Search

This paper reports on the first investigation of the characteristics of 3D structures formed in silicon carbide for the realisation of ultra-high performance nanoscale transistors, based on the FINFET topology. Capacitance–voltage characteristics show evidence of a second flatband voltage, located at a higher bias than that seen for purely planar devices. Two distinct peaks in the conductance–voltage characteristics are observed, centred at the flatband voltages, where the amplitude of the high voltage peak correlates with the sidewall area. This suggests that the chemical behaviour of the sidewalls differ from those of the (0001) wafer surface. The breakdown electric field of the dielectric film grown on the 3D structure is in excess of 3 MV cm−1. It is demonstrated that 3D transistors (FINFETs) do not utilise the gate voltage range where the abnormal characteristics exist and so this work reports for the first time the possibility of high performance nanoscale transistors in silicon carbide that can operate at high temperatures.

This paper reports the first benchmarking of smallholder tilapia farming practices in rural northern Zambia, measuring fish growth, feed utilization and water quality parameters, using a participatory action research approach. The effects of three different feeding regimes that are currently used in smallholder farming systems in northern Zambia on the growth of the native tilapia, Oreochromis macrochir, were tested with 15 farmers (40% female), each having three ponds (one pond per feed treatment per farmer = 45 in total), over eight months in 2016–2017. The three feed treatments included: (1) formulated feed only comprising maize and soybean; (2) goat manure only; and (3) formulated feed and manure together. Three hundred (300) O. macrochir mixed-sex fingerlings, 0.5 ± 0.1 g average weight, were stocked in each pond. Water quality (pH, dissolved oxygen, and water temperature and transparency) was measured monthly, as was fish growth in each pond by randomly sampling thirty (30) fish and taking individual weights and lengths. Results showed that the water quality parameters were broadly similar in the different feed treatments over the eight-month experiment period, except for the water transparency values that were higher in the feed only treatment when compared to the other two treatments at months four through eight. Means for survival rate, weight gain, specific growth rate, total amount of fish harvested, and yield were all higher for the feed and manure together treatment than the other two feed treatments at the end of the experiment. A linear mixed-effects regression analysis that controlled for several fixed effects and included pond as a random effect found that the feed and manure together treatment increased fish growth by 10.8 ± 3.7 g at month eight compared to the manure only treatment. The analysis also found a negative effect on fish growth (−9.4 ± 3.8 g) in ponds that used the feed only treatment. The pond effect accounted for 10.1% of the total variance in the data at month eight. Future research and development efforts in Zambia should examine more closely the factors that influence fish growth and that make up the pond effect to determine which innovations and better management practices could help smallholder farmers increase the productivity of their ponds.

This paper studies the impact of high-speed rail (HSR) on the consumption of urban residents from a spatial perspective. Using the panel data of 285 prefecture-level cities in China from 2003 to 2017, we examine the effects of HSR operations on consumption density. In order to solve the spatial correlation problem and better reflect the spillover effect, we use the spatial Durbin model to do the empirical analysis. The results show that, first, the opening of HSR significantly affects consumption density. This effect is brought about by the local and spatial effects of HSR. Secondly, HSR transmits the competition of consumption demand and resources, which promotes the intensification of consumption competition among cities. This competition is manifested as competition between cities with similar levels of economic development. In addition, the study finds that HSR has different effects on consumption density in cities in eastern, central, and western China due to different levels of urban economic development and inter-city relationships. Policy makers in Chinese cities need to adjust their consumption guidance strategies to promote local consumption according to the characteristics of different regions.

The existing literature has depicted market-oriented land governance by comparing it with other types of governance, such as government-led or state-intervention modes. However, unanswered questions remain. What are the nuances of market-oriented land governance in terms of institutional components and performance? Furthermore, how do context-specific conditions, including the versatile roles of the government, contribute to divergences in market-oriented land governance? To investigate these questions, a comparative analysis of two typical cases of urban land redevelopment (ULR) in China was performed based on a theoretical framework consisting of contextual factors, institutional components, and performance. The results show the following: 1) market-oriented governance of ULR is composed of price mechanisms with divergent competition and cooperation mechanisms induced by various biophysical conditions, actor characteristics, and transaction properties; 2) nuanced market-oriented land governance leads to ULR performance that differs in physical outcomes, distribution effects, and process efficiency; and 3) the local government plays an overarching role in fostering and sustaining market-oriented ULR. This paper sheds light on the emergence of different market-oriented land governance practices and the indispensable role of the local government using empirical evidence from China that is distinct from that found for the Western context.

Nuclear medicine imaging techniques have become fundamental modalities for metabolic evaluation of the human body. Single Photon Emission Computed Tomography (SPECT) is among the most used tools for functional image analysis. When combined with anatomical imaging such as Computed Tomography (CT), functional information can be analyzed with better performance since lesion localization can be substantially improved. In this paper, we present a transform-based fusion scheme applied to bone SPECT/CT image analysis. The method uses the discrete Hermite transform for image feature coding. It consists of a powerful mathematical tool that locally projects an input image onto the set of Hermite polynomials, providing low frequency and detail information. The image fusion is performed by using coefficients obtained from the transformation. Two different fusion strategies were designed based on coefficient content. Low frequency coefficients are fused through a sparse representation mechanism meanwhile coefficients with fine details are combined using local directional information and variance. Prior knowledge based on segmentation of bone tissues is included in the SR-based fusion rule. The final fused image is recovered by using the inverse transform from the resulting fused coefficients. The approach was evaluated with registered bone SPECT and CT images. It was also compared with other approaches of the state of art demonstrating its feasibility. We also present an evaluation of our method with brain tissues by using common images found in literature. Frequently used metrics were considered to quantitatively assess the performance of the proposed method.

With the increasing proportion of distributed generation (DG) in electrical power systems (EPSs), the lack of grid inertia and damping has posed great challenges to the stability and operation of EPSs. To address these problems, the concept of a virtual synchronous generator (VSG) has been proposed. As the physical basis of virtual inertia, the energy storage unit directly affects the performance and operating cost of the VSG. In this paper, the energy change of the energy storage unit during the frequency control process is calculated. Based on this calculation, the charge and discharge behavior of the energy storage unit can be inferred according to the VSG parameters and the frequency deviation data. Then, combined with the improved battery cycle life model, the operating cost of the energy storage unit when the VSG participates in frequency control can be obtained. Based on the data provided by the British electricity system operator (ESO), a case is studied, and the effects of the control parameters and battery rated capacity on the operating cost is analyzed. The analysis results can provide an economic reference for the parameter selection and energy storage unit capacity configuration of the VSG. Moreover, the simulation results validate the accuracy and efficiency of the proposed method.

This paper reports the results obtained in a techno-economic analysis of the Steam Methane Reforming (SMR) technology aided with solar heat, developed and demonstrated in the European FCH JU project CoMETHy: a compact membrane reformer heated with molten salt up to 550 °C allowed to simultaneously carry out methane steam reforming, water-gas-shift reaction and hydrogen separation. This reactor can be integrated with new generation Concentrating Solar Thermal (CST) systems to supply the process heat. Experimental validation of the technology has been successfully achieved in a pilot scale plant and the results recently published. In this paper, we introduce a fully-integrated scheme and operation strategies of a plant on the 1500 Nm3/h hydrogen production scale. Then, techno-economic analysis of this new solar-driven process is presented to evaluate its competitiveness. Considering a plant capacity of 1500 Nm3/h (pure hydrogen production) and today's costs for the methane feed and the CST technology, obtained Hydrogen Production Cost (HPC) are in the range of 2.8–3.3 €/kg for a “solar-hybrid” system with high capacity factor (8000 h/year operation) and 4.7 €/kg for a “solar-only” case, while HPC≅1.7 €/kg can be obtained with the conventional route under equivalent assumptions. However, a sensitivity analysis shows that the expected drop of the cost of the CST technology will bring the HPC around 2.4 €/kg for the “solar-hybrid” case and close to 3.4 €/kg for the “solar-only” case, thus making the cost of solar reforming closer to conventional SMR with CO2 capture and with wind/solar electrolysis in the future. In the “solar-hybrid” case total CO2 production can be reduced by 13–29% with 58–70% of produced CO2 recovered as pure stream (at 1.3 bar); in the “solar-only” case total CO2 production can be reduced by 52% and 100% of produced CO2 recovered as pure stream (at 1.3 bar). However, compared to the conventional route, CO2 avoidance costs are still relatively high (≥137 €/tonCO2) and process optimization measures required. Therefore, optimization measures have been outlined to increase the overall process efficiency and further reduce the HPC.

In the field of human-computer interaction, the detection, extraction and classification of the electroencephalogram (EEG) spectral and spatial features are crucial towards developing a practical and robust non-invasive EEG-based brain-computer interface. Recently, due to the popularity of deep end-to-end learning, the applicability of algorithms such as convolutional neural networks (CNN) has been explored to achieve the mentioned tasks. This paper presents an improved and compact CNN algorithm for motor imagery decoding based on the adaptation of SincNet, which was initially developed for speaker recognition task from the raw audio input. Such adaptation allows for a compact end-to-end neural network with state-of-the-art (SOTA) performances and enables network interpretability for neurophysiological validation in cortical rhythms and spatial analysis. In order to validate the performance of proposed algorithms, two datasets were used; the first is the publicly available BCI Competition IV dataset 2a, which was often used as a benchmark in validating motor imagery classification algorithms, and the second is a dataset consists of primary data initially collected to study the difference between motor imagery and mental-task associated motor imagery BCI and was used to test the plausibility of the proposed algorithm in highlighting the differences in terms of cortical rhythms. Competitive decoding performance was achieved in both datasets in comparisons with SOTA CNN models, albeit with the lowest number of trainable parameters. In addition, it was shown that the proposed architecture performs a cleaner band-pass, highlighting the necessary frequency bands that were crucial and neurophysiologically plausible in solving the classification tasks.

With the development of hand-held photographic devices, document images in unconstrained environments can be captured in high-speed and high-resolution. It will be more efficient to process the text information of multiple documents simultaneously. In this paper, we propose a multi-document detection approach. It can estimate the amount of documents and also detect their accurate locations via iteratively searching the four corners and their direction maps from individual document in the image. Even for slightly occluded documents, the proposed method can infer the hidden corner positions. The model is designed to jointly learn the corner categories, locations and their directions in attentional regions via two branches of the same sequential prediction process. The association score is calculated based on the them between two corner connections. The graph theory, considering corners as nodes and association scores as edges, is applied to get the quadrangle for each document in image. For evaluation, we collect a Multi-Doc data set which contains 2,200 document images in various natural scenes. We show that the baseline model trained on this collected data and bench-mark SmartDoc 2015 can detect both single and multiple documents accurately and effectively.

Infectious keratitis is the most common entities of corneal diseases, in which pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues. Infectious keratitis is a medical emergency, for which a rapid and accurate diagnosis is needed for speedy initiation of prompt and precise treatment to halt the disease progress and to limit the extent of corneal damage; otherwise it may develop sight-threatening and even eye-globe-threatening condition. In this paper, we propose a sequential-level deep learning model to effectively discriminate the distinction and subtlety of infectious corneal disease via the classification of clinical images. In this approach, we devise an appropriate mechanism to preserve the spatial structures of clinical images and disentangle the informative features for clinical image classification of infectious keratitis. In competition with 421 ophthalmologists, the performance of the proposed sequential-level deep model achieved 80.00% diagnostic accuracy, far better than the 49.27% diagnostic accuracy achieved by ophthalmologists over 120 test images., Comment: Accepted by Engineering

In this paper, we utilized the ternary Mo(1-x)WxS2 alloys to fabricate Mo0.5W0.5S2 film, which serve as a ultrashort pulse mode-locker in erbium-doped all fiber lasers (EDFL). The modulation depth, saturable intensity and unsaturated loss of the film SA were 12%, 4.5 MW/cm2 and 67.1% respectively. Stable self-mode-locking pulses could be achieved with a threshold pump power of 60 mW. With the increasing pump power, the pulse was centered at 1556.8 nm ~ 1558.1 nm with pulse width of 575 fs ~ 800 fs. The shortest pulse width, fundamental repetition rate and maximum pulse energy is 575 fs, 4.873 MHz, and 0.154 nJ, respectively. The signal to noise ratio (SNR) was measured to be about 53 dB. For the first time, we have experimentally demonstrated a mode-locked EDFL based on Mo0.5W0.5S2 SA. To the best of our knowledge, this was the first realization of femtosecond photonics based on Mo0.5W0.5S2 SAs. Our results suggest that Mo0.5W0.5S2 SA is a promising candidate for mode-locked ultrafast fiber laser in optoelectronic and optical communications applications.

Background The value of antitachycardia pacing (ATP) in the overall cohort of primary prevention patients who receive implantable cardioverter-defibrillators (ICDs) remains uncertain. ATP success reported in prior trials potentially included a large number of patients receiving unnecessary ATP for arrhythmias that may have self-terminated owing to the prematurity of the intervention. Although some patients derive benefit from initial ATP in terminating rapid ventricular arrhythmias and thereby preventing shocks, there are limited data allowing us to identify those patients a priori. Objective The purpose of APPRAISE ATP is to understand the role of ATP in primary prevention patients currently indicated for ICD therapy in a large prospective randomized controlled trial with modern programming parameters. Methods The study is a global, prospective, randomized, multicenter clinical trial conducted at up to 150 sites globally, enrolling approximately 2600 subjects The primary endpoint of the trial is time to first all-cause shock in a 2-arm study with an equivalent study design in which the incidence of all-cause shocks will be compared between primary prevention subjects programmed with shocks only vs subjects programmed to standard therapy (ATP and shock). Results An Electrogram and Device Interrogation Core Laboratory will review interrogation data to determine primary endpoints that occur in APPRAISE ATP. Their decisions are based on independent physician review of the data from device interrogation. Conclusion The ultimate purpose of the study is to aid clinicians in the selection of ICD technologies based on hard endpoint evidence across the spectrum of indications for primary prevention implantation.

The segmentation of images under biased conditions such as low contrast, high-intensity inhomogeneity, and noise is a challenge for any image segmentation model. The ideal image segmentation model must be capable of segmenting images with maximum accuracy and a minimum false-positive rate under biased conditions. In this paper, we propose a region-based active contour model (ACM), called global signed pressure and K-means clustering based on local correntropy with the exponential family (GSLCE), to address segmentation challenges under biased conditions. An adaptive weighted function is formulated based on the global and local image differences such that a single weighted function can drive both the global and local intensities. Further, the Riemannian steepest descent method is used for convergence of the proposed GSLCE energy function, and a Gaussian kernel is applied for spatial smoothing to obviate the computationally expensive level-set re-initialization. The experimental results show that, compared with state-of-the-art ACMs, the proposed GSLCE model obtained the best visual image segmentation results for synthetic and real images under biased conditions. Further, the qualitative and quantitative experimental results validate that the proposed model outperforms the state-of-the-art ACMs by yielding higher values of performance metrics. Moreover, the proposed GSLCE model requires substantially less processing time compared to the state-of-the-art ACMs.

Current as-built Building Information Model (BIM) deliverables generated following design and construction generally do not fulfill facility management needs. To provide total value to the facility owner, as-built models should be configured to provide needed critical operation and maintenance information, solve operational challenges, and improve safety and efficiency in various ways. In addition to having complete and accurate data, as-built BIMs should be configured to be systems-centric enabling facility owners to visualize model graphics from a systems perspective. This is critical during a maintenance emergency when the model user needs to identify components belonging to the building systems that are contributing to or impacted by the emergency and be able to make well-informed timely decisions. This paper contributes to the existing body of knowledge by defining and documenting requirements and procedures for configuring systems-centric as-built BIM models. A multi-level building systems classification is developed, and fourteen specific properties are defined. The implementation is discussed using a case study approach analyzing the mechanical and plumbing systems for a science and classroom building of a 4-year academic institution. By developing systems-centric models, facility staff can respond to emergency maintenance and analyze the impact of the event by isolating the elements of a specific system related to the emergency and display these elements explicitly.

The present paper concerns the numerical study of the behavior of exterior RC beam–column joints under seismic actions by using a macro-modelling approach. The beam-column joint element is modelled through the well-known “scissors model” in which the two main mechanisms governing the overall behavior of the RC joints are considered by means of two nonlinear rotational springs in series. In particular, the first spring represents the shear deformation of the joint panel, while the second one represents the “fixed-end-rotation” of the beam due to the debonding of the longitudinal steel rebars at the beam-joint interface. In the model, the two springs are defined by proper moment-rotation constitutive laws selected from the literature. In particular, for the spring simulating the shear behavior of the joint panel, various combinations of some literature proposals are taken into account with the purpose to identify the laws better simulating the overall response of the RC joint. The numerical simulations are performed by using the OpenSees software in which the ability of the considered constitutive laws to reproduce the monotonic behavior of RC beam-column joints is assessed by considering an experimental database of cyclic tests collected from the literature. Finally, preliminary cyclic analyses are also carried out by considering the backbone envelopes of the best constitutive models previously identified in the calibration process. It is worth highlighting that, with respect to other similar literature approaches − often validated on the basis of a few experimental tests performed by the same proposals’ developers − the model presented herein is characterized by the following twofold advantage: a) easy to implement but, at the same time, capable of simulating both monotonic and cyclic response of tests with appreciable accuracy and, b) more robust and powerful since it was calibrated by considering experimental test results made available from a variety of researchers on the same typology of RC joints investigated.

Documents on the social media platform Twitter are formulated in short and simple style, instead of being written extensively and elaborately. Further, the core message of a post is often encoded into characteristic phrases called hashtags. These hashtags illustrate the semantics of a post or tie it to a specific topic. In this paper, we propose multiple approaches of using hashtags and their surrounding texts to improve topic modeling of short texts. We use transfer learning by applying a pre-trained word embedding of hashtags to derive preliminary topics. These function as supervising information, or seed topics and are passed to Archetypal LDA (A-LDA), a recent variant of Latent Dirichlet Allocation. We demonstrate the effectiveness of our approach using a large corpus of posts exemplarily on Twitter. Our approaches improve the topic model's qualities in terms of various quantitative metrics. Moreover, the presented algorithms used to extract seed topics can be utilized as form of lightweight topic model by themselves. Hence, our approaches create additional analytical opportunities and can help to gain a more detailed understanding of what people are talking about on social media. By using big data in terms of millions of tweets for preprocessing and fine-tuning, we enable the classification algorithm to produce topics that are very coherent to the reader.

This paper presents a method for automatic segmentation of tympanic membranes (TMs) from video-otoscopic images based on deep fully convolutional neural network. Built upon the UNet architecture, the proposed EAR scheme is based on three main paradigms: EfficientNet for the encoder, Attention gate for the skip connection path, and Residual blocks for the decoder. The paper also introduces a new loss function term for the neural networks to perform segmentation tasks. Particularly, we propose to integrate EfficientNet-B4 into the encoder part of the UNet. In addition, the decoder part of the proposed network is constructed based on residual blocks from ResNet architecture. By this way, the proposed approach could take advantages of the EfficientNet and ResNet architectures such as preserving efficient reception field size for the model and avoiding overfitting problem. In addition, in the skip connection path, we employ the attention gate that can handle the varieties in shapes and sizes of interested objects, which are common issues in TM regions. Moreover, for network training, we proposed a new loss function term based on the shape distance between predicted and ground truth masks, and exploited the stochastic weight averaging to avoid being trapped in local minima. We evaluate the proposed approach on a TM dataset which includes 1012 otoscopic images from patients diagnosed with and without otitis media. Experimental results show that the proposed approach achieves high segmentation performance with the average Dice similarity coefficient of 0.929, without any pre- or post-processing steps, that outperforms other state-of-the-art methods.

This paper investigates the effect of brick dust on the water-releasing properties and dewatering of hydrated lime mortars. Dewatering in masonry occurs when freshly mixed mortar is applied to a dry substrate (e.g. fired bricks, stone) and can influence the physical and mechanical properties of the mortar and the mortar-masonry bond. Controlling these properties provides an important opportunity to optimise performance. This study evaluates the water transport kinetics of hydrated lime-brick dust mortars. The study demonstrates that the particle size distribution of the brick dust can effectively control transfer sorptivity and dewatering time. The measured dewatering times correlated to theoretically calculated values demonstrating the ability to accurately predict the dewatering characteristics of a mortar from a knowledge of the mix design. The use of waste brick dust in hydrated lime mortars provides an environmentally friendly alternative to waste disposal by routes such as landfill.

The quantum-resistant attribute is a new design criterion for cryptography algorithms in the era of quantum supremacy. Lattice-based cryptography is proved to be secure against quantum computing. CRYSTALS-Kyber is a lattice-based promising candidate in the post-quantum cryptography standardization process. This paper proposes a high-performance polynomial ring processor for the CRYSTALS-Kyber algorithm. The processor executes optimized polynomial ring arithmetic, which cuts off over 20%/50% on the times of modular multiplication/addition compared with the straightforward implementations. Besides, the forward and inverse Number Theoretic Transform (NTT) reuse the control logic with the help of an efficient configurable butterfly unit to minimize the area of the finite state machine. Further, the underlying dual-column sequential storage scheme breaks the bottleneck of memory accessing. To evaluate the performance, a fully pipelined architecture is implemented on a low-cost FPGA platform. Benefiting from these optimizations, the Kyber1024processor can perform NTT operation for a 4-dimensional polynomial vector in 17.1 μ s, and it achieves speedup by a factor of 2.1 compared with the state-of-the-art implementation.

The composition of worldwide energy consumption is undergoing tremendous changes due to the consumption of non-renewable fossil energy and emerging global warming issues. Renewable energy is now the focus of energy development to replace traditional fossil energy. Energy storage system (ESS) is playing a vital role in power system operations for smoothing the intermittency of renewable energy generation and enhancing the system stability. We divide ESS technologies into five categories, mainly covering their development history, performance characteristics, and advanced materials. Biomass storage and gas storage are also discussed, which are not considered in most reviews. After detailed research, the rapid development of each technology in recent years is introduced, and some representative research works are surveyed. We comprehensively summarized the advantages and disadvantages of various ESS technologies and presented several evaluation indicators for quantitative analysis. Hybrid ESS is also considered based on the complex market demand. Then, we investigate the applications of various ESS technologies as short-term, medium-term, and long-term storages in power systems, covering the power generation, transmission and distribution, and end-user. Finally, this paper reviews global developing trends, and identifies critical challenges and promising opportunities.

Despite being considered the safest means to transport oil and gas, pipelines are susceptible to degradation. Pipeline integrity management (PIM) is implemented to lower the risk of failure due to degradation and to maintain the functionality and safety of pipelines. PIM consists of a set of activities for assessing the operational conditions of pipelines. These activities generate data with high volume, velocity, and variety, due to the length of a pipeline and the number of sensors and tools used to assess the pipeline's condition. This paper provides a comprehensive review in relation to the applications of machine learning (ML) in managing and processing data generated from PIM activities. ML applications in the elements of a PIM process (e.g., inspection, monitoring, and maintenance) are investigated. The aspects of ML techniques (i.e., type of input, pre-processing, learning algorithm, output and evaluation metric) applied in each element of PIM are examined. Current research challenges and future research opportunities in the application of ML in PIM are also discussed.

Differential evolution is one of the most popular evolutionary algorithms for continuous optimization. In this paper, we introduce a new algorithm named the adaptive differential evolution with ensembling populations. In the proposed algorithm, two sets of mutation and crossover operators are utilized to generate offspring to better balance the exploitation and exploration abilities of the algorithm. Besides, an adaptive parameter control strategy is integrated to dynamically adjust the parameter setting of the algorithm so as to further improve the search efficacy. In the experimental studies, it is demonstrated that the proposed algorithm presents competitive performance on benchmark functions as well as on the real-world wireless sensor localization application, in terms of global search ability and search efficiency.

Introduction: This paper investigates how members of a culinary and hospitality arts program generate, share, and learn safety knowledge via social and identity mechanisms. Method: We conducted semi-structured interviews with 20 participants of varying roles and experience (i.e., students, culinary instructors, and restaurant chefs) in the culinary and hospitality arts program at a large polytechnic in western Canada. Results: The emergent themes from these interviews indicated that the circulation of safety knowledge relied on the interaction among individuals with various levels of experience, such that those who were more experienced in the culinary arts were able to share safety knowledge with novices, who had less experience. Comparing safety knowledge gleaned from within the school against that gleaned from within the industry highlighted differences between the construction of safety in the two contexts. Notably, many aspects of safety knowledge are not learned in school and those that are may not apply in the industry context. We found that safety knowledge was shared through informal means such as storytelling, a process that allowed members to come to a deep, collective understanding of what safety meant, which they often labeled “common sense.” Conclusion: We found that safety knowledge was a currency through which participants achieved legitimacy, generated through continual practical accomplishment of the work in interaction with others. Practical Applications: Our findings provide novel insights into how safety knowledge is shared, and we discuss the implications of these findings for classroom, work-based learning, and other forms of curricula.

In this paper a two-phase neuro-modal solution for seismic analysis of skeletal structures was developed. Seismic analyses are required to design resisting structures against potential ground motions. Such analyses are, however, computationally intense because of coupled systems of differential equations, time-dependent analyses, uncertainty of seismic loads, and large number of degrees of freedom in high-rise structures. Here, through integration of modal analysis with Long Short-Term Memory neural networks, a method was developed to model and solve dynamic equations of motion more efficiently. Specifically, the method allowed us to convert a time-dependent problem to a recurrent neural network with fixed architecture, functions, and parameters so that the required CPU time for analysis of the problem reduced from order of 10−2 s to the order of 10−5 s for a single degree-of-freedom system under a seismic load. The model was validated through comparison between model predictions and ground truth values obtained from simulated and real data. The correlation between predictions and target values was between 0.98626 and 1 for different loading conditions. This level of accuracy was equivalent to error values ranging from ~ 0 to 1.7% for predicted displacements of the structures under the seismic loads. The developed model can be used to tackle iterative procedures such as design optimizations, risk analysis, Monte Carlo simulations, etc. for large systems such as high-rise skeletal buildings in more efficient time. Also, the proposed platform can be extended to perform vibration and dynamic analyses for continuous systems, plates and shells, bridges, and offshore structures, and under loading conditions such as tornadic wind loads, moving Loads, wave and sea loads, etc.

This paper investigates the impacts of offshore and onshore outsourcing on China's upgrading in global value chains (GVCs). First, based on Antras and Chor (2018) and Wang et al., (2017), we build a model to theoretically structure the GVC networks and then calibrate China's positioning in GVCs. Next we incorporate both offshore and onshore outsourcing into one analysis framework to examine the effects of the two types of outsourcing on China's industrial upgrading. Then using its bilateral trade data and sectoral data from the WIOD (World Input-Output Database), we test empirically the impacts of offshore and onshore outsourcing on the positions of Chinese manufacturing and service sectors in GVCs in particular. We find that offshore outsourcing is a driving force behind China's upgrading along GVCs, while the effect of onshore outsourcing is insignificant. Additional empirical results also indicate that the headquarters economy and human capital strengthen the impacts of offshore outsourcing on China's GVC upgrading. Finally, based on the empirical findings, we derive some policy implications for Chinese government.

This paper presents the development of an experimental campaign to investigate the cyclic, out-of-plane response of reinforced concrete frames containing masonry infill walls, using non-contact optical techniques to measure contour strains and deformations. Bare RC frames, as well as fully infilled walls, both with and without window and door openings, were tested in the laboratory. From the analysis of the results, it was found that neither the infill walls nor the openings significantly affected the overall behaviour of the specimens. However, the infill walls suffered substantial damage within a storey drift ratio of 1.25−2.50%, which poses a risk to life and livelihoods. In addition, it was found that masonry walls containing openings, sustained further damage, especially those with eccentrically positioned openings, when compared with the fully infilled frames. Finally, in the case of masonry infills containing openings and when debonding of the infill from the surrounding frame occurred, parts of infill walls could fall out due to inertia and could influence the overall combined, in-plane behaviour of the specimen.

Background and objective An important task of the brain-computer interface (BCI) of motor imagery is to extract effective time-domain features, frequency-domain features or time-frequency domain features from the raw electroencephalogram (EEG) signals for classification of motor imagery. However, choosing an appropriate method to combine time domain and frequency domain features to improve the performance of motor imagery recognition is still a research hotspot. Methods In order to fully extract and utilize the time-domain and frequency-domain features of EEG in classification tasks, this paper proposed a novel dual-stream convolutional neural network (DCNN), which can use time domain signal and frequency domain signal as the inputs, and the extracted time-domain features and frequency-domain features are fused by linear weighting for classification training. Furthermore, the weight can be learned by the DCNN automatically. Results The experiments based on BCI competition II dataset III and BCI competition IV dataset 2a showed that the model proposed by this study has better performance than other conventional methods. The model used time-frequency signal as the inputs had better performance than the model only used time-domain signals or frequency-domain signals. The accuracy of classification was improved for each subject compared with the models only used one signals as the inputs. Conclusions Further analysis shown that the fusion weight of different subject is specifically, adjusting the weight coefficient automatically is helpful to improve the classification accuracy.

A discriminative pyramid (DP) network-based method is presented in this paper, which aims to perform semantic segmentation on tea geometrid in natural scene images. The method uses image flipping, translation, mirroring, random zooming, and other techniques for training sample augmentation and adopts local histogram equalization to reduce nonuniform light influence on segmentation. Moreover, this method constructs a DP network to improve segmentation accuracy. The DP network contains two sub networks: pyramid attention and border networks. The pyramid attention network can capture the global context information of targets at different scales, increase the receptive fields to focus on small targets, and solve the problems of large shape change, small size, and difficult detection of tea geometrids. The border network can increase the differences between tea geometrids and tea tree stalks, diseased leaves, and other types of backgrounds with similar appearances by extracting and supervising semantic boundaries to help the network learn additional discriminative features. Experiment results show that the proposed method has outstanding accuracy for semantic segmentation of tea geometrid in natural scene images.

Numerous estuaries worldwide have been modified in the past decades by human interventions. The shoreline and bathymetry in the Pearl River Estuary (PRE) have changed greatly over the past 40 years due to the influence of land reclamation and waterway dredging, which have resulted in the corresponding adjustment of its hydrodynamics. Utilizing the ROMS numerical model, this paper studies the hydrodynamic responses to changes in shoreline and bathymetry in the PRE from 1971 to 2012. The results show that, on one hand, during neap tide, the change in the shoreline makes the residual current in the West Channel (WC) increase by 0.10 (0.05) m/s at maximum in the surface (near bottom) layer. Therefore, the exchange flow increases by 9.5% and the longitudinal circulation strengthens. The surface isohalines move southward up to 18 km, but the bottom isohalines move northward ∼2 km in the WC and East Channel (EC), which is different from the previous conclusion that the seaward extension of coastlines inhibits saltwater intrusion. The decrease in salinity in the upper layer reduces the upper seaward salt transport, resulting in a larger net landward salt transport from 25.21 × 103 kg/s to 35.44 × 103 kg/s. During spring tide, the changes are relatively weaker, but the direction of salt transport changes to seaward and the net seaward transport also increases. Moreover, the change in shoreline reduces the water area and volume in the PRE by 21.3% and 15.6% respectively, which causes a reduction of 11.3% (17.1%) in tidal prism during spring (neap) tide. The wave celerity is enhanced (>23%) and the amplification of semidiurnal tide is strengthened (>20%) in the WC and West Shoal (WS). The reduction in tidal prism together with the strengthened reflection of tidal waves with a larger phase lag between elevation and velocity of M2 tidal component cause a significant decrease of 19.0% in tidal energy flux entering the PRE. However, the tidal range increases by ∼0.30 m (mainly due to the increase in M2 tidal amplitude) in the EC, which is induced by lower tidal energy dissipation there. On the other hand, during neap tide, the change in bathymetry greatly enhances the bottom landward residual current whose peak value increases by 80% in the WC, thereby increasing approximately 14 km of the intrusion distance of saline water, enhancing the exchange flow by 27.5% and strengthening the longitudinal circulation. The net landward salt transport increases to 38.39 × 103 kg/s. The results during spring tide are similar to those during neap tide but with smaller changes, and the net seaward salt transport decreases. Moreover, the wave celerity is slightly reduced (

Risk of both ischemic and hemorrhagic stroke is increased in the chronic kidney disease (CKD) population, particularly in end-stage kidney disease patients. Uremic factors that contribute to stroke risk include blood pressure variability, vascular calcification, build-up of vascular toxins, chronic inflammation, platelet dysfunction and increased brain microbleeds. This paper discusses the controversial evidence for stroke prevention strategies including blood pressure control, statins, antiplatelet agents, and anticoagulation in the CKD population. Only a few randomized clinical trials included patients with advanced CKD, thus evidence is derived mostly from observational cohorts and real-world data. Overall, targeting a lower systolic blood pressure below 120 mmHg and statin prescription do not appear to decrease stroke risk in CKD. Antiplatelet agents have not shown a clear benefit for secondary stroke prevention, but aspirin may reduce incident stroke in hypertensive CKD stage 3B-5 patients. Observational data suggests that the factor Xa inhibitor apixaban has a favorable profile over warfarin in dialysis patients with atrial fibrillation; apixaban being associated with lower stroke risk and fewer major bleeding events.

The aim of this paper is to identify a business strategy for high-speed rail (HSR), including market positioning and price strategy, in the Chinese express delivery market using a bilevel programming model. Three routes were selected for case studies. The results showed that parcels with a high value of time (VOT) are mostly distributed to self-owned airlines. Compared with highway and conventional rail transport, HSR is relatively competitive for parcels with high VOT in both long- and middle-distance transport and parcels with extremely high VOT in middle-distance transport. Highway transport is the dominant mode for parcels with extremely low VOT in both long-distance and middle-distance routes. The freight rate of HSR must be more than 30% lower than that of air to be more competitive. Additionally, a sensitivity analysis considering increased speed and relocation of parcel distribution centres was performed to identify factors that affect the competitiveness of HSR in the express delivery sector. The results showed that the competitiveness of HSR for parcels with high VOT is improved significantly in middle-distance routes when either measure is adopted. However, there is no remarkable change in the performance of HSR for long-distance parcel transport and parcels with low VOT.

COVID-19 pandemic caused a massive impact on healthcare, social life, and economies on a global scale. Apparently, technology has a vital role to enable ubiquitous and accessible digital health services in pandemic conditions as well as against “re-emergence” of COVID-19 disease in a post-pandemic era. Accordingly, 5G systems and 5G-enabled e-health solutions are paramount. This paper highlights methodologies to effectively utilize 5G for e-health use cases and its role to enable relevant digital services. It also provides a comprehensive discussion of the implementation issues, possible remedies and future research directions for 5G to alleviate the health challenges related to COVID-19. European Commission University College Dublin Academy of Finland Update citation details during checkdate report - AC

With image object detection techniques progressing, video object detection attracts a lot of attention more than ever before. However, the performance of detecting the object from a video suffers a lot as object occlusion can always lead to the object in the presence of appearance deterioration. To deal with this problem, the temporal correlation of video sequences is often used to reduce the effects of object occlusion in video object detection. In this paper, an optical-flow-feature fusion-based video object detection method is proposed with consideration of temporal coherence among video frames. To further reduce computational complexity, this paper also proposes a packet video processing method. Specifically, video frames are grouped first, and all frames in current group share the same optical flow feature map by feature fusion. Then the Target Image can be formed with object information enhanced by fusing the shared feature map with current frame. The proposed method gives rise to effective background information masking, so that the object detection network can focus more on the foreground object. This method effectively improves the object detection performance and the scene migration performance. Experimental results prove that the proposed method significantly improves the detection accuracy to 78.6% on ImageNet VID. In addition, VGG and ResNet are compared to further verify the effectiveness of the proposed method, and the results can be a persuasive evidence with the highest detection accuracy and acceptable time consumption.

In this paper, we propose a blind high-definition image watermarking scheme named multiscale-fusion dilated ResNet-based high-definition watermarking (MDResNet-HDWM), which is based on a key-point detection and deep learning framework. The proposed watermarking scheme embeds watermark in multiple non-overlapping regions, whose locations are secured with a private key referring to several dominant key points. To achieve scale-invariant watermark embedding, regions are first determined in a normalized image copy and then mapped back to its origin. The watermarks are embedded in the central part of the region in such a way that the watermarks will always be located inside the regions even if the image is geometrically transformed. The watermarks are embedded and extracted with MDResNet, which is trained with a curriculum learning strategy that makes it be robust to signal processing operations and geometric transforms. Experimental results demonstrate that the proposed MDResNet-HDWM achieves good performance and is robust to both common signal operations and geometric attacks.

Hydropower is one environmentally friendly option for meeting these energy demands. Because such technologies do not require a weir or a dam, they may be used with minimal environmental effect. The drag-based Savonius turbine is characterized by working at low-flow speeds which fits well the natural water streams. In spite of its simple and robust design, the Savonius turbine suffers from low efficiency and high static torque fluctuations. In view of this, CFD-based optimization is applied to a Savonius-type hydrokinetic turbine inspired by a couple of swimming Koi fish. The two main design parameters; overlap ratio and gap ratio are used in this paper to define the gap between the blades. The maximum power coefficient can be obtained by employing an evolution strategy optimization technique over the constructed metamodel. The results revealed that the optimal design for the bio-inspired Savonius hydrokinetic turbine corresponds to overlap and gap ratios of 0.2085 and 0.0057, respectively. The optimal configuration generates a maximum power coefficient of 0.2521, which is 17.6% higher than that for a conventional shape at the same tip speed ratio. The new bio-inspired design performs better than the conventional Savonius hydrokinetic turbine for the considered operating range.

In the present work, effect injection timing (IT) on the modified dual fuel (DF) engine performance with effective utilization of biodiesel and gaseous fuel combinations is reported. Biodiesel prepared Ceiba Pentandra oil called Ceiba Pentandra oil methyl ester (CPNTOME) and its B20 blend (CPNTOME B20) are used as pilot injected fuels while the biogas (raw and purified) is used as the inducted fuel in the modified DF engine. Hence, the present research focus on the study of DF engine performance fuelled with liquid and gaseous fuel combinations. Meanwhile, the effect of IT on modified DF engine performance is investigated. Brake thermal efficiency (BTE), carbon monoxide (CO), hydrocarbon (HC) and smoke emissions were found to be less besides higher emissions of NOx were observed. Combustion parameters such as ignition delay (ID), and peak pressure (PP) are analysed. The DF engine operated on renewable fuel combinations in DF mode can cover the way for partial substitution of fossil fuel along with reduction in greenhouse gas emissions. Advancing the IT improves the DF engine performance and reduces the smoke, CO and NOx emissions.

Background: Intracranial atherosclerosis is a common cause of stroke with a high recurrence rate. Haemodynamically significant lesions are associated with a particularly high risk of recurrence. Computational fluid dynamics (CFD) is a tool that has been investigated to identify haemodynamically significant lesions. CFD in the intracranial vasculature benefits from the precedent set by cardiology, where CFD is an established clinical tool. This precedent is particularly important in CFD as models are very heterogenous. There are many decisions-points in the model-creation process, usually involving a trade-off between computational expense and accuracy. Objectives: This study aimed to review published CFD models in intracranial atherosclerosis and compare them to those used in cardiology. Methods: A systematic search for all published computational fluid dynamics models applied to intracranial atherosclerosis was performed. Each study was analysed as regards to the different steps in creating a fluid dynamics model and findings were compared with established cardiology CFD models. Results and conclusion: 38 papers were screened and 12 were included in the final analysis. There were important differences between coronary and intracranial atherosclerosis models in the following areas: area of interest segmented, use of transient models vs steady-state models, boundary conditions, methods for solving the fluid dynamics equations and validation. These differences may be high-yield areas to explore for future research.

Arguably the most influential climatic load affecting the structural design of gulf coast refining and petrochemical facilities is wind. Despite this fact, little has been done to customize wind design for these unique facilities. This paper proposes a probabilistic model to predict the magnitude of shielding that is provided by the vast network of pipe rack structures that encircle these densely arranged facilities. The probabilistic model is built from data obtained through a broad sampling of pipe rack configurations simulated with hurricane force winds using computational fluid dynamics (CFD). The CFD simulation approach is validated against published wind tunnel data that relate the proposed methodology to code recognized procedures (i.e. wind load reductions corroborated by wind tunnel testing). A reliability analysis is then carried out to re-calibrate the LRFD design equation to include the proposed shielding effects while maintaining the code specified level of reliability.

Renewable energy is widely applied in the world. The key problem of wind energy application is to improve the reliability of wind turbine and reduce its downtime. Supervisory control and data acquisition (SCADA) has created reliable and cost-effective status data for health conditioning of wind turbine operation. Effectively extracting useful information from SCADA is critical to the reliability of applied wind energy. In this paper, a new method is proposed to extract multidirectional spatio-temporal features of SCADA data for wind turbine condition monitoring based on convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with attention mechanism. Firstly, the quartile method is developed to distribute the SCADA data for cleaning and deleting the abnormal data so as to improve the data validity. Then, the input variables are selected through Pearson correlation coefficient, and they are transformed into high-dimensional features by using CNN. These features are input into BiGRU network through attention mechanism layer. Attention mechanism strengthens the impact of important information to improve learning accuracy. In the end, it is verified that the proposed method can detect early abnormal operation and identify failed components of wind turbine by real case analysis from wind farm. The proposed method presents better feasibility of practical wind energy application, which can promote the application of clean energy.

Fraud in food commodities is very common around the world and today occurs at different places of the supply chain: the demand for rapid and confirmatory analytical methods has increased in recent years. This paper intends to present a comparison between analytical chromatographic approaches, coupled with mass spectrometry (MS), solutions to detect food fraud suggested in scientific literature and practices actually implemented by food companies, taking into consideration four different types of matrixes/food chains (wheat/cereals, fruits, nuts and nut products, eggs & egg products). A selection of the more relevant papers published to date is presented and compared with the information about industrial behaviour directly collected through informal interviews and official open-days & workshops; executed during FoodIntegrity European Funded EU-FP7 Project. The academic and food industry worlds are not aligned thus far and should interact better, exactly revolving around the flexibility and performances of chromatography-MS solutions.

In current researches of performance evaluation with evidential reasoning (ER) rule, the referential points of evidence are generally set as a quantitative value or an interval value. However, these two types cannot handle fuzzy uncertainty and probabilistic uncertainty simultaneously, which decreases the evaluation accuracy. In this paper, a new ER rule-based performance evaluation model for aerospace relay with distributed referential points is proposed. Firstly, in order to effectively embed the expert knowledge and capture the performance information, the Gaussian distribution is used to describe the referential points. Secondly, a complete performance evaluation model is constituted by calculating and optimizing the evidence parameters. Finally, the sensitivity analysis is performed on the model to locate the weak component of aerospace relay. A case study of the JRC-7 M aerospace relay is carried out to validate the effectiveness of the proposed model.

Altres ajuts: acords transformatius de la UAB This work has been supported by the Barcelona Council (20S01909-001). Jordi Perdiguero thanks the finantial support of Secretaria d'Universitats i Recerca del departament d'Empresa i Coneixement de la Generalitat de Catalunya (2020 PANDE 00058). Altres ajuts: Barcelona Council/20S01909-001 Altres ajuts: Generalitat de Catalunya/2020 PANDE 00058 Many Spanish cities' air contains excessive levels of pollutants caused by traffic. These pollutants are associated with high concentrations of vehicles in urban and surrounding areas, such as Madrid and Barcelona. According to the World Health Organization (WHO), 96.8% of Spain's population breathe polluted air. This paper provides empirical evidence on the effect of public transport strikes on Barcelona's air quality through an econometric analysis. The period analyzed included 147 days of some kind of public transport strike: bus (57), metro (21), trains (71), and tram (4); and also four general strikes. The estimates show that public transport strikes increases the concentration level of SO2, CO, PM, and NOX throughout the city, especially in the case of metro and RENFE trains. These results also allow us to understand how commuters substitute transport modes and to propose specific policies to increase public transport use.

Large greenhouse gas reductions are possible with a fully decarbonised grid and electric land transport. Additional electric load could pose a significant challenge to a grid with high levels of variable and non-dispatchable renewable energy sources. This scenario is not well-examined, nor is the use of pumped hydro energy storage for low-cost energy balancing. In this paper, we investigate the electrification of land transport within a photovoltaics and wind dominated 100% renewable electricity system. Only technologies that are deployed at scale and widely available globally are considered, namely photovoltaics, wind, battery electric vehicles, high voltage transmission, and pumped hydro. As a case study we present an hourly energy balance analysis of the Australian National Electricity Market with 100% renewables and 100% uptake of electric vehicles for land transport. The cost of the system is determined by occasional periods (days-weeks) of low renewable generation, and therefore only weakly dependent on the charging regime. The 40% increase in electricity demand due to electric land transport can be incorporated with a 4%–8% increase in the levelized cost of electricity. An exception occurs if most passenger vehicle charging occurs during the evening peak period, in which case the average price increases by about 18%.

Semantic similarity measures play an important role in many natural language processing and information retrieval activities. It is highly challenging to measure semantic similarity with higher accuracy. A notable branch of semantic similarity evaluation based on information content (IC) is popular in this aspect. Intrinsic information content (IIC) models are another wing of IC based evaluation. Both IC based and IIC based approaches majorly handled similarity evaluation of nouns. Research related to semantic similarity assessment of verb pairs are rarely discussed. To bridge this gap, this work examines various IC based, IIC based approaches on verb pairs. A detailed discussion of the existing measures and their drawbacks are mentioned in this work. Strategies based on information content, length and depth of the concepts are discussed and tested on benchmark datasets. Existing intrinsic information content models are enhanced by addressing various issues like (a) dealing concepts with no path in WordNet and (b) handling the synonym sets of verb concepts. Measures based on path length, intrinsic information content, combined strategies and non-linear strategies for verb pairs are thoroughly inspected. This paper also presents novel strategies to understand novel aspects that are not addressed before. The strategies are experimented by generating the synonym sets of required parts-of-speech which proved very effective in improving the correlation with human judgment. Results on benchmark datasets specify that the proposed approaches for verb similarity will be a guiding factor for understanding the natural language processing tasks.

This paper shows how a design of an indirectly heated rotary kiln for the pyrolysis of waste tyres can be developed based on numerical simulation results. The design is up from a laboratory scale tyre particle mass flow to an industrial scale tyre particle mass flow, preserving the heat- and mass-transfer characteristics. This ensures that findings from the laboratory scale mass flow kiln, which is relatively cheap to build and to operate, are meaningful and representative for the industrial scale mass flow kiln. Due to high cost the scaled-up industrial scale design cannot be tested in advance. The development process based on numerical simulation minimizes the risk that scaled up designs do not reach desired performance values. A one-dimensional simulation model is described, enabling simulation of the temperature and mass flow profiles of the particle bed and the gas phase along the axis of the indirectly heated rotary kiln. The simulation considers the bed height development and the influence of fouling. The model is successfully validated against experimental measurements. It is then used to develop several rotary kiln designs covering a tyre particle mass flow range of two orders of magnitude, from laboratory up to industrial scale, fulfilling a variety of pre-formulated requirements. It is found that the kiln designs increase in slenderness as the bed particle mass flow increases, while the inclination and rotational speed can be kept constant.

Mobility-as-a-service (MaaS) is a concept that has gained popularity and interest in the last few years. A significant determinant of the successful integration of a MaaS scheme is demand from passengers and potential users. The current research explores demand and willingness-to-pay for MaaS, focusing on different MaaS plans and offered services. An online survey in Greater Manchester, United Kingdom is utilized in order to explore and model such preferences and choices. Data from the survey includes socio-demographic data, current travel patterns, and a menu-based, stated preference experiment where the users were able to design their ideal MaaS plan. We model user choices by estimating a multinomial logit model, calculating also willingness-to-pay (WTP) for different levels of offered services in the MaaS package (public transport, car-sharing, bike-sharing and taxi). Results of the paper shed light into demand and quantitative analysis regarding MaaS, which the current literature lacks and outlines a price range for mobility services which is close to relevant literature in other locations for public transport and car-sharing and provides WTP estimates for taxi and bike-sharing. Results from this paper are useful both to potential MaaS operators and mobility service providers who would consider joining such initiatives; as we highlight user preferences and willingness-to-pay and to policymakers; outlining the overall preference for MaaS and the effect of socio-demographic and travel habits on MaaS plan choices.

Remotely Operated (underwater) Vehicles (ROV) have a wide range of maritime applications, including repair and maintenance. Quantifying hydrodynamic loads is important for the design and control of these ROVs. A novel approach with eight tethers was used to restrain a commercially available ROV, namely the BlueROV2 (Blue Robotics, Torrance, USA), in the mid depth of the FloWave wave and current test tank. This experimental set-up allowed the measurement of the forces under realistic flow around the ROV without introducing significant interference. The paper presents the analysis of the load cell data as forces and moments in relation to the observed motion and rotation of the ROV. In addition to active propelled cases, a variation of current speed (up to 1 m/s) coming out of the four directions as well as different regular waves were tested. Three different distances of a cylindrical obstacle provided a quantification of the effect of flow shadowing from a structure in front of the ROV. The results can also be used as a validation experiment to expand the application of ROVs and the influence of obstacles based on numerical simulations.

The application of tire pyrolysis oil (TPO) as an alternative fuel has attained attention owing to the exponentially raise demand and price of fossil fuels, environmental impacts, and landfilling of the waste tires. Globally, the pyrolysis process has become the leading solution to this problem by converting the waste tires to the TPO. So, this review paper studies the application of TPO in diesel engines related to the purification and physicochemical properties of TPO with their effects on the combustion, performance, and emission characteristics of the fuel. Oxidative desulfurization and catalyst process is the most reliable method to remove the sulfur contents and purified the tire pyrolysis oil. Higher cylinder peak pressure, heat release rate, and ignition delay has been observed and noted its impact on the combustion analysis of the TPO. An increase in brake power, brake thermal efficiency, and lower brake specific fuel consumption have been found in the performance analysis of TPO. In emission analysis literature, mixed trends have been seen in nitrogen oxides and hydrocarbon emission, so in some cases, emission tends to increase, but the opposite direction has also been observed in other cases. Carbon monoxide and carbon dioxide and particulate matter emission are reduced owing to the excess of oxygen shares in TPO. In general, no alteration in the diesel engine is required for a low share of TPO in the TPO-diesel blend. Finally, it is concluded that the studies recommended the TPO-diesel (10%–90%) blend in the engine as an alternative fuel.

[EN] Non-uniform heating in structures under fire involves the appearance of 3D-phenomena and typically requires the use of complex models built with finite elements shell or solid. Although different procedures have been developed to model the complex thermo-mechanical phenomenon, there is no simple, accurate, and low-cost computational methodology involving the space-time variation of the temperature and displacement fields that opens the path advancing more easily towards modeling more complex structural problems in a fire situation. To overcome this knowledge-gap, this paper presents a new methodology that fulfills those conditions, making it possible to carry out more complex analyses that require many simulations in a short time and at low computational costs. The new methodology to obtain the thermo-mechanical response to non-uniform heating and mechanical loads is general, simple, accurate, and avoids using complex and high-cost finite elements, simplifying the structural modeling, and reducing the computational analysis cost. As a result, complex structural fire engineering problems such as probabilistic and optimization analysis can be handled much more easily, representing a significant step toward the generalized application of performance-based approaches to deal with fire effects on structures. The procedure uses simple but advanced Timoshenko¿s beam-type finite elements and represents the non-uniform temperature space-time field through a mean value of the temperature and the two mean values of the section thermal gradients which are variable in time during the fire. The methodology is satisfactorily validated with results (experimental and numerical) of the Cardington frame test and captures 3D-phenomena such as buckling, flexural-torsional buckling, and warping., Thanks are due to the Fundación Carolina, the Universitat Politècnica de València, and the Universidad Surcolombiana for the support given to this research through the 2018-2019 Ph.D. scholarship.

International audience; This paper analyzes French winemakers' decision-making process to adapt to climate change, and how the institutional and relational context of an innovation system, including a clean technological regime, affect these decisions. Our study used a mixed method research based on original face-to-face interviews with 92 winemakers in three French regional wine clusters that have been affected by climate change: Bordeaux, Champagne and Languedoc. We perform a logistic model to tests how managers' personal backgrounds, wine-producing company characteristics, and innovation system components, including cleaner technological regime, might explain the adaptation decision-making process. Our results show that economic variables have little influence on climate change adaptation decision-making. On the contrary, variables expressing the relationship built by wine producing companies within the Innovation System, their involvement in organic wine production, and the manager's personal background affect the decision-making process to adapt to climate change. Furthermore, many of the adaptation strategies rely on adopting cleaner production approach. Our findings show that the decision-making process depends on networks and clean technological regimes embedded in an innovation system, with regional and sector dimensions.