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In molecular communications, the direct detection of signaling molecules may be challenging due to a lack of suitable sensors and interference in the environment. Motivated by research in molecular biology, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. We consider two implementations of the proposed detection mechanism, i.e., unrestricted probe movement and probes restricted to a volume around the receiver. The reaction-diffusion equations describing the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed form. Therefore, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables of the equations in each time step. Our results show that the concentrations of the product molecules and the signalling molecules share a similar characteristic over time, i.e., a single peak and a long tail. The peak and tail values of the product molecule concentration can be controlled by choosing probes with suitable parameters. By carefully choosing the molecular probe and optimizing the decision threshold, the BER can be improved significantly and outperform that of a direct detection system., 13 pages, 11 figures, 1 table. This journal version was submitted in April 2022 to the IEEE for possible publication. A part of this article was presented at the IEEE Wireless Communications and Networking Conference 2020 and stored in the previous version on arXiv

Nanoelectrochemistry, where electrochemical processes are controlled and investigated with nanoscale resolution, is gaining more and more attention because of the many potential applications in energy and sensing and the fact that there is much to learn about fundamental electrochemical processes when we explore them at the nanoscale. The development of instrumental methods that can explore the heterogeneity of electrochemistry occurring across an electrode surface, monitoring single molecules or many single nanoparticles on a surface simultaneously, have been pivotal in giving us new insights into nanoscale electrochemistry. Equally important has been the ability to synthesise or fabricate nanoscale entities with a high degree of control that allows us to develop nanoscale devices. Central to the latter has been the incredible advances in nanomaterial synthesis where electrode materials with atomic control over electrochemically active sites can be achieved. After introducing nanoelectrochemistry, this paper focuses on recent developments in two major application areas of nanoelectrochemistry; electrocatalysis and using single entities in sensing. Discussion of the developments in these two application fields highlights some of the advances in the fundamental understanding of nanoelectrochemical systems really driving these applications forward. Looking into our nanocrystal ball, this paper then highlights: the need to understand the impact of nanoconfinement on electrochemical processes, the need to measure many single entities, the need to develop more sophisticated ways of treating the potentially large data sets from measuring such many single entities, the need for more new methods for characterising nanoelectrochemical systems as they operate and the need for material synthesis to become more reproducible as well as possess more nanoscale control.

For land management and planning, information on the Land Use Land Cover (LULC) is vital. In this research, three optimizers of the Multi-Verse Optimizer (MVO), Genetic Algorithm (GA), and Derivative-free Function (DF) are developed in MATLAB programming language to improve the accuracy of remote sensing image classification using a Small-sized Neural Network (SNN). The results are compared to a Medium-sized Neural Network (MNN) developed in MATLAB programming language. Based on the test data, the MNN has the best performance with the Overall Accuracy (OA) of 92.64% for the object-based Landsat-8 imagery with a spatial resolution of 15 m. Based on the test data, the Derivative-free Function Multi-layer Perceptron (DFMLP) for the pixel-based Landsat-8 imagery with a spatial resolution of 15 m has the best performance with the OA of 89.31%. The Genetic Algorithm Multi-layer Perceptron (GAMLP) for the pixel-based Landsat-8 imagery with a spatial resolution of 30 m has the least performance with a value of 74.47% for the OA. The most significant improvement was for the pixel-based Landsat-8 imagery with a spatial resolution of 15 m where the DF and GA optimizers have improved the results of the SNN classifier with 7.37% and ~8% for the OA index, respectively.

In this study, films of iron disulfide (FeS2) nanostructures are prepared by electrochemical deposition on the Fluorine-doped tin oxide (FTO) glass substrate. The effect of deposition parameters such as voltage, time, molarity, and also sulfurization temperature are studied as parameters affecting the physical properties of the deposited films. The prepared films are studied and characterized by analysis of X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX), Raman spectroscopy, photoluminescence (PL), and UV–Vis–NIR spectroscopy. Following sulfurization, the XRD analysis shows the cubic phase of pyrite. The phase purity of the prepared samples are confirmed by Raman spectroscopy and the presence of Fe and S elements is verified by EDX analysis. The micrograph of FESEM shows that different morphologies in porous, spherical, and flower-like forms in nanometer dimensions are obtained by changing the effective parameters. The PL spectrum shows the presence of emission bands in the visible region. With the increase in the sulfurization temperature, the light absorption intensity of the films increases and the energy band gap increases from 1.53 to 1.73 eV, confirmed by UV–Vis–NIR spectroscopic analysis. The Mott-Schottky's analysis shows the p-type conductivity. Based on the results, the amount of photosensitivity, photoresponsivity, and specific detectivity increases with the rise in temperature. The sample sulfurized at 250 °C, compared to the sample sulfurized at 200 °C, shows a remarkable photoresponsivity in the range of visible spectrum. The values of photoresponsivity, photosensitivity, and specific detectivity at 100 mW/cm2 intensity are 22.7 mA/W, 18.80%, and 0.37×109 (Jones), respectively. According to the obtained results, this material has the potential for use in the fabrication of high-efficiency optoelectronic devices.

Grid-connected photovoltaic (PV) systems play an important role in reducing emissions resulting from conventional fossil-fuel-based power plants. However, in order to effectively integrated PV systems into the power system, many challenges regarding these renewable resources such as extracting maximum power under various conditions should be solved. This paper suggests an enhanced maximum power point tracking (MPPT) by the fuzzy logic controller (FLC) and a modified bat algorithm (MBA) to fine-tune the parameters of the controller. The FLC is greatly affected by rule base and membership functions (MFs). The fine-tuning of such parameters cannot be appropriate when accurate information regarding the system is not available. To overcome the above-mentioned challenges, the MBA algorithm is utilized to optimize the scaling factors of MFs. Simulation results confirm that the suggested MBA-FLC method can effectively cope with the global maxima under different weather circumstances with high efficiency, faster tracking and stable output.

In this paper, we present a low-complexity recursive approach for massive and scalable code-domain nonorthogonal multiple access (NOMA) with applications to emerging low-capacity scenarios. The problem definition in this paper is inspired by three major requirements of the next generations of wireless networks. Firstly, the proposed scheme is particularly beneficial in low-capacity regimes which is important in practical scenarios of utmost interest such as the Internet-of-Things (IoT) and massive machine-type communication (mMTC). Secondly, we employ code-domain NOMA to efficiently share the scarce common resources among the users. Finally, the proposed recursive approach enables code-domain NOMA with low-complexity detection algorithms that are scalable with the number of users to satisfy the requirements of massive connectivity. To this end, we propose a novel encoding and decoding scheme for code-domain NOMA based on factorizing the pattern matrix, for assigning the available resource elements to the users, as the Kronecker product of several smaller factor matrices. As a result, both the pattern matrix design at the transmitter side and the mixed symbols' detection at the receiver side can be performed over matrices with dimensions that are much smaller than the overall pattern matrix. Consequently, this leads to significant reduction in both the complexity and the latency of the detection. We present the detection algorithm for the general case of factor matrices. The proposed algorithm involves several recursions each involving certain sets of equations corresponding to a certain factor matrix. We then characterize the system performance in terms of average sum rate, latency, and detection complexity. Our latency and complexity analysis confirm the superiority of our proposed scheme in enabling large pattern matrices., Accepted for publication in the IEEE Transactions on Communications

In this letter, we focus on large intelligent reflecting surfaces (IRSs) and propose a new codebook construction method to obtain a set of predesigned phase-shift configurations for the IRS unit cells. Since the overhead for channel estimation and the complexity of online optimization for IRS-assisted communications scale with the size of the phase-shift codebook, the design of small codebooks is of high importance. We show that there exists a fundamental tradeoff between power efficiency and the size of the codebook. We first analyze this tradeoff for baseline designs that employ a linear phase-shift across the IRS. Subsequently, we show that an efficient design for small codebooks mandates higher-order phase-shift variations across the IRS. Consequently, we propose a quadratic phase-shift design, derive its coefficients as a function of the codebook size, and analyze its performance. Our simulation results show that the proposed design yields a higher power efficiency for small codebooks than the linear baseline designs.

In several phenomena in biology and industry, it is required to understand the comprehensive behavior of sedimenting micro-particles in fluids. Here, we use the numerical refocusing feature of digital holographic microscopy (DHM) to investigate the slippage effect on micro-particle sedimentation near a flat wall. DHM provides quantitative phase contrast and three-dimensional (3D) imaging in arbitrary time scales, which suggests it as an elegant approach to investigate various phenomena, including dynamic behavior of colloids. 3D information is obtained by post-processing of the recorded digital holograms. Through analysis of 3D trajectories and velocities of multiple sedimenting micro-particles, we show that proximity to flat walls of higher slip lengths causes faster sedimentation. The effect depends on the ratio of the particle size to (1) the slip length and (2) its distance to the wall. We corroborate our experimental findings by a theoretical model which considers both the proximity and the particle interaction to a wall of different hydrophobicity in the hydrodynamic forces.

Carbon nanotubes (CNTs) are stiff, all-carbon macromolecules with diameters as small as one nanometer and few microns long. Solutions of CNTs in chlorosulfonic acid (CSA) follow the phase behavior of rigid rod polymers interacting via a repulsive potential and display a liquid crystalline phase at sufficiently high concentration. Here, we show that small-angle X-ray scattering and polarized light microscopy data can be combined to characterize quantitatively the morphology of liquid crystalline phases formed in CNT solutions at concentrations from 3 to 6.5 % by volume. We find that upon increasing their concentration, CNTs self-assemble into a liquid crystalline phase with a pleated texture and with a large inter-particle spacing that could be indicative of a transition to higher-order liquid crystalline phases. We explain how thermal undulations of CNTs can enhance their electrostatic repulsion and increase their effective diameter by an order of magnitude. By calculating the critical concentration, where the mean amplitude of undulation of an unconstrained rod becomes comparable to the rod spacing, we find that thermal undulations start to affect steric forces at concentrations as low as the isotropic cloud point in CNT solutions.

Due to the numerous downsides associated with the non-renewable energy sources, the global attention is increasingly diverting towards using renewable sources. Among many others, the biomass of agricultural crops is shown to be a promising non-renewable energy source, hence becoming a preferable choice to extract/synthesize value added products. In this respect, rice husk has shown its potential time and again. As an example, rice husk can be converted into ash which is already an investigated source of rich silica content, and therefore can be utilized in the relevant applications. Agricultural biomass and its waste have gravitated a significant interest of researchers around the globe due to their low reaction time and high utilization. The present review explores the novel and recent green/sustainable applications of rice husk/rice husk ash in various engineering, industrial, and other renewable energy applications. Moreover, it gives a background of agro-waste applications along with a comparative study of achievable silica from rice husk and other agro-waste sources.

As the occurrence of disasters has increased frequently and has resulted in growing concern about their adverse effects on the environment, Sustainable Humanitarian Logistics (SHL) has received great attention recently. SHL aims to reduce disaster damages in an environmentally-friendly manner in the shortest possible time. The terms including ‘environmentally-friendly’ and ‘shortest possible time’ refer to the environmental and social aspects of sustainability. This research proposes a stochastic multi-objective mixed-integer programming model to configure an SHL network during the response phase. Having compared to the research literature, this is the first study that considers economic, social, and environmental aspects of sustainability by incorporating relief cost, deprivation cost, and carbon emissions, respectively. Then, the improved multi-choice goal programming approach is applied to solve the proposed multi-objective model. To indicate the validity of the proposed model, an earthquake that occurred in a region of Kermanshah, Iran, in 2017 is investigated as a real case study. Finally, sensitivity analysis is performed and several managerial and theoretical insights are provided. The results show that exerting environmental issues in humanitarian logistics does not necessarily increase the relief costs, but can be in contrast with the social aspect. Furthermore, a minor increase in the budget of the preparedness phase drastically decreases the response costs.

The objective of this paper is to investigate the thermal behaviour and loadability characteristic of a yokeless and segmented armature axial-flux permanent-magnet (YASA-AFPM) generator, which uses an improved 3-D coupled electromagnetic-thermal approach. Firstly, a 1-kW YASA-AFPM generator is modelled and analysed by using the proposed approach; the transient and steady-state temperatures of different parts of the generator are determined. To improve the modelling accuracy, the information is exchanged between the thermal and electromagnetic models at each step of the co-simulation, considering both the accurate calculation of losses and the impacts of temperature rise on the temperature-dependent characteristics of the materials. Then, by using the proposed approach, the impact of the slot opening width and the turn number of stator segments on the generator loadability are investigated. After that, the experimental tests are performed. The results reveal the effectiveness and accuracy of the approach to predict the machine loadability and thermal behavior.

The influence of overall particle shape, mean particle size, initial relative density and effective normal stress on the global and local soil–geosynthetic interaction is investigated here through an extensive series of direct shear interface tests between six sands in contact with one multifilament woven geotextile, one non-woven geotextile and one geomembrane. Analysis of the soil–geosynthetic data highlights mobilisation of higher peak friction angles with increases in initial relative density and angularity of sand particles and decrease in the effective normal stress. The average frictional efficiencies of the sand–geosynthetic interfaces are 0·95 and 0·79, respectively, for the sand–geotextile and sand–geomembrane interfaces. It is shown that the interface slip increases with the initial relative density of sand and regularity of particles, and decreases with increasing the mean particle size and effective normal stress. Analysis of the local particle movements by means of digital particle image velocimetry revealed that the average thickness of the shear zone for the sand–geosynthetic interfaces tested ranges from 3·8 to 11·9 (with an average of 7·0) times the mean particle size of the contact sand. The experimental data indicate that thicknesses of the shear and dilation zones are almost equal for the sand–geosynthetics interfaces tested.

Strategies to enhance economic opportunities, while minimizing environmental impacts, are primary goals in watershed development. This study aims to evaluate ecotourism and socioeconomic strategies using the strengths, weaknesses, opportunities, and threats (SWOT) decision framework. This framework is especially suitable to evaluate complex issues facing many watershed systems. A survey with attributes of the SWOT framework is used to study future strategies for watershed development. An expert panel is used to develop and evaluate the questionnaires and tested for reliability using statistical measures. The Cronbach’s alpha coefficient was calculated (0.92). Independent variables are based on attributes related to each of the strengths, weaknesses, opportunities, and threats categories in the watershed. Internal factors (IF = 2.77 of 4) and external factors (EF = 2.53 of 4) were assigned by weighted scores in the evaluation process. The results show that an aggressive strategy or strength–opportunity (SO) is an optimal strategy for the watershed. An offensive strategy can be used to promote economic development, ecotourism expansion, morale boosting, and raising capital for investment and rural development, including watershed ecotourism.

The present study implemented an integrative approach facilitated by the developed model called ‘the location-based model of sustainable-development-of-energy' to select the optimal location for deploying Run-of-River (RoR) hydropower plants in Fomanat plain, Gilan, Iran. The suggested model was developed based on a geospatial information system (GIS) and multi-criteria-decision-making (MCDM) methods. A best-worst method (BWM) was further applied to weigh the criteria and calculate a land suitability index (LSI) to evaluate the potential sites. The results were grouped into five categories from the ‘least suitable' to the ‘optimal' based on LSI. Finally, the potential power generation by RoR hydropower plants was estimated for each river in the output suitable areas from the model after validating the result by the field survey process. Accordingly, 0.44% (19.15 km2) of the study area was optimal for deploying RoR hydropower plants, and the results had the highest infrastructure, geographical, and economic potentials due to the aspects of the model. Furthermore, the results indicated that installing RoR hydropower plant facilities in the Fomanat plain produce an average of 4371 KWH per month, which suffices to meet the residential need of more than 12,000 households.

Due to the advent of powerful parallel processing tools, including modern Graphics Processing Units (GPU), new deep learning algorithms, such as Convolutional Neural Networks (CNNs), have significa...

Natural soil deposition process results in formation of anisotropic planes with different properties along different directions, and hence, such natural deposits possess inherent anisotropy. On the other hand, an estimate of the elastic settlement of surface foundations placed on such anisotropic deposits is very important. In this research, the effect of anisotropy (in fact, cross-anisotropy) on the elastic settlement of shallow foundations resting on anisotropic granular soils, often sands, has been investigated. The most important properties in this work, in addition to the geometry of the footing, are the soil modulus of elasticity, Poisson’s ratio and the ratio of these moduli along anisotropic planes in a cross-anisotropic medium. The governing equations of deformation in such media are first presented. Then, the elastic settlement of shallow foundations resting on such media has been estimated using the finite difference method, and then, some influence factors are presented. The factors are useful in estimating the elastic settlement of shallow foundations on cross-anisotropic soils, when the soil properties along two perpendicular directions are known. A series of design charts are provided in non-dimensional form so as to avail their practical use in a general form.

This article presents the first experimental-computational study on the centrifugal detachment of a compound droplet (e.g., a primary water droplet cloaked by an immiscible oil) from a fiber. The work was intended to establish a method for quantifying the force needed to detach compound droplets of different compositions from a fiber. More importantly, our study was aimed at improving the understanding of the interplay between interfacial and external forces acting on a compound droplet during forceful detachment. The experiments were conducted using DI water, for the primary droplet, and silicone or mineral oil, for the cloaking fluid. It was observed from the experiments that the silicone-oil-cloaked droplets behave differently from the mineral-oil-cloaked droplets. It was also observed that detachment force decreases with increasing the oil-to-water volume ratio. The simulations were performed using the Surface Evolver (SE) finite element code programmed for the complicated four-phase (air, water, oil, and solid) interfacial problem at hand. Our simulations revealed the evolution of the interfacial forces between the interacting phases under an increasing external body force on the droplet. The simulations also allowed us to define effective interfacial tensions and contact angles for detaching compound droplets, for the first time. Reasonable agreement was observed between the experimental measurements and computational results.

In this work, solubility of hydrogen in some alkenes was investigated at different temperatures and pressures. Solubility values were calculated using the Peng–Robinson equation of state. Binary interaction parameters were calculated using fitting the equation of state on experimental data, Group contribution method and Moysan correlations and total average absolute deviation for these methods was 3.90, 17.60 and 13.62, respectively. Because hydrogen solubility in Alkenes is low, Henry’s law for these solutions were investigated, too. Results of calculation showed with increasing temperature, Henry’s constant was decreased. The temperature dependency of Henry’s constants of hydrogen in ethylene and propylene was higher than to other alkenes. In addition, using Van’t Hoff equation, the thermodynamic parameters for dissolution of hydrogen in various alkenes were calculated. Results indicated that the dissolution of hydrogen was spontaneous and endothermic. The total average of dissolution enthalpy ($${\Delta H}^{^\circ }$$ Δ H ∘ ) and Gibbs free energy ($${\Delta G}^{^\circ }$$ Δ G ∘ ) for these systems was 3.867 kJ/mol and 6.361 kJ/mol, respectively. But dissolution of hydrogen in almost of alkenes was not an entropy-driven process.

© Copyright 2021, The Author(s). An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to change its affiliation with other clusters based on a deep learning modified Element-wise Attention Gate. The modified Element-wise Attention Gate has the ability to handle the buffer capacity in all the network, thereby enriching the Quality of Service. A deep learning modified training algorithm is proposed to learn the artificial intelligent system allowing the neurons to have greater concentration ability. The simulation results demonstrate that the Root Mean Square error is minimized by 37.14% when using modified Element-wise Attention Gate when compared with a Deep Learning Recurrent Neural Network. Also, the Quality of Service of the network is improved, for example, the network lifetime is enhanced by 12.7% more than with Deep Learning Recurrent Neural Network.

Reinforcement learning (RL) is an unsupervised learning technique used in many real-time applications. The essence of RL is a decision-making problem. In RL, the agent constantly interacts with the environment and selects the next action according to previous feedback in terms of reward. In this paper, RL trains Software-Defined Wireless Sensor Networks (SDWSNs) controller to optimize the routing paths. We combine RL and SDN, where RL is applied to the SDN controller to generate the routing tables. We also propose four different reward functions for optimization of the network performance. RL-based SDWSN improves network performance by 23% to 30% in terms of lifetime compared with RL-based routing techniques. RL-based SDWSN performs well because it can intelligently learn the routing path at the controller. In addition, it has a faster network convergence rate than RL-based WSN.

Eutectic Hardeners (EH) are multi component mixtures that activate one or more of their constituents for curing at low temperatures and are characterized by a low melting point. In this paper, natural acids and their mixtures (Citric Acid (CA), Malic Acid (MA), and Tartaric Acid (TA)) are used in conjunction with the liquefactor Ethyl Lactate (EL) to cure epoxidized linseed oil (ELO). Their influence on curing speed and final material properties are investigated. Mixtures with MA proved to have a lower viscosity, which is beneficial for the preparation of homogenous samples, but showed slightly diminished tensile properties compared to materials cured with only CA. TA had an increased reaction speed thanks to a lower pKa value and the final material properties of a CATA mixture with small amounts of TA were found to have improved toughness and tensile strength.

In this letter, we consider diffusive molecular communication (MC) systems affected by signal-dependent diffusive noise, inter-symbol interference, and external noise. We design linear and nonlinear fractionally-spaced equalization schemes and a detection scheme which combines decision feedback and sequence detection (DFSD). In contrast to the symbol-rate equalization schemes in the MC literature, the proposed equalization and detection schemes exploit multiple samples of the received signal per symbol interval to achieve lower bit error rates (BERs) than existing schemes. The proposed DFSD scheme achieves a BER which is very close to that achieved by maximum likelihood sequence detection, but with lower computational complexity.

The copper flue dust is an important secondary source for various metals and also a potential threat to the environment. In this study, a process was developed to convert a local copper flue dust c...

The analysis of adjacent grounding systems under lightning currents which is responsible for many safety problems to human beings and damages to devices is very important. In this study, the behaviours of such grounding grids under lightning conditions which are not well documented in the literature are analysed using the multi-conductor transmission line method in a two-layer soil model. To achieve this goal, the expressions related to the study of a single grounding grid extracted from the recent study of the authors are developed to be appropriate to the analysis of adjacent grounding systems under lightning currents. Also, through the representation of the connectivity matrix, the analysis of the adjacent grounding grids with any size and degree of complexity is facilitated. Furthermore, the behaviours of the adjacent grounding grids under different lightning and soil characteristics are examined and some conclusions are derived.

Water jet peening is a surface modification technique which can introduce compressive residual stresses beneath the surface of the engineering components. In this paper, the effects of water jet pe...

Efficacy of chemotherapy is limited by the resistance of cancer cells. Phytochemicals especially Essential Oils (EOs) provide an alternative mode of cancer therapy. However, EOs utilization is restricted because of low bioavailability, and high degradation. Nanoemulsification is a method developed to overcome these obstacles. Accordingly, Citrus-Pectin nanoemulsion of Zataria Essential Oil (CP/ZEONE) was prepared to evaluate the anticancer activity and the mechanisms responsible for the caused cytotoxicity. Physical properties and FTIR spectra of CP/ZEONE were characterized. CP/ZEONE progressively improves the suppression of viability of drug-resistant MCF-7, MDA-MB-231 breast cancer cells, and spheroids. It triggers apoptosis by increasing Reactive Oxygen Species (ROS), mitochondrial membrane potential (MMP) loss, DNA damage, G2 and S-phase arrest in MDA-MB-231 cells and spheroids respectively. Additionally, spectroscopy techniques revealed the interaction of CP/ZEONE with DNA via the formation of a groove binding/partial intercalative complex. Thus, ZEO-loaded CP Nano-particles can be further explored as a promising antiproliferative and therapeutic candidate against cancer.

Water resources optimal allocation in large complex water systems such as multi-reservoir dams is a challenging task for decision makers. Wide range of decision variables as well as difficult nonlinear optimization problems (highly nonlinear problems) make it difficult to use optimization approaches. Alternatively, simulation–optimization approaches are then providing more realistic and applicable solution. This study presents a simulation–optimization framework for extracting monthly long-term operation rules. This method is applied to Karun River Basin including six multi-objective (hydropower generation, agricultural and environmental water supplement) cascade dams. In this regard, Water Evaluation and Planning System is used as the simulation model coupled with an optimization model. Decision variables include (1) monthly variation of top of buffer parameter in reservoirs where the reservoir releases water to meet the required demand and (2) monthly priority for filling of the reservoirs. Two-objective NSGA-II algorithms are used to minimize sum of squares unmet energy and agricultural water demands within two scenarios. The results show that the reliability of the generation of hydroelectricity in Karun River Basin has been increased sufficiently. Results showed that the scenario 2 (an aggregate energy demand at the system level) has better performance in terms of reliability (91.4% compared to 89.6%) and efficiency of centralized approach in which all reservoirs are operated in an integrated management scheme.

We have studied the embryo-fetal toxicity of mephedrone in mice during organogenesis period on gestation day (GD) 6–15 in mice during organogenesis period intraperitoneally. Our observation showed ...

This study aims to estimate the depth of anesthesia (DOA) at a safe and appropriate level taking into account the patient characteristics during the induction phase. Bi-spectral Index signal (BIS) as a common approach of controlling DOA generates noise and delays in the initial phase of induction. This may lead to useless information in the process of controlling. Moreover, using the BIS index entails a time-consuming process, high equipping costs, and a lack of accessibility to device accessories. To overcome these problems, we propose a new model of controlling DOA with no need for the use of such an index. Hence, an estimation strategy for DOA is developed applying a feedforward neural network and an adaptive neuro-fuzzy inference estimation model. This model estimates the dose of intravenous anesthetic drugs concerning the patients’ needs resulting in optimal drug dose and stable anesthesia depth. The proposed estimations are tested by sensitivity analysis being compared with real data obtained from the classical model (PK-PD) revised approach and BIS approach on 13 patients undergoing surgery. The results show an accuracy of 0.999, indicative of a high-validated model. Compared to BIS, our proposed model not only controls DOA accurately but also achieves outcomes in practice successfully. Some practical implications for future research and clinical practice are also suggested.

Hydraulic jump is a phenomenon observed in open channel flow, and studying it has been a challenge for scientific communities. Most of the previous studies on this topic have been devoted to the cases of clear water in rectangular geometry. However, in nature river flows carry sediment loads; meanwhile, trapezoidal stilling basins are proved to be more economical as well. In this paper, the RNG turbulence model has been used to simulate jump characteristics in a symmetrical trapezoidal channel of 45° side slope for clear water flow and three different sediment concentrations of 4%, 8% and 12% and a range of initial Froude number between 1.5 and 12. The results indicate that by increasing the sediment concentration the bed shear stress amounts and energy loss ratio increase while the length of jump ratio decreases. In comparison with the clear water flow, for the case of Fr1 = 7 and sediment concentration of 12%, the length of jump ratio decreases by 18.1%, the sequent depth ratio increases by 17.2% and the energy loss ratio increases by 3.2%. Furthermore, the bed shear stress at the beginning and at the end of the jump is innovatively investigated. The results show that the shear stress at the initial depth section is not dependent on the sediment content, but at the secondary depth of flow, it is highly dependent on the suspended sediment loads content.

During the era of Industry 4.0, the industrial robot monitoring process is getting success and popularity day by day. It also plays a vital role in the enhancement of robot vision algorithms. This paper proposed a model Deep Three Dimensional Spearman Correlation Analysis (D3D-SCA) to address nonlinear dimensionality reduction in robot vision for three-dimensional data. Dealing with three-dimensional multimedia datasets using traditional algorithms, to date, researchers have been facing limitations and challenges because mostly sub-space learning algorithms and their developments cannot perform satisfactorily in most of the time with linear and non-linear data dependency. The proposed model directly finds the relations between two sets of three-dimensional data without reshaping the data into 2D-matrices or vectors and dramatically reduces the dimensional reduction and computational algorithm complexity. The proposed model extracts deep information and translates it into a decision. To do so, three components are employed in the proposed model: customized deep learning model Inception_V3 for deep feature mapping, three-dimensional spearman correlation analysis for comparing pairwise deep features without a singular matrix and spatial dilemma problem, and the customized Xception classifier with automatic online updating ability and adjustable neural architecture for low latency models. The motivation of the proposed model is to advance the scalability of existing industrial robot vision applications which based on recognition, detection and re-identification approaches. Extensive findings on industrial datasets named “3D Objects on turntable and Caltech 101” demonstrate the effectiveness of the proposed model.

Tin (Sn)-based perovskites are a low-toxic and eco-friendly alternative because of their faster tendency to form inert oxides which have much lower bio-availability than lead-based perovskites. Therefore, at present paper, different properties of Pb-Sn binary perovskite solar cells (CH3NH3SnxPb1 – xI3 [0 ≤ x ≤ 1]) were investigated under ambient conditions without the use of encapsulation techniques. For the first time, cauliflower-like morphologies of Sn/Pb mixed perovskites were synthesized and their structural quantitatively analyzed using modified (Monshi) Scherrer (M-Sch) and Williamson-Hall (W-H) methods. The electrical characterization of the cells revealed that incorporating Sn ions into the Pb-based perovskite not only dramatically reduces the carrier density but also from the molar ratio of x = 0.25 to higher values, it converts the electrical conduction behavior from p- to n-type. This effect could be considered as one of the effective factors in reducing the efficiency (η) of solar cells from 16.83% for CH3NH3PbI3 to 0.86% for CH3NH3SnI3. Therefore, choosing an appropriate strategy against this electrical conductivity behavior could help us to achieve a new approach to reducing the toxicity of perovskite solar cells with the least reduction in their efficiency.

Thin films of Zn-doped copper selenide (Cu3Se2) nanostructures with different Zn concentrations were deposited on fluorine-doped tin oxide (FTO) glass substrates by electrodeposition method. The structural, optical, electrical, and photovoltaic properties of the deposited films were investigated by different analyses. X-ray diffraction (XRD) pattern showed the crystalline and tetragonal structure of the samples. The results of XRD pattern also showed that the peaks shifted to higher angles by increasing the Zn dopant, indicating the influence of dopant on the crystalline structure of Cu3Se2. Electron microscopy analysis showed that the films had a sheet-like shape whose thickness changed by altering the dopant percentage; the highest number of sheets belonged to the samples with higher concentrations of dopant. The photoluminescence (PL) analysis of the Cu3Se2 nanostructures revealed that the peaks in the green and infrared regions shifted to shorter wavelengths and the intensity of emission peaks increased for the Zn-doped sample with the highest concentration, compared with the un-doped sample. Based on the absorption spectrum analysis, the optical energy band gap increased with raising the percentage of Zn dopant. Finally, the electrical and photovoltaic parameters of the solar cells prepared via Cu3Se2 nanosheets were examined. The doped sample which had the highest percentage of doping, showed the highest efficiency (η) of ~1.30%.

We examined the potential of thermal evaporation for the controlled production of aerosols for the administration of drugs to the lungs. A novel volumetric heater was designed and fabricated allowing better control of the heating power transferred to the solvent compared to conventional coil heaters that are commonly used in e-cigarettes. The whole silicon chip is used as a resistive heater. Therefore, we call this approach a volumetric heater. We demonstrate that the vapor production of the volumetric heater can be controlled over a wide dynamic range by varying the heater temperature. [2020-0105]

In this work, polymer solar cells (PSCs) with the configuration of ITO/PEDOT:PSS/P3HT:PCBM/back electrode with different back electrodes of silver (Ag), aluminum (Al), and Ag/Al alloy with various weight percentages were investigated. Different electrodes were deposited at the same conditions using the thermal evaporation process on the P3HT:PCBM active layer. Moreover, the effect of different electrodes on the cell's performance was investigated. The electrical properties of components, such as ideality factor ( n ), inverse saturation current, turning- on voltage ( Vt ), and mobility ( μ ), were evaluated using the J–V curves of the cells in the dark and irradiated conditions. The results showed that the use of Ag/Al alloys with higher Ag percentages as the back electrode reduced both series resistance ( R ss) and the reverse saturation current, and increased both shunt resistance ( R sh) and the mobility ( μ ). Furthermore, it increased the fill factor (FF) and power conversion efficiency (PCE) by improving the metal/polymer junction in these types of cells.

eAssessment The application of multiterminal (MT), high-voltage dc (HVdc) (MTdc) grid technology requires test procedures for the operation and implementation of the protection solutions. The test procedures are usually derived from experience and from extensive measurement data, which, at present, are still not widely available. Based on a hardware-inthe- loop (HIL) method, advanced dc protection testing strategies, utilizing existing experience for ac grids and requirements for MTdc grids, may overcome this gap.

Purpose This paper reviews an area of interdisciplinary collaboration in the design of healthcare facilities that attempts to optimize hospital space-planning using automated statistical techniques from the discipline of Operations Research (OR). This review articulates Facility Layout Problems (FLPs) as a general class of OR problems. Furthermore, the review highlights limitations of these techniques, which necessitate an ethical and participatory engagement with computerized processes of healthcare architecture. Design/methodology/approach An in-depth critical review was carried out, which revealed a number of common themes, collectively theorized as metamodeling processes, or models of models, through which various FLP modelling techniques can be challenged and debated in terms of their architectural viability, and ethical ramifications. Findings This review provides a methodological basis for the further evaluation of computational models. It was found that most of the reviewed studies are functionally focused on flow efficiency and, in general, do not consider broader contextual, relational, social, or salutogenic design values. Originality/value This review is the first on the subject written from an architectural perspective. It can be used by a broad range of readers as its critical review of past and present hospital layout modelling techniques discusses their capabilities and limitations. As such, it also enables them to consider ethical values while critiquing the epistemology of computational processes hidden beneath algorithmic outputs.

Moving randomly without any centralized authority, dynamic nodes constitute the Mobile Adhoc Networks on the basis of fully-fledged cooperation and native trustworthiness. Unfortunately, in real scenarios, the malicious nodes take advantage of this inherent trustworthiness to settle in and perform their suspicious activities. In the light of the growing concerns about security attacks in hostile environments, new challenges have emerged to thwart routing attacks including the smart grayhole attack, which adversely affects the availability and accuracy of the network by dropping data packets. This paper addresses this disturbing attack by monitoring the behavior of the participating nodes through a bio-inspired trust management model. The distributed trustworthiness assessment model is based on the beta reputation system combined with the Ant Colony Optimization (ACO) metaheuristic. The main focus of the beta reputation system is rating the nodes according to their successful tasks and their consumed energy, while the ACO metaheuristic maintains this reputation metric during the discovery process and calculates the preference value of each traversed path to select the most secure one. The proposed model improves the traditional Dynamic Source Routing (DSR) protocol by isolating the malicious nodes from participation in data packets transmission. The simulations conducted with the network simulator 2, show that despite the presence of numerous gray holes, the reputation-based ACO DSR (RACODSR) outperforms the standard DSR in terms of packet loss ratio by a decrease of 9.8%, packet delivery ratio by an increase of 0.22%, throughput by an increase of 0.4%, jitter by a decrease of 22.76%, the end to end delay by a decrease of 2.51% and the consumed energy by a decrease of 0.17%.

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation (DG) and microgrids. The method uses the voltage and current data from the measurement points at the main substation, and the connection points of DG and microgrids. The data is used in a single feedforward artificial neural network (ANN) to estimate the distances to fault from all the measuring points. A k-nearest neighbors (KNN) classifier then interprets the ANN outputs and estimates a single fault location. Simulation results validate the accuracy of the fault location method under different fault conditions including fault types, fault points, and fault resistances. The performance is also validated for non-synchronized measurements and measurement errors.

This study presents a single-end protection algorithm for current source converter (CSC) high-voltage direct current (HVDC) transmission lines to solve shortcomings such as inadequate sensitivity, low speed and the need for communication link of conventional protection schemes. The proposed algorithm uses the average of DC line current at the rectifier terminal, sampled at 4 kHz within a 5 ms window, for simultaneous fault detection and faulted pole selection. For fault discrimination, the algorithm uses the average of second-order difference of the sampled current data. The algorithm is verified by extensive simulation studies using a CSC-HVDC test system modelled in PSCAD, as well as field data. The simulation results show fast and reliable protection of CSC-HVDC lines under different fault conditions including high-fault resistances. Moreover, the novel protection algorithm is stable against resistive–inductive faults, power flow changes, change of data window length, lightning interference and size of smoothing reactor. The algorithm does not require a communication link as used by numerous previous methods. It has a low computational burden, low sampling frequency and can be easily implemented in practical CSC-HVDC systems without extra arrangements as the required signals are readily available in the existing measuring hardware platforms.

Optimal design of controllers without considering uncertainty in the plant dynamics can induce feedback instabilities and lead to obtaining infeasible controllers in practice. This paper presents a multi-objective evolutionary algorithm integrated with Monte Carlo simulations (MCS) to perform the optimal stochastic design of robust controllers for uncertain time-delay systems. Each potential optimal solution represents a controller in the form of a transfer function with the optimal numerator and denominator polynomials. The proposed methodology uses genetic programming to evolve robust controllers. Using GP enables the algorithm to optimize the structure of the controller and tune the parameters in a holistic approach. The proposed methodology employs MCS to apply robust optimization and uses a new adaptive operator to balance exploration and exploitation in the search space. The performance of controllers is assessed in the closed-loop system with respect to three objective functions as (1) minimization of mean integral time absolute error (ITAE), (2) minimization of the standard deviation of ITAE and (3) minimization of maximum control effort. The new methodology is applied to the first-order and second-order systems with dead time. We evaluate the performance of obtained robust controllers with respect to the upper and lower bounds of step responses and control variables. We also perform a post-processing analysis considering load disturbance and external noise; we illustrate the robustness of the designed controllers by cumulative distribution functions of objective functions for different uncertainty levels. We show how the proposed methodology outperforms the state-of-the-art methods in the literature.

Purpose The purpose of this study is to numerically simulate the Lamb wave propagation through a clamp-on ultrasonic gas flowmeter (UGF) in contact mode, using a new semi three-dimensional approach. Moreover, experimental and analytical modeling results for transit time difference method have been used to confirm the simulation results at different gas flow velocities from 0.3 to 2.4 m/s. Design/methodology/approach The new semi three-dimensional approach involves the simulation of the flow field of the gas in a three-dimensional model and subsequently the simulation of wave generation, propagation and reception in a two-dimensional (2D) model. Moreover, the analytical model assumes that the wave transitions occur in a 2D mode. Findings The new approach is a semi three-dimensional approach used in this work, has better accuracy than a complete 2D simulation while maintaining the computing time and costs approximately constant. It is faster and less expensive than a complete 3D simulation and more accurate than a complete 2D simulation. It was concluded that the new approach could be extended to simulate all types of ultrasonic gas and non-gas flowmeters, even under harsh conditions. Originality/value In this work, a new approach for the numerical simulation of all types of ultrasonic flowmeters is introduced. It was used for simulation of a Lamb wave ultrasonic flow meter in contact mode.

Recent trends in research and investigation on nanoemulsion based products is the result of many reasons such as food security as a global concern, increasing demand for highly efficient food and agricultural products and technological need for products with the ability of manipulation and optimization in their properties. Nanoemulsions are defined as emulsions made up of nano sized droplets dispersed in another immiscible liquid which exhibit properties distinguishing them from conventional emulsions and making them suitable for encapsulation, delivery and formulations of bioactive ingredients in different fields including drugs, food and agriculture. The objective of this paper is to present a general overview of nanoemulsions definition, their preparation methods, properties and applications in food and agricultural sectors. Due to physicochemical properties of the nanoemulsion composition, creating nanosized droplets requires high/low energy methods that can be supplied by special devices or techniques. An overview about the mechanisms of these methods is also presented in this paper which are commonly used to prepare nanoemulsions. Finally, some recent works about the application of nanoemulsions in food and agricultural sectors along with challenges and legislations restricting their applications is discussed in the last sections of the current study.

In this paper, we investigate the statistics of the free space optics (FSO) communication channel between a hovering unmanned aerial vehicle (UAV) and a central unit. Two unique characteristics make UAV-based FSO systems significantly different from conventional FSO systems with static transceivers. First, for UAV-based FSO systems, the incident laser beam is not always orthogonal to the receiver lens plane. Second, both position and orientation of the UAV fluctuate over time due to dynamic wind load, inherent random air fluctuations in the atmosphere around the UAV, and internal vibrations of the UAV. On the contrary, for conventional FSO systems, the laser beam is always perpendicular to the receiver lens plane and the relative movement of the transceivers is limited. In this paper, we develop a novel channel model for UAV-based FSO systems by quantifying the corresponding geometric and misalignment losses (GML), while taking into account the non-orthogonality of the laser beam and the random fluctuations of the position and orientation of the UAV. In particular, for diverse weather conditions, we propose different fluctuation models for the position and orientation of the UAV and derive corresponding statistical models for the GML. We further analyze the performance of a UAV-based FSO link in terms of outage probability and ergodic rate and simplify the resulting analytical expressions for the high signal-to-noise ratio (SNR) regime. Finally, simulations validate the accuracy of the presented analysis and provide important insights for system design. For instance, we show that for a given variance of the fluctuations, the beam width should be properly adjusted to minimize the outage probability., This paper is accepted for publication in the IEEE Transactions on Communications

A molecularly imprinted polymer micro-solid phase extraction in pipette tip was employed for selective extraction and spectrophotometric determination of acetaminophen and nalidixic acid in pills and seawater samples. Parameters affecting microextraction, including pH of the sample, sample volume, type and elution solvent, number of cycles of extraction and desorption and amount of molecular imprinted polymer, were optimized utilizing both response surface methodology and one-variable-at-a-time methods. Four parameters in three levels were applied for response surface methodology experimental design. The proposed method indicated good linear range of 1.0–150.0 µg L−1 for nalidixic acid and 1.0–100.0 µg L−1 for acetaminophen by low detection limits of 0.2 and 0.3 µg L−1, respectively, for nalidixic acid and acetaminophen. Found recoveries were between 88.7 and 99.8% for real samples, with relative standard deviations less than 5.8%. The suggested protocol was successfully utilized to the separation, preconcentration and determination of nalidixic acid and acetaminophen in commercial pills and seawater samples.

This paper presents a new approach to measure the force required to detach a water (a polar liquid) droplet from a hydrophobic surface. This is done by partially cloaking the droplet with a high-surface-tension oil-based ferrofluid and using a magnet to apply a controllable body force to the resulting compound droplet. Placing the assembly on a sensitive scale, the magnet can then be brought closer to the droplet to detach it from the surface while recording the forces applied to the droplet. The work presented here is novel as it uses the concept of partial cloaking in which the solid-droplet contact area is not contaminated by the ferrofluid (and the measured forces do not need postprocessing). Our study is accompanied by numerical simulations aimed at improving our understanding of the interplay between the interfacial forces in a two-phase droplet under the influence of a strong (detaching) body force and at providing additional data for in-depth analyses of these forces. In particular, the minimum ferrofluid volume required for successful water droplet detachment from hydrophobic surfaces is computed for ferrofluids of different surface tensions, and they are compared to experimental data obtained from detaching water droplets from electrospun polystyrene coatings. It is also shown that the detachment force measured via partial cloaking is independent of the volume of the ferrofluid used for the experiment.

This study proposes a novel deep eutectic solvent based-ferrofluid to develop a dispersive liquid phase microextraction method. The efficiency of the protocol was evaluated by determining the trace...

Smartphones and a growing number of wearable devices are equipped with powerful sensors. This has led to increased interest in developing applications using sensor feeds from such devices to offer services across a wide variety of domains including healthcare, entertainment, environmental monitoring and transportation. However, most of these applications require continuous sensing, which places a heavy demand on the typically limited battery power of devices. This paper presents ShareSens, our approach to opportunistically merge sensing requirements of independent applications. We achieve this using sensing schedulers for sensors, which determine the lowest sensing rate which would satisfy all requests, and then use custom filters to send out only the required data to each application. Applications can request fixed sampling rates or ranges of rates, creating the opportunity for sending them higher rates than minimally required—“for free.” Sensing requests made through our ShareSens API are forwarded to the relevant schedulers, which determine the optimum sensing rates to satisfy all requests, and set up filters to deliver required feeds to the applications. The design and implementation of ShareSens is presented, along with results from our experimental work on the power savings that can be achieved by using it.