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In this study, CO₂ conversion to acetate and CH₄ was achieved in a flow-through laboratory-scale microbial electrosynthesis (MES) cell composed of a 3D conductive polylactic acid (cPLA) lattice cathode with electrodeposited metal electrocatalyst coatings. The MES cell with a bare cPLA cathode showed the poorest performance with the lowest H₂ and CH₄ production rates and low Coulombic efficiency. This was ascribed to a poor electrocatalytic activity of cPLA towards H₂ production and high electrode resistivity. When the cPLA electrode was modified with metal coatings, the CH₄, acetate and H₂ production rate increased significantly, with the following trend: cPLA < Ni < NiFe < NiFeMn. The better performance of the metal-coated cPLA in terms of CH₄ production was attributed to the lower electrical resistance, enhanced H₂ production and enhanced electron transfer between the cathode and the biofilm. At the cell potential of 2.8 V, the best-performing NiFeMn cPLA cathode showed stable production of CH₄ (50 ± 6 mL d⁻¹), acetate (185 ± 27 mg d⁻¹), and H₂ (545 ± 175 mL d⁻¹) at close to 100% Coulombic efficiency.

Funding Information: This research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement (EnergyMatching project number 768766), and J. Gust. Richert foundation in Sweden (grant number: 2020-00586). Funding Information: This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement (EnergyMatching project number 768766 ), and J. Gust. Richert foundation in Sweden (grant number: 2020-00586 ). Publisher Copyright: © 2021 The Author(s) Peer-to-peer (P2P) energy sharing among neighboring households is a promising solution to mitigating the difficulties of renewable power (such as solar Photovoltaics (PV)) penetration on the power grid. Until now, there is still a lack of study on the impacts of future climate change on the P2P energy trading performances. The future climate change will cause variances in the renewable energy production and further lead to changes in the economic performances of households with various energy uses and affect the decision making in PV ownership and pricing strategies. Being unaware of these impacts could potentially hinder the P2P energy sharing application in practice. To bridge such knowledge gap, this paper conducts a systematic investigation of the climate change impacts on the energy sharing performance in solar PV power shared communities. The future weather data is generated using the Morphine method, and an agent-based modeling method is used for simulating the energy trading behaviors of households. Four comparative scenarios of different PV ownerships and pricing strategies are designed. The detailed energy trading performances (including the PV power self-sufficiency, cost saving, revenues, and compound annual growth rate) for the four comparative scenarios are analyzed under both the present and future climates and compared. The study results of a building community located in Sweden show that the future climate change is more beneficial to large energy use households while less beneficial to small households. High price of energy trading can improve the fairness of the economic performances in the community, especially when some of the households do not have any PV ownership. This study can help understand the future climate impacts on the energy sharing performances of building communities, which can in turn guide decision making in PV ownership and price setting for different households under the future climate change to facilitate real applications.

This paper discusses the digital automation workflows and co-design methods that made possible the comprehensive robotic prefabrication of the BUGA Wood Pavilion - a large-scale production case study of robotic timber construction. Latest research in architectural robotics often focuses on the advancement of singular aspects of integrated digital fabrication and computational design techniques. Few researchers discuss how a multitude of different robotic processes can come together into seamless, collaborative robotic fabrication workflows and how a high level of interaction within larger teams of computational design and robotic fabrication experts can be achieved. It will be increasingly important to discuss suitable methods for the management of robotics and computational design in construction for the successful implementation of robotic fabrication systems in the context of the industry. We present here how a co-design approach enabled the organization of computational design decisions in reciprocal feedback with the fabrication planning, simulation and robotic code generation. We demonstrate how this approach can implement direct and curated reciprocal feedback between all planning domains - paving the way for fast-paced integrative project development. Furthermore, we discuss how the modularization of computational routines simplify the management and computational control of complex robotic construction efforts on a per-project basis and open the door for the flexible reutilization of developed digital technologies across projects and building systems., European Regional Development Fund, Deutsche Forschungsgemeinschaft, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Projekt DEAL, Bundesgartenschau Heilbronn 2019 GmbH, GETTYLAB

Mixing and evaporation processes play an important role in fluid injection and disintegration. Laser-induced thermal acoustics (LITA) also known as laser-induced grating spectroscopy (LIGS) is a promising four-wave mixing technique capable to acquire speed of sound and transport properties of fluids. Since the signal intensity scales with pressure, LITA is effective in high-pressure environments. By analysing the frequency of LITA signals using a direct Fourier analysis, speed of sound data can be directly determined using only geometrical parameters of the optical arrangement no equation of state or additional modelling is needed at this point. Furthermore, transport properties, like acoustic damping rate and thermal diffusivity, are acquired using an analytical expression for LITA signals with finite beam sizes. By combining both evaluations in one LITA signal, we can estimate mixing parameters, such as the mixture temperature and composition, using suitable models for speed of sound and the acquired transport properties. Finally, direct measurements of the acoustic damping rate can provide important insights on the physics of supercritical fluid behaviour., Deutsche Forschungsgemeinschaft, Projekt DEAL

As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (ACC) that is augmented by the sharing of preceding vehicle acceleration through the vehicle to vehicle communication in a feedforward control path. In general, the desired velocity optimization for vehicles in the convoy is based on fuel economy optimization, rather than driveability. This paper is a preliminary study on the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles and the controller gain impact on the driveability. A simple low-level longitudinal model of the vehicle has been used along with a PD type cruise controller and a generic spacing policy for ACC/CACC. The acceleration of the previous vehicle is available to the next vehicle as input, and the simulations are performed as Cooperative Adaptive Cruise Control of a convoy of vehicles. Individual vehicle acceleration profiles have been analyzed for driveability for two different velocity profiles that are followed in a stretch of 720 m between stop signs. The controller gains have been re-tuned based on the parameter space robust control PID approach for driveability and compared with the original gains. The US06 SFTP drive cycle has also been used for the comparison of the two different controller gain sets.

Advanced transportation biofuels have been the focus of intensive development since the early 2000s, and gasification in combination with synthesis technologies represents a flexible production pathway to deliver fuels for heavy-duty transport sectors that are difficult to electrify. This article is related to the pilot-scale development of a process concept aiming to smaller-scale production plants than are feasible with fluidized-bed gasifiers. Five test weeks with a total gasification time of 347 hours were realized at a pilot plant that consisted of the pressurized staged fixed-bed gasifier, raw gas cooling to 500–600 °C, filtration with robust metal filters, and catalytic reforming of tars and methane. The gasifier combined an updraft primary stage and a catalytically enhanced secondary stage where most of the updraft tars were decomposed. The tar content of the product gas, 2–12 g/m3, was of the same order of magnitude as determined previously for fluidized-bed gasifiers. Consequently, similar filtration and reforming methods could be successfully applied. After the reformer, the contents of C2-hydrocarbon gases and high-molecular-weight tars were negligible. Esa Kurkela,* Minna Kurkela, Ilkka Hiltunen Pilot-scale development of pressurized fixed-bed gasification for synthesis gas production from biomass residues Biomass Conversion and Biorefinery https://doi.org/10.1007/s13399-021-01554-2

Concentrator PhotoVoltaics (CPV) may generate considerable heat as a byproduct in addition to power. Using that heat as concentrated solar power (CSP) to thermally desalinate salt water would allow for value addition in arid regions by supplementing produced electricity with fresh water. This paper discusses the primary trade-off between the net electricity amount and the net distilled water amount, both of which are desirable products in a hybrid concentrated photovoltaic and thermal system (CPV-T). While higher temperatures promote freshwater generation, they also cause a quick fall in cell efficiency, reducing the quantity of power generated and causing cell deterioration. The CPV-T receiver, which is partially covered with cells and then supplementary heated in a cell-free portion, is modeled and established. Furthermore, simple-stage and multistage desalination unit models are presented, linked to the receiver, and examined to cope with the reduced temperature limits of this receiver. The results demonstrate a CPV-T receiver's ability to generate both electrical power and freshwater, while also demonstrating that the cell temperature constraint can become quite important beyond 50% of the receiver surface coverage. These first order results establish the system's validity and potential and will aid the system designer in the initial system design.

In this paper, the influence of the variation in transformer oil temperature on the accuracy of the all-acoustic non-iterative method for partial discharge location in a power transformer is researched. The research can improve power transformers’ testing and monitoring, particularly given the large transformer oil temperature variations during real-time monitoring. The research is based on quantifying the contribution of oil temperature to the standard combined measurement uncertainty of the non-iterative algorithm by using analytical, statistical, and Monte Carlo methods. The contribution can be quantified and controlled. The contribution varied significantly with different mutual placements of partial discharge and acoustic sensors. The correlation between the contribution and the mean distance between partial discharge and acoustic sensors was observed. Based on these findings, the procedure to quantify and control the contribution in practice was proposed. The procedure considers the specificity of the method’s mathematical model (the assumption that the oil temperature is constant), the non-iterative algorithm’s nonlinearity, and the large variations in transformer oil temperature. Existing studies did not consider the significant effect of the oil temperature on the combined measurement uncertainty of partial discharge location influenced by those phenomena. The research is limited to partial discharge located in the transformer oil.

As the demand for lithium-ion batteries increases, a better understanding of the complex phenomena involved in their operation becomes crucial. In this work, we propose a coupled thermo-chemo-mechanical model for electrode particles of Li-ion batteries. To this end, we start with a general finite strain continuum framework for the coupled thermo-chemo-mechanical problem and then narrow it down to cathode active particles of Li-ion batteries, particularly to lithium manganese oxide particles. Electrochemical kinetics at the surface of the particle and also heat generation due to current exchange are taken into account. Next, the numerical treatment of the problem using the finite element method is presented. Specific line elements are needed to evaluate the flux of ions at the surface of the particle. Finally, the performance of the proposed model is evaluated using a few representative boundary value problems., Deutsche Forschungsgemeinschaft, Projekt DEAL

Production of applicable and homogeneous biochar for soil amendment purposes would benefit from knowledge on how feedstock heterogeneity impacts key biochar pore properties and how the properties are transformed due to pyrolysis. This study aimed (1) to quantify how clonal differences and within-tree heterogeneity of a hybrid aspen feedstock (wood) impact biochar pore properties and (2) to estimate how pore properties of non-pyrolysed wood materials are transformed when pyrolysed into biochar. The study was conducted by collecting samples from a hybrid aspen (Populus tremula L. × Populus tremuloides Michx.) clonal field trial. Key pore properties of non-pyrolysed and pyrolysed wood samples were quantified with 3D X-ray imaging and quantitative image analyses. The results demonstrated how pyrolysis shifted distinctively bi-modal pore size distributions of the wood samples towards smaller pore size regions. The bi-modal wood tissue structure controlled the pore structure also in the biochars. Due to decreasing cell wall thicknesses, the pyrolysis increased the porosity of the materials. While the thermal process homogenized differences in the wall thicknesses, the thicknesses of the feedstock were also shown to control the resulting thicknesses in the biochars. Mechanisms of biochar pore property formation can be considered important when designing applicable biochars for a chosen purpose. Clonal differences and within-tree heterogeneity had a direct impact only on the wall thicknesses and the pore diameters of vessels. These impacts can be of interest when planning feedstock utilization in biochar production. However, the results suggest that relatively homogeneous biochar can be produced from hybrid aspen feedstocks.

Integration of the pulsed power load (PPL) into the power system leads to high fluctuations in the frequency and voltage at the point of common coupling (PCC). This paper presents an effective solution to overcome these issues related to the PPL using the superconducting magnetic energy storage (SMES) system that has the features of high power capability and fast response. The fuzzy logic control technique (FLC) is employed to control the interfaced DC–DC chopper of the SMES to regulate the active power of the PCC and the state-of-charge (SOC) of the SMES coil. Two scenarios have been addressed with/without integrating the SMES conversion system. The on-time of the studied PPL is 0.1s with a total power of 5.7 MW, which is considered a high power demand in a short period. The simulation results based on MATLAB/Simulink show the high performance of the proposed system based on the FLC-SMES to minimize the voltage and frequency fluctuations during the operation of PPL.

Hybrid renewable energy systems (HRES) are seeing as a solution to overcome the fluctuation and randomness of certain renewable energy sources, such as solar and wind power. Coupling the fluctuating renewable energy sources with the controllable sources, such as biomass-fueled micro-cogeneration, constitute an HRES that significantly reduces CO2 emissions and primary energy consumption. The purpose of this work is to review research works on hybrid renewable energy systems based on micro-cogeneration and to present a case study of optimizing a solar-based micro-cogeneration system. First, renewable energy-fueled micro-cogeneration systems are presented according to the prime mover technology: Stirling engine, organic Rankine cycle and photovoltaic-thermal (PVT). The different prime movers are assessed according to their advantages, disadvantages and market availability. Next, several research works on hybrid renewable energy systems including solar and micro-cogeneration technologies are summarized and key findings are highlighted. Finally, the results of the case study are presented for reasoning the necessity of system hybridization. The results indicated that more experimental data on HRES and research effort on energy management strategies and stochastic optimization models are required. The results of the case study showed maximum thermal and electrical reliability of 68% and 70%, respectively. The optimized PVT/battery/thermal storage system was not able to cover all energy demand of the case study but supporting heat and electricity sources are required.

he transition from high- to lower-carbon production systems increasingly creates regulatory and market risks for high-emitting firms. We test to what extent equity market investors demand a premium to compensate for such risks and thus might raise firms' cost of equity capital (CoE). Using data for 1,897 firms spanning 50 countries over the years 2008–2016, we find a distinct and robust positive impact of carbon intensity (carbon emissions per unit of output) on CoE: On average, a standard deviation higher (sector-adjusted) carbon intensity is associated with a CoE premium of 6 (9) basis points or 1.7% (2.6%). This effect is primarily explained by systematic risk factors: high-emitting assets are significantly more sensitive to economy-wide fluctuations than low-emitting ones. The CoE impact of carbon intensity is more pronounced in high-emitting sectors, EU countries, and firms subject to carbon pricing regulation. Our results suggest that carbon emission reduction might serve as a valuable risk mitigation strategy.

In this study, the in situ chemical polymerization process was used to synthesize polystyrene/magnetite nanocomposite (PS-DVB/ Fe3O4). The prepared composite was characterized by XPS, SEM, XRD, HRTEM, FT-IR, and TGA technique. The XPS as an essential elemental analysis tool proved the proposed general formulae of the prepared nanocomposite (PS-DVB/ Fe3O4). FT-IR data proved the functional groups in the proposed structural formula of the prepared nanomaterials. The thermal analysis confirmed the high thermal stability of the prepared core–shell polymer. XRD results prove the crystallinity and single phase. Scanning electron microscope (SEM) and the presence of Fe2O3 and PS-DVB/Fe3O4 composite has been confirmed by HRTEM. The particle size analysis was used to determine the distribution of particle size within the polymer matrix. The adsorption potential of Tartrazine azo dye from polluted water samples onto cross-linked (PS-DVB/Fe3O4) using fixed-bed adsorption column was investigated. The adsorption capacity of Tartrazine onto PS-DVB greatly improved when Fe3O4 is added to the porous composite of PS-DVB copolymer to be 0.15 mol with removal efficiency reach 98%. In this respect, the effect of liquid flow rate, initial Tartrazine concentration, and PS-DVB/Fe3O4 bed height on the adsorption technique's breakthrough features was taken. In this work, the mass transfer model, which involved the two parameters of τ (50% breakthrough time) and k (adsorption rate constant), was suggested for discussion the effect of PS-DVB/Fe3O4on such values. It was found that the adsorption capacity Qe and τ values were decreased with the flow rate increases. The flow rate had little effect, at least for the PS-DVB/Fe3O4, on the k value. On the other hand, both Qe and τ values decreased with increasing the initial Tartrazine concentration, whereas the k value was slightly increased. We concluded that the liquid flow rate, initial Tartrazine concentration, and bed height were1mL/min, 5 M, and 7 cm, respectively. Finally, the PS-DVB/Fe3O4 was a suitable Tartrazine adsorbent using a fixed-bed adsorption column, which fitted well with the mass transfer model.

A novel design of printed CPW-fed planar elliptical slot antenna with transformed circular patch is presented here for dual-band application. The obtained impedance band finds application in lower C-band satellite communication and entire X-band with significant gain. Electromagnetic wave simulator HFSS® is used to simulate the structure and the study of parameter behaviour is carried out. The performance of the proposed antenna in terms of return loss, radiation pattern and gain are analysed. Furthermore, the same structure is designed and simulated with microstrip feed line. An imperial formula in terms of axial length and ellipticity ratio of the elliptical slot is formulated to find the lower edge frequency of impedance bandwidth.

The thermogravimetric (TGA) responses of PA6 composites filled with synthetic Graphite (G) and Graphite nanoplatelets (GNP) each on 5 cumulative loading levels were investigated using two processing streams each of a common processing strain. To determine the effect of the 2 carbon fillers on the thermal stability of unfilled PA6, the temperatures at which 5, 20 and 50 wt% losses occurred were observed for the composites and the unfilled PA6. Weight lost at 460 °C and the carbon char at 600 °C were also noted. Effects of varying extrusion screw speeds as well as sonication amplitudes with time were given cognisance. To evaluate the effect of processing on the thermal response of the composites, two in situ polymerised streams and 2 melt extrusion streams with each set being equivalent in ‘magnitude of strain histories’ were characterised using TGA. The TGA for the unfilled PA6 and its graphite composite were conducted under inert (N2) blanketing and reactive (air) media. Initially, TGA responses of the fillers were studied and both GNP and G remained stable in N2. The onset of weight loss under N2 blanketing for G and GNP were respectively 820 ± 27.6 °C and 777.3 ± 17.7 °C. At temperature close to 900 °C, G showed relatively poorer thermal stability compared to GNP. A residue of 1.3% for G and 2.0% for GNP were left. The thermal stability of the streams G200/3 and G100/6 remained within a close range thus indicating that varying screw speeds and durations did not play a significant role on influence the thermal stability of the melt processed composites. For the composites, thermal stability were comparable with unfilled PA6, especially in the melt-processed systems, while Some changes occurred in the 2 in situ polymerized systems (20/20 and 40/10) which was ascribed to the presence of small molecules from the catalysing species or unconverted monomers.

The separation of azeotropic mixtures, particularly, pressure sensitive azeotropes is intriguing, challenging, and inevitable in chemical industries in comparison to pressure insensitive azeotropic mixtures. The main bottleneck for separating pressure sensitive azeotropes lies in the pressure selection and sequencing of distillation columns. A conundrum in pressure swing distillation for both minimum and maximum boiling azeotropes primarily about the feasibility of column configurations has been discussed in this work. The two column configurations, Low pressure Column-High Pressure Column (LPC-HPC) and High Pressure Column-Low Pressure Column (HPC-LPC) are found to be dependent on the feed composition, type of azeotrope (whether, minimum or maximum), and the effect of pressure on azeotropic composition. This article has explored concomitantly pressure swing distillation technique (continuous one) and the effect of various feed compositions on the column sequencing and process feasibility. A comprehensive strategy for both minimum and maximum boiling azeotropes has been proposed depending upon the effect of pressure on azeotropic composition. Two example case studies have been also presented in support of the proposed strategies for pressure swing distillation technique.

Fused deposition modeling (FDM) is a widely applied rapid prototyping technology that produces layered models from specially designed thermoplastic filaments. Thermoplastic materials can be layered using several parameters that influence the mechanical properties of the printed object. In this study, the effects of freeze–thaw cycles on the physical characteristics of FDM objects printed using the ULTEM 9085 filament is assessed. The samples used in this study were printed with an air gap of –0.025 mm and at raster angles of 0°, 45°, and 90°. The results indicate that freeze–thaw conditioning reduces the tensile properties of FDM samples. Results also can help enhance FDM material properties to withstand significant temperature changes whilst having a prolonged service life.

The present work carries about a unique experimental investigation in the thermal management and cooling realms of the Photovoltaic (PV) panels under the most harsh environmental conditions. The performance of the heat sink and thermal storage heat sink techniques were analysed amongst the three passively cooled PV panels. Moreover, the efficacy of the immersion and coolant thermal properties was evaluated and discussed for the four actively cooled Photovoltaic Thermal (PVT) collectors. Even though, it was found that the heat sink-based PV panel configuration is more performant relatively compared to the PCM heat sink based-PV one since a drop of ten-degree in PV cell temperature was recorded, but still, the heat dissipation rate is not enough to withstand the severe mechanical and electrical degradation challenges under the harsh environmental climate of Saudi Arabia. For the actively cooled PVT collectors, it was found that the immersion technique protects the PV panel from the hot spot apparatuses and lead to better heat dissipation uniformity. The heat pipes based-PVT collector immersed in regular water has recorded the optimal temperature distribution and maximum temperature drop by 53% relatively compared to best passively cooled configuration. With ethylene glycol, engine oil, and active cooling without immersion, the steady-state PV temperature has been reduced relatively compared to a passively cooled PV panel by about 48%, 25%, and 21%, respectively.

Geopolymer is an eco-friendly material used as an alternative to Portland cement and it can be used in construction practices as a repair material to concrete/as a structural element in construction/ as a whole. This study was mainly intended to understand the efficiency of geopolymers in the structural system when special concretes are involved in construction. Shear bond strength was evaluated for the combination of self compacting geopolymer concrete (SCGC) and normal self compacting concrete (SCC). SCGC was developed under external exposure curing conditions enabling it to be used in cast-in-situ production. This was achieved by binary blending of mineral admixtures, use of activator solutions, and superplasticizer. Comparative study on two types of push-off tests namely (1) Conventional Push-off test (Type 1) and (2) Push-off test with normal load acting over the shear interface (Type 2) were conducted to evaluate the interface shear strength. Test results were compared with selected shear friction models available from literature and the effect of introducing self compacting geopolymer concrete in composite interfaces were analyzed.

At the end of December 2019, the Wuhan Municipal Health Commission, revealed several cases of pneumonia of unknown etiology. Later, this etiology was called the coronavirus disease 2019 (COVID-19). COVID-19 disease is rapidly spreading around the globe, affected millions of people, compelling governments to take serious actions. Due to this deadly disease, a number of deaths have been occurred and still increasing exponentially. In the practice and application of big data sciences, it is always of interest to provide the best description of the data. In this present article, the event background, symptoms, and preventions from COVID-19 are discussed. The steps were taken by the Chinese government to control the COVID-19 has also been discussed. Up to date, details, and data of daily discovered cases, total discovered cases, daily deaths, and total deaths around the world are presented. Moreover, a new statistical distribution is introduced to provide the best characterization of the survival times of the patients affected by the COVID-19 in China. By analyzing the survival times of the COVID-19 patient’s data, it is showed that the new model provides a closer fit to COVID-19 events.

The rapid rise in the application of novel treatment techniques, such as intensity-modulated radiotherapy (IMRT), motivated us to survey the status of Korea's radiation safety management and the shielding designs of facilities employing medical linear accelerators (LINACs). To this end, a questionnaire was used to collect information on LINAC facilities and treatments, workload, shielding design, shielding management, and path of obtaining shielding information. Out of 100 domestic institutions, 52 responded to the survey. Approximately 70% of the institutions utilized IMRT for more than 60% of their cases, and an IMRT factor of 5 was adopted by 75% of these institutions. Over 80% of the institutions accounted for the applied time-averaged dose rate per week and instantaneous dose equivalent rates in their shielding designs. Approximately 45% of the institutions obtained important shielding information via a radiation shielding design company and the NCRP-151 report. Overall, most facilities were shown to follow the standards recommended by the relevant international agencies. However, the requirement to establish standardized shielding design information and clarify ambiguous paths for information acquisition was also highlighted. Therefore, the study's results can be used as a foundation for establishing a safety control system and for creating adequate shielding designs.

International audience; This paper is a contribution to fasten integration of battery pack innovation in commercial Electric Vehicles (EV) through massive digitalization: a seamless process detailed for battery design, battery safety, and battery management. Selected results of studies carried out on the EV value chain from design to recycling steps are presented, highlighting the importance of seamless integration and holistic state of mind when designing EV. Association between experimental and numerical approaches for efficient innovative EV production is crucial to achieve easy commercialisation. Successful forecasting of aging and thermal runaway evolution from single cell failure at module level using such methods illustrates their great potential. Hardware key counterparts under development are also introduced and give an idea of future architecture of EV battery packs and overall improvement of EV energy efficiency. Finally, a flexible and easily modifiable solution for battery electric vehicle (BEV) that allows rapid and cost-effective integration of future innovation is presented. This paper globally illustrates key breakthroughs gained in the context of the collaborative research project named ‘DEMOBASE’, for DEsign and MOdelling for improved BAttery Safety and Efficiency successfully submitted for funding by the European Commission in response to a 2017 call dedicated to ‘Green Vehicles’ under the EU Horizon 2020 work programme “Smart, green and integrated transport”.

Typical fire extinguishing agents were considered: water; bischofite solutions (with a mass fraction of 5% and 10%); bentonite slurries (with a mass fraction of 5% and 10%); foaming agent emulsions (with a mass fraction of 5% and 10%). The heating temperature range of 150-400°С was chosen to correspond to the conditions of rapid thermal decomposition of forest fuels. The experimental research findings suggest that the rates of moisture release depend exponentially on the heating temperature. It was established that the rates of moisture release in the above temperature range may differ significantly for the forest fuels and fire extinguishing agents under study. Conditions were identified when the general approximation equations, presented in this paper, can be used to predict the vaporization characteristics of firefighting liquids.

Railway passenger comfort has been considered a growingly important field to attract more passengers from other public transports such as air flights. To allow passengers and train companies to estimate the onboard passenger comfort level, we propose a phone-based hybrid machine learning (ML) model combining pre-train convolutional neural network as a feature extractor and support vector regressor as a predictor. To better demonstrate the capacity of the proposed model, two sub-models of the hybrid model and the same hybrid model but with non-pre-train feature extractor are adopted to be benchmarks. The raw field data is acquired from a corridor between the University of Birmingham station and Birmingham International station using phones, subsequently calculated to corresponding comfort level according to UIC 513. The four models are trained by the dataset in two domains – time domain and frequency domain, then optimized by random search and validated by 10-fold cross-validation. The proposed method yields the best performance with an $R^{2}$ of 0.988 ± 0.004, a root-mean-square error (RMSE) of 0.028 ± 0.015, and a mean-absolute-error (MAE) of 0.02 ± 0.005. The results of this study underpin the possibility that the railway passenger has the access to quantify the level of comfort and the real-time assistance for the train driver to calibrate the driving style from the proposed system.

Data imputation studies include reconstruction or estimation of imperfect data gaps caused by system sensing failure, and non-responsive data transmission remains an open issue. In space weather applications, imputation of ground electromagnetism is significant in capturing the complex interaction of sun–earth prior to the subsequent analysis of the space weather effects. Key contributions to the demonstration of supervised machine learning (ML) imputation approach with artificial neural network, K-nearest neighbour, support vector regression (SVR), and General Regression Neural Network (GRNN) for MAGDAS-9 ground electromagnetism dataset have not yet been established. A total of 1,585,950 data points were analysed with supervised ML models which included performance benchmark with statistical analysis namely zero value substitution, listwise deletion, mean substitution, and hot deck imputation. To achieve low reconstruction errors, different imputation models with hyperparameter tuned settings are varied, and computational time execution has been shown to contribute to imputation performance. Performance metrics measured by mean square error (MSE), mean absolute error (MAE),mean absolute percentage error (MAPE), and execution time respectively demonstrate the capability of SVR to perfectly impute missing data for all ground electromagnetism components at an average of 0.314 MSE, 0.738 MAPE, closeness to 0.510 MAE and 0.91-second at various percentage level of data missingness. A comparison with traditional imputation shows that the supervised ML with SVR model has improved imputation performance by up to 80% of data gap. The outcome of the proposed imputation will benefit space weather applications for event characterisation, which will cover a large number of missing data in the MAGDAS-9 dataset.

Human failure event (HFE) dependency analysis is a part of human reliability analysis (HRA). For efficient HFE dependency analysis, a maximum number of minimal cut sets (MCSs) that have HFE combinations are generated from the fault trees for the probabilistic safety assessment (PSA) of nuclear power plants (NPPs). After collecting potential HFE combinations, dependency levels of subsequent HFEs on the preceding HFEs in each MCS are analyzed and assigned as conditional probabilities. Then, HFE recovery is performed to reflect these conditional probabilities in MCSs by modifying MCSs. Inappropriate HFE dependency analysis and HFE recovery might lead to an inaccurate core damage frequency (CDF). Using the above process, HFE recovery is performed on MCSs that are generated with a non-zero truncation limit, where many MCSs that have HFE combinations are truncated. As a result, the resultant CDF might be underestimated. In this paper, a new method is suggested to incorporate HFE recovery into the MCS generation stage. Compared to the current approach with a separate HFE recovery after MCS generation, this new method can (1) reduce the total time and burden for MCS generation and HFE recovery, (2) prevent the truncation of MCSs that have dependent HFEs, and (3) avoid CDF underestimation. This new method is a simple but very effective means of performing MCS generation and HFE recovery simultaneously and improving CDF accuracy. The effectiveness and strength of the new method are clearly demonstrated and discussed with fault trees and HFE combinations that have joint probabilities.

The spread of COVID-19 to more than 200 countries has shocked the public. Therefore, understanding the dynamics of transmission is very important. In this paper, the COVID-19 mathematical model has been formulated, analyzed, and validated using incident data from West Java Province, Indonesia. The model made considers the asymptomatic and symptomatic compartments and decreased immunity. The model is formulated in the form of a system of differential equations, where the population is divided into seven compartments, namely Susceptible Population ( S 0 ) , Exposed Population ( E ) , Asymptomatic Infection Population ( I A ) , Symptomatic Infection Population ( Y S ) , Recovered Population ( Z ) , Susceptible Populations previously infected ( Z 0 ) , and Quarantine population ( Q ) . The results show that there has been an outbreak of COVID-19 in West Java Province, Indonesia. This can be seen from the basic reproduction number of this model, which is 3.180126127 ( R 0 > 1 ) . Also, the numerical simulation results show that waning immunity can increase the occurrence of outbreaks; and a period of isolation can slow down the process of spreading COVID-19. So if a strict social distancing policy is enforced like a quarantine, the outbreak will lessen.

The mVINIS ion source, a part of FAMA installation at “Vinca” Institute of Nuclear Sciences, is able to produce multiple charged heavy ion beams through the utilization of vapors created by the process of melting solids inside the miniature oven (mini-oven). The mini-oven that was used previously could only reach the maximum temperature of 800°C, which is far too low for evaporating most metals. For this purpose, a higher operating-temperature of 1500°C was needed. Our study focuses on numerical finite element method (FEM) analysis of the temperature distribution of newly designed mini-oven.

In this study, a free energy model of the Lattice Boltzmann Method (LBM) is performed to simulate the motion, deformation, and decomposition of a droplet in the presence of electrohydrodynamic flow in porous media. To simulate the multi-phase flow in the presence of dielectric current by using the LBM, three distribution functions are used. To implement the free energy mode of HCZ, two equilibrium distribution functions are considered, one for solving the Navier-Stokes equation and the other for solving the Cahn-Hillard equation. Initially, the ability of the code to apply surface tension is tested by using the Laplace law and the droplet release test. Results show that there is a linear relation is between surface tension and κ which is a parameter in the HCZ model. The results show that the present numerical program is capable of modeling the regulated surface tension force as well. Then, the Rayleigh–Taylor instability simulation is used to evaluate the code's ability to apply volume forces. Also, the obtained results of the written numerical program are in good agreement with the numerical results of other valid references. Obtained results show that the difference between droplet deformation measured by numerical method and Taylor function is less than 2%. After modeling the droplet motions to investigate the droplet deformation, two electric fields are inserted into the droplet with reverse directions of each other. Then, by various tests, it is shown that at a given potential difference the droplet breaks down after much deformation and is divided into smaller droplets. The decomposition of droplets in a pre-mixed emulsion is a common technique to produce the monodisperse droplets. The presence of monodisperse droplets in an emulsion improves the physical properties of polymer from the science expert's perspective. The results of this study are used to improve the quality of polymer components.

Lightweight constrained application protocol (CoAP) has emerged as a common communication protocol for resource-constrained equipment in distribution internet of things (IoT). CoAP introduces two modes for data transmission, i.e., non-confirmed mode for reducing transmission delay and confirmed mode for reducing packet-loss ratio, which can be dynamically selected to satisfy the service requirements. However, there are still some challenges in dynamic CoAP mode selection, including incomplete information and differentiated quality of service (QoS) requirements of distributed IoT services. In this paper, we propose a upper confidence bound (UCB)-based dynamic CoAP mode selection algorithm for data transmission to address these challenges. The simulation results validate that, compared with the fixed mode selection algorithm, the proposed algorithm can flexibly balance the tradeoff between packet-loss ratio and transmission delay as well as satisfy the differentiated QoS in distribution IoT.

Understanding nucleate pool boiling heat transfer and, in particular the accurate prediction of conditions that can lead to critical heat flux, is of the utmost importance in many industries. Due to the safety issues related to the nuclear power plants, and for the efficient operation of many heat transfer units including fossil fuel boilers, fusion reactors, electronic chips, etc., it is important to understand this kind of heat transfer. In this paper, a comprehensive review of analytical and numerical work on nucleate pool boiling heat transfer is presented. In order to understand this phenomenon, existing studies on boiling heat transfer coefficient and boiling heat flux are also discussed, as well as characteristics of boiling phenomena such as bubble departure diameter, bubble departure frequency, active nucleation site density, bubble waiting and growth period and their impact on pool boiling heat transfer.

In vehicles, air conditioning tends to offer efficient thermal conditioning and air circulation inside cabin for passenger comfort from different climate variation. The working performance of air conditioning system in vehicles is an essential factor for thermal comfort and driving safety of the passenger compartment of vehicles. Therefore, it is of great significance to construct a numerical simulation model for theoretical analysis and performance evaluation of vehicle air conditioning system. In current research, the air conditioning system of an electric vehicle is taken as the research subject. Internal flow field characteristics of air conditioning system is investigated by Computational Fluid Dynamics (CFD). Results show that the air flow in air-conditioning system is relatively smooth, and the velocity distribution in the front windshield A area and the driver's field of vision of the left and right windows is not ideal when the defrosting-demisting mode is on; the velocity and air quantity distribution of the two outlets in the middle air duct are not uniform under blowing mode. In view of the uneven distribution of velocity and air quantity, the corresponding optimization scheme is proposed. After optimization, the cell relative velocity of the front windshield is significantly improved, and velocity distribution of the side window glass is slightly increased and the defrosting-demisting performance meets the vehicle defrosting requirements of national standard; after optimization, the air quantity distribution of the air ducts at blowing mode is more uniform, which is conducive to the comfort of the passenger compartment.

This paper describes the SPOWTT project. The intention of this project was to understand how sailing by crew transfer vessel (CTVs) to offshore wind farms affects the mental and physical wellbeing of individuals on board. The focus was on quantifying this impact, understanding the key drivers, with an aim to ensuring personnel can arrive to the wind turbines in a fit state to work safely and effectively. Impacts looked at subjective state beyond simply vomiting. Key results include the ability now to predict vessel motions from given Metocean conditions and vessel designs. We also discovered that the impact of vessel motions on seasickness is different for different symptoms and is driven not only by vertical z‐axis accelerations but also by certain frequencies of motion in the y‐axis. Frequencies other than 0.16 Hz were found to be impactful, and x‐axis movements appeared to have a longer‐lasting effect on the day's work. Through the formulation of a new, evidence‐based understanding of seasickness, we have created an operational planning tool, designed to have a direct benefit on the safety and productivity of offshore wind farm operations.

Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).

The Shell-and-tube type heat exchangers have long been widely used in many fields of industry. These types of heat exchangers are generally easy to design, manufacturing and maintenance, but require relatively large spaces to install. Therefore the optimization of such heat exchangers from thermal and economical points of view is of particular interest. In this article, an optimization procedure based on the minimum total cost (initial investment plus operational costs) has been applied. Then the flow analysis of the optimized heat exchanger has been carried out to reveal possible flow field and temperature distribution inside the equipment using computational fluid dynamics. The experimental results were compared with computational fluid dynamics analyses results. It has been concluded that the baffles play an important role in the development of the shell side flow field. This prompted us to investigate new baffle geometries without compromising from the overall thermal performance. It has been found that the heat exchanger with the new baffle design gives rise to considerably lower pressure drops in the shell side, which in turn reducing operating cost. The new baffle design is particularly well suited for shell-and-tube heat exchangers, where a viscous fluid flows through shell side with/out phase change.

This study investigates the hidden thermal losses of glass plate, collector plate, water pipe and storage tank of solar water heater in the process of energy conversion. The present non-conventional energy methods are insufficient, whereas the exergy analysis provides a remarkable solution. Thus, employing the exergy analysis, entropy generation, exergy destruction and exergy efficiency of each subsystem of solar water heater are computed. The obtained results showed that the entropy generation and exergy destruction are high during the heat transfer in each subsystem. Henceforth, the existing solar water heater design is modified placing hexagonal honeycomb structure between the glass plate and the collector plate and also water pipe is insulated to trap huge amount of solar energy. The proposed design exhibits improved exergy efficiency when compared with the existing model, which enhances the performance of the system.

Phishing attackers spread phishing links through e-mail, text messages, and social media platforms. They use social engineering skills to trick users into visiting phishing websites and entering crucial personal information. In the end, the stolen personal information is used to defraud the trust of regular websites or financial institutions to obtain illegal benefits. With the development and applications of machine learning technology, many machine learning-based solutions for detecting phishing have been proposed. Some solutions are based on the features extracted by rules, and some of the features need to rely on third-party services, which will cause instability and time-consuming issues in the prediction service. In this paper, we propose a deep learning-based framework for detecting phishing websites. We have implemented the framework as a browser plug-in capable of determining whether there is a phishing risk in real-time when the user visits a web page and gives a warning message. The real-time prediction service combines multiple strategies to improve accuracy, reduce false alarm rates, and reduce calculation time, including whitelist filtering, blacklist interception, and machine learning (ML) prediction. In the ML prediction module, we compared multiple machine learning models using several datasets. From the experimental results, the RNN-GRU model obtained the highest accuracy of 99.18%, demonstrating the feasibility of the proposed solution.

The main contribution of this paper is proposing a new approach for retrieving the five parameters of the single diode equivalent model (SDM) of photovoltaic cells/modules including the series and shunt resistances. The least square method is used as an error minimization technique for fitting the non-linear transcendental model equation of the solar panel to the measured I–V characteristics. Newton Raphson method is applied to solve the system of five non-linear equations which represent the error of each parameter. Initial guess values are calculated with an optimised algorithm depending on information extracted from the same measured data. MATLAB programming script was used in all implementation steps. This approach is useful for a wide variety of applications where the five SDM parameters have to be determined from the measured curves, particularly, for self-fabricated cells/modules or in case of no available datasheet. One of the strengths of this method is the higher level of accuracy because of the absence of mathematical simplifications and physical assumptions. The method was validated on different types of PV devices, including a crystalline silicon cell, a polycrystalline module, and an amorphous module by using measurement data obtained at a wide range of solar irradiance conditions and temperatures.

The fuel efficiency of gas engines is effected by the temperature of intake air at the suction of turbocharger. The data on dependence of fuel consumption and engine electric power on the intake air temperature were monitored for Jenbacher gas engine JMS 420 GS-N.LC to evaluate its influence. A waste heat of engine is rejected for heating water to the temperature of about 90°С. The heat received is used in absorption lithium-bromide chiller to produce a cold in the form of chilled water. A cooling capacity of absorption chiller firstly is spent for technological needs and then for feeding the central air conditioner for cooling the ambient air incoming the engine room, from where the air is sucked by the engine turbocharger. The monitoring data revealed the reserves to enhance the efficiency of traditional cooling system of intake air by absorption chiller through deeper cooling. This concept can be realized in two ways: by addition cooling a chilled water from absorption chiller to about 5-7°С for feeding engine intake air cooler or by two-stage cooling with precooling ambient air by chilled water from ACh in the first stage and subsequent deep cooling air to the temperatures 7-10°С in the second stage of intake air cooler by using a refrigerant as a coolant. In both cases the ejector chiller could be applied as the most simple in design.

More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event. This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.

Ammonia synthesis is a crucial section of ammonia and urea plant. Many industries are facing a challenge of efficient ammonia production every day. In this study, steady state one dimensional pseudo-homogeneous models of an axial flow industrial catalytic packed bed ammonia converter have been developed. The converter is a vertical two catalytic bed reactor with varying volumes of catalysts. Required industrial data for the design were collected from a real fertilizer industry in Bangladesh (RFIB) and the Halder Topsoe process was followed. Differential equations of the the developed mathematical model was solved using Runge-Kutta-Fehlberg (RKF45) method by Polymath solver software. For a 23.8% single-pass conversion total 22.5 (15 + 7.5) m catalyst bed required. Pressure vessel wall and head thickness were found 112 mm and 62 mm respectively. Skirt support was selected and the thickness was calculated 12 mm. Pressure drop along the length of the bed was calculated and the value was found 2.55 atm. Finally, parameters of the optimized model were compared with the real plant data and quite satisfactory result were obtained.

Our devices can use a wide range of communication technologies such as multiple cellular technologies (4G/5G), WiFi, and also Ethernet. At the same time, applications have a choice of a wide range of transport protocols such as QUIC and TCP that can be fine-tuned and optimized according to their needs. However, in spite of these advances, offering seamless multiconnectivity to applications continues to be a hard problem. The key factors that continue to be a roadblock towards achieving seamless multiconnectivity include a) applications cannot specify the communication technologies to be used by their flows, and b) the traditional definition of a connection endpoint was not designed to support mobile nodes. In this paper we discuss the key challenges that make this problem hard. We also present MULTI, a session layer approach that can be leveraged to address some of the key sub-problems of this problem. For instance, we observe that MULTI incurred a small overhead (less than 5% decrease in throughput) when using TCP compared to the native asyncio python library.

In this study, mixed convection in a nanofluid filled cavity induced by thermal buoyancy force, moving wall and rotating flat plate subjected to external magnetic field is numerically investigated. The cavity is partially heated from its bottom wall and cooled from top wall moving with constant velocity in ±x direction and other walls are kept adiabatic. A counter-clockwise rotating flat plate is placed at the centre of the cavity. The cavity is permeated by a transverse magnetic field. Conservation equations are simulated through implementing finite element method. Numerical results are presented using streamlines, isotherms and bar charts to explore the effects of physical parameters on the flow and temperature fields. It is found that flow and thermal fields are impressively affected with the variations in length and speed of rotating flat plate. Besides, higher length and rotational speed of the plate causes maximum amount of heat transfer. Best heat transfer is ensured while the direction of rotating plate is same as the direction of lid wall. Moreover, optimal heat transfer performance is obtained up to 5% nanoparticles concentration which is 123.02% more than base fluid. Higher magnetic field strength attenuates the fluid motion and hence heat transfer rate significantly.

To achieve fast, safe and economical drilling operations through formations containing natural fractures or vugs, all fractures and vugs must be perfectly sealed using proper fractures seal materials in a process called wellbore strengthening. Wellbore strengthening may elevate the overall tensile strength of the treated formation to a value approaches the intrinsic tensile strength of unfractured rock. Numerous studies have been conducted for testing fractures seal materials suitability for wellbore strengthening application in various formations. However, there is no solid and simple theoretical explanation on how wellbore strengthening process can recover the tensile strength and hence the fracturing pressure of the treated naturally fractured formation. In this study, test specimens cored from an artificial (building) sandstone bricks were used to measure the unconfined compressive strength (UCS), Brazilian indirect tensile strength (BTS), and establishing the multi-stage triaxial compression (MS-TCS) test. Core samples from the same sandstone were previously used for wellbore strengthening studies using crushed dates palm seeds. Crushed dates palm seeds succeeded to completely stop mud losses at a pressure equal to 6.9 MPa. Data obtained from unconfined compression, multi-stage triaxial compression, Brazilian indirect tensile, and wellbore strengthening tests were collectively used to develop a clear theoretical understanding of the mechanism of tensile strength recovery caused by wellbore strengthening effect. It was found that wellbore strengthening effect in sealing natural fractures and vugs is similar to the confining pressure effect in closing the induced shear fractures in the test specimen during a multi-stage triaxial compression test. For the tested sandstone, using crushed dates palm seeds as a fracture seal material has improved wellbore strengthening from zero tensile strength (complete loss of mud circulation) to 2.56 MPa tensile strength at which there was a complete fracture seal (zero mud loss). The recovered tensile strength (2.56 MPa) is approaching the intrinsic tensile strength (BTS = 2.7 MPa) of the same sandstone in its initial unfractured state.

We consider a model of wavelet shrinkage with two normal distributions where the variance of the noise is estimated using exponential distribution. Instead of normal and double exponential distributions on the parameters domain of wavelet coefficients, which is usually the case in the existing literature, the ∊ -contamination prior distribution is involved that is particularly attractive to work with when one seeks robust priors in Bayesian analysis. The proposed method starts with computing the posterior mean and the posterior mean under the mass point. This article covers the major topic of theoretical approaches. Wavelet methods are applied, involving decimated wavelet transform with a single parameter for each level. The proposed method is illustrated on the standard test functions of Donoho and Johnstone, where the level of noise corrupts these functions. This procedure can be explained as a type of inverse problem. We compared some standard wavelet-based methods with the existing process. Extensive simulation is implemented to perform the proposed method and improve signal’s reconstruction using average mean-squared error to compare different approaches. As a practical illustration, we present an application to a real-life data set. The main interest of the paper is to compare two different likelihoods-the first is the normal distribution, and the other is the double exponential distribution.

Missing data are unavoidable in the real-world application of unsupervised machine learning, and their nonoptimal processing may decrease the quality of data-driven models. Imputation is a common remedy for missing values, but directly estimating expected distances have also emerged. Because treatment of missing values is rarely considered in clustering related tasks and distance metrics have a central role both in clustering and cluster validation, we developed a new toolbox that provides a wide range of algorithms for data preprocessing, distance estimation, clustering, and cluster validation in the presence of missing values. All these are core elements in any comprehensive cluster analysis methodology. We describe the methodological background of the implemented algorithms and present multiple illustrations of their use. The experiments include validating distance estimation methods against selected reference methods and demonstrating the performance of internal cluster validation indices. The experimental results demonstrate the general usability of the toolbox for the straightforward realization of alternate data processing pipelines. Source code, data sets, results, and example macros are available on GitHub. https://github.com/markoniem/nanclustering_toolbox peerReviewed

The implementation of the “Internet +” policy advocated by the state has also led to rapid development of Internet finance. In order to promote changes in business development models, as a pioneering work for banks serving the real economy, supply chains are being developed to address small and medium-sized enterprises. The financing of enterprises, the transformation and development needs of banks themselves, and the promotion of logistics technology. Edge computing refers to an open platform that integrates network, data processing, storage and application core functions, and can provide the closest end-of-page service near the object data source to meet real-time, application intelligence, security and privacy Sexual needs. The core of supply chain financing is to establish an optimized plan that can effectively control supply chain financing. By integrating the financing literature of the supply chain, the settlement cost in the supply chain can be solved. Based on theoretical research, this article analyzes supply chain financing and block chain technology. Combined with the current specific situation of block chain in supply chain financing, the management system, cash flow of the supply chain, and risk control system are analyzed. All parties to the supply chain financing optimize the supply chain financing risk control system while reducing business costs and improving corporate efficiency, which greatly reduces the risks of all parties in the supply chain financing. The block chain Iota environment based on shared data and advanced data processing has very powerful theoretical and practical significance for promoting the development of commercial banks and enterprises.

In this work, we examine a nonlinear partial differential equation of fluid mechanics, namely, the generalized nonlinear advection–diffusion equation, which portrays the motion of buoyancy driven plume in a bent-on porous medium. Firstly, we classify all (point) symmetries of the equation, which prompt three cases of n. Next, for each case, we construct an optimal system of one-dimensional subalgebras and use them to perform symmetry reductions and symmetry invariant solutions. In a bid to explain the physical significance of some invariant solutions secured, we present a graphic display of some solutions in 3D, 2D as well as density plots via the exploitation of numerical simulations. Besides, we categorically state here that the results obtained in this study are new when compared with the outcomes previously achieved by Loubens et al., 2011 Quart. Appl. Math. 69 389–401. Interestingly, kink shape soliton, dark soliton, singular soliton together with exponential function solution wave profiles are displayed to make this work more valuable. Furthermore, we determine the conserved vectors in two different ways: engaging the general multiplier approach and Ibragimov’s conservation law theorem. Finally, we provide the physical meaning of these conservation laws.

In the field of ecommerce, most recommendation algorithms are based on user-item bipartite graph network (BGN). But this kind of recommendation algorithm is severely lacking in accuracy and diversity. In this paper, a novel ecommerce recommendation algorithm is proposed based on BGN link prediction. Firstly, all the user-item data were imported into distance formula to calculate the similarity between the attributes. Then, the BGN was projected into a single-mode network (SMN), making it more efficient to extract potential links from the BGN. On this basis, the potential links were predicted based on similarity. Through experiments on real ecommerce datasets, it was proved that our algorithm has a higher accuracy and coverage than typical recommendation algorithms.