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The capacitated vehicle routing problem (CVRP) is regarded as an NP-hard problem. Moreover, the CVRP is described as a model that can be used in many applications such as transport, logistics, and distribution. The exact algorithms can find exact optimal solutions on the small-sized problem instances; however, for large-sized instances it is difficult to find the exact optimal solutions in polynomial time. This reason motivated the researchers to present heuristic/metaheuristic algorithms to solve large-sized problem instances within a reasonable computational time. One of the good algorithms that deal with the CVRP is the ant colony optimization (ACO) algorithm. Several ACO algorithms have been suggested in the literature, such as the ant system (AS) algorithm, ant colony system (ACS) algorithm, and so on. On the other hand, ACO is designed to solve the path problem that finds the best way. However, this algorithm still lacks exploratory mechanisms, which results in premature convergence and stagnation issues. Therefore, we propose to develop an enhanced ACS (EACS) algorithm for solving the CVRP based on subpaths. In our proposed algorithm, we propose to utilize the K-nearest neighbour (KNN) algorithm for finding the best initial solution and then enhance the diversity mechanism of the proposed algorithm by avoiding the generation of the same solution using subpaths. This uses the diversity of the generated solution to find a better solution with a shorter route in a reasonable amount of computational time. Furthermore, we propose to apply the three-opt algorithm to the completed subtour and the k-opt algorithm to the subpath gained from the experience of the subpath. Finally, to verify the effectiveness of the proposed EACS algorithm, the algorithm is tested on some CVRP instances and is compared with one of the state-of-the-art methods, namely, the enhanced simulated annealing algorithm. The comparative study showed a better performance of our EACS compared to the enhanced simulated annealing algorithm. [ABSTRACT FROM AUTHOR]

We aim to suggest a simple genetic algorithm (GA) and other four hybrid GAs (HGAs) for solving the asymmetric distance-constrained vehicle routing problem (ADVRP), a variant of vehicle routing problem (VRP). The VRP is a difficult NP-hard optimization problem that has numerous real-life applications. The VRP aims to find an optimal tour that has least total distance (or cost) to provide service to n customers (or nodes or cities) utilizing m vehicles so that every vehicle starts journey from and ends journey at a depot (headquarters) and visits every customer only once. The problem has many variations, and we consider the ADVRP for this study, where distance traveled by every vehicle must not exceed a predefined maximum distance. The proposed GA uses random initial population followed by sequential constructive crossover and swap mutation. The HGAs enhance the initial solution using 2-opt search method and incorporate a local search technique along with an immigration procedure to obtain effective solution to the ADVRP. Experiments have been conducted among the suggested GAs by solving several restricted and unrestricted ADVRP instances on asymmetric TSPLIB utilizing several vehicles. Our experiments claim that the suggested HGAs using local search methods are very effective. Finally, we reported a comparative study between our best HGA and a state-of-the-art algorithm on asymmetric capacitated VRP and found that our algorithm is better than the state-of-the-art algorithm for the instances. [ABSTRACT FROM AUTHOR]

The facility layout problem (FLP) is a very important class of NP-hard problems in operations research that deals with the optimal assignment of facilities to minimize transportation costs. The quadratic assignment problem (QAP) can model the FLP effectively. One of the FLPs is the hospital facility layout problem that aims to place comprehensive clinics, laboratories, and radiology units within predefined boundaries in a way that minimizes the cost of movement of patients and healthcare personnel. We are going to develop a hybrid method based on discrete differential evolution (DDE) algorithm for solving the QAP. In the existing DDE algorithms, certain issues such as premature convergence, stagnation, and exploitation mechanism have not been properly addressed. In this study, we first aim to discover the issues that make the current problem worse and to identify the best solution to the problem, and then we propose to develop a hybrid algorithm (HDDETS) by combining the DDE and tabu search (TS) algorithms to enhance the exploitation mechanism in the DDE algorithm. Then, the performance of the proposed HDDETS algorithm is evaluated by implementing on the benchmark instances from the QAPLIB website and by comparing with DDE and TS algorithms on the benchmark instances. It is found that the HDDETS algorithm has better performance than both the DDE and TS algorithms where the HDDETS has obtained 42 optimal and best-known solutions from 56 instances, while the DDE and TS algorithms have obtained 15 and 18 optimal and best-known solutions out of 56 instances, respectively. Finally, we propose to apply the proposed algorithm to find the optimal distributions of the advisory clinics inside the Azadi Hospital in Iraq that minimizes the total travel distance for patients when they move among these clinics. Our application shows that the proposed algorithm could find the best distribution of the hospital's rooms, which are modeled as a QAP, with reduced total distance traveled by the patients. [ABSTRACT FROM AUTHOR]

The facility layout problem (FLP) is a very important class of NP-hard problems in operations research that deals with the optimal assignment of facilities to minimize transportation costs. The quadratic assignment problem (QAP) can model the FLP effectively. One of the FLPs is the hospital facility layout problem that aims to place comprehensive clinics, laboratories, and radiology units within predefined boundaries in a way that minimizes the cost of movement of patients and healthcare personnel. We are going to develop a hybrid method based on discrete differential evolution (DDE) algorithm for solving the QAP. In the existing DDE algorithms, certain issues such as premature convergence, stagnation, and exploitation mechanism have not been properly addressed. In this study, we first aim to discover the issues that make the current problem worse and to identify the best solution to the problem, and then we propose to develop a hybrid algorithm (HDDETS) by combining the DDE and tabu search (TS) algorithms to enhance the exploitation mechanism in the DDE algorithm. Then, the performance of the proposed HDDETS algorithm is evaluated by implementing on the benchmark instances from the QAPLIB website and by comparing with DDE and TS algorithms on the benchmark instances. It is found that the HDDETS algorithm has better performance than both the DDE and TS algorithms where the HDDETS has obtained 42 optimal and best-known solutions from 56 instances, while the DDE and TS algorithms have obtained 15 and 18 optimal and best-known solutions out of 56 instances, respectively. Finally, we propose to apply the proposed algorithm to find the optimal distributions of the advisory clinics inside the Azadi Hospital in Iraq that minimizes the total travel distance for patients when they move among these clinics. Our application shows that the proposed algorithm could find the best distribution of the hospital's rooms, which are modeled as a QAP, with reduced total distance traveled by the patients. [ABSTRACT FROM AUTHOR]

Numerous techniques have been proposed in the literature to improve the performance of high‐gain observers with noisy measurements. One such technique is the linear extended state observer, which is used to estimate the system's states and to account for the impact of internal uncertainties, undesirable nonlinearities, and external disturbances. This observer's primary purpose is to eliminate these disturbances from the input channel in real‐time. This enables the observer to precisely track the system states while compensating for the various sources of uncertainty that can influence the system's behaviour. So, in this paper, a novel nonlinear higher‐order extended state observer (NHOESO) is introduced to enhance the performance of high‐gain observers under noisy measurement conditions. The NHOESO is designed to observe the system states and total disturbance while eliminating the latter in real time from the input channel. It is capable of handling disturbances of higher‐order derivatives, including internal uncertainties, undesirable nonlinearities, and external disturbances. The paper also presents two innovative schemes for parametrizing the NHOESO parameters in the presence of measurement noise. These schemes are named time‐varying bandwidth NHOESO (TVB‐NHOESO) and online adaptive rule update NHOESO (OARU‐NHOESO). Numerical simulations are conducted to validate the effectiveness of the proposed schemes, using a nonlinear uncertain system as a test case. The results demonstrate that the OARU technique outperforms the TVB technique in terms of its ability to sense the presence of noise components in the output and respond accordingly. However, it is noted that the OARU technique is slower than the TVB technique and requires more complex parameter tuning to adaptively account for the measurement noise. [ABSTRACT FROM AUTHOR]

It has been confirmed that structures with micro dimensions display size-dependent thermomechanical behaviors. Moreover, according to the findings of empirical and theoretical researches, thermoelastic damping (TED) has been recognized as one of inescapable causes of energy dissipation in microstructures. The current article is an effort to provide a novel size-dependent framework for approximating the amount of TED in microring resonators with rectangular cross section. To include size effect into structural and thermal constitutive relations, the modified couple stress theory (MCST) and the Moore–Gibson–Thompson (MGT) heat equation are utilized, respectively. By solving the coupled heat equation in the purview of MGT model, the fluctuation temperature throughout the ring is determined. By employing the obtained temperature distribution and constitutive relations of MCST, the peak values of strain and wasted thermal energies during one cycle of vibration are computed. Based on the description of TED in the energy dissipation (ED) method, a mathematical expression containing the scale parameters of MCST and MGT model is derived for estimating TED value. To ensure the correctness and veracity of the established solution, a comparative study is carried out on the basis of the data released by other researchers for more plain models. A section is also designated for an all-out study to ascertain the association between TED spectrum and some influential factors like scale parameters of MCST and MGT model, vibration mode number, one-dimensional (1D) and two-dimensional (2D) heat conduction, geometry and material. The extracted data enlighten that the impact of applying MCST and MGT model on TED has a close relationship with the vibration mode number of the ring. [ABSTRACT FROM AUTHOR]

Currently, as carbon emissions rise and global temperatures increase, climate change models have gained significant importance. These models are capable of yielding insights into historical and forthcoming climate variables. Despite their extensive scope, these models continue to exhibit considerable uncertainties. Selecting suitable methodologies can be crucial and beneficial for effective downscaling to ensure precise forecasts. In this study, an LSTM model was employed to downscale the outputs of two global climate models, i.e., MRI-ESM2-0 and GFDL-ESM4. The climate data (e.g., precipitation and temperatures) from two global climate models, as well as observations from Urmia, Zanjan, Zahedan, and Birjand stations, were collected for the 1995–2014 period. Some indices were employed to evaluate the performance of the LSTM network for the downscaling of climate data over employed stations. As per the results, the LSTM model indicated acceptable downscaling outputs. The indices exhibit higher improvement values in the precipitation variable, outperforming the LSTM network in this variable. The LSTM neural network improved the GFDL-ESM4 precipitation correlation coefficient values by up to 350%. Furthermore, the maximum temperature remained unchanged and exhibited minimal improvement as the values were already high in the original state. Similarly, the minimum temperature improved insignificantly. The GFDL-ESM4 model demonstrated a 75% improvement in the mean square error of precipitation values after the LSTM was used. Additionally, the maximum temperature increased by 75%, whereas the minimum temperature decreased by 50%. The MRI-ESM2-0 model’s correlation coefficient values for precipitation were enhanced by up to 110% following the implementation of the LSTM neural network. There was no significant alteration in the maximum temperature, and the correlation changes observed in both models across all four stations remained below 10%. This stability can be attributed to the initial conditions being within the optimal range, while the minimum temperature exhibited only marginal improvements across the various stations. The implementation of the LSTM resulted in a significant improvement in RMSE, 70%, 75%, and 60% for precipitation, maximum and minimum temperatures, respectively. The results of this research underscore the capability of the LSTM model in enhancing the precision of downscaled climate data, especially concerning precipitation. These advancements indicate that the LSTM model may serve as an effective instrument for refining regional climate forecasts, which could be essential for multiple purposes such as managing water resources, planning for agriculture, and preparing for disasters. By delivering more precise local climate predictions, this method can facilitate improved decision-making in areas that are vulnerable to climate fluctuations and transformations. [ABSTRACT FROM AUTHOR]

The national economy of Iranians depends greatly on agriculture. Wheat and barley are two strategic crops in Iran, where their scarcity can adversely affect people's quality of life. Therefore, it is essential to analyze the climatic, humidity-related, and socioeconomic factors affecting the cultivation areas of these crops. This study analyzed the single and mutual effects of 17 parameters on wheat and barley cultivation areas in Iran within the 2007–2020 period. The geographical detector model (GDM) was used in RStudio to analyze the interactive effects of parameters. According to the results, topography had greater effects than other parameters on the cultivation areas of wheat. Spring precipitation had a larger impact with 26% of effects than winter precipitation (SC) with 24% of effects on the cultivation area of wheat. However, winter precipitation and temperature with 42% and 34% effects, respectively, had greater impacts than spring precipitation on the cultivation area of barley. NDVI and SC have the highest impact on the cultivated area of wheat and barley crops. Also, elevation is the most important parameter in wheat cultivation with 54% influence, and this product performs better in high altitudes, on the other hand, elevation is not a vital factor in barley. Also, the winter parameters have a greater effect on wheat than barley, and the average wheat harvest showed high performance in heavy snowfall lands. Also, the results showed that barley has a higher resistance to dry conditions. Moreover, socioeconomic parameters had more effects on barley than on wheat. In other words, livestock cover with 52% of effects had the greatest impact on the cultivation area of barley, whereas population with 36% of effects was ranked the third factor affecting the cultivation area of wheat. According to the interactive results of parameters, the interaction between elevation and NDVI was 88% in wheat. However, the interaction between population and livestock cover was 72% in barely. The optimal results of barley and wheat indicated that both crops had the largest cultivation areas at 22.9–24.9 °C. • The single and mutual effects of various parameters on the cultivation areas of wheat and barley are quantified. • The geographical detector model (GDM) is used to analyze the interactive effects of the parameters. • A comprehensive analysis was conducted on the proposed methodology. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources. [ABSTRACT FROM AUTHOR]