bluuebunny/crossref_metadata_2025_split huggingface dataset:
EXPERIENCE IN APPLYING THE TECHNOLOGY OF CYTOREDUCTIVE SURGERY WITH HYPERTHERMIC INTRAOPERATIVE INTRAPERITONEAL CHEMOTHERAPY IN THE TREATMENT OF PATIENTS WITH PERITONEAL CARCINOMATOSIS
Authors: Shelekhov A. V. , Dvornichenko V. V. , Radostev S. I. , Morikov D. D. , Rasulov R. I. , Ushakova I. V. , Medvednikov A. A. , Zaharov A. G. , Demonov R. N. , Gladkova O. V. , Chernorubashkina N. M. 
Published: July 2018

We present our experience in using cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (HIPEC) for ovarian cancer patients treated at Irkutsk Regional Cancer Center. All patients were divided into 2 groups. Group I consisted of 15 patients, who underwent cytoreductive surgery only. Group II comprised 17 patients, who underwent surgery and HIPEC. The main eligibility criteria for this study were verified peritoneal carcinomatosis and resectable ovarian cancer. The primary analysis of these groups included: preoperative period, length of operation, postoperative length of stay, and postoperative complications. The technique of performing HIPEC using Performer HT® (RAND, Medolla (MO), Italy) was completely described. Further study is required to estimate the difference in overall and disease-free survival between study groups.
Surface Complexation Modeling of Fluoride Adsorption by Soil and the Role of Dissolved Aluminum on Adsorption
Authors: Padhi Sakambari , Tokunaga Tomochika 
Published: July 2018

Core Ideas
A surface complexation model for F− adsorption on soil was developed.
Dissolved Al and Al–F complex adsorption added to the model better explained F− adsorption.
The model can be extended to study the effect of existing co‐anions or humic substances.
The adsorption processes of F− on a natural soil as a function of varying pH and F− concentration were evaluated by applying a surface complexation model (SCM) based on a single surface functional group with monodentate binding sites. A granitic soil from Tsukuba, Japan, was chosen as an example, and the SCM was developed to explain the pH dependency of F− sorption isotherms on the soil. Four possible surface complexation reactions were postulated with and without including dissolved Al. Optimized constants for F− surface complexation, and those for protonation and deprotonation, were used for the simulations. The SCM including dissolved Al and the adsorption of Al–F complex can simulate the experimental results, the decreasing trend of F− adsorption with the increase in pH, quite successfully. Also, including dissolved Al and the adsorption of Al–F complex to the model explained the change in solution pH after F− adsorption. Therefore, incorporation of dissolved Al and Al–F complex in model calculations of a soil–F system is an important improvement to predict F− concentrations of soil solutions.
Estimation of hydrological response to future climate change in a cold watershed
Authors: Sha Jian , Wang Zhong-Liang , Zhao Yue , Xu Yan-Xue , Li Xue 
Published: July 2018

Abstract
The vulnerability of the natural water system in cold areas to future climate change is of great concern. A coupled model approach was applied in the headwater watershed area of Yalu River in the northeastern part of China to estimate the response of hydrological processes to future climate change with moderate data. The stochastic Long Ashton Research Station Weather Generator was used to downscale the results of general circulation models to generate synthetic daily weather series in the 2050s and 2080s under various projected scenarios, which were applied as input data of the Generalized Watershed Loading Functions hydrological model for future hydrological process estimations. The results showed that future wetter and hotter weather conditions would have positive impacts on the watershed runoff yields but negative impacts on the watershed groundwater flow yields. The freezing period in winter would be shortened with earlier snowmelt peaks in spring. These would result in less snow cover in winter and shift the monthly allocations of streamflow with more yields in March but less in April and May, which should be of great concern for future local management. The proposed approach of the coupled model application is effective and can be used in other similar areas.
Assessment of climate change impact on flow regimes over the Gomti River basin under IPCC AR5 climate change scenarios
Authors: Abeysingha N. S. , Islam Adlul , Singh Man 
Published: July 2018

AbstractClimate change impact on flow regimes in the Gomti River basin, India was studied using the Soil and Water Assessment Tool (SWAT) driven by climate change scenarios generated from multiple general circulation model (GCM) projections. The SWAT-CUP (SWAT-Calibration and Uncertainty Programs) was used for calibration and validation of SWAT using multi-site data. Climate change scenarios were generated from multiple GCM projections using the hybrid-delta ensemble method. Calibration of SWAT using the nine most sensitive parameters showed that the model performed reasonably well with P-factor &gt;0.7 and R-factor &lt;1.0. The annual rainfall is projected to increase by 3.4–4.5, 4.7–10.0, and 5.0–18.0% during the 2020s, 2050s, and 2080s respectively under different Representative Concentration Pathways (RCPs). There is a decrease in rainfall during the winter season. The annual streamflow is projected to increase by 1–9, 1–22, and 2–38% during the 2020s, 2050s, and 2080s, respectively. However, winter and summer streamflow is projected to decrease. Magnitude and frequency of high flows is also projected to increase in the range of 3.6–27.3 and 12–87%, respectively under different RCPs. The results of this study will be helpful in developing suitable water management adaptation plans for the study basin.
Multiple Access using Time Modulated Array with Single RF Chain
Authors: He Xu , Ding Yuan , Huang Gaojian 
Published: July 2018

Background:
A new wireless multiple access technology enabled by using Time Modulated
Arrays (TMAs) is proposed in this paper.

Methods:
It benefits due to the requirement of only a single Radio Frequency (RF) chain, compared
with other multiple-RF-chain schemes.

Results:
As a result, it is able to greatly reduce the system cost, energy consumption, and complexity.

Conclusion:
In addition, the signal through the single RF chain is narrow-band modulated, reducing the
signal Peak-to-Average-Power-Ratio (PAPR), thus, further enhancing the power efficiency of the RF
chain, especially for power amplifiers. The operation principle and synthesis approach are elaborated in
this paper, and are demonstrated with two examples.
Perbandingan hasil stimulasi ovarium dengan kombinasi klomifen sitrat gonadotropin dan klomifen sitrat pada inseminasi intra uteri
Authors: Indriani Farida , Widad Shofwal , Sofoewan Sulchan 
Published: July 2018

Background: Infertile couple with the cause of unexplained infertility and male factor can follow intrauterine insemination (IUI) program as a cheap and easy method for infertility therapy. Ovarian stimulation by combination of clomiphene citrate and gonadotropin and also clomiphene citrate alone was done as a superovulation mode to get higher number of mature follicle to gain higher pregnancy rate. Objective : To compare the amount of mature follicles and endometrial thickness as an outcome of ovarian stimulation between combination drug of clomiphene citrate and gonadotrophin with clomiphene citrate alone.Methods: Observational retrospective cohort. Subject is one cycle intrauterine insemination stimulated either by combination CC gonadotrophin or CC alone, eligible in inclusion criteria and exclusion criteria. Statistic test used was independent t-test, chi-square and logistic regression.Location of study: Infertility Clinic of Permata Hati, dr. Sardjito Hospital, Yogyakarta.Result: A total of 142 cycle from 98 couples followed IUI and stimulated by combination CC gonadotrophin (n=72) or CC alone (n=70). Multiple mature follicle number was different and statistically significant 77% vs 54%, RR 1,433 (IK95% 1,118-1,836), p=0,005, while endometrial thickness > 7mm was more frequent in combination group but not statistically different with percentage 81,9% vs 74,3%, RR 1,103 (CI95% 0,926-1,315), p=0,367.Conclusion: Ovarian stimulation by combination CC gonadotropin resulting of more multiple mature follicle number while good endometrial thickness was more in combination group but not statistically significant.Keywords: ovarian stimulation, clomiphene citrate, gonadotrophin, multifollicular, endometrial thickness
TOURISM VILLAGE EXPANSION IN THE CONCEPT OF SUSTAINABLE TOURISM DEVELOPMENT (INDONESIA EXPERIENCE)
Authors: Hamzah Any Suryani , Irfan Mohammad 
Published: July 2018

ABSTRACT
The purpose of this research is to find the model of traditional tourism governance format so that it can be used as a benchmark in the development of village tourism or sustainable hamlet tourism by re-arranging regulations based on local wisdom that support traditional tourism. Another objective is to evaluate and refine the implementation of village regulations that have been developed and implemented in the village tourism domain to determine the extent of their implications on improving the economy of people in tourist villages. This paper is authored using a qualitative approach with the orientation of literature study as an employed strategy in this study. The literature study is used in social research methodologies to trace historical data, as is found in this study.
The results showed that the development of rural tourism is able to minimize the potential of urbanization from rural to urban society caused by the ability of local people to create economic activities based on tourism activities (tourism economy) in rural areas. The productive power of local potentials, including the potentials of rural areas, can be encouraged to grow and develop by utilizing the resources owned by the village, so that it can be an effective instrument in encouraging the development of socio-cultural and economic fields of rural communities. Furthermore, efforts will be encouraged to conserve and empower the potential of local culture and local wisdom values ​​that tend to be threatened with extinction due to the heavily globalized currents that have entered rural areas. In the development of a tourist village, certain guidelines are needed so that the impact of the development of tourism activities in each rural area can be controlled. The guidelines are expected to encourage the development and management of tourist villages to be more focused, planned, and sustainable. Besides, the tourist village will be supported by all parties and it will provide significant benefits for all villagers through tourism village-based economic growth and development.

Keywords: tourism village, sustainable tourism development, economic empowerment, local wisdom.
SUPORTE TEXTUAL, COERÊNCIA E PRODUÇÃO DE SENTIDOS: DISCUSSÃO A PARTIR DE TEXTOS DE MÍDIA DE AMBIENTE
Authors: Lima Juliana De Mello Chagas , Romualdo Edson Carlos 
Published: July 2018

: Este artigo tem como ponto de partida a observação da existência de novos espaços para a publicação de peças publicitárias, em especial o uso de diferentes suportes e formatos. Nosso objetivo, neste trabalho, é contribuir para a discussão sobre a importância dos suportes na construção dos sentidos dos textos, principalmente no que se refere a sua coerência, com base em autores como Marchuschi (2003; 2008), Koch (2009), Koch e Travaglia (2011), Bonini (2011), entre outros. Analisamos textos da chamada "mídia de ambiente", uma vertente da criação e veiculação publicitária, que tem como característica a instalação de peças publicitárias em locais inusitados e de grande fluxo de pessoas. Consideramos que essas novas formas de publicidade permitem novas relações para a produção de sentidos, devido à interação com o ambiente físico em que estão inseridas e as relações entre sujeitos e objetos promovidas a partir de sua publicação, levando-nos a propor o suporte como um dos fatores de coerência. Palavras-chave: linguística textual; suporte; mídia de ambiente.
Megacolon Tóxico De Origen Idiopático: Reporte De Caso
Authors: Siado Guerrero Sergio Andrés , Jiménez Sánchez Héctor Conrado , Martínez Montalvo Carlos Mauricio 
Published: July 2018

El megacolon tóxico (MT) es una patología con una tasa de mortalidad superior al 80% desencadenada por un proceso inflamatorio progresivo que compromete la pared del colon con dilatación secundaria de la luz intestinal debido a procesos inflamatorios o infecciosos. Su presentación clínica es bizarra y los pilares básicos en su manejo, son un diagnóstico oportuno, un manejo médico adecuado (antibiótico, reanimación hídrica, corrección metabólica) y de ser necesario, un manejo quirúrgico eficaz, que evite al máximo las complicaciones que empeoran el pronóstico de los pacientes. En este artículo presentamos un caso de una paciente con choque séptico secundario a MT, con desenlace fatal, con sospecha de un cuadro de colangitis grado III descartado por ecografía, lo cual genero distorsiones en su enfoque y manejo inicial. Por deterioro clínico y distensión abdominal, la paciente fue llevada a laparoscopia diagnóstica donde se evidenció un compromiso isquémico severo de todo el colon, sin compromiso de intestino delgado, razón por la que se le realizó colectomia total. El reporte de patología y la historia clínica descartan colitis ulcerativa o enfermedad de Crohn, confirmando megacolon tóxico. La paciente no presentaba factores de riesgo para el desarrollo de colitis pseudomembranosa. Concluimos que es la presentación de un caso de megacolon tóxico idiopático.
Multi-task Layout Analysis for Historical Handwritten Documents Using Fully Convolutional Networks
Authors: Xu Yue , Yin Fei , Zhang Zhaoxiang , Liu Cheng-Lin 
Published: July 2018

Layout analysis is a fundamental process in document image analysis and understanding. It consists of several sub-processes such as page segmentation, text line segmentation, baseline detection and so on. In this work, we propose a multi-task layout analysis method that use a single FCN model to solve the above three problems simultaneously. The FCN is trained to segment the document image into different regions and detect the center line of each text line by classifying pixels into different categories. By supervised learning on document images with pixel-wise labels, the FCN can extract discriminative features and perform pixel-wise classification accurately. After pixel-wise classification, post-processing steps are taken to reduce noises, correct wrong segmentations and find out overlapping regions. Experimental results on the public dataset DIVA-HisDB containing challenging medieval manuscripts demonstrate the effectiveness and superiority of the proposed method.
Learning Optimal Decision Trees with SAT
Authors: Narodytska Nina , Ignatiev Alexey , Pereira Filipe , Marques-Silva Joao 
Published: July 2018

Explanations of machine learning (ML) predictions are of fundamental importance in different settings. Moreover, explanations should be succinct, to enable easy understanding by humans. Decision trees represent an often used approach for developing explainable ML models, motivated by the natural mapping between decision tree paths and rules. Clearly, smaller trees correlate well with smaller rules, and so one challenge is to devise solutions for computing smallest size decision trees given training data. Although simple to formulate, the computation of smallest size decision trees turns out to be an extremely challenging computational problem, for which no practical solutions are known. This paper develops a SAT-based model for computing smallest-size decision trees given training data. In sharp contrast with past work, the proposed SAT model is shown to scale for publicly available datasets of practical interest.
Vocabulary Alignment for Collaborative Agents: a Study with Real-World Multilingual How-to Instructions
Authors: Chocron Paula , Pareti Paolo 
Published: July 2018

Collaboration between heterogeneous agents typically requires the ability to communicate meaningfully. This can be challenging in open environments where participants may use different languages. Previous work proposed a technique to infer alignments between different vocabularies that uses only information about the tasks being executed, without any external resource. Until now, this approach has only been evaluated with artificially created data. We adapt this technique to protocols written by humans in natural language, which we extract from instructional webpages. In doing so, we show how to take into account challenges that arise when working with natural language labels.The quality of the alignments obtained with our technique is evaluated in terms of their effectiveness in enabling successful collaborations, using a translation dictionary as a baseline. We show how our technique outperforms the dictionary when used to interact.
Distance Metric Facilitated Transportation between Heterogeneous Domains
Authors: Ye Han-Jia , Sheng Xiang-Rong , Zhan De-Chuan , He Peng 
Published: July 2018

Lacking training examples is one of the main obstacles to learning systems. Transfer learning aims to extract and utilize useful information from related datasets and assists the current task effectively. Most existing methods restrict tasks connection on the same feature sets, or require aligned examples cross domains, even cannot take full advantage of the limited label information. In this paper, we focus on transferring between heterogeneous domains, i.e., those with different feature spaces, and propose the Metric Transporation on HEterogeneous REpresentations (MapHere) approach. In particular, an asymmetric transformation map is first learned to compensate the cross-domain feature difference based on linkage relationship between objects; then the inner-domain discrepancy is further reduced with learned optimal transportation. Note that both source domain and cross-domain relationship are fully utilized in MapHere, which helps improve target classification task a lot. Experiments on synthetic dataset validate the importance of the ''metric facilitated'' consideration, while results on real-world image and text classification also show the superiority of the proposed MapHere approach.
Online Kernel Selection via Incremental Sketched Kernel Alignment
Authors: Zhang Xiao , Liao Shizhong 
Published: July 2018

In contrast to offline kernel selection, online kernel selection must rise to the new challenges of passing the training set once, selecting optimal kernels and updating hypotheses at each round, enjoying a sublinear regret bound for online kernel learning, and requiring a constant maintenance time complexity at each round and an efficient overall time complexity integrated with online kernel learning. However, most of existing online kernel selection approaches can not meet the new challenges. To address this issue, we propose a novel online kernel selection approach via the incremental sketched kernel alignment criterion, which meets all the new challenges. We first define the incremental sketched kernel alignment (ISKA) criterion, which estimates the kernel alignment and can be computed incrementally and efficiently. When applying the proposed ISKA criterion to online kernel selection, we adopt the subclass coherence to maintain the hypothesis space, select the optimal kernel at each round using the median of the ISKA criterion estimates, and update the hypothesis following the online gradient decent method. We prove that the ISKA criterion is an unbiased estimate of the maximum mean discrepancy, enjoys the optimal logarithmic regret bound for online kernel learning, and has a constant maintenance time complexity at each round and a logarithmic overall time complexity integrated with online kernel learning. Empirical studies demonstrate that the proposed online kernel selection approach is computationally efficient while maintaining comparable accuracy for online kernel learning.
Robust Graph Dimensionality Reduction
Authors: Zhu Xiaofeng , Lei Cong , Yu Hao , Li Yonggang , Gan Jiangzhang , Zhang Shichao 
Published: July 2018

In this paper, we propose conducting Robust Graph Dimensionality Reduction (RGDR) by learning a transformation matrix to map original high-dimensional data into their low-dimensional intrinsic space without the influence of outliers. To do this, we propose simultaneously 1) adaptively learning three variables, \ie a reverse graph embedding of original data, a transformation matrix, and a graph matrix preserving the local similarity of original data in their low-dimensional intrinsic space; and 2) employing robust estimators to avoid outliers involving the processes of optimizing these three matrices. As a result, original data are cleaned by two strategies, \ie a prediction of original data based on three resulting variables and robust estimators, so that the transformation matrix can be learnt from accurately estimated intrinsic space with the helping of the reverse graph embedding and the graph matrix. Moreover, we propose a new optimization algorithm to the resulting objective function as well as theoretically prove the convergence of our optimization algorithm. Experimental results indicated that our proposed method outperformed all the comparison methods in terms of different classification tasks.
Localized Incomplete Multiple Kernel k-means
Authors: Zhu Xinzhong , Liu Xinwang , Li Miaomiao , Zhu En , Liu Li , Cai Zhiping , Yin Jianping , Gao Wen 
Published: July 2018

The recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) optimally integrates a group of pre-specified incomplete kernel matrices to improve clustering performance. Though it demonstrates promising performance in various applications, we observe that it does not \emph{sufficiently consider the local structure among data and indiscriminately forces all pairwise sample similarity to equally align with their ideal similarity values}. This could make the incomplete kernels less effectively imputed, and in turn adversely affect the clustering performance. In this paper, we propose a novel localized incomplete multiple kernel k-means (LI-MKKM) algorithm to address this issue. Different from existing MKKM-IK, LI-MKKM only requires the similarity of a sample to its k-nearest neighbors to align with their ideal similarity values. This helps the clustering algorithm to focus on closer sample pairs that shall stay together and avoids involving unreliable similarity evaluation for farther sample pairs. We carefully design a three-step iterative algorithm to solve the resultant optimization problem and theoretically prove its convergence. Comprehensive experiments on eight benchmark datasets demonstrate that our algorithm significantly outperforms the state-of-the-art comparable algorithms proposed in the recent literature, verifying the advantage of considering local structure.
On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach
Authors: Natarajan Sriraam , Das Srijita , Ramanan Nandini , Kunapuli Gautam , Radivojac Predrag 
Published: July 2018

We consider the problem of actively feature elicitation in which given a few examples with all the features (say the full EHR) and a few examples with some of the features (say demographics), the goal is to identify the set of examples on whom more informationÂ (say the lab tests) needs to be collected. The observation is that some set of features may be more expensive, personal or cumbersome to collect. We propose an active learning approach which identifies examples that are dissimilar to the ones with the full set of data and acquire the complete set of features for these examples. Motivated by real clinical tasks, our extensive evaluation on three clinical tasks demonstrate the effectiveness of this approach.