ALBERT

Description: Present-day direct injection (DI) diesel engines with a high power density of displacement are not just promoting an expansion in the utilization of high-temperature resistant alloys in pistons yet, in addition, the expanded cylinder air pressures. When the temperature of the diesel engines piston exceeds a certain limit, it assumes a critical role at the start of sprays. The target of the present investigation was to look at the effects of cylinder air pressures (CAP) (10–25 bar) and high hot surface temperatures (HST) (350–450 °C). The ignition delay (ID) of pure diesel and that of diesel with Iftex clean system D (a cetane enhancer) are investigated experimentally. The experiments are performed by using a constant volume combustion chamber (CVCC) with a single hole pintle-type nozzle mounted on its head. A strong dependence of ID on the CAPs and HSTs was observed. A CAP of 25 bar is much inferior to the precombustion pressure of DI diesel engines; however, it is the case that combustion typical features are the same in spite of an inferior CAP, HST, and injection pressure. The ID tends to decrease to very small values with an increase in either of the two parameters. At a CAP of 25 bar, the measured ID of diesel with fuel additive is 45.8% lower than the pure diesel. Further, the ID of diesel with fuel additive at a 300 bar injection pressure and 25 bar CAP decreases at a rate of close to 0.2 ms/bar.

Description: Accurate built-up area extraction is one of the most critical issues in land-cover classification. In previous studies, various techniques have been developed for built-up area extraction using Landsat images. However, the efficiency of these techniques under different technical and geographical conditions, especially for bare and sandy areas, is not optimal. One of the main challenges of built-up area extraction techniques is to determine an optimum and stable threshold with the highest possible accuracy. In many of these techniques, the optimum threshold value fluctuates substantially in different parts of the image scene. The purpose of this study is to provide a new index to improve built-up area extraction with a stable optimum threshold for different environments. In this study, the developed Automated Built-up Extraction Index (ABEI) is presented to improve the classification accuracy in areas containing bare and sandy surfaces. To develop and evaluate the accuracy of the new method for built-up area extraction with Landsat 8 OLI reflective bands, five test sites located in the Iranian cities (Babol, Naqadeh, Kashmar, Bam and Masjed Soleyman), eleven European cities (Athens, Brussels, Bucharest, Budapest, Ciechanow, Hamburg, Lyon, Madrid, Riga, Rome and Porto) and high resolution layer imperviousness (HRLI) data were used. Each site has varying environmental and complex surface coverage conditions. To determine the optimal weights for each of the Landsat 8 OLI reflective bands, the pure pixel sets for different classes and the improved gravitational search algorithm (IGSA) optimization were used. The Kappa coefficient and overall error were calculated to evaluate the accuracy of the built-up extraction map. Additionally, the ABEI performance was compared with the urban index (UI) and normalized difference built-up index (NDBI) performances. In each of the five test sites and eleven cities, the extraction accuracy of the built-up areas using the ABEI was higher than that using the UI, and NDBI (P-value of 0.01). The relative standard deviations of the optimal threshold values for the ABEI and UI were 27 and 155% (at five test sites) and were 16 and 37% (at eleven European cities), respectively, which indicates the stability of the ABEI threshold value when the location and environmental conditions change. The results of this study demonstrated that the ABEI can be used to extract built-up areas from other land covers. This index is effective even in bare soil and sandy areas, where other indices experience major challenges.

Description: Iterative methods were employed to obtain solutions of linear and non-linear systems of equations, solutions of differential equations, and roots of equations. In this paper, it was proved that s-iteration with error and Picard–Mann iteration with error converge strongly to the unique fixed point of Lipschitzian strongly pseudo-contractive mapping. This convergence was almost F -stable and F -stable. Applications of these results have been given to the operator equations F x = f and x + F x = f , where F is a strongly accretive and accretive mappings of X into itself.

Description: The ever-growing Internet of Things (IoT) data traffic is one of the primary research focuses of future mobile networks. 3rd Generation Partnership Project (3GPP) standards like Long Term Evolution-Advanced (LTE-A) have been designed for broadband services. However, IoT devices are mainly based on narrowband applications. Standards like LTE-A might not provide efficient spectrum utilization when serving IoT applications. The aggregation of IoT data at an intermediate node before transmission can answer the issues of spectral efficiency. The objective of this work is to utilize the low cost 3GPP fixed, inband, layer-3 Relay Node (RN) for integrating IoT traffic into 5G network by multiplexing data packets at the RN before transmission to the Base Station (BS) in the form of large multiplexed packets. Frequency resource blocks can be shared among several devices with this method. An analytical model for this scheme, developed as an r-stage Coxian process, determines the radio resource utilization and system gain achieved. The model is validated by comparing the obtained results with simulation results.

Description: Worldwide, urban areas are expanding both in size and number, which results in a decline in habitats suitable for urban flora and fauna. The construction of urban green features, such as green roofs, may provide suitable habitat patches for many species in urban areas. On green roofs, two approaches have been used to select plants—i.e., matching similar habitat to green roofs (habitat template approach) or identifying plants with suitable traits (plant trait approach). While both approaches may result in suitable habitats for arthropods, how arthropods respond to different combinations of plants is an open question. The aim of this study was to investigate how the structural complexity of different plant forms can affect the abundance and richness of arthropods on green roofs. The experimental design crossed the presence and absence of annuals with three Sedum sediforme (Jacq.) Pau (common name: stonecrops) treatments—i.e., uniformly disrupted Sedum, clumped disrupted Sedum, and no Sedum. We hypothesized that an increased structural diversity due to the coexistence of different life forms of plants on roofs is positively related to the abundance and richness of arthropods. We found that arthropod abundance and richness were positively associated with the percent of vegetation cover and negatively associated with substrate temperature. Neither arthropod abundance nor richness was influenced by the relative moisture of substrate. We also found that arthropod abundance and richness varied by green roof setups (treatments) and by seasonality. Arthropod abundance on green roofs was the highest in treatments with annuals only, while species richness was slightly similar between treatments containing annuals but varied between sampling periods. This study suggests that adding annuals to traditional Sedum roofs has positive effects on arthropods. This finding can support the development of biodiverse cities because most extensive green roofs are inaccessible to the public and can provide undisturbed habitat for several plant and arthropod species.

Description: The potential of Caulerpa lentillifera, Gracilaria coronopifolia and Chaetomorpha linum, as biomass feedstock was investigated in this study. It was concluded that seaweed is more suitable for bio-based products synthesis, i.e., bioplastic and bio-lubricants, instead of biofuels due to its relatively low calorific value (~12 MJ/kg). Since seaweed has high moisture content (~80%), hydrothermal liquefaction is recommended, and its efficiency can be further enhanced through microwave technology. Besides, it is found that the thermal degradation of seaweed was best described with the reaction order of 1. The kinetic results also indicated that seaweed consists of lower activation energy (〈30 kJ/mol) in comparison with terrestrial biomass (50–170 kJ/mol). Hence, seaweed has a high potential to be used as biomass feedstock, particularly Chaetomorpha linum, as it has no conflict with other interests. Lastly, acetic-acid pre-treatment was suggested to be an optional process in order to increase the algal conversion efficiency as it can reduce up to 25% of ash content.

Description: Most power transformer faults are caused by iron core and winding faults. At present, the method that is most widely used for transformer iron core and winding faults identification is the vibration analysis method. The vibration analysis method generally determines the degree of fault by analyzing the energy spectrum of the transformer vibration signal. However, the noise reduction step in this method is complicated and costly, and the effect of denoising needs to be further improved to make the fault identification results more accurate. In addition, it is difficult to perform an accurate determination of the early mild failure of the transformer due to the effect of noise on the results. This paper presents a novel mathematical statistics method based on the vibration signal to optimize the vibration analysis method for the short-circuit failure of the transformer winding. The proposed method was used for linear analysis of the transformer vibration signal with different degrees of short-circuit failure of the transformer winding. By comparing the slope value of the transformer vibration signal cumulative probability distribution curve and analyzing the energy spectrum of the signal, the degree of short-circuit failure of the transformer winding was identified quickly and accurately. This method also simplified the signal denoising process in transformer fault detection, improved the accuracy of fault detection, reduced the time of fault detection, and provided good predictability for early mild faults of the transformer, thereby reducing the hidden hazards of operating the power transformer. The proposed optimization procedure offers a new research idea in transformer fault identification.

Description: Higher educational institutes (HEI) are adopting ubiquitous and smart equipment such as mobile devices or digital gadgets to deliver educational content in a more effective manner than the traditional approaches. In present works, a lot of smart classroom approaches have been developed, however, the student learning experience is not yet fully explored. Moreover, module historical data over time is not considered which could provide insight into the possible outcomes in the future, leading new improvements and working as an early detection method for the future results within the module. This paper proposes a framework by taking into account module historical data in order to predict module performance, particularly the module result before the commencement of classes with the goal of improving module pass percentage. Furthermore, a video streaming server along with blended learning are sequentially integrated with the designed framework to ensure correctness of teaching and learning pedagogy. Simulation results demonstrate that by considering module historical data using time series forecasting helps in improving module performance in terms of module delivery and result outcome in terms of pass percentage. Furthermore, the proposed framework provides a mechanism for faculties to adjust their teaching style according to student performance level to minimize the student failure rate.

Description: Efficient and reliable systems are required to detect and monitor disasters such as wildfires as well as to notify the people in the disaster-affected areas. Internet of Things (IoT) is the key paradigm that can address the multitude problems related to disaster management. In addition, an unmanned aerial vehicles (UAVs)-enabled IoT platform connected via cellular network can further enhance the robustness of the disaster management system. The UAV-enabled IoT platform is based on three main research areas: (i) ground IoT network; (ii) communication technologies for ground and aerial connectivity; and (iii) data analytics. In this paper, we provide a holistic view of a UAVs-enabled IoT platform which can provide ubiquitous connectivity to both aerial and ground users in challenging environments such as wildfire management. We then highlight key challenges for the design of an efficient and reliable IoT platform. We detail a case study targeting the design of an efficient ground IoT network that can detect and monitor fire and send notifications to people using named data networking (NDN) architecture. The use of NDN architecture in a sensor network for IoT integrates pull-based communication to enable reliable and efficient message dissemination in the network and to notify the users as soon as possible in case of disastrous situations. The results of the case study show the enormous impact on the performance of IoT platform for wildfire management. Lastly, we draw the conclusion and outline future research directions in this field.

Description: We present a nonintrusive approach for combining high-fidelity simulations using Finite-Volume (FV) methods with Proper Orthogonal Decomposition (POD) and Galerkin Reduced-Order Modeling (ROM) methodology. By nonintrusive we here imply an approach that does not need specific knowledge about the high-fidelity Computational Fluid Dynamics (CFD) solver other than the velocity and pressure results given on an element mesh representing the related discrete interpolation spaces. The key step in the presented approach is the projection of the FV results onto suitable finite-element (FE) spaces and then use of classical POD Galerkin ROM framework. We do a numerical investigation of aerodynamic flow around an airfoil cross-section (NACA64) at low Reynolds number and compare the ROM results obtained with high-fidelity FV-generated snapshots against similar high-fidelity results obtained with FE using Taylor–Hood velocity and pressure spaces. Our results show that we achieve relative errors in the range of 1–10% in both H 1 -seminorm of the computed velocities and in the L 2 -norm of the computed pressure with reasonably few ROM modes. Similar accuracy was obtained for lift and drag.