggunakan uji bivariat chi square. Hasil Hasil penelitian menunjukkan 26 responden (63,4% mempunyai kualitas tidur buruk dan komponen tidur yang paling buruk adalah efisiensi tidur (x=2.82) Sedangkan responden yang berisiko mengalami depresi postpartum adalah 25 orang (61% Sebanyak 20 orang mempunyai kualitas tidur yang buruk dan berisiko mengalami depresi postpartum. Analisis bivariat menunjukkan nilai p=0,006 (p<0,05) Kesimpulan Dapat disimpulkan, kualitas tidur dan risiko depresi postpartum mempunyai hubungan yang signifikan. Oleh karena itu, edukasi mengenai upaya perbaikan kualitas tidur perlu direkomendasikan untuk mendapatkan kualitas tidur yang lebih baik serta mengurangi risiko depresi postpartum. Daftar Pustaka 2009 2019 Kata Kunci kualitas tidur, depresi postpartum, ibu postpartum, EPDS, PSQI.
Recovering the feminine other: masculinity, femininity, and gender hegemony
Year: 2007

R. W. Connell's path breaking notion of multiple masculinities (Connell, 1995) and hegemonic masculinity (Connell, 1987, 1995) have been taken up as central constructs in the sociology of gender. Although there has been a great deal of empirical research and theory published that has built upon and utilized Connell's concepts, an adequate conceptualization of hegemonic femininity and multiple femininities has not yet been developed. To redress this, the author presents a theoretical framework that builds upon the insights of Connell and others, offers a definition of hegemonic masculinity and hegemonic femininity that allows for multiple configurations within each, and that can be used empirically across settings and groups. The author also outlines how hegemonic masculinity and hegemonic femininity are implicated in and intersect with other systems of inequality such as class, race, and ethnicity.
From conventional to IT based visual management: a conceptual discussion for lean construction
Venue: J. Inf. Technol. Constr.
Year: 2017

Lean construction and construction automation are two of the important efforts to improve the performance of the construction industry. However, apart from a small number of scholarly articles and implementation prototypes, the lean and digital construction movements seem to be largely running independent of each other. This paper aims at exploring those connections between Visual Management (VM) a fundamental information management strategy in lean construction, and emerging technologies, demonstrating the synergy between the two concepts over potential implementation scenarios and establishing their conceptual connections in construction. Consequently, the hypothesis of the paper is there is a significant synergy between emerging technologies (construction automation) and visual/sensory information management strategies (Visual Management) in lean construction. The hypothesis is explored by (i) discussing how emerging technologies can support conventional VM tools and techniques and (ii) presenting a conceptual architecture to integrate emerging technologies, such as the Internet of Things, Augmented Reality, context aware and mobile computing, the use of drones and quadcopters, auto identification (AutoID) systems and laser scanning, to support lean construction and VM on construction sites. Futuristic scenarios for the implementation of the context aware VM in application areas such as production control, production levelling, quality control, project planning and control, plant maintenance and safety control are examined from a lean construction perspective, alongside the presentation of a higher level implementation architecture to integrate various VM and emerging technology components to support the implementation in a holistic picture. The use of such scenario based approach was found useful in summarising the technology components, their interconnections and possible implementation areas in relation with VM. This paper demonstrates how the integration of conventional and IT based visual management approaches is within reach and holds the potential to enhance the construction and maintenance phase of complex, large scale construction projects by reviewing the synergies between operational VM concepts and IT.