•We propose a survey of soft computing techniques applied to financial market.•We surveyed several primary studies proposed in the literature.•A framework for building an intelligent trading system ...was proposed.•Future directions of this research field are discussed.
Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field.
Increased energy demand has led to plans for building many new dams in the western Amazon, mostly in the Andean region. Historical data and mechanistic scenarios are used to examine potential impacts ...above and below six of the largest dams planned for the region, including reductions in downstream sediment and nutrient supplies, changes in downstream flood pulse, changes in upstream and downstream fish yields, reservoir siltation, greenhouse gas emissions and mercury contamination. Together, these six dams are predicted to reduce the supply of sediments, phosphorus and nitrogen from the Andean region by 69, 67 and 57% and to the entire Amazon basin by 64, 51 and 23%, respectively. These large reductions in sediment and nutrient supplies will have major impacts on channel geomorphology, floodplain fertility and aquatic productivity. These effects will be greatest near the dams and extend to the lowland floodplains. Attenuation of the downstream flood pulse is expected to alter the survival, phenology and growth of floodplain vegetation and reduce fish yields below the dams. Reservoir filling times due to siltation are predicted to vary from 106-6240 years, affecting the storage performance of some dams. Total CO2 equivalent carbon emission from 4 Andean dams was expected to average 10 Tg y-1 during the first 30 years of operation, resulting in a MegaWatt weighted Carbon Emission Factor of 0.139 tons C MWhr-1. Mercury contamination in fish and local human populations is expected to increase both above and below the dams creating significant health risks. Reservoir fish yields will compensate some downstream losses, but increased mercury contamination could offset these benefits.
Climate change and its effects on the hydrologic regime of the Amazon basin can impact biogeochemical processes, transportation, flood vulnerability, fisheries and hydropower generation. We examined ...projections of climate change on discharge and inundation extent in the Amazon basin using the regional hydrological model MGB-IPH with 1-dimensional river hydraulic and water storage simulation in floodplains. Future projections (2070–2099) were obtained from five GCMs from IPCC’s Fifth Assessment Report CMIP5. Climate projections have uncertainty and results from different climate models did not agree in total Amazon flooded area or discharge anomalies along the main stem river. Overall, model runs agree better with wetter (drier) conditions over western (eastern) Amazon. Results indicate that increased mean and maximum river discharge for large rivers draining the Andes in the northwest contributes to increased mean and maximum discharge and inundation extent over Peruvian floodplains and Solimões River (annual mean-max: +9 % - +18.3 %) in western Amazonia. Decreased river discharges (mostly dry season) are projected for eastern basins, and decreased inundation extent at low water (annual min) in the central (−15.9 %) and lower Amazon (−4.4 %).
Recent advances in deep learning methods have redefined the state-of-the-art for many medical imaging applications, surpassing previous approaches and sometimes even competing with human judgment in ...several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabeled data.
•Deep Learning models suffer from poor generalization when applied to other centers.•Unsupervised domain adaptation can mitigate this issue.•Here we show that self-ensembling technique shows better performance even with small amount of training data.•Ablation study demonstrates that unlabeled data provides significant improvements.
Abstract
Objective
The purpose of this article was to summarize the available evidence from systematic reviews on telerehabilitation in physical therapy.
Methods
We searched Medline/PubMed, EMBASE, ...and Cochrane Library databases. In addition, the records in PROSPERO and Epistemonikos and PEDro were consulted. Systematic reviews of different conditions, populations, and contexts—where the intervention to be evaluated is telerehabilitation by physical therapy—were included. The outcomes were clinical effectiveness depending on specific condition, functionality, quality of life, satisfaction, adherence, and safety. Data extraction and risk of bias assessment were carried out by a reviewer with non-independent verification by a second reviewer. The findings are reported qualitatively in the tables and figures.
Results
Fifty-three systematic reviews were included, of which 17 were assessed as having low risk of bias. Fifteen reviews were on cardiorespiratory rehabilitation, 14 on musculoskeletal conditions, and 13 on neurorehabilitation. The other 11 reviews addressed other types of conditions and rehabilitation. Thirteen reviews evaluated with low risk of bias showed results in favor of telerehabilitation versus in-person rehabilitation or no rehabilitation, while 17 reported no differences between the groups. Thirty-five reviews with unclear or high risk of bias showed mixed results.
Conclusions
Despite the contradictory results, telerehabilitation in physical therapy could be comparable with in-person rehabilitation or better than no rehabilitation for conditions such as osteoarthritis, low-back pain, hip and knee replacement, and multiple sclerosis and also in the context of cardiac and pulmonary rehabilitation. It is imperative to conduct better quality clinical trials and systematic reviews.
Impact
Providing the best available evidence on the effectiveness of telerehabilitation to professionals, mainly physical therapists, will impact the decision-making process and therefore yield better clinical outcomes for patients, both in these times of the COVID-19 pandemic and in the future. The identification of research gaps will also contribute to the generation of relevant and novel research questions.
Aims Struvite (MgNH₄PO₄.6H₂O) recovered from wastewater can be used as fertilizer. The agronomic effectiveness of struvite has mostly been evaluated using ground fertilizer mixed through soil. ...However, fertilizers are most commonly applied in granular form in the field. In this study, we assessed the dissolution and effectiveness of different struvites when applied in granular or powdered form. Methods Phosphorus (P) diffusion in soil, determined using a visualization technique and chemical analyses, and P uptake by 6-week old wheat was compared for soluble fertilizer (monoammonium phosphate, MAP), a commercial struvite and three synthesized struvites with different excess MgO, in both granular and ground form. Results Ground struvite mixed through soil quickly dissolved and its agronomic effectiveness was similar to that of MAP. For pure granular struvite, the granule dissolution rate ranged from circa 0.03 mg d⁻¹ in alkaline soil to 0.43 mg d⁻¹ in acidic soil. Excess base in the struvite fertilizer reduced its dissolution rate. The P uptake by wheat followed the order MAP >> struvite ≥ control (no P), with no significant difference between the control and the struvite treatment in alkaline soil. Conclusions Both fertilizer characteristics (particle size, excess base) and soil pH strongly affect the dissolution rate of struvite and hence its agronomic effectiveness.
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One of the fundamental steps needed to design functional tissues and, ultimately organs is the ability to fabricate thick and densely populated tissue constructs with controlled ...vasculature and microenvironment. To date, bioprinting methods have been employed to manufacture tissue constructs with open vasculature in a square-lattice geometry, where the majority lacks the ability to be directly perfused. Moreover, it appears to be difficult to fabricate vascular tissue constructs targeting the stiffness of soft tissues such as the liver. Here we present a method for the fabrication of thick (e.g. 1 cm) and densely populated (e.g. 10 million cells·mL−1) tissue constructs with a three-dimensional (3D) four arm branch network and stiffness in the range of soft tissues (1–10 kPa), which can be directly perfused on a fluidic platform for long time periods (>14 days). Specifically, we co-print a 3D four-arm branch using water-soluble Poly(vinyl alcohol) (PVA) as main material and Poly(lactic acid) (PLA) as the support structure. The PLA support structure was selectively removed, and the water soluble PVA structure was used for creating a 3D vascular network within a customized extracellular matrix (ECM) targeting the stiffness of the liver and with encapsulated hepatocellular carcinoma (HepG2) cells. These constructs were directly perfused with medium inducing the proliferation of HepG2 cells and the formation of spheroids. The highest spheroid density was obtained with perfusion, but overall the tissue construct displayed two distinct zones, one of rapid proliferation and one with almost no cell division and high cell death. The created model, therefore, simulate gradients in tissues of necrotic regions in tumors. This versatile method could represent a fundamental step in the fabrication of large functional and complex tissues and finally organs.
Vascularization within hydrogels with mechanical properties in the range of soft tissues remains a challenge. To date, bioprinting have been employed to manufacture tissue constructs with open vasculature in a square-lattice geometry that are most of the time not perfused. This study shows the creation of densely populated tissue constructs with a 3D four arm branch network and stiffness in the range of soft tissues, which can be directly perfused. The cells encapsulated within the construct showed proliferation as a function of the vasculature distance, and the control of the micro-environment induced the encapsulated cells to aggregate in spheroids in specific positions. This method could be used for modeling tumors and for fabricating more complex and densely populated tissue constructs with translational potential.
The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and ...non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.
The diversity of the uterine bacterial composition in dairy cows is still poorly understood, although the emerging picture has shown to be increasingly complex. Understanding the complexity and ...ecology of microorganisms in the uterus of postpartum dairy cows is critical for developing strategies to block their action in reproductive disorders, such as metritis/endometritis. Here, we used PCR-Denaturing Gradient Gel Electrophoresis (DGGE) and DNA pyrosequencing to provide a comprehensive description of the uterine bacterial diversity and compare its succession in healthy, metritic and endometritic Holstein dairy cows at three intervals following calving. Samples were collected from 16 dairy cows housed in a dairy farm located in upstate New York. PCR-DGGE revealed a complex profile with extensive differences in the community structure. With few exceptions, clustering analysis grouped samples from cows presenting the same health status. Analysis of >65,000 high-quality 16S rRNA gene sequences showed that the uterine bacterial consortia, regardless of the health status, is mainly composed of members of the phyla Bacteroidetes, Fusobacteria, Firmicutes, Proteobacteria, and Tenericutes. In addition to these co-dominant phyla, sequences from Spirochaetes, Synergistetes, and Actinobacteria appear less frequently. It is possible that some sequences detected in the uterine fluid resulted from the presence of fecal or vaginal contaminants. Overall, the bacterial core community was different in uterine fluid of healthy cows, when compared to cows suffering from postpartum diseases, and the phylogenetic diversity in all the combined samples changed gradually over time. Particularly at the 34-36 days postpartum (DPP), the core community seemed to be specific for each health status. Our finding reveals that the uterine microbiota in dairy cows varies according with health status and DPP. Also, it adds further support to the hypothesis that there is uterine contamination with diverse bacterial groups following calving and emphasizes the role of unidentified microorganisms in this context.
In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques. The proposed RDB approach mitigates the effects of channel errors in ...wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques. The relay nodes are equipped with an amplify-and-forward (AF) protocol and the channel errors are modeled using an additive matrix perturbation, which results in degradation of the system performance. The proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB), considers a total relay transmit power constraint in the system and the goal of maximizing the output signal-to-interference-plus-noise ratio (SINR). We carry out a performance analysis of the proposed LRCC-RDB technique along with a computational complexity study. The proposed LRCC-RDB does not require any costly online optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.
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