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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.

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.

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.

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”.

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.

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.

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.

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 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.

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.

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.

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.

In this article, we introduce the discrete fractional sum equations and inequalities. Some new generalized Gruss type discrete fractional sum inequalities are developed. We employ a nabla or backward difference; we employ the Riemann-Liouville definition of the fractional difference. Furthermore, the current research is a discrete variant of fundamental inequalities set up in the recent literary works and extends a few discrete version for ∇ -fractional sum specifically on time scale ℏ N , where 0 ℏ ⩽ 1 .

In this paper, a computational approach based on the operational matrices in conjunction with orthogonal shifted Legendre polynomials (OSLPs) is designed to solve numerically the multi-order partial differential equations of fractional order consisting of mixed partial derivative terms. Our computational approach has ability to reduce the fractional problems into a system of Sylvester types matrix equations which can be solved by using MATLAB builtin function lyap(.). The solution is approximated as a basis vectors of OSLPs. The efficiency and the numerical stability is examined by taking various test examples.

This current study investigates the effect of blending bio-lubricant with commercial lubricant on physico-chemical and tribological properties. The synthesis of TMP and PE ester are optimized to obtain 100% long chain tetra-esters and extreme care was taken during preparation of bio-lubricant to lessen deterioration of physio-chemical properties by blending with commercial oil. SM grade commercial petrol engine oil was blended with Tri-methylolpropane (TMP) ester, Penta-erythritol (PE) ester derived from Calophyllum-inophyllum in 10, 15, 20 and 25% v/v. The tests for anti-wear, anti-friction and extreme pressure properties are conducted on the sample oils on a four-ball wear tester. The friction coefficient and wear of bio-lubricant – commercial oil blends have decreased significantly up to 20% blending percentage. In the extreme pressure test, there is a substantial improvement in the weld load and marked improvement in the load wear index signifying the better load-bearing capacity of commercial oil – bio-lubricant blends. A synergy between bio-lubricant and additives in the commercial oil was found from a metallographic examination of worn balls after the EP test. The optimum volume percent of blend was found to be 15% PE blend and 10% TMP blend for optimum overall performance.

Wind energy technology is based on the ability to capture the energy contained in air motion. Wind power quantifies the rate of this kinetic energy extraction. Wind power is also the rate of kinetic energy flow carried by the moving air. Because the motion is both the source of the energy and the means of its transport, the efficiency of wind power extraction is a balance of slowing down the wind while maintaining a sufficient flow. This chapter quantifies these fundamental concepts and discusses the nature of wind.

A facile solution process to synthesis ZnO thin films was successfully conducted in this work. Zinc acetate dihydrates were used as the main precursor, dissolved in the mixture of acetylacetone and ethanol whichact as solvent and precursor simultaneously. This mixture deposited by spin coating and dried under ambient atmosphere at room temperature. The reaction mechanism, structure, morphology, chemical composition and optoelectrical properties of the resulted ZnO thin film were studied. The as deposited films were annealed at 500 °C for 2 h under O2 gas flow added with H2O vapor and CO2 gas. The ZnO thin films showed a dense, homogenous morphology and high transparency. The film is an n-type semiconductor with 3.18 eV bandgap, has 1.25 × 10−19 cm3 carrier concentration, 12 cm2/Vs electron mobility and 2.42 × 10−3 Ω·cm resistivity making it suitable for application as window layer in a photovoltaic cell.

Banana peel-derived porous carbon from natural biowaste had been prepared with different annealing temperatures at 300, 400, 500, 600, and 700 °C. The as-prepared carbon powders were carefully characterized with XRD, SEM, TEM, EIS, and XPS measurements and their antibacterial activities were tested toward Escherichia coli (E. coli - ATCC 25922). It was found that 600 °C as-prepared porous carbon exhibited the most significant inhibitor against E. coli colonies’ growth. It was found that the natural potassium chloride in the banana peel-derived porous carbon played an essential role in preventing the bacterial growth. Furthermore, the as-prepared nanosized porous carbon also directly contacted the bacterial cell wall, which seriously affected the cellular activities, including the metabolic processes and transfer of substances in the cell interfaces. Therefore, the synergistic effect between natural potassium chloride and porous carbon gave rise to antimicrobial activity. The generated electron and hole from low thermal-induced excitation in porous carbon and released electron from potassium significantly deactivated the bacteria activity. The bactericides of banana peel-derived porous carbon were investigated at 37 °C without light illumination and the mechanism of antibacterial growth was elucidated and proposed in this work.

In this study, volcanic ash was used as raw material to prepare waste forms with different silicon/aluminum (Si/Al) molar ratios to immobilize sodium-salt waste (SSW) containing simulated 137Cs. Effects of Si/Al molar ratios (3:1 and 2:1) and sodium salts on sintering behavior of waste forms and immobilization mechanism of Cs+ were investigated. Results indicated that the main mineral phase of sintered waste-form matrixes was albite, and the formation of major phases was found to depend on Si/Al molar ratios. Si/Al molar ratio of 2 was favorable for the formation of pollucite, and the formation and crystallization of mineral phases were also decided based on physicochemical characteristics of sodium salts. Furthermore, product consistency test results indicated that the immobilization of Cs+ was related to Si/Al molar ratio, types of sodium salts, and glassy phase. Waste forms with Si/Al molar ratio of 2 exhibited better ability to immobilize Cs+, whereas the influence of sodium salts and glassy phases on the immobilization of SSW showed more complicated relationship. In waste forms with Si/Al molar ratio of 2, Cs+ leaching concentrations of samples containing Na2B4O7·10H2O and NaOH were low. Na2B4O7‧10H2O easily transformed into liquid phase during sintering to consequently achieve low temperature liquid-phase sintering, which is beneficial to avoid the volatilization of Cs+ at high temperature. Results clearly reveal that waste forms with Si/Al molar ratio of 2 and containing Na2B4O7‧10H2O show excellent immobilization of Cs+.

Swarm-based nature-inspired optimization techniques are developing rapidly due to their universal acceptance and capability in solving various real-life challenges efficiently. Hybridization of swarm intelligence based optimization algorithms with multi-objective based solution techniques is creating a wide door in the field of optimization. The main focus of this work is, developing a hybrid Non-dominated Sorting Whale Optimization Algorithm (NSWOA), where swarm-based Whale Optimization Algorithm (WOA) is hybridized with multi-objective based, non-dominated sorting technique. This is done to develop an algorithm with efficient optima searching ability and faster computational speed. The application of NSWOA in optimizing the controller parameters of an islanded microgrid consisting of both static and dynamic load has been also described. SPSS software has been used to compare the performance of proposed NSWOA with non-dominated sorting genetic algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA) technique in optimizing the controller parameters of islanded microgrid model with multi-objective problem. It is obtained that, NSWOA requires an average of 4 iterations to reach the best optimum solution, which is less than other existing algorithms. Moreover, the computational time required by NSWOA is 2.9201 s, which proves that, it converges at a much faster rate compared to existing NSGA-II and SPEA algorithms.

Owing to the improved thermophysical properties of nano-materials, the nanofluid convey novel applications in industries, engineering as well as bio-medicals. With high efficiency performances, the nanoparticles include applications in various cooling and heating systems, energy production, aerospace engineering, thermal extrusion, bio-medical applications like treatment of diseases, brain tumour, surgical applications and many more. Moreover, the magneto-nanofluids have the characteristics in the flow of blood in the human artery. The current analysis deals with the free convective unsteady flow of viscous nanofluid subject to the magnetic force over a vertical plate in presence of porous space. The heat transfer phenomenon is accessed by utilizing the additional features like Joule heating, viscous dissipation and absorption coefficients. In addition to the volume fraction of the nanofluid, the effect of the chemical reaction is also introduced. The solution procedure for the modified time-dependent boundary value problem is performed by using the code in-build MATLAB namely Built-in-Shooting method. The change in velocity, temperature and concentration of nano-materials is examined through various graphs. The physical consequences of flow parameters like porosity parameter K p 1 ⩽ K p ⩽ 3 , thermal Grashoff number Gr 1 ⩽ G r ⩽ 3 , solutal Grashoff number Gc 1 ⩽ G c ⩽ 3 , magnetic field parameter M 1 ⩽ M ⩽ 3 , thermal radiation parameter Nr 0.1 ⩽ N r ⩽ 1 , radiation absorption parameter Q 0 ⩽ Q ⩽ 0.5 , Prandtl number Pr 2 ⩽ Pr ⩽ 21 , Eckert number Ec 0 ⩽ E c ⩽ 0.4 , heat source S - 0.2 ⩽ S ⩽ 0.2 , chemical reaction parameter Kc 1 ⩽ K c ⩽ 4 , nanoparticles volume fraction ϕ 0.0 ⩽ ϕ ⩽ 0.1 , Lewis number Le 1 ⩽ L e ⩽ 10 on velocity, temperature and concentration distributions is graphically analyzed. The results show that skin friction coefficients decline with buoyant forces. A lower numerical variation in Nusselt number is resulted with heat source parameter. Moreover, a sharp fall in nanofluid concentration is noticed with increment of chemical reactants and Lewis number.

SACOS series of subchannel analysis codes have been developed by XJTU-NuTheL for many years and are being used for the thermal-hydraulic safety analysis of various reactor cores. To achieve fine whole core pin-level analysis, the input preprocessing and parallel capabilities of the code have been developed in this study. Preprocessing is suitable for modeling rectangular and hexagonal assemblies with less error-prone input; parallelization is established based on the domain decomposition method with the hybrid of MPI and OpenMP. For domain decomposition, a more flexible method has been proposed which can determine the appropriate task division of the core domain according to the number of processors of the server. By performing the calculation time evaluation for the several PWR assembly problems, the code parallelization has been successfully verified with different number of processors. Subsequent analysis results for rectangular- and hexagonal-assembly core imply that the code can be used to model and perform pin-level core safety analysis with acceptable computational efficiency.

Preflex beam girders with remarkable advantages are widely used in bridge structures. With aging, preflex beam bridges undergo concrete cracking and degrade as the cracks propagate and deteriorate due to repeated service load application and exposure to humid and saline environment. The objective of this study was to evaluate concrete cracking, changes in prestress, and consequent changes in sectional properties as the preflex beam undergoes deterioration in its concrete casing under cyclic loading. The experimental study investigated the behavior of a preflex beam specimen under multiple cyclic loading ranges, reproducing different deterioration levels in the concrete casing of the specimen. Relating concrete deterioration to resulting changes in the girder’s structural behavior, the study outlined recommendations for monitoring approaches in actual bridges. The study concluded that with advancement in concrete deterioration, the preflex beam girder loses prestress that results in reduced strength of the girder and remarkable changes in its sectional properties. The study also concluded to minimize prestress loss and changes in sectional properties of the girder, closer spacing for shear ribs welded to the lower flange can be helpful., 論文

In this paper, we implemented the Exp-function method and the extended tanh method to finds the analytical solutions of one of the important nonlinear partial differential equations with variable coefficients called the new (3 + 1)-dimensional Date-Jimbo-Kashiwara-Miwa with time-dependent coefficients. Moreover, we find the numerical solutions to that equation using the finite difference method. The results demonstrate the effectiveness and convenience of the used methods. Comparison between our results through some tables and graphs to illustrate the accuracy of the applied methods. We obtained the different types of solutions-like bright, dark, periodic, rational and singular periodic soliton solutions using the different values of constant parameters. The obtained exact solutions may be useful to understand the mechanism of the complicated nonlinear physical phenomena which are related to wave propagation in (3 + 1)-dimensional Date-Jimbo-Kashiwara-Miwa with time-dependent coefficients model.

Most of the previous studies on mobile robot path planning consider the obstacles as polygons. However, the complex shaped obstacles should be considered as curves rather than polygons, since the latter may result in non-optimal paths. Three new algorithms are proposed for finding the shortest path of a mobile robot among curved obstacles: TAD which uses a tangent drawing method, TRAD which uses trapezoidal decomposition, and TAD-TRAD which uses a combination of the TAD and the TRAD algorithms. The time complexities of the TAD algorithm, the TRAD algorithm, and the TAD-TRAD algorithm are O ( n 3 log n ) , O ( n log n ) , and O ( n ( log n ) 2 ) respectively, where n is the total number of arcs in all the obstacles. The TAD and the TAD-TRAD algorithms compute an optimal shortest path, whereas the TRAD algorithm computes an approximate shortest path, among curved obstacles. The TAD algorithm has a lower time complexity than existing algorithms which are implemented for the computation of the optimal shortest path. The TRAD algorithm has a lower time complexity than existing algorithms which compute an approximate shortest path and the TAD-TRAD algorithm has a lower time complexity than existing algorithms which compute an optimal shortest path. The experimental comparison of the TAD, TRAD, and TAD-TRAD algorithms on 200 randomly generated problems shows that the TAD-TRAD algorithm has the least running time. Moreover, the TAD-TRAD algorithm computes an optimal path using a lesser number of tangents and with lesser time complexity when experimental comparisons are performed with existing algorithms in the literature.

Corrosion has a significant deteriorative effect on fatigue life of aged ship and offshore structures; hence reliable numerical fatigue life analysis of aged ship structures is crucial for safe operation. The reliability of the fatigue damage assessment techniques depends on the quality of the S-N curve which should account for corrosion degradation. In this paper, the different factors affecting the randomness of stress concentration and fatigue life of corroded specimens have been studied. It was demonstrated that the Stress Concentration, which results from the distinctive thickness variation of the corroded specimens, significantly depends on the mean and minimum thickness of the corroded plate. The equivalent thickness of a typical plate, which has the same fatigue strength of the corresponding corroded one under the same cyclic load, was investigated. A time-dependent S-N curve of corroded structures considering the deterioration of material mechanical properties was proposed. The corroded surfaces were modeled using random spatial distributions and the effect of corrosion degradation on the material mechanical properties was taken into account. The numerically estimated fatigue life and the proposed S-N curve were compared with experimental fatigue test results. The comparison showed good agreement between the experimental results and the adopted approaches for fatigue life assessment.

This study focuses on staff scheduling problems in a natural gas combined cycle power plants with demanding shift systems. Scheduling the personnel working in power plants to the most suitable shift program can be arduous in terms of energy supply security. Natural gas combined cycle power plants are labor units where an expensive resource such as labor is used intensively. The primary aim in these types of enterprises is to ensure maximum employee satisfaction with minimum operating and labor costs. To meet this goal, companies must use various techniques. Shift scheduling models can also be considered as one of the useful tools in this respect. A constraint programming model has been developed for the shift scheduling problem. This model allows personnel to be assigned equally to shifts, minimizing the cost increase due to overtime. The proposed model satisfies the need for electricity generation in a big natural gas combined cycle power plant was operated in Turkey with real data has an acceptable calculation time to obtain the optimal solution. Staff scheduling model with the proposed improvements to the problem is provided close to 33% compared with the present situation. The calculation results show that the constraint programming model gives better results than the current schedule. The specificity of this study can be listed as follows: Firstly; the different model is presented in detail for the personnel scheduling problem, compared with the actual results and the results of the proposed model. Secondly, the personnel scheduling problem is modelled as a task-source match with the standard scheduling logic and modelled effectively with constraint programming. With the model, suitable solutions can be produced in a short time calculation period.

In this paper, Crank-Nicolson differential quadrature method based on cubic exponential B-spline (CExpB-spline) functions is presented to approximate the 1D nonlinear hyperbolic Sine-Gordon equation (SGE). The time derivative is discretized by the usual forward difference scheme while the differential quadrature method (DQM) is used to integrate the spatial derivatives. The discretization of the problem gives systems of linear equations. Three numerical examples are chosen to investigate the efficiency and accuracy of the method. It is observed that the proposed method provides excellent results than the existing methods. The rate of convergence (ROC) of the present method is obtained numerically showing that the method is second-order accurate in space.

In indoor navigation and visual odometry, cameras may be used as an aiding algorithm for inertial navigation. Fast and accurate image matching is an important task used in various applications in computer vision and visual odometry. In this research, the performances of all recently available detection, description, and matching techniques were compared. The detection techniques included HARRIS, GFTT, SIFT, SURF, STAR, FAST, ORB, MSER, Dense, and SimpleBlob. Feature description techniques included SIFT, SURF, ORB, and BRIEF. Finally, image matching techniques included BruteForce, BruteForce-L1, BruteForce-Hamming, BruteForce-HammingLUT, and FlannBased. The comparison was made between different kinds of geometric distortions and deformations, such as scaling, rotation, fish-eye distortion, and shearing. In this research, the lighting conditions and the shadowing effects were not taken into consideration. To perform this task, different types of transformations were manually applied to original images and computed with the matching evaluation parameters, such as the number of detected keypoints in images, the processing time, and the matching accuracies for each algorithm to show which algorithm was the best and most robust against these distortions. This work provided us with a perspective on and contributed to the field of image-based indoor navigation systems to recommend further research. The results showed that the ORB detector, the ORB descriptor, and either the BruteForce-Hamming or the BruteForce-HammingLUT matchers were favored to be used in indoor environments.

The hydrocarbon supply chain (HCSC) is a significant part of the world's energy sector. The energy market has experienced erratic behavior over the last few years results in financial risks such as exceeding certain limits of the budget or not achieving the desired levels of cash in-flow, i.e., revenue. In this work, robust optimization and multi-objective mathematical programming are used to develop a model that eliminates or at least mitigates the impact of uncertain market behavior. Robust optimization provides tactical plans that are feasible and robust over market scenarios. The model assesses the trade-offs between alternatives and guides the decision-maker towards the effective management of the HCSC. The economic objectives are to minimize total cost and maximize revenue, while the non-economic objective is to minimize the depletion rate. The model considers the environmental aspect by limiting the emission of CO2 and the sustainability aspect by reducing the depletion rate of natural resources. Uncertain behavior of the oil market is modeled on scenario representation. A case study based on real data from Saudi Arabia HCSC is provided to demonstrate the model's practicality, and a sensitivity analysis is conducted to provide some managerial insights. The results indicate that Saudi Arabia can cover its entire expenditure, break-even-point, by producing oil at 7.18 MMbbld and gas at 3,543.48 MMcftd. Besides, the robust approach provides a preferred plan with the highest cash inflow and the lowest sustainability over other approaches, e.g., deterministic, stochastic, and risk-based. The differences show that the robust model increases oil production to compensate for the variability of the scenario.

Color medical images hold noteworthy impact in clinical conclusion and treatment. Medical image segmentation reduces the uncertainty by providing detailed information about the shape, size, or location characteristics. However, traditional methods suffer from low accuracy, high complexity, and are less robust. To overcome these drawbacks, this paper proposes an efficient metaheuristic algorithm, exchange market algorithm (EMA) for multilevel thresholding (MLT) of distinct medical images. Optimal threshold is effectively obtained through the most promising objective functions such as Kapur, Otsu and minimum cross entropy (MCE) aided with EMA. The EMA involves exchange of shares among the investors in stable and unstable market situations to achieve profit. Exploration and exploitation are achieved by second and third groups of stable and unstable modes of EMA. Moreover, the execution time is reduced by the highly competent shareholders retaining their top rank without any changes in their shares. The efficacy of the proposed paper is evaluated on three distinct medical images at 4, 5, 6 and 7th threshold levels and compared with the recent algorithms such as Krill herd (KHA), Teaching-learning based optimization (TLBO) and Cuckoo search algorithm (CSA). Quantitative and qualitative validation by metrics such as computational time, Peak signal to noise ratio (PSNR), Structural similarity index (SSIM) and Wilcoxon rank sum test affirm that the EMA is superior to other algorithms. On the other hand, Otsu based EMA method is found to be more accurate and robust for improved clinical decision making and diagnosis.

This study discusses the acoustic property variations by introducing new absorption materials. An experimental set up established to simulate virtually as well as experimentally with various biodegradable, biocompatible and naturally absorbable materials for a truck cabin of carrier capacity 3.5Tonnes. The materials were embedded on the sidewall and base of the cabin covering the entire cabin not leaving any air pockets. Experimental and simulation study was carried out for six materials and in the later part a combination of these materials also carried out to see through the possible optimal cases providing due Weightage to cost and weight. A sound quality index study being made to see with each case of material how the sound pressure level affects the rider and the subordinates seating in a cabin. A detailed simulation study carried out to understand the material behavioral characteristics along with convergence study using ANSYS Workbench. The exhaustive work emphasis on percentage error study with simulation and experimental work and proposes an optimal material from sound pressure level and rider comfort aspects.