1,629 research outputs found
Impact of Symmetric Vertical Sinusoid Alignments on Infrastructure Construction Costs: Optimizing Energy Consumption in Metropolitan Railway Lines Using Artificial Neural Networks
[EN] Minimizing energy consumption is a key issue from both an environmental and economic perspectives for railways systems; however, it is also important to reduce infrastructure construction costs. In the present work, an artificial neural network (ANN) was trained to estimate the energy consumption of a metropolitan railway line. This ANN was used to test hypothetical vertical alignments scenarios, proving that symmetric vertical sinusoid alignments (SVSA) can reduce energy consumption by up to 18.4% compared with a flat alignment. Finally, we analyzed the impact of SVSA application on infrastructure construction costs, considering different scenarios based on top-down excavation methods. When balancing reduction in energy consumption against infrastructure construction costs between SVSA and flat alignment, the extra construction costs due to SVSA have a return period of 25-300 years compared with a flat alignment, depending on the soil type and construction method used. Symmetric vertical sinusoid alignment layouts are thus suitable for scattered or soft soils, up to compacted intermediate geomaterials.This paper was realized thanks to the collaboration agreement signed between Ferrocarrils de la Generalitat Valenciana and Universitat Politecnica de Valencia, and funding obtained by the Spanish Ministry of Economy and Competitiveness through the project ''Strategies for the design and energy-efficient operation of railway and tramway infrastructure'' (Ref. TRA2011-26602).Pineda-Jaramillo, J.; Salvador Zuriaga, P.; Martínez Fernández, P.; Insa Franco, R. (2020). Impact of Symmetric Vertical Sinusoid Alignments on Infrastructure Construction Costs: Optimizing Energy Consumption in Metropolitan Railway Lines Using Artificial Neural Networks. Urban Rail Transit. 6(3):145-156. https://doi.org/10.1007/s40864-020-00130-714515663International Energy Agency (2018) Key world energy statistics. ParisGarcía Álvarez A (2010) High speed, energy consumption and emissions. Study and Research Group for Railway Energy and Emissions, MadridKim K, Chien S (2010) Optimal train operation for minimum energy consumption considering schedule adherence. In: TRB annual meeting compendium. Transportation Research Board, Washington, USAKosinski A, Schipper L, Deakin E (2011) Analysis of high-speed rail’s potential to reduce CO2 emissions from transportation in the United States. In: TRB annual meeting compendium. Transportation Research Board, Washington, USAEuropean Comission (2017) EU transport in figures-statistical Pocketbook 2017Douglas H, Roberts C, Hillmansen S, Schmid F (2015) An assessment of available measures to reduce traction energy use in railway networks. Energy Convers Manag 106:1149–1165Dundar S, Sahin I (2011) A genetic algorithm solution for train scheduling. In: TRB annual meeting compendium. Transportation Research Board, Washington, USALiu M, Haghani A, Toobaie S (2010) A genetic Algorithm-based column generation approach to passenger rail crew scheduling. In: TRB annual meeting compendium. Transportation Research Board, Washington, USASalvador P, García C, Pineda-Jaramillo JD, Insa R (2016) The use of driving simulators for enhancing train driver’s performance in terms of energy consumption. In: 12th conference on transportation engineering (CIT 2016), 7–9 June 2016, Valencia, SpainSicre C, Cucala P, Fernández-Cardador A, Lukaszewicz P (2012) Modeling and optimizing energy-efficient manual driving on high speed lines. IEEJ Trans Electr Electron Eng 7:633–640Brenna M, Foiadelli F, Longo M (2016) Application of genetic algorithms for driverless subway train energy optimization. Int J Veh Technol 2016:8073523. https://doi.org/10.1155/2016/8073523Fernández A, Fernández-Cardador A, Cucala P, Domínguez M, Gonsalves T (2015) Design of robust and energy-efficient ATO speed profiles of metropolitan lines considering train load variations and delays. IEEE Trans Intell Transp Syst 16:2061–2071Lukaszewicz P (2000) Driving techniques and strategies for freight trains. In: Allan J, Brebbia CA, Hill RJ, Sciutto G, Sone S (eds) Computers in railways VII. WIT Press, Southampton, pp 1065–1073Bai Y, Mao B, Zhou F, Ding Y, Dong C (2009) Energy-efficient driving strategy for freight trains based on power consumption analysis. J Transp Syst Eng Inf Technol 9(3):43–50Lukaszewicz P (2001) Energy consumption and running time for trains. Modelling of running resistance and driver behaviour based on full scale testing. Dissertation, KTH Royal Institute of TechnologySicre C, Cucala P, Fernández A, Jiménez J, Ribera I, Serrano A (2010) A method to optimise train energy consumption combining manual energy efficient driving and scheduling. WIT Trans Built Environ 114:549–560Bocharnikov YV, Tobias AM, Roberts C, Hillmansen S, Goodman CJ (2007) Optimal driving strategy for traction energy on DC suburban railways. IET Electr Power Appl 1(5):675–682Tian Z, Hillmansen S, Roberts C, Weston P, Zhao N, Chen L, Chen M (2015) Energy evaluation of the power network of a DC railway system with regenerating trains. IET Electr Syst Transp 6:1–9Domínguez M, Fernández A, Cucala P, Blanquer J (2010) Efficient design of automatic train operation speed profiles with on board energy storage devices. WIT Trans Built Environ 114:509–520Kim K, Chien SI (2010) Simulation-based analysis of train controls under various track alignments. J Transp Eng 136(11):937–948Pineda-Jaramillo JD, Salvador-Zuriaga P, Insa-Franco R (2017) Comparing energy consumption for rail transit routes through symmetric vertical sinusoid alignments (SVSA), and applying artificial neural networks. A case study of MetroValencia (Spain). DYNA 84(203):17–23Huang S, Sung H, Ma C (2015) Optimize energy of train simulation with track slope data. In: IEEE conference on intelligent transportation systems, 15–18 Sept 2015, Las Palmas, SpainLai X, Schonfeld P (2010) Optimizing rail transit alignment connecting several major stations. In: TRB annual meeting compendium. Transportation Research Board, Washington, USASamanta S, Jha MK (2011) Modeling a rail transit alignment considering different objectives. Transp Res A 45(1):31–45Kelly J, Knottenbelt W (2015) Neural NILM: deep neural networks applied to energy disaggregation. In: Proceedings of the 2nd ACM international conference on embedded systems for energy-efficient built environments, Seoul, South KoreaDatta D, Tassou SA, Marriott D (1997) Application of neural networks for the prediction of the energy consumption in a supermarket. In Proceedings of the international conference CLIMA 2000. Brussels, Belgium.Khosravani HR, Castilla MD, Berenguel M, Ruano AE, Ferreira PM (2016) A comparison of energy consumption prediction models based on neural networks of a bioclimatic building. Energies 9(1):57. https://doi.org/10.3390/en9010057Moon JW, Jung SK, Lee YO, Choi S (2015) Prediction performance of an artificial neural network model for the amount of cooling energy consumption in hotel rooms. Energies 8:8226–8243Abolfazli H, Asadzadeh SM, Nazari-Shirkouhi S, Asadzadeh SM, Rezaie K (2014) Forecasting rail transport petroleum consumption using an integrated model of autocorrelation functions—artificial neural network. Acta Polytech Hung 11(2):203–214Feng J, Li XM, Xie MQ, Gao LP (2016) A neural network model for calculating metro traction energy consumption. In: international conference on power, energy engineering and management (PEEM 2016), Bangkok, ThailandGattuso D, Restuccia A (2014) A tool for railway transport cost evaluation. Procedia Soc Behav Sci 111:549–558Flyvbjerg B, Bruzelius N, Van-Wee B (2008) Comparison of capital costs per route-kilometre in urban rail. J Transp Infrasruct Res 8(1):17–30Von-Brown JT (2011) A planning methodology for railway construction cost estimation in North America. Dissertation, Iowa State UniversityGarcía-Álvarez A (2010) Relationship between rail service operating direct costs and speed. Fundación de los Ferrocarriles Españoles, MadridOlsson NOE, Økland A, Halvorsen SB (2012) Consequences of differences in cost-benefit methodology in railway infrastructure appraisal—a comparison between selected countries. Transp Policy 22:29–35Treasury HM (2010) Infrastructure cost review. Infrastructure UK, LondonBernardos A, Paraskevopoulou C, Diederichs M (2013) Assessing and benchmarking the construction cost of tunnels. In: GéoMontréal, Montreal, CanadaMing-Guang L, Jin-Jian C, An-Jun X, Xiao-He X, Jian-Hua X (2014) Case study of innovative top-down construction method with channel-type excavation. J Construct Eng Manag 140(5):05014003. https://doi.org/10.1061/%28ASCE%29CO.1943-7862.0000828Fox Halcrow (2000) World bank urban transport strategy review: mass rapid transit in developing countries, Final report. World Bank, WashingtonBB&J Consult (2000) The world bank group urban transport strategy review: Implementation of rapid transit. Final report. World Bank, WashingtonPineda-Jaramillo JD, Insa R, Martínez P (2018) Modelling the energy consumption of trains applying neural networks. Proc Inst Mech Eng F J Rail Rapid Transit 232(3):816–823Pineda-Jaramillo JD (2017) Modelo de optimización del consumo energético en trenes mediante el diseño geométrico vertical sinusoidal y su impacto en el coste de la construcción de la infraestructura Dissertation, Polytechnical University of ValenciaKarlik B (2014) Machine learning algorithms for characterization of EMG signals. Int J Inf Electron Eng 4(3):189–194Bishop C (1995) Neural networks for pattern recognition. Clarendon Press, OxfordMcCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133Karlaftis M, Vlahogianni E (2011) Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transp Res C Emerg Technol 19(3):387–399Cantarella G, De Luca S (2003) Modeling transportation mode choice through artificial neural networks. In: 4th international symposium on uncertainty modeling and analysis. 21–24 Sept 2003, College Park, MD, USACelikoglu H (2006) Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling. Math Comput Model 44(7):640–658Zhao D, Shao C, Li J, Dong C, Liu Y (2010) Travel mode choice modeling based on improved probabilistic neural network. In: Traffic and transportation studies, 3–5 Aug 2010, Kunming, ChinaOmrani H, Charif O, Gerber P, Awasthi A, Trigano P (2013) Prediction of individual travel mode with evidential neural network model. In: TRB annual meeting compendium. Transportation Research Board, Washington, USAJha MK, Schonfeld P, Samanta S (2007) Optimizing rail transit routes with genetic algorithms and geographic information systems. J Urban Plann Dev 133(3):161–171Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117Yeh S (2003) Integrated analysis of vertical alignment and speed profiles for rail transit routes. Dissertation, University of MarylandMolines J (2011) Stability of crown walls of cube and cubipod armoured mound breakwaters. PIANC E-Mag 144:29–44CYPE Ingenieros SA (2019) Prices database. www.generadordeprecios.info . Accessed 3 March 2019Ministerio de Fomento. Gobierno de España (2011) Cuadro de precios de referencia de la dirección general de carreteras. MadridHydraulics of Wells Task Committee (2014) In: Ahmed N, Taylor S, Sheng Z (eds) Hydraulics of wells: design, construction, testing and maintenance of water well systems. American Society of Civil Engineers, Resto
Predicting the traction power of metropolitan railway lines using different machine learning models
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Rail Transportation on 2021, available online: http://www.tandfonline.com/10.1080/23248378.2020.1829513[EN] Railways are an efficient transport mean with lower energy consumption and emissions in comparison to other transport means for freight and passengers, and yet there is a growing need to increase their efficiency. To achieve this, it is needed to accurately predict their energy consumption, a task which is traditionally carried out using deterministic models which rely on data measured through money- and time-consuming methods. Using four basic (and cheap to measure) features (train speed, acceleration, track slope and radius of curvature) from MetroValencia (Spain), we predicted the traction power using different machine learning models, obtaining that a random forest model outperforms other approaches in such task. The results show the possibility of using basic features to predict the traction power in a metropolitan railway line, and the chance of using this model as a tool to assess different strategies in order to increase the energy efficiency in these lines.This work was supported by the Ministerio de Economia y Competitividad [TRA2011-26602].Pineda-Jaramillo, J.; Martínez Fernández, P.; Villalba Sanchis, I.; Salvador Zuriaga, P.; Insa Franco, R. (2021). Predicting the traction power of metropolitan railway lines using different machine learning models. International Journal of Rail Transportation. 9(5):461-478. https://doi.org/10.1080/23248378.2020.1829513S4614789
implications for first line treatment recommendations
Introduction: Treatment for All recommendations have allowed access to antiretroviral (ARV) treatment for an increasing number of patients. This minimizes the transmission of infection but can potentiate the risk of transmitted (TDR) and acquired drug resistance (ADR). Objective: To study the trends of TDR and ADR in patients followed up in Portuguese hospitals between 2001 and 2017. Methods: In total, 11,911 patients of the Portuguese REGA database were included. TDR was defined as the presence of one or more surveillance drug resistance mutation according to the WHO surveillance list. Genotypic resistance to ARV was evaluated with Stanford HIVdb v7.0. Patterns of TDR, ADR and the prevalence of mutations over time were analyzed using logistic regression. Results and Discussion: The prevalence of TDR increased from 7.9% in 2003 to 13.1% in 2017 (p < 0.001). This was due to a significant increase in both resistance to nucleotide reverse transcriptase inhibitors (NRTIs) and non-nucleotide reverse transcriptase inhibitors (NNRTIs), from 5.6% to 6.7% (p = 0.002) and 2.9% to 8.9% (p < 0.001), respectively. TDR was associated with infection with subtype B, and with lower viral load levels (p < 0.05). The prevalence of ADR declined from 86.6% in 2001 to 51.0% in 2017 (p < 0.001), caused by decreasing drug resistance to all antiretroviral (ARV) classes (p < 0.001). Conclusions: While ADR has been decreasing since 2001, TDR has been increasing, reaching a value of 13.1% by the end of 2017. It is urgently necessary to develop public health programs to monitor the levels and patterns of TDR in newly diagnosed patients.publishersversionpublishe
Molecular epidemiology of hiv-1 infected migrants followed up in Portugal: Trends between 2001-2017
Migration is associated with HIV-1 vulnerability. Objectives: To identify long-term trends in HIV-1 molecular epidemiology and antiretroviral drug resistance (ARV) among migrants followed up in Portugal Methods: 5177 patients were included between 2001 and 2017. Rega, Scuel, Comet, and jPHMM algorithms were used for subtyping. Transmitted drug resistance (TDR) and Acquired drug resistance (ADR) were defined as the presence of surveillance drug resistance mutations (SDRMs) and as mutations of the IAS-USA 2015 algorithm, respectively. Statistical analyses were performed. Results: HIV-1 subtypes infecting migrants were consistent with the ones prevailing in their countries of origin. Over time, overall TDR significantly increased and specifically for Non-nucleoside reverse transcriptase inhibitor (NNRTIs) andNucleoside reverse transcriptase inhibitor (NRTIs). TDR was higher in patients from Mozambique. Country of origin Mozambique and subtype B were independently associated with TDR. Overall, ADR significantly decreased over time and specifically for NRTIs and Protease Inhibitors (PIs). Age, subtype B, and viral load were independently associated with ADR. Conclusions: HIV-1 molecular epidemiology in migrants suggests high levels of connectivity with their country of origin. The increasing levels of TDR in migrants could indicate an increase also in their countries of origin, where more efficient surveillance should occur. © 2020 by the authors
Sub-epidemics explain localized high prevalence of reduced susceptibility to Rilpivirine in treatment-naive HIV-1-infected patients: subtype and geographic compartmentalization of baseline resistance mutations
This Open Access article is distributed under the terms of the Creative Commons Attribution Noncommercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited."Objective: The latest nonnucleoside reverse transcriptase inhibitor (NNRTI) rilpivirine (RPV) is indicated for human immunodeficiency virus type-1 (HIV-1) patients initiating antiretroviral treatment, but the extent of genotypic RPV resistance in treatment-naive patients outside clinical trials is poorly defined.
Study Design: This retrospective observational study of clinical data from Belgium and Portugal evaluates genotypic information from HIV-1 drug-naive patients obtained for the purpose of drug resistance testing. Rilpivirine resistance-associated mutations (RPV-RAMs) were defined based on clinical trials, phenotypic studies, and expert-based resistance algorithms. Viral susceptibility to RPV alone and to the single-tablet regimen was estimated using expert-based resistance algorithms.
Results: In 4,631 HIV-1 treatment-naive patients infected with diverse HIV-1 subtypes, major RPV-RAMs were detected in 4.6%, while complete viral susceptibility to RPV was estimated in 95% of patients. Subtype C- and F1-infected patients displayed the highest levels of reduced viral susceptibility at baseline, respectively 13.2% and 9.3%, mainly due to subtype- and geographic-dependent occurrence of RPV-RAMs E138A and A98G as natural polymorphisms. Strikingly, a founder effect in Portugal resulted in a 138A prevalence of 13.2% in local subtype C-infected treatment-naive patients. The presence of transmitted drug resistance did not impact our estimates.
Conclusion: RPV is the first HIV-1 inhibitor for which, in the absence of transmitted drug resistance, intermediate or high-level genotypic resistance can be detected in treatment-naive patients. The extent of RPV susceptibility in treatment-naive patients differs depending on the HIV-1 subtype and dynamics of local compartmentalized epidemics. The highest prevalence of reduced susceptibility was found to be 15.7% in Portuguese subtype C-infected treatment-naive patients. In this context, even in the absence of transmitted HIV-1 drug resistance (TDR), drug resistance testing at baseline should be considered extremely important before starting treatment with this NNRTI.
Oligodendrocytes contribute to motor neuron death in ALS via SOD1 dependent mechanism
Oligodendrocytes have recently been implicated in the pathophysiology of ALS. Here we show that, in vitro, mutant SOD1 mouse oligodendrocytes induce wild-type motor neuron hyperexcitability and death. Moreover, we efficiently derived human oligodendrocytes from a large number of controls, sporadic and familial ALS patients using two different reprogramming methods. All ALS oligodendrocyte lines induced motor neuron death through conditioned medium and in co-culture. Conditioned medium-mediated motor neuron death was associated with decreased lactate production and release, while toxicity in co-culture was lactate independent, demonstrating that motor neuron survival is not only mediated by soluble factors.
Remarkably, human SOD1 shRNA treatment resulted in motor neuron rescue in both mouse and human cultures when knockdown was achieved in progenitor cells, while it was ineffective in differentiated oligodendrocytes.
Early SOD1 knockdown, in fact, rescued lactate impairment and cell toxicity in all lines tested with exclusion of samples carrying C9orf72 repeat expansions. These did not respond to SOD1 knockdown nor showed lactate release impairment.
Our data indicate that SOD1 is directly or indirectly involved in ALS oligodendrocyte pathology and suggest that in this cell type some damage might be irreversible. In addition, we demonstrate that C9ORF72 patients represent an independent patient group that might not respond to the same treatment
Tomato POLLEN DEFICIENT 2 encodes a G-Type lectin receptor kinase required for viable pollen grain formation
Pollen development is a crucial biological process indispensable for seed set in flowering plants and for successful crop breeding. However, little is known about the molecular mechanisms regulating pollen development in crop species. This study reports a novel male-sterile tomato mutant, pollen deficient 2 (pod2), characterized by the production of non-viable pollen grains and resulting in the development of small parthenocarpic fruits. A combined strategy of mapping-by-sequencing and RNA interference-mediated gene silencing was used to prove that the pod2 phenotype is caused by the loss of Solanum lycopersicum G-Type lectin receptor kinase II.9 (SlG-LecRK-II.9) activity. In situ hybridization of floral buds showed that POD2/SlG-LecRK-II.9 is specifically expressed in tapetal cells and microspores at the late tetrad stage. Accordingly, abnormalities in meiosis and tapetum programmed cell death in pod2 occurred during microsporogenesis, resulting in the formation of four dysfunctional microspores leading to an aberrant microgametogenesis process. RNA-seq analyses supported the existence of alterations at the final stage of microsporogenesis, since we found tomato deregulated genes whose counterparts in Arabidopsis are essential for the normal progression of male meiosis and cytokinesis. Collectively, our results revealed the essential role of POD2/SlG-LecRK-II.9 in regulating tomato pollen development.This work was supported by research grants PID2019-110833RB-C31, PID2019-110833RB-C32, and PID2020-113324GB-100 funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033), and the Research and Innovation Programme of the European Union Horizon 2020 (BRESOV Project, ID 774244). A PhD fellowship to MGA was funded by the FPU Programme of the Spanish Ministry of Education and Culture (ref. AP2010-4528). RLe was supported by a Junta de Andalucía and FEDER research contract (DOC_01129)
Predicción de las características de la canal en ovejas Pelibuey de desecho por medio de ultrasonido
The objective of present study was to predict the carcass characteristics of 28 discarded Pelibuey ewes (41.01 ± 8.43 kg) using ultrasonography. The ultrasonic measurements of fat thickness (FT), area, (LDA), depth (DLD) and width (WLD) of the Longissimus dorsi, between the 12th and 13th thoracic vertebra and between the 3rd and 4th lumbar vertebra, were performed 24 h before slaughter. At the slaughter, hot carcass, internal organs and internal fat were weighed. The carcasses were divided in two half, refrigerated (1 °C; 24 h) and the chilled carcass were weighed. Then were dissected and weighed in the main tissues. With the data it was calculated the correlation coefficients between the variables and their relationships were estimated using regression models. It was observed that the ultrasonic measurements of thoracic and lumbar backfat thickness had a positive r2 that ranged from 0.51 to 0.66 (P<0.001) for prediction of the carcass weights; and an r2 from 0.44 to 0.57 (P<0.001) to predict the carcass muscle quantity. It is possible to use the measurements of ultrasound as a tool for the evaluation of carcass characteristics in discarded Pelibuey ewes and it is possible to predict the carcass weights and edible tissues.El objetivo fue predecir mediante ultrasonografía las características de la canal de 28 ovejas Pelibuey (41.01 ± 8.43 kg) de desecho alimentadas en sistema intensivo, clínicamente sanas, no gestantes y no lactantes. Las mediciones ultrasónicas de espesor de grasa dorsal (EGD), área, (ALD), profundidad (PLD) y ancho (ALD) del músculo Longissimus dorsi, entre la 12.a y 13.a vertebra torácica y entre la 3.a y 4.a vértebra lumbar, se realizaron 24 h antes del sacrificio. Al sacrificio se pesó la canal caliente, así como los órganos internos y la grasa interna. Las canales se dividieron a la mitad, se refrigeraron (1°C; 24 h) y se pesaron en canal frío. Luego fueron diseccionadas y pesadas en sus principales componentes. Con los datos se calcularon los coeficientes de correlación entre variables y las relaciones se estimaron mediante modelos de regresión. Se observó que las mediciones ultrasónicas de grasa dorsal, torácica y lumbar tuvieron un r2 positiva de entre 0.51 y 0.66 (P<0.001) en la predicción de los pesos de la canal; y un r2 de entre 0.44 a 0.57 (P<0.001) para predecir el tejido muscular en la canal. Es posible utilizar las mediciones de ultrasonido como una herramienta para la evaluación de características de la canal de ovejas Pelibuey de desecho, y es posible predecir el peso de la canal y los tejidos comestibles
- …