Search

Abstract The economic yield of saffron (Crocus sativus L.) has a wide range in different parts of the world, and it is not clear why this considerable difference exists. In this research saffron yield and yield components of 13 fields with varied geographic and climatic conditions were studied to determine which factor(s) are more important. Among the studied factors, temperature, field age, soil texture, bulk density, soil and water pH, irrigation events, and growth period had the greatest effect on saffron yield. The highest dry stigma weight, as economic yield, was obtained in three regions of Birjand (27 kg ha−1), Sarayan (24 kg ha−1), and Golshan (23.5 kg ha−1), followed by Neyshabur (18 kg ha−1) and Kashmar (17.5 kg ha−1), which had lower temperatures, coarse soil, balanced soil, and water pH, and longer growth periods. The average yields were increased until the sixth or seventh year (20.8 kg ha−1) and then decreased, however, it seems to be economic before the 10th year. Lower temperatures in early fall were important to stimulate flowering and increase yield in that year, and warm and sunny days in the spring are important for next year yields. We found that the optimal temperature for the first irrigation is ∼16°C and for flowering is ∼5°C–10°C. High‐yield fields did not have higher irrigation water volumes but more irrigation events (6.3), resulting in less water volume per irrigation. No direct relationship was observed between manure consumption and yield; however, processed manure increases yield by improving the soil structure and moisture retention ability. Fields with a complete chemical fertilizer composition had higher yields. It was concluded higher yields are achieved in saffron fields where regions are higher in altitude (at least 1300 m) and lower temperature in early autumn with complete fertilizer composition (especially sulfur and iron). There was no evidence of high salinity sensitivity of saffron.

Introduction: With increasing of population and the valuable water resource pollutions, a demand has been felt for new and inexpensive methods in order to remediation and improving of water quality. Cadmium is a trace element. In low concentration, this heavy metal is harmful to life, and considered as a dangerous pollutant. Cadmium leads to pollution and reduction of water quality; sometimes even toxicity through contaminated sources such as wastewater (Agricultural, municipal and industrial). Phytoremediation with aquatic macrophytes is an effective and inexpensive method for improving water quality and wastewater. The aim of this study was to investigatethe cadmium phytoremediation by Ceratophyllumdemersum L. as a potential method for remediation of cadmium pollution in aquatic medium. Materials and Methods: In this study, the remediation of cadmium pollution in aquatic medium monitored, within 14 days cultivation of coontail (Ceratophyllumdemersum L.). At first, for estimating the level of local wastewater cadmium pollutions, five-month cadmium concentration measurement of steel industrial wastewater and urban wastewater set. Then, plants collected from the irrigation channels of ShahidChamran University of Ahvaz. After finding the best pH of nutrient solution for Ceratophyllumdemersum L. growth by cultivating the plants in 2 liters pots filled by the solutions withthree different pH(5.5, 7 and 9.5) within three weeks; 12 grams of plants cultivated in 2 liters of Hoagland nutrient solution contaminated by cadmium(pH = 7). The initial contamination levels were setasfive different concentrations of cadmium (0, 1, 2, 4, and 6 mg l-1) with three replications. The cadmium concentrations of the pots were measured every day and on the last day of cultivation, plants wet weight, plants dry weight and Cd concentration in plants weremeasured. Then,biomass production, Cd bioconcentration factor (BCF), Cd uptake index, and Cd uptake percentage of plants were calculated. Standard deviations calculationand correlation and regression analysis were performed using Microsoft Office Excel2007 and SPSS 16. One-way ANOVA performed to identify significant differences in metal concentrations in the different treatments. Differences considered significant atp < 0.05. Results and Discussion: Among three pH (5.5, 7 and 9.5) for plants cultivation, C. demersum L.grewbetter in pH = 7. In fact, the average amount of produced biomasses were 46.6 g (pH = 5.5), 79.6 g (pH = 7) and 68.4 g (pH = 9.5). Therefore, to investigate the Cd remediation, the pH of nutrient solution set equal to 7. The final Cd concentrations in nutrient solution for initial Cd concentrations of 1, 2, 4 and 6 mg l-1 were 0.30, 0.36, 2.76 and 3.85 mg l-11respectively. Moreover, the Cd uptake percentage after 14days cultivation of C. demersum L.in nutrient solution for initial Cd concentrations of 1, 2, 4 and 6 mg l-1 were 70.00, 82.01, 31.00 and 35.83 %respectively. Cd uptake percentage of plants for initial concentrations of 4 and 6 mg l-1weresignificantly lesser than those of 1and 2 mg l-1.The decreased uptake efficiency percentage maybe caused by the effect of Cd toxicity on plant cell membrane permeability and efficiency.The average of BCF in plants for initial Cd concentrations of 1, 2, 4 and 6 mg l-1 were 384.4, 707.9, 66.5 and 75.0respectively. High reduction ofBCF amounts with increasing the initial concentration of 2to 4 and 6mg l-1, maybe caused by cadmium physiological adverse effects on plants. The averages of uptake index in plants were 1.26, 2.95, 2.24 and 3.92 mg for initial Cd concentrations of 1, 2, 4 and 6 mg l-1respectively. The results showed a reduction between 2 and 4 mg l-1concentrations that probablycaused by Cd toxicity disruption on plants uptake mechanism and growth. Moreover, the increase of plants uptake index in initial concentration of 6 mg l-1 could be explain by partial losing of the selective permeability of the plants cell membrane. The maximum (3.60 g/day) and minimum (1.62 g/day) of biomass production related to pollutant concentrations of 0 and 6 mg l-1 respectively, and it shows a greatefect of the Cd on C. demersum L.growth. Conclusion: The plant accumulated cadmium efficiently, and the remediation efficiency was near to 82%. However, the pollutant removal was not complete in a short time.In total, phytoremediation of cadmium and other pollutants from wastewater or other aqueoussolutions by Ceratophyllumdemersum, as a native aquatic plant of most of Iran’s rivers, could be anefficient and appropriatemethod.

Efficient crowd evacuation from railway platforms is critical for passenger safety during emergencies. This study introduces a novel dynamic emergency evacuation route generator using the Proximal Policy Optimization (PPO) algorithm within a custom-built 3D simulation environment developed in Unity. We independently created a detailed digital twin of Krung Thep Aphiwat Central Terminal, Thailand’s largest train station, and implemented all elements of the simulation, including the Social Force Model, to accurately replicate crowd behaviors and interactions during evacuation scenarios. Through extensive training over 3,000,000 episodes, our PPO-based model achieved significant improvements in evacuation efficiency. The results indicate that in a major emergency scenario, increasing the number of agents in the station reduced the number of remaining passengers from 111 to just 6, highlighting the model’s effectiveness. Similarly, in a minor emergency scenario, the average number of remaining passengers dropped from 38 to 1 with the addition of more agents. These findings confirm the model’s ability to adapt to different emergency conditions, offering a practical and scalable solution for enhancing evacuation strategies in high-density environments. Furthermore, increasing the agents’ sight range also improved evacuation efficiency, with a 20-meter sight range yielding the best results.

Channel estimation is a critical component in orthogonal frequency division multiplexing (OFDM) systems for ensuring reliable wireless communication. In this study, we propose a fast super-resolution convolutional neural network (FSRCNN) model for channel estimation, designed to reduce computational complexity while maintaining high estimation accuracy. The proposed FSRCNN model incorporates modifications such as replacing linear interpolation with zero padding and leveraging a new fast CNN architecture to estimate channel coefficients. Our numerical experiments and simulations demonstrate that the FSRCNN model significantly outperforms traditional methods, such as least square (LS) and linear minimum mean square error (LMMSE), in terms of mean square error (MSE) across various signal-to-noise ratios (SNRs). Specifically, the FSRCNN model achieves MSE values comparable to MMSE estimation, particularly at higher SNRs, while maintaining lower computational complexity. At an SNR of 20 dB, the FSRCNN model shows a notable improvement in MSE performance compared to the ChannelNet and LS methods. The proposed model also demonstrates robust performance across different SNR levels, with optimal results observed when the training SNR is close to the operating SNR. These findings validate the effectiveness of the FSRCNN model in providing a low-complexity, high-accuracy alternative for channel estimation, making it suitable for real-time applications and devices with limited computational resources. This advancement holds significant promise for enhancing the reliability and efficiency of current and future wireless communication networks.

Abstract This paper aims the development of a new reduced-cost algorithm for a multi-objective robust transmitter placement under uncertainty. Toward this end, we propose a new hybrid Kriging/Grey Wolf Optimizer (GWO) approach combined with robust design optimization to estimate the set of Pareto frontier by searching robustness as well as accuracy (lower objective function) in a design space. We consider minimization of the energy power consumption for transmitting as well as maximization of signal coverage in a multi-objective robust optimization model. The reliability of the model to control signal overlap for multiple transmitting antennas is also provided. To smooth computational cost, the proposed method instead of evaluating all receiver test points in each optimization iteration approximates signal coverages using Kriging interpolation to obtain optimal transmitter positions. The results demonstrate the utility and the efficiency of the proposed method in rendering the robust optimal design and analyzing the sensitivity of the transmitter placement problem under practically less-expensive computational efforts (350% and 320% less than computational time elapsed using standalone GWO and NSGAII respectively).

Futuristic transportation systems would be requiring more effective sensing system that are not only cost effective but also more efficient in sensing multiple targets while acquiring minimal area and utilizing low input power. To achieve that, frequency modulated continuous wave (FMCW) based Photonic Radar is proposed. For multiple targets detection, wavelength division multiplexing (WDM) scheme is used in conjugation with polarization division multiplexing (PDM) scheme. Impact of varying bandwidth is analyzed upon the performance of the proposed system using numerical simulations. The system is simulated for detecting and ranging over 100 meters by analyzing received echoes and verifying the range frequency with theoretically calculated values while effective operation is measured in terms of received power under varying level of attenuation up to 75 dB/Km under rain and fog conditions. Lastly, the system is replicated for range resolution with four varying operational bandwidths from 600 MHz to 4 GHz.

The majority of the Earth’s surface is covered by water, with oceans holding approximately 97% of this water and serving as the lifeblood of our planet. These oceans are essential for various purposes, including transportation, sustenance, and communication. However, establishing effective communication networks between the numerous sub-islands present in many parts of the world poses significant challenges. Underwater optical wireless communication, or UWOC, can indeed be an excellent solution to provide seamless connectivity underwater. UWOC holds immense significance due to its ability to transmit data at high rates, low latency, and enhanced security. In this work, we propose polarization division multiplexing-based UWOC system under the impact of salinity with an on–off keying (OOK) modulation format. The proposed system aims to establish high-speed network connectivity between underwater divers/submarines in oceans at different salinity levels. The numerical simulation results demonstrate the effectiveness of our proposed system with a 2 Gbps data rate up to 10.5 m range in freshwater and up to 1.8 m in oceanic waters with salinity up to 35 ppt. Successful transmission of high-speed data is reported in underwater optical wireless communication, especially where salinity impact is higher.

Today, most manufacturing control systems are complex and expensive, so they are limited to employ a small number of function evaluations for optimal design. Yet, looking for optimization methods with the less-computational cost is an open issue in engineering control systems. This paper aims to propose an effective adaptive optimization approach by integrating Kriging surrogate and Particle Swarm Optimization (PSO). In this method, a novel iterative adaptive approach is utilized using two sets of training samples including initial training and adaptive sample points. The initial training points are designed by space-filling design, while the adaptive points are generated using a new jackknife resampling approach. The proposed approach can effectively convergence towards the global optimal point using a small number of function evaluations. The efficiency and applicability of the proposed algorithm are evaluated using the optimal design of the fractional-order PID (FOPID) controller for some benchmark transfer functions. Then, the introduced approach is applied for tuning the parameters and the sensitivity analysis of the FOPID controller for a dynamic production-inventory control system. The results are in good agreement with the results reported in the literature, while the proposed approach is executed with a lower computational burden.

The ultra-wideband (UWB)-based real-time localization system (RTLS) is a promising technology for locating and tracking assets and personnel in real-time within a defined indoor environment since it provides high-ranging accuracy. However, its performance can be affected by the underlying antenna delays of UWB nodes, which act as a source of error during range estimations. Usually, measurement of the antenna delays is performed separately as a dedicated standalone procedure. Such an additional measurement procedure makes the UWB-based RTLS more tedious with manual interventions. Moreover, the air-time occupancy during the transmission and reception of signaling messages for range estimations between UWB node pairs also limits the serviceable capability of these networks. In this regard, we present a novel simultaneous ranging scheme that requires limited air-time occupancy during range estimations between UWB node pairs and also compensates for the error from the antenna delays. This paper provides a detailed mathematical modeling, system design, and implementation procedure of the proposed scheme. The effectiveness of the proposed scheme for locating a mobile node in an indoor environment is validated through experimental analysis. The results show that, compared to the state-of-the-art two-way ranging (TWR) method, the proposed scheme eliminates the requirement of dedicated standalone antenna delay measurement procedures of the nodes, increases air efficiency through the provision of simultaneous ranging, and provides relative root-mean-square errors (RMSEs) improvement for range and position estimations of approximately 54.52% and 39.96%, respectively.

Because of their simple design structure, end-to-end deep learning (E2E-DL) models have gained a lot of attention for speech enhancement. A number of DL models have achieved excellent results in eliminating the background noise and enhancing the quality as well as the intelligibility of noisy speech. Designing resource-efficient and compact models during real-time processing is still a key challenge. In order to enhance the accomplishment of E2E models, the sequential and local characteristics of speech signal should be efficiently taken into consideration while modeling. In this paper, we present resource-efficient and compact neural models for end-to-end noise-robust waveform-based speech enhancement. Combining the Convolutional Encode-Decoder (CED) and Recurrent Neural Networks (RNNs) in the Convolutional Recurrent Network (CRN) framework, we have aimed at different speech enhancement systems. Different noise types and speakers are used to train and test the proposed models. With LibriSpeech and the DEMAND dataset, the experiments show that the proposed models lead to improved quality and intelligibility with fewer trainable parameters, notably reduced model complexity, and inference time than existing recurrent and convolutional models. The quality and intelligibility are improved by 31.61% and 17.18% over the noisy speech. We further performed cross corpus analysis to demonstrate the generalization of the proposed E2E SE models across different speech datasets.

The number of function evaluations in many industrial applications of simulation-based optimization problems is strictly limited. Therefore, only little analytical information on objective and constraint functions is available. This paper presents an adaptive algorithm called the Surrogate-Based Constrained Global-Optimization (SCGO) method to solve black-box constrained simulation-based optimization problems involving computationally expensive objective function and inequality constraints. Firstly, Kriging surrogate is constructed over a new overall objective function (called loss function) to approximate the behavior of a true model. Then, an adaptive approach is provided to improve the optimal results sequentially while enforcing a feasible solution. The SCGO method is tested on several classical engineering design problems namely design of a tension/compression spring, design of a welded beam, design of a pressure vessel, and three-bar truss design. The results demonstrate that SCGO has advantages in solving the costly constrained problems and needs less costly function evaluations. Optimization results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms.

In the real world of engineering problems, in order to reduce optimization costs in physical processes, running simulation experiments in the format of computer codes have been conducted. It is desired to improve the validity of simulation-optimization results by attending the source of variability in the model’s output(s). Uncertainty can increase complexity and computational costs in Designing and Analyzing of Computer Experiments (DACE). In this state of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation models under uncertainty. This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation optimization can be handled by surrogates. At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed.

Computational complexity and time-consuming iteration of simulation for tuning of Proportional-Integral-Derivative (PID) controller is a common drawback in many types of existing methods. This paper aims to propose a new method for achieving an optimal design for PID gains parameters with the least number of simulation runs. To achieve this purpose, we combine polynomial regression and Latin Hypercube Sampling (LHS) in order to Design and Analyze of Computer Experiments (DACE). In this method, the LHS is performed three times to design the associated sample points for different usage that includes training sample points to fit polynomial regression as a common surrogate model; validating sample points to scale standardized residuals; grid search sample points for investigating optimal point over whole design space. To show the flexibility and applicability of the proposed method, we serve a numerical case in the tuning of PID controller for linear speed control of Direct Current (DC) motor. Four different polynomial regression fit input/output (I/O) data over separately four model’s performances that includes Integral-Square-Error (ISE), Integral-Absolute-error (IAE), Integral-Time-Square-Error (ITSE), and Integral-Time-Absolute-Error (ITAE). Comparison of the result with two existing approaches such as traditional Zeigler-Nichols method and Taguchi-Gray Relational Analysis (Taguchi-GRA) confirms the reliability and superiority of the proposed method.

This paper aims to further increase the reliability of optimal results by setting the simulation conditions to be as close as possible to the real or actual operation to create a Cyber-Physical System (CPS) view for the installation of the Fractional-Order PID (FOPID) controller. For this purpose, we consider two different sources of variability in such a CPS control model. The first source refers to the changeability of a target of the control model (multiple setpoints) because of environmental noise factors and the second source refers to an anomaly in sensors that is raised in a feedback loop. We develop a new approach to optimize two objective functions under uncertainty including signal energy control and response error control while obtaining the robustness among the source of variability with the lowest computational cost. A new hybrid surrogate-metaheuristic approach is developed using Particle Swarm Optimization (PSO) to update the Gaussian Process (GP) surrogate for a sequential improvement of the robust optimal result. The application of efficient global optimization is extended to estimate surrogate prediction error with less computational cost using a jackknife leave-one-out estimator. This paper examines the challenges of such a robust multi-objective optimization for FOPID control of a five-bar linkage robot manipulator. The results show the applicability and effectiveness of our proposed method in obtaining robustness and reliability in a CPS control system by tackling required computational efforts.