Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-seq is one of the most commonly used techniques to perform transcriptional experiments. Computational analysis on such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-seq data-sets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsig and identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50\% of the genes identified as influential have some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes have crucial roles or participate in processes associated with response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

In 1895, Korteweg and de Vries (KdV), derived their celebrated equation describing the motion of waves of long wavelength in shallow water. In doing so they made a number of quite reasonable assumptions, incompressibility of the water and irrotational fluid. The resulting equation, the celebrated KdV equation, has been shown to be a very reasonable description of real water waves. However there are other phenomena which have an impact on the shape of the wave, that of vorticity and viscosity. This paper examines how a constant vorticity affects the shape of waves in electrohydrodynamics. For constant vorticity, the vertical component of the velocity obeys a Laplace equation and also has the usual lower boundary condition. In making the vertical component of the velocity take central stage, the Burns condition can be thus bypassed.

The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

This paper proposes a method for determining the bridge condition index (BCI) in concrete bridges, which is based on the views of bridge experts. First, eight indices were defined for a concrete bridge including structure, hydrology, safety, load impact, geotechnical and seismicity, strategic importance, facilities, and finally traffic and pavement. Each index consists of several sub-indices. Next, a series of questionnaires about the relative importance of indices and their sub-indices were prepared and distributed among bridge experts. Experts’ views were analyzed by Expert Choice software and the relative importance (weight) of each index and each sub-index was determined using the analytical hierarchy process (AHP). Then, based on experts’ views, an average score was assigned to each sub-index for any condition. Now the bridge inspectors can examine the bridge and determine the scores of sub-indices. Each index’s score is the sum of the weighted score assigned to its’ sub-indices and BCI is the sum of weighted scores assigned to indices. Higher values of BCI indicate a better condition. Therefore, bridges with lower BCI take priority in maintenance activities. To apply the proposed method, five bridges were selected in Semnan province, Iran, and BCI calculation of these bridges were conducted.

The study investigates the determinants of mobile Internet banking adoption among Gen-Y in Malaysia. Align the issue of the study and research objectives, and the study underpinned Technology Acceptance Model (TAM) as the main guideline or a blueprint to analyze the research model. The study applied survey research design and analyzed using Structural Equation Modelling (SEM) under the PLS-SEM technique with SmartPLS 2.0. A total of 358 mobile internet banking users in Malaysia were random proportional selected as respondents and analyzed. Results from the partial least square structural equation modeling (PLS-SEM) revealed that perceived ease of use, perceived usefulness, facilitating condition, and personal innovativeness in IT have significantly influenced the attitude of mobile internet banking adoption in Malaysia. Additionally, perceived ease of use and facilitating conditions found to have a significant influence on the perceived usefulness of Mobile Internet Banking. Lastly, facilitating conditions and personal innovativeness in IT are significantly influence the perceived ease of use of mobile internet banking among Gen-Y in Malaysia

Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well their hybrids, i.e., Levenberg-Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS model outperforms other models with the promising results of APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.

Remaining service life (RSL) of pavement, as a sign of future pavement performance, has always received growing attention from pavement engineers. The RSL describes the time from the moment of pavement inspection until such a time when a major repair or reconstruction is required. The conventional approach to determining RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise users' safety. In this paper, surface distresses of pavement have been used to estimate the pavement’s RSL in order to eliminate the aforementioned problems and challenges. To implement the proposed theory, 105 flexible pavement segments were taken from Shahrood-Damghan Highway (Highway 44) in Iran. For each pavement segment, the type, severity, and extent of surface damage and pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include Support Vector Regression (SVR), Support Vector Regression Optimized by Fruit Fly Optimization Algorithm (SVR-FOA), and Gene Expression Programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The Correlation Coefficient (CC), Nash-Sutcliffe efficiency (NSE), Scattered Index (SI), and Willmott’s Index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

The conventional method used for calculating pavement condition index (PCI) has two major drawbacks: safety problems during pavement inspection, and human error. This paper proposes a method for removing these problems. The proposed method uses surface deflection data in falling weight Deflectometer test to estimate PCI. The data used in this study were derived from 236 pavement segments taken from Tehran-Qom freeway in Iran. The data set was analyzed using multi layers perceptron (MLP) and radial basis function (RBF) neural networks. These neural networks were optimized by levenberg-marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic (RBF-GA) algorithms. After initial modeling with four neural networks mentioned, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of analysis have been verified by the four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SD). The best reported results belonged to CMIS, including APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.

In South Korea, landslides are caused by localized heavy rainfall and typhoons, which often occur in the summer season at natural slopes in mountainous areas and artificial slopes in urban surroundings. Flow-type landslides frequently occur in mountainous areas. To evaluate flow-type landslides, it is essential to identify the physical characteristics of soil, giving focus to the soil on the top layers of various types of slope. This study conducts a survey and an analysis of the characteristics of landslides that occurred in the study area with different geological conditions of granite and gneiss. The characteristics of soil in the area and its surroundings that have or have not undergone landslides for every geological condition is also evaluated. Based on these characteristics and a statistics method, it extracts the triggering factors, permeability coefficients (k), and shearing strength with cohesion (c) and internal friction angel (φ) of soils that are highly related to landslides around weathered soil layers. As a result, the permeability coefficients show significant relevance with void ratio (e), the effective size of grains (D10), and uniformity coefficient (cu), while the shearing strength with the proportion of fine-grained soil (Fines), uniformity coefficient (cu), degree of saturation (S), dry weight density (rd), and void ratio (e). By obtaining this result, the study uses the regression analysis to suggest models to estimate the permeability coefficients and shearing strength. For the gneiss area, the statistics-based estimation model (SEM) is proposed as k_gn = (1.488 × 10^-02 × e) + (1.076 × D₁₀) + (-1.629 × 10^-04 × c_u) - (1.893 × 10^-02) for permeability coefficients; cgn = (-0.712 × Fines) + (-0.131 × c_u) + 15.335 for cohesion; and φgn = (27.01 × r_d) + (-12.594 × e) + 6.018 for internal frictional angle of soils. For the granite area, the statistics-based estimation model (SEM) is proposed as kgr = (8.281 × 10^-03 × e) + (0.639 × D₁₀) + (-2.766 × 10^-05 × c_u) - (9.907 × 10^-03) for permeability coefficients; c_gr = (-0.689 × Fines) + (-0.0744 × S) + 18.59 for cohesion; and φ_gr = (33.640 × r_d) + (-0.875 × e) - 9.685 for internal frictional angle of soils. The use of statistics-based estimation models (SEMs) for landslide-triggering factors that trigger landslides will support the simple calculation of permeability coefficient and shearing strength (cohesion and internal frictional angle), only requiring information about the physical properties of soil at the natural slopes that have different geological features such as gneiss and granite areas.

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

Fluid mechanics of flow in hydrophobic, rectangular microchannels with finite aspect ratios is of paramount importance. In such microchannels, not only the effect of the side walls should be taken into account, but also the classical assumption of no-slip boundary condition (BC) is no longer valid at the solid-liquid interface. Accordingly, slip flow can occur in microchannels fabricated from surfaces with low wetting conditions, hydrophobic surfaces. Determining the interactions of liquid molecules adjacent to solid surface is still a challenging issue, and it is especially important in small scale domains. Herein, the fluid mechanics of flow through rectangular hydrophobic microchannels has been reconsidered by taking into account the general Navier-slip BCs at the solid-liquid interface. For fully developed incompressible flow in microchannels at low Reynolds number, partial differential equation (PDE) of the momentum equation simplifies to the classical Poisson equation. Accordingly, by analytically solving the Poisson equations with general Navier-slip BCs, the most general forms of velocity distributions, flow rate, friction factor and Poiseuille number have been obtained.

Trigonella foenum is one of the oldest medicinal plants that grow in many parts of Iran with the diverse ecological situation. Employing this plant for treating diabetes and high cholesterol has a long history, because of some metabolites. Due to the habitat of fenugreek is a wide range of climatic conditions, it may have power to cope with climate variation. The main intention of this inquiry was to understand the effect of the environmental variables on this therapeutic plant features. It was also interesting for us to understand which environment variables are more impressive for enhancing of trigonelline and 4-hydroxy isoleucine content as the most important metabolites of this plant. For achieving this goal, environmental information and vegetal data were analyzed to discover the role of nature on the seed part of fenugreek life in 50 different regions of Iran. Canonical correspondence analysis (CCA) displayed that high content of metabolites and some morphological characteristics happened in high temperature and solar irradiation. Partial least squares regression (PLSR) and path analysis used to find the best predictors and direct and indirect effect of all variables on 4-hydroxy isoleucine and trigonelline. Ecological condition were the best predictors and had the highest direct and indirect impact on 4-hydroxy isoleucine. However, for trigonelline, the environment did not play a senior role. It seems that the reaction of components of fenugreek does not follow the same way. Studying on morphological, primary and secondary metabolites, and surrounding environment of fenugreek, helped us to have a more precise judgment about the life of this plant.

Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults the use of vibrations or sound generally offers better results in the accuracy of the detection although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has much more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal

In this study, the multi-relaxation-time lattice Boltzmann method is applied to investigate the oscillatory instability of lid-driven flows in two-dimensional semi-elliptical cavities with different vertical to horizontal aspect ratios K in the range of 1.0--3.0. The program implemented in this study is parallelized using CUDA (compute unified device architecture), a parallel computing platform, and computations are carried out on NVIDIA Tesla K40c GPU. To carry out precise calculations, the CUDA algorithm is extensively investigated, and its parallel efficiency indicates that the maximum speedup is 47.6 times faster. Furthermore, the steady--oscillatory Reynolds numbers are predicted by implementing the CUDA-based programs. The amplitude coefficient is defined to quantify the time-dependent oscillation of the velocity magnitude at the monitoring point. The simulation results indicate that the transition Reynolds numbers correlate negatively with the aspect ratio of the semi-elliptical cavity, and are smaller than those of the rectangular cavity at the same aspect ratio. In addition, the detailed vortex structures of the semi-elliptical cavity within a single period are also investigated when the Reynolds number is larger than the steady--oscillatory value to determine the effects of periodic oscillation of the velocity magnitude.

Riparian restoration is an important objective for landscape managers seeking to redress the widespread degradation of riparian areas and the ecosystem services they provide. This study investigated the long-term outcomes of ‘one-off’ restoration activities undertaken in the Upper Murrumbidgee Catchment, NSW, Australia. The objective of the restoration was to protect and enhance riparian vegetation and control erosion, and consequently reduce sediment and nutrient delivery into the Murrumbidgee River. To evaluate the outcomes 10 years after restoration, rapid riparian vegetation and geomorphological assessments were undertaken at 29 sites spanning the four different restoration methods used (at least five replicates per treatment), as well as at nine comparable untreated sites. We also trialed the use of aerial imagery to compare width of riparian canopy vegetation and projective foliage cover prior to restoration with that observed after 10 years. Aerial imagery demonstrated the width of riparian canopy vegetation and projective foliage cover increased in all restored sites, especially those with native plantings. The rapid assessment process indicated that 10 years after riparian restoration, the riparian vegetation was in a better condition at treated sites compared to untreated sites. Width of riparian canopy vegetation, native mid-storey cover, native canopy cover and seedling recruitment were significantly greater in treated sites compared to untreated sites. Geomorphological condition of treated sites was significantly better than untreated sites, demonstrating the importance of livestock exclusion to improve bank and channel condition. Our findings illustrate the value of ‘one-off’ restoration activities in achieving long-term benefits for riparian health. We have demonstrated that rapid assessments of the vegetation and geomorphological condition can be undertaken post-hoc to determine the long-term outcomes, especially when supported with analysis of historical aerial imagery.

The growing shortage of human resources to manage landscape in the water environment has led to problems concerning preservation of the landscape in Japan. To cultivate people with awareness about preserving the landscape, it is important to understand the process of how a region’s residents develop an attachment toward the water environment in their area. This paper identified factors influencing the attachment of residents toward the Fukushima-gata lagoon, Niigata prefecture, Japan, as a case study while focusing on changes in the lifestyle of the regional residents. The findings are as follows: (1) The target households frequently used Fukushima-gata lagoon for “fishing and for picking edible wild plants” and “gathering reeds to make thatched roofs” before “the reclamation project was carried out by the government in Fukushima-gata lagoon.” They also frequented Fukushima-gata lagoon for “walking and cherry blossom viewing” after “the reclamation project” was implemented. (2) Even the fact that distance from the lagoon to their place of residence increased after the project did not lose their attachment to the lagoon. (3) It was suggested that the project contributed to increase the attachment of regional residents towards Fukushima-gata lagoon because of multiple factors such as “playing in the water,” “livelihood opportunity,” “recreational use,” and so on. (4) Changes in the attachment of regional residents toward Fukushima-gata lagoon before and after the reclamation project was carried out by the government corresponded to “changes in the lifestyle of the local residents” before and after the project.

There have been many discussions on the globalisation of SMEs, but it is true that there is not enough academic achievement after such the study of Born global (BG) ventures. The internationalisation of SMEs (Small and Medium Enterprises) is not easy because they lack resources or capabilities compared to multinational corporations. This study investigated the role of government in assisting the internationalisation of SMEs. In particular, SMEs lacked the ability to acquire market-oriented information, so we’ve established the scheme of efficient information support system for the internationalisation of SMEs. In other words, we proposed an information analysis system through the establishment of a relational database constructed for market-oriented information support. KISTI (Korea Institute of Science and Technology Information), which is one of the government-funded research institutes in the Republic of Korea, provided information support to the SMEs dealing with hydrazine related products. This study suggests this case for the market-oriented information support of the government in the internationalisation of SMEs. The research on information support of the government is meaningful in that it suggests a way to support SMEs in practical level.

Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

The Australian dairy sheep industry is small and mostly based on a natural grass grazing system which can limit productivity. The current study tested different plant oil-infused and rumen protected polyunsaturated fats and their interactions with sire breeds to improve lactation traits and body condition score (BCS) of ewes grazing low quality pastures. It was hypothesised that supplementing lactating ewe diets plant-derived polyunsaturated oils will improve milk production and composition without compromising BCS. Sixty ewes (n=10/treatment) in mid-lactation, balanced by sire breed, parity, milk yield, body condition score, and liveweight were supplemented with: 1) control: wheat-based pellets without oil inclusion; wheat-based pellets including 2) canola oil (CO); 3) rice bran oil (RBO); 4) flaxseed oil (FSO), 5); safflower oil (SFO) and 6) rumen protected fat containing eicosapentaenoic acid and docosahexaenoic acid (RPO). Except for the control group, all supplementary diets included the same level of 50 ml/kg DM of oil and all diets were isocaloric and isonitrogenous. Experimental animals were grazed in the same paddock with ad libitum access to pasture, hay and water during the 10-week study. RPO was the most effective diet that enhanced milk, fat and protein yields by approximately 30, 13, and 31% respectively (P<0.0001). Significant increase in milk production was also observed in CO, RBO, and SFO (P<0.0001). Breed significantly influenced animal performance with higher milk yield recorded for crossbred Awassi x East Friesian (AW x EF) (578 g/day) vs purebred Awassi (452 g/day) (P<0.0001). This study provides empirical evidence for the use of rumen-protected and plant-derived oil-infused pellets as supplements under low quality pasture grazing conditions, to improve production performance of purebred Awassi and crossbred AW x EF ewes.

The paper presents a case study and a model for condition monitoring of Diesel engines’ oil of urban buses, through the accompaniment of the evolution of its degradation, with the objective to implement a predictive maintenance policy. Along time, because the usage, there is some decay in the lubricant properties. However, in normal functioning conditions, the lubricants properties, at the time the manufacturers recommend its changing, regardless of they are within the safety limits. Then, based on the accompaniment of the lubricants’ oil condition, the intervals of oil replacement can be enlarged what implies the availability increasing and the corresponding production increasing of the equipment. The model presented in this paper shows its potential to be spread to other types of equipment and organisations that want can implement similar maintenance policies, to achieve the best availability based on the real equipment health conditioning conditions

In this paper, we study the third order semilocal convergence of the Newton-like method for finding the approximate solution of nonlinear operator equations in the setting of Banach spaces. First, we discuss the convergence analysis under ω-continuity condition, which is weaker than the Lipschitz and Hölder continuity conditions. Second, we apply our approach to solve Fredholm integral equations, where the first derivative of involved operator not necessarily satisfy the Hölder and Lipschitz continuity conditions. Finally, we also prove that the R-order of the method is 2p + 1 for any p $\in$ (0,1].

An important moment in the establishment of forestry seed orchards is the formation of their optimal composition, avoiding inbreeding depression in the outgoing seed material which occurs due to crossbreeding between closely related plus trees, the clones of which comprise the orchards. It is possible to minimize the negative effect of inbreeding by considering the hereditary aspect of the heterogeneous seed orchard material. The purpose of our work is to provide a comparative assessment of the formation and lignification of xylem in annual shoots of the plus trees of Scots pine. We investigated the formation and lignification of xylem in the annual shoots of the plus trees of Scots pine (Pinus sylvestris L.), found in natural forests in the Nizhny Novgorod region of the Russian Federation. Their vegetative progeny were cultivated in the clone archive stationed in the same region. Analyzing the one-type of the 1-year shoots, the time that the shoots were cut from the branches corresponded to the presence of plants in the characteristic phenological phases of seasonal development. A histochemical study of xylem was conducted, using the qualitative reactions of phloroglucinol to lignin. The power of xylem development was estimated by counting, in the radial direction, the cell rows from the core to the cambium in the microscope’s field of view. A significant differentiation of plus trees was revealed in a complex of signs characterizing the level of xylem development and the degree of xylem cell lignification in the tissues of annual shoots. Phenotypic differences in the physiological state of plus trees appeared given a leveled ecological background, indicating their genotypic determinism. This was confirmed by an analysis of variance. The share of the influence of differences between the proper plus trees was between 25.16% ± 8.91% and 53.98% ± 5.48% of the total phenotypic variance of the signs of the seasonal state of xylem. Factor analysis was used to reduce the number of considered indicators of xylem physiological state. The results allowed a cluster analysis to be carried out on the basis of the normalized values of the initial xylem features as well as the principal components derived from them. The association of plus trees was done on the basis of the similarity of the multidimensional estimates of xylem seasonal condition. The objects that were the most remote from the others were identified. This information provides a reasonable approach to the formation of an assortment of Scots pine seed orchards.

In this work, we extend the fundamental results of Schu to the class of monotone asymptotically nonexpansive mappings in modular function spaces. In particular, we study the behavior of the Fibonacci-Mann iteration process defined by $$x_{n+1} = t_n T^{\phi(n)}(x_n) + (1-t_n)x_n,$$ for $n \in \mathbb{N}$, when $T$ is a monotone asymptotically nonexpansive self-mapping.

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new immobile multi-sensing robot, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (UAVs, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal processing techniques. We show an example of this approach for monitoring offshore wind turbines. We demonstrate the use of four different communication systems (WiFi, serial, LoRa and optical communication) for the condition monitoring process. By processing the sensor data on-board, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. We train our classifier offline and transfer its parameters to the Limpet for online classification, where it makes an autonomous decision based on the condition of the monitored structure.

Ferulic acid esters have been suggested as a group of natural chemicals with sunscreen function. The study aimed to utilize an environment-friendly enzymatic method to produce octyl ferulate by esterification of ferulic acid with octanol. The Box-Behnken design with response surface methodology (RSM) was adopted to evaluate the effects of synthesis variables, including reaction temperature (70–90 °C), enzyme amount (1000–2000 PLU) and stir speed (50–150 rpm), on the molar conversion of octyl ferulate. According to the joint test, both the reaction temperature and enzyme amount had great impacts on the molar conversion. RSM-developed second-order polynomial equation further showed great ability on data-fitting. Based on ridge max analysis, the optimum parameters for the biocatalyzed reaction were: 72 h reaction time, 92.2 °C reaction temperature, 1831 PLU enzyme amount and 92.4 rpm stir speed, respectively. Finally, the molar conversion of octyl ferulate under optimum condition was verified to be 93.2 ± 1.5%. In conclusion, high yield of octyl ferulate synthesized by commercial immobilized lipase under elevated temperature conditions has been suggested, which our findings could broaden the utilization of the lipase and provide a biocatalytic approach, instead of the chemical method, for ferulic acid ester synthesis.

Oil immersed paper insulation condition is a crucial aspect of power transformer’s life condition diagnostic. The measurement testing database collected over the years made it possible for researchers to implement classification analysis to in-service power transformer. This article presents classification analysis of transformer oil-immersed paper insulation condition. The measurements data (dielectric characteristics, dissolved gas analysis, and furanic compounds) of 149 transformers with primary voltage of 150 kV had been gathered and analyzed. The algorithm used for developing classification model is Support Vector Machine (SVM). The model has been trained and tested using different datasets. Different models have been created and the best chosen, resulting in 90.63% accuracy in predicting the oil-immersed paper insulation condition. Further implementation was executed to classify oil-paper condition of 19 Transformers which Furan data is not available. The classification results combined, reviewed, and compared to conventional assessment methods and standards, confirming that the model developed has the ability to do classification of current oil-paper condition based on Dissolved Gasses and Dielectric Characteristics.

A real-time two-way direct load control (TWDLC) of central air-conditioning chillers in wide area is proposed to provide demand response. The proposed TWDLC scheme is designed to optimize the load shedding ratio of every customer under control to ensure the target load to be shed is met at every scheduling period. In order to overcome the load reduction uncertainties of TWDLC, an innovative solution is proposed by applying a certain degree of loosening on the constraint of the actual shed load. Fuzzy linear programming is utilized to solve the optimization problem with fuzzy constraints. The proposed fuzzy linear programming problem is solved by delicately transforming it into a regular liner programming problem. A selection scheme used to obtain the feasible candidates set for load shedding at every sampling interval of TWDLC is also designed along with the fuzzy linear programming.

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

For offshore wind power generation, accessibility is one of the main factors that has great impact on operation and maintenance due to constraints on weather conditions for marine transportation. This paper presents a framework to explore the accessibility of an offshore site. At first, several maintenance types are defined and taken into account. Next, a data visualization procedure is introduced to provide an insight into the distribution of access periods over time. Then, a rigorous mathematical method based on finite state Markov chain is proposed to assess the accessibility of an offshore site from the maintenance perspective. A five-year weather data of a marine site is used to demonstrate the applicability and the outcomes of the proposed method. The main findings show that the proposed framework is effective in investigating the accessibility for different time scales and is able to catch the patterns of the distribution of the access periods. Moreover, based on the developed Markov chain, the average waiting time for a certain access period can be estimated. With more information on the maintenance of an offshore wind farm, the expected production loss due to time delay can be calculated.