nils-herrmann/clean_scirepeval huggingface dataset:
ARDL Bounds Testing Approach to Cointegration: Relationship International Trade Policy Reform and Foreign Trade in Vietnam

Starting from in 1986, Vietnam began economic reforms, from an economy centrally planned economy to market oriented socialist where the government still playing a leading role. The breakthrough in policy reform has succeeded in creating faster economic growth and improving people's living standards. Among the policy reforms since renovation, trade policy is the most important ones and has great impacts not only on Vietnam's foreign trade activity but also the whole economy. This paper focuses on the evaluation of the impact of reforms in trade policy on Vietnam's foreign trade, the achievements and the limitations still exist after implement the reform of trade policy. Also, together with the mention of the content and the process of trade policy reform in Vietnam. Finally, it proposes some suggestions to improve Vietnam's trade policy.
An Exploratory Study of the Economic Potential of Philippine Pili Pulp Oil From Waste Pulp

This paper aims to determine the status of the pili nut and pili pulp oil industry in the Philippines and the competitiveness of processing oil from pili Canarium ovatum waste pulp using the domestic resource cost approach. Results of analysis reveal that producing locally one kg of pili pulp oil costs PhP 599. This is almost half the cost of importing olive oil a close substitute from Spain estimated at PhP 1,310/kg. In terms of domestic resource cost, there exists comparative (economic) advantage in pili pulp oil production. With a domestic resource cost (DRC) of 0.72, pili pulp oil production in the Philippines is competitive and hence manifests considerable economic potential. It has both employment and income effects that any similar program on inclusive growth aims to generate. If all the pili pulp were processed and converted into pili pulp oil, a total of 262,000 metric tons worth about PhP 212 million could have been generated in 2014.  Canarium ovatum, cost and returns, domestic resource cost, import competitiveness, Philippines
Abundant NDRG2 Expression Is Associated with Aggressiveness and Unfavorable Patients' Outcome in Basal Like Breast Cancer
Venue: PloS one

NDRG2, a member of the N myc downstream regulated gene family, is thought to be a putative tumor suppressor gene with promising clinical impact in breast cancer. Since breast cancer comprises heterogeneous intrinsic subtypes with distinct clinical outcomes we investigated the pivotal role of NDRG2 in basal type breast cancers. Based on subtype classified tumor (n 45) and adjacent normal tissues (n 17) we examined NDRG2 mRNA expression and CpG hypermethylation, whose significance was further validated by independent data sets from The Cancer Genome Atlas (TCGA) In addition, NDRG2 protein expression was evaluated immunohistochemically using a tissue micro array (TMA, n 211) In vitro, we investigated phenotypic effects caused by NDRG2 silencing in the basal A like HCC1806 as well as NDRG2 over expression in basal A like BT20 compared to luminal type MCF7 breast cancer cells. Our tissue collections demonstrated an overall low NDRG2 mRNA expression in breast cancer subtypes compared to normal breast tissue in line with an increased CpG hypermethylation in breast cancer tissue. Independent TCGA data sets verified a significant (P<0.001) expression loss of NDRG2 in breast tumors. Of interest, basal like tumors more frequently retained abundant NDRG2 expression concordant with a lower CpG hypermethylation. Unexpectedly, basal like breast cancer revealed an association of NDRG2 expression with unfavorable patients' outcome. In line with this observation, in vitro experiments demonstrated reduced proliferation and migration rates ~20% in HCC1806 cells following NDRG2 silencing. In contrast, NDRG2 over expressing luminal type MCF7 cells demonstrated a 26% decreased proliferation rate. Until now, this is the first study investigating the putative role of NDRG2 in depth in basal type breast cancer. Our data indicate that the described putative tumor suppressive function of NDRG2 may be confined to luminal and basal B type breast cancers.
Sustainable textile production: cleaner production assessment/eco efficiency analysis study in a textile mill

Cleaner production assessment studies were carried out according to the Integrated Pollution Prevention and Control and Industrial Emission Directive in a cotton/polyester fabric finishing dyeing textile mill, located in Denizli, Turkey. Following detailed on site process evaluation, environmental performance of the mill was evaluated. Data of the material flow and the energy consumption in all processes was collected. Mass energy balances and specific input and output values based on the production processes were calculated. Also, a chemical inventory list was prepared and all material safety data sheets were collected. Environmental performance of the mill was benchmarked against similar textile mills in the literature. 92 Best Available Techniques options were listed. Each suggested BAT option was discussed with the mill management in terms of techno economic applicability and implementation of 22 Best Available Techniques were decided. In the decision making process, statistical Multi criteria Decision Making Methods (Simple Ranking Method, Weighting Criteria Method, and Weighted Sum Method) were used. Moreover, technical and environmental performances, potential benefits and savings were determined with the implementation of identified 22 Best Available Techniques such as good management practices, water and energy consumption optimization minimization techniques, chemical consumption optimization and substitution. These evaluations have revealed that after the implementation of suggested 22 Best Available Techniques, following reductions could be achieved if those techniques were implemented in the future: 43 51% water consumption, 11 26% energy consumption, 16 39% chemical consumption, 42 52% wastewater flowrate, 26 48% chemical oxygen demand load, 12 32% waste flue gas emissions, and 8 18% solid waste generation. Payback periods of the suggested Best Available Techniques were estimated as 1 26 months.
Cubreme(r) and Sustainable Value Creation: A Diagnosis

Cubreme(r) is a small textile company created by designer Alejandra Gottelli whose purpose is to promote the use of organic and natural textiles by designing classical contemporary garments that transcend fashion trends. Therefore, the collections are not divided into 6 week "seasons" that change continuously, but rather they are featured in line with cold and warm seasons. The brand was conceived in response to the designer's need to express creatively the culture of Argentine native communities. The fibers used by Alejandra Gottelli come from the shearing of domestic species, such as animals from the sheep, camel, and goat families from the Andean Patagonian and Andean Cuyo regions of Argentina, which are bred in their natural habitat. The fibers obtained from shearing animals from both the camel and sheep families, as well as the harvest of vegetable fibers are treated in premium spinning mills that develop highly refined products on a very small scale. Fabrics are developed using handlooms, and craft tailor shops are in charge of the final tailoring to give garments a haute couture finish; this helps keep a small production line, using renewable resources and contributing both actively and voluntarily to social, economic, and environmental improvement. This case study introduces Cubreme and then shows the model of sustainable value creation that integrates four elements: environment, innovation, stakeholder management, economic value and potential of growth (Hart, Harvard Bus Rev 75: 66 76, 1997; Capitalism at the Crossroads, 2005; Capitalism at the crossroads: capitalism at the crossroads aligning business, earth, and humanity, 2007; Hart and Milstein, MIT Sloan Manag Rev 41:23 33,1999, Acad Manag Exec 17: 56 67, 2003) as a tool to diagnose the brand, ending with an analysis of the company in the light of the above model and a few conclusions.
Event coincidence analysis for quantifying statistical interrelationships between event time series

Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. In this paper, we describe the method of event coincidence analysis to provide a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter event time distribution and other higher order properties. Applying the framework to country level observational data yields evidence that flood events have acted as triggers of epidemic outbreaks globally since the 1950s. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.
Pattern discovery from stock time series using self organizing maps

This work was supported by the RGC CERG project PolyU 5065/98E and the Departmental Grant H ZJ84 Corresponding author ABSTRACT Pattern discovery from time series is of fundamental importance. Particularly when the domain expert derived patterns do not exist or are not complete, an algorithm to discover specific patterns or shapes automatically from the time series data is necessary. Such an algorithm is noteworthy in that it does not assume prior knowledge of the number of interesting structures, nor does it require an exhaustive explanation of the patterns being described. In this paper, a clustering approach is proposed for pattern discovery from time series. In view of its popularity and superior clustering performance, the self organizing map (SOM) was adopted for pattern discovery in temporal data sequences. It is a special type of clustering algorithm that imposes a topological structure on the data. To prepare for the SOM algorithm, data sequences are segmented from the numerical time series using a continuous sliding window. Similar temporal patterns are then grouped together using SOM into clusters, which may subsequently be used to represent different structures of the data or temporal patterns. Attempts have been made to tackle the problem of representing patterns in a multi resolution manner. With the increase in the number of data points in the patterns (the length of patterns) the time needed for the discovery process increases exponentially. To address this problem, we propose to compress the input patterns by a perceptually important point (PIP) identification algorithm. The idea is to replace the original data segment by its PIP's so that the dimensionality of the input pattern can be reduced. Encouraging results are observed and reported for the application of the proposed methods to the time series collected from the Hong Kong stock market.
Semi Supervised Learning for Neural Machine Translation
Venue: ACL

While end to end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source to target and target to source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese English dataset show that our approach achieves significant improvements over state of the art SMT and NMT systems.
Modeling Coverage for Neural Machine Translation
Venue: ACL

Attention mechanism has enhanced state of the art Neural Machine Translation (NMT) by jointly learning to align and translate. It tends to ignore past alignment information, however, which often leads to over translation and under translation. To address this problem, we propose coverage based NMT in this paper. We maintain a coverage vector to keep track of the attention history. The coverage vector is fed to the attention model to help adjust future attention, which lets NMT system to consider more about untranslated source words. Experiments show that the proposed approach significantly improves both translation quality and alignment quality over standard attention based NMT.
Chronic Disease Prevention and Health Promotion

Chronic diseases include conditions such as heart disease, stroke, cancer, diabetes, respiratory conditions, and arthritis. In high income countries, chronic diseases have long been the leading causes of death and disability. Globally, more than 70 of deaths are due to chronic diseases, in the United States, more than 87 (World Health Organization [WHO] 2011) Almost one in two Americans has at least one chronic condition (Wu and Green 2000) Aside from the cost in terms of human welfare, treatment of chronic disease accounts for an estimated three quarters of U.S. health care spending (Centers for Disease Control and Prevention [CDC] 2012) Chronic diseases directly affect overall health care budgets, employee productivity, and economies. Globally, noncommunicable diseases account for two thirds of the overall disease burden in middle income countries and are expected to rise to three quarters by 2030, typically in parallel to economic development (World Bank 2011) Of particular concern to many low and middle income countries is that threats to population health occur on two fronts simultaneously: "In the slums of today's megacities, we are seeing noncommunicable diseases caused by unhealthy diets and habits, side by side with undernutrition"
Organizational change theory: implications for health promotion practice.
Venue: Health promotion international

Sophisticated understandings of organizational dynamics and processes of organizational change are crucial for the development and success of health promotion initiatives. Theory has a valuable contribution to make in understanding organizational change, for identifying influential factors that should be the focus of change efforts and for selecting the strategies that can be applied to promote change. This article reviews select organizational change models to identify the most pertinent insights for health promotion practitioners. Theoretically derived considerations for practitioners who seek to foster organizational change include the extent to which the initiative is modifiable to fit with the internal context; the amount of time that is allocated to truly institutionalize change; the ability of the agents of change to build short term success deliberately into their implementation plan; whether or not the shared group experience of action for change is positive or negative and the degree to which agencies that are the intended recipients of change are resourced to focus on internal factors. In reviewing theories of organizational change, the article also addresses strategies for facilitating the adoption of key theoretical insights into the design and implementation of health promotion initiatives in diverse organizational settings. If nothing else, aligning health promotion with organizational change theory promises insights into what it is that health promoters do and the time that it can take to do it effectively.
Improvement of LMI controllers of Takagi Sugeno models via Q learning

This paper presents a preliminary attempt to bridge the conservative (shape independent) results from guaranteed cost LMIs and the reinforcement learning setups which learn optimal controllers from data. In this sense, the proposed approach uses an initialization based on the LMI solution and proposes an approximation of the Q function using polynomials of the membership functions in Takagi Sugeno models. The resulting controller is shape dependent, that is, uses the knowledge of membership functions and data to clearly improve LMI solutions.
From transformation to metastasis: deconstructing the extracellular matrix in breast cancer
Venue: Cancer and Metastasis Reviews

The extracellular matrix (ECM) is a guiding force that regulates various developmental stages of the breast. In addition to providing structural support for the cells, it mediates epithelial stromal communication and provides cues for cell survival, proliferation, and differentiation. Perturbations in ECM architecture profoundly influence breast tumor progression and metastasis. Understanding how a dysregulated ECM can facilitate malignant transformation is crucial to designing treatments to effectively target the tumor microenvironment. Here, we address the contribution of ECM mechanics to breast cancer progression, metastasis, and treatment resistance and discuss potential therapeutic strategies targeting the ECM.
An agent based electric vehicle ecosystem model: San Francisco case study

The widespread commercial availability of plug in electric vehicles (EVs) in recent years motivates policies to encourage EV adoption and infrastructure to cope with the increasing number of EVs. We present an agent based EV ecosystem model that incorporates EV adoption and usage with spatial and temporal considerations and that can aid different EV industry stakeholders such as policymakers, utility operators, charging station planners, and EV manufacturers. The model follows an ecological modeling approach, and is used to determine how different policies and battery technologies affect EV adoption, EV charging, and charging station activity. We choose model parameters to fit San Francisco as a test city and simulate different scenarios. The results provide insight on potential changes to the San Francisco EV ecosystem as a result of changes in rebates, availability of workplace charging, public awareness of lower EV operational costs, and denser EV batteries. We find that our results match those obtained using other approaches and that the compact geographical size of San Francisco and its relative wealth make it an ideal city for EV adoption.
Electricity Trading Agent for EV enabled Parking Lots
Venue: AMEC/TADA

The reduction of greenhouse gas emissions is seen as an important step towards environmental sustainability. Perhaps not surprising, many governments all around the world are providing incentives for consumers to buy electric vehicles (EVs) A positive response from consumers means that the demand for the charging infrastructure increases as well. We investigate how an existing traditional parking lot, upgraded with chargers, can suit the present demand for charging stations. In particular, a resulting EV enabled parking lot is an electricity trading agent (i.e. broker) which acts as an energy retailer and as a player on a target electricity market. In this paper, we use agent based simulation to present the EV enabled parking lot ecosystem in order to model the underlying dynamics and uncertainties regarding parking lots with electricity trading agent functionalities. We instantiate our agent based simulations using real life data in order to perform the what if analysis. Several key performance indicators (KPIs) including parking utilization, charging utilization and electricity utilization, are proposed. We also illustrate how those KPIs can be used to choose the effective investment strategy with respect to the number and speed of chargers.
Proximal Gradient Temporal Difference Learning Algorithms
Venue: IJCAI

In this paper, we describe proximal gradient temporal difference learning, which provides a principled way for designing and analyzing true stochastic gradient temporal difference learning algorithms. We show how gradient TD (GTD) reinforcement learning methods can be formally derived, not with respect to their original objective functions as previously attempted, but rather with respect to primal dual saddle point objective functions. We also conduct a saddle point error analysis to obtain finite sample bounds on their performance. Previous analyses of this class of algorithms use stochastic approximation techniques to prove asymptotic convergence, and no finite sample analysis had been attempted. An accelerated algorithm is also proposed, namely GTD2 MP, which use proximal "mirror maps" to yield acceleration. The results of our theoretical analysis imply that the GTD family of algorithms are comparable and may indeed be preferred over existing least squares TD methods for off policy learning, due to their linear complexity. We provide experimental results showing the improved performance of our accelerated gradient TD methods.
Kernel Based Least Squares Temporal Difference With Gradient Correction
Venue: IEEE Transactions on Neural Networks and Learning Systems

A least squares temporal difference with gradient correction (LS TDC) algorithm and its kernel based version kernel based LS TDC (KLS TDC) are proposed as policy evaluation algorithms for reinforcement learning (RL) LS TDC is derived from the TDC algorithm. Attributed to TDC derived by minimizing the mean square projected Bellman error, LS TDC has better convergence performance. The least squares technique is used to omit the size step tuning of the original TDC and enhance robustness. For KLS TDC, since the kernel method is used, feature vectors can be selected automatically. The approximate linear dependence analysis is performed to realize kernel sparsification. In addition, a policy iteration strategy motivated by KLS TDC is constructed to solve control learning problems. The convergence and parameter sensitivities of both LS TDC and KLS TDC are tested through on policy learning, off policy learning, and control learning problems. Experimental results, as compared with a series of corresponding RL algorithms, demonstrate that both LS TDC and KLS TDC have better approximation and convergence performance, higher efficiency for sample usage, smaller burden of parameter tuning, and less sensitivity to parameters.
Fiber Bragg Grating Sensors Based In Situ Monitoring and Safety Assessment of Loess Tunnel
Venue: J. Sensors

Compared with electrical strain gauges, fiber Bragg grating (FBG) sensing technology is a relatively novel method for tunnel structural health monitoring, which has a number of advantages including high accuracy, multiplexing, electromagnetic interference resistance, and good repeatability. In order to study the internal force of the tunnel liner and detect the potential safety hazards, series of strain monitoring tests of a loess tunnel, taking into account the complex stress and strain variation of the loess during tunnelling, were performed by employing the tandem linear FBG sensor arrays controlled by the wavelength division multiplexing (WDM) technology. The concrete strain has obvious linear characteristics over time in the early stage and then gradually tends to a stable value. Moreover, after the necessary temperature compensation, loess tunnel structure safety was assessed through the analysis of real time strain and internal force of the liner concrete, and the FBG monitoring data and safety assessment results indicate that the safety factors of various liner sections all meet the code requirements, which verify the safety and stability of the tunnel liner structure. The FBG sensors based in situ monitoring technology can be well applied in the loess tunnel structure safety assessment.