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The integration of distributed generation resources in power systems offers various advantages, such as peak load management and reduced transmission line congestion. However, it also introduces challenges related to voltage stability. This paper presents a novel multi-objective model for optimizing the allocation of solar resources in radial distribution systems. The model aims to achieve an optimal voltage profile, minimize losses, and maximize penetration levels. To address the conflicting nature of these objectives, a refined multi-objective slime mold algorithm (MOSMA) is proposed. This algorithm demonstrates exceptional capabilities in finding Pareto fronts, avoiding local optima, and effectively solving multi-objective problems compared to other optimization methods. Additionally, the corrected social hierarchy method is integrated to enhance performance. The proposed method is evaluated using a standard system under various operational conditions, showing superior results in terms of maintaining an acceptable voltage profile and significantly reducing losses. The study reveals that while losses decrease for penetration levels ranging from low to medium, they start to increase for levels exceeding 100 %. Notably, the proposed method achieves approximately 12 % system efficiency improvement, as measured by the voltage profile, at a penetration level of 300 %. These findings highlight the effectiveness of the proposed method, even at high penetration levels, surpassing other optimization approaches based on the inverse generation distance parameter.

Background and objective: This scientific review involves a sequential analysis of randomized trial research focused on the incidence of shivering in patients undergoing cardiac surgery. The study conducted a comprehensive search of different databases, up to the end of 2020. Only randomized trials comparing magnesium administration with either placebo or no treatment in patients expected to experience shivering were included. The primary objective was to evaluate shivering occurrence, distinguishing between patients receiving general anesthesia and those not. Secondary outcomes included serum magnesium concentrations, intubation time, post-anesthesia care unit stay, hospitalization duration, and side effects. Data collection included patient demographics and various factors related to magnesium administration. Material and methods: This scientific review analyzed 64 clinical trials meeting inclusion criteria, encompassing a total of 4303 patients. Magnesium was administered via different routes, primarily intravenous, epidural, and intraperitoneal, and compared against placebo or control. Data included demographics, magnesium dosage, administration method, and outcomes. Heterogeneity was assessed using the I2 statistic. Some studies were excluded due to unavailability of data or non-responsiveness from authors. Result: and discussion: Out of 2546 initially identified articles, 64 trials were selected for analysis. IV magnesium effectively reduced shivering, with epidural and intraperitoneal routes showing even greater efficacy. IV magnesium demonstrated cost-effectiveness and a favorable safety profile, not increasing adverse effects. The exact dose-response relationship of magnesium remains unclear. The results also indicated no significant impact on sedation, extubation time, or gastrointestinal distress. However, further research is needed to determine the optimal magnesium dose and to explore its potential effects on blood pressure and heart rate, particularly regarding pruritus prevention. Conclusion: This study highlights the efficacy of intravenous (IV) magnesium in preventing shivering after cardiac surgery. Both epidural and intraperitoneal routes have shown promising results. The safety profile of magnesium administration appears favorable, as it reduces the incidence of shivering without significantly increasing costs. However, further investigation is required to establish the ideal magnesium dosage and explore its potential effects on blood pressure, heart rate, and pruritus prevention, especially in various patient groups.

Diabetes is a dreaded disease that can be identified by elevated blood glucose levels in the blood, and undiagnosed diabetes can cause a host of related complications, such as retinopathy and nephropathy. In terms of type, the main categories are type 1 diabetes (T1DM), type 2 diabetes (T2DM) and gestational diabetes mellitus (GDM). Machine learning models and metaheuristic optimization algorithms can play an important role in the early detection, diagnosis and treatment of this disease. To this end, we propose AHDHS-Stacking, an ensemble learning framework for diabetes mellitus classification and diagnosis that is based on the harmony search (HS) algorithm and stacking and includes two stages of feature selection and optimization of base-learner combinations. To improve the model’s overall performance, the average performance of all base learners is used as the feature selection target, and an adaptive hyperparameter strategy is used to accelerate the iterative process. HS is then used to optimize to find the best combination of base learners, which improves model performance while reducing complexity. Following that, we conducted experiments on the Pima Indians Diabetes (PID) dataset and the Chinese and Western Medicine Diabetes (CWMD) dataset, achieving accuracy of 93.09%, precision of 93.22%, recall of 91.60% , F-measure of 92.25%, and MCC of 84.79% on PID dataset, which is better than all benchmark models and validated the model’s validity. CWMD dataset experimental results showed that AHDHS-Stacking screened for key features such as age, gender, urinary glucose, fasting glucose, BMI and cholesterol, and can be used as a practical and accurate method for early diabetes prediction.

Abstract Visible-infrared person re-identification aims to match pedestrian images between visible and infrared modalities, and its two main challenges are intra-modality differences and cross-modality differences between visible and infrared images. To address these issues, many advanced methods attempt to design new network structures to extract modality-sharing features, mitigate modality differences, or learn part-level features to overcome background interference. However, they ignore the parameter sharing of the convolutional layers to obtain more modality-sharing features. At the same time, only using part-level features lack discriminative pedestrian representations such as body structure and contours. To handle these problems, a parameter sharing and feature learning network is proposed in this paper to mitigate modality differences and further enhance feature discrimination. Firstly, a new two-stream parameter sharing network is proposed, by sharing the convolutional layers parameters to obtain more modality-sharing features. Secondly, a multi-granularity feature learning module is designed to reduce modality differences at both coarse and fine-grained levels while further enhancing feature discriminability. In addition, a center alignment loss is proposed to learn relationships between identities and to reduce modality differences by clustering features into their centers. For the part-level feature learning, the hetero-center triplet loss is adopted to alleviate the strict constraints of triplet loss. Finally, extensive experiments are conducted to validate our method outperforms state-of-the-art methods on two challenging datasets. In the SYSU-MM01 dataset, the Rank-1 and mAP reach $$74.0\%$$ 74.0 % and $$70.51\%$$ 70.51 % in the all-search mode, which is an increase of $$3.4\%$$ 3.4 % and $$3.61\%$$ 3.61 % to baseline, respectively.

This study explores why a sustainable cycle is induced when manufacturers implement auditing in combination with lean production. Furthermore, it verifies whether this sustainable cycle enhances process integration and risk resilience, thereby allowing the manufacturer to build strong cooperation with suppliers, which further produces a positive effect on the green supply chain. Sociotechnical systems theory is our theoretical basis, and calculating Spearman’s rank correlation coefficient and estimating PLS regressions are the main methods used. The results show that the implementation of auditing induces two driving forces: internal responsibility and the ability to respond to emergencies. These two forces drive suppliers to actively and positively cooperate with lean practices to ensure that the effect of those practices is strengthened. Moreover, stronger lean practices also produce two feedback forces – expanded tolerance for auditing and expanded acceptance of auditing interventions – that strengthen auditing practices. As a result, the mutually continuous strengthening of lean production and auditing practices is produced, which further becomes a sustainable cycle. This cycle can continue to enhance process integration and increase risk resilience, build strong cooperation with suppliers, and improve the green supply chain.

The machine learning method of Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed as a data-driven technique to model the dew point temperature (DPT). The input patterns, of T min, T max, and T mean, are utilized for the training. The results indicate thatANFIS method is capable of identifying data patterns with a high degree of accuracy. However, the approach demonstrates that processing time and computer resources may substantially increase by adding additional functions. Based on the results, the number of iterations and computing resources might change dramatically if new functionalities are included. As a result, tuning parameters have to be optimized inside the method framework. The findings demonstrate a high agreement between results by the proposed machine learning method and the observed data. Using this prediction toolkit, DPT can be adequately predicted based on the temperature distribution. The modeling approach has shown to be promising for predicting DPT at various sites. Besides, this study thoroughly compares the Bilayered Neural Network (BNN) and ANFIS models on various scales where the ANFIS model remains stable for almost all the numbers of the membership functions.

Abstract Scour hole that occurs downstream of the hydraulic structures threatens the safety and stability of the hydraulic structures. The scour around the structures is a complex and important hydraulic phenomenon; hence, it requires a data extensive research for the accurate estimation of scour depth. Although many analytical models are available for scour depth estimation, they suffer from huge limitations. In this research, the support vector regression (SVR) model and SVR ensemble with the metaheuristic algorithm of innovative gunner (SVR-AIG) models have been developed for accurate prediction of scour depth downstream of the ski-jump spillways. Field measurements including head and discharge intensity are used for developing the models. The performances of the models are compared using root mean square error (RMSE), mean average error (MAE), and correlation coefficient (CC) criteria and some statistical plots. The results showed that the hybrid SVR-AIG-based estimations (with CC = 0.987, 0.991, RMSE = 2.839, 1.987, and MAE = 2.247, 1.201) are more accurate than the SVR standalone model estimations (with CC = 0.942, 0.975, RMSE = 5.686, 4.040, and MAE = 4.114, 3.201) at the training and testing phases. This study is an important reference for analyzing the high capability of the AIG as an optimization tool in improving scour estimations of a standalone model. Also, this algorithm eliminates the trial-and-error procedure to optimize the internal parameters during the model development. Graphical abstract

Determination of pore pressure (PP), a key reservoir parameter that is beneficial for evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields development. Accurate estimation of PP is also essential for safe drilling of oil and gas wells since PP data are used as the input for safe mud window determination. In the present study, empirical equations along with machine learning methods, namely random forest algorithm, support vector regression (SVR) algorithm, artificial neural network (ANN) algorithm, and decision tree (DT) algorithm, are employed for PP prediction applying well log data. To this end, 2827 data records collected from three wells (Well A, Well B, and Well C) drilled in one of the Middle East oil fields are used. The dataset of Wells A and B is used for models’ training, validating, and testing, while Well C dataset is applied for evaluating the models’ generalizability in PP prediction in the field under study. To construct the predictive algorithms, 12 input variables are initially considered in the study. A feature selection analysis is conducted to find the most influential input variables set for developing PP predictive models. The results obtained suggest that the 9-input-variable set is the most efficient combination of inputs used in the ML models construction. Among all the four ML algorithms proposed, the DT algorithm presents the most accurate predictions for PP, delivering R2 and RMSE values of 0.9985 and 14.460 psi, respectively. Furthermore, the model generalization analysis results reveal that the 9-input-variable DT model developed can be used for PP prediction throughout the field of study since it presented an excellent accuracy performance in predicting PP when applied to Well C dataset.

Nonnegative Matrix Factorization (NMF) has become a popular technique for dimensionality reduction, and been widely used in machine learning, computer vision, and data mining. Existing unsupervised NMF methods impose the intrinsic geometric constraint on the encoding matrix, which only indirectly affects the base matrix. Moreover, they ignore the global structure of the data space. To address these issues, in this paper we propose a novel unsupervised NMF learning framework, called Robust Graph regularized Nonnegative Matrix Factorization (RGNMF). RGNMF constructs a sparse graph imposed on the basis matrix to catch the global structure and preserve the discriminative information. And it models the local structure by building a k-NN graph constrained on the encoding matrix, which gains the compact representation. Consequently, RGNMF not only respects the global structure, but also depicts the local structure. In addition, it employs such a $\text{L}_{2,1}$ -norm cost function to decompose the basis matrix and encoding matrix that its robustness can be improved. Further, it imposes the $\text{L}_{2,1}$ -norm constraint on the basis matrix to choose the discriminative feature. Hence, RGNMF can gain the robust discriminative representation by combining structure learning and $\text{L}_{2,1}$ -norm constraints imposed on the basis matrix and encoding matrix. Extensive experiments on real-world problems demonstrate that RGNMF achieves better clustering results than the state-of-the-art approaches.

Abstract E-learning is achieved by the deep integration of modern education and information technology, and plays an important role in promoting educational equity. With the continuous expansion of user groups and application areas, it has become increasingly important to effectively ensure the quality of e-learning. Currently, one of the methods to ensure the quality of e-learning is to use mutually independent e-learning behaviour data to build a learning performance predictor to achieve real-time supervision and feedback during the learning process. However, this method ignores the inherent correlation between e-learning behaviours. Therefore, we propose the behaviour classification-based e-learning performance (BCEP) prediction framework, which selects the features of e-learning behaviours, uses feature fusion with behaviour data according to the behaviour classification model to obtain the category feature values of each type of behaviour, and finally builds a learning performance predictor based on machine learning. In addition, because existing e-learning behaviour classification methods do not fully consider the process of learning, we also propose an online behaviour classification model based on the e-learning process called the process-behaviour classification (PBC) model. Experimental results with the Open University Learning Analytics Dataset (OULAD) show that the learning performance predictor based on the BCEP prediction framework has a good prediction effect, and the performance of the PBC model in learning performance prediction is better than traditional classification methods. We construct an e-learning performance predictor from a new perspective and provide a new solution for the quantitative evaluation of e-learning classification methods.

Solar radiation (SR) is considered as a critical factor in determining energy management. In this research, the potential of the Bayesian averaging model (BMA) was investigated for estimating monthly SR. The inputs were monthly average temperature, wind speed, relative humidity, and sunshine duration. The BMA model was employed to estimate SR by extracting information from multiple adaptive neuro-fuzzy systems (ANFIS) and multi-layer perceptron (MLP) models. In this study, Archimedes optimization algorithm (AOA), particle swarm optimization (PSO), genetic algorithm (GA), and bat algorithm (BA) were used to tune the parameters of the ANIFS and MLP. In addition, a multitude of error indices such as root mean square error (RMSE), and Nash Sutcliff efficiency (NSE), and several graphical tools were used to investigate the accuracy of the models. The results showed the better performance of the BMA model than other models for estimating solar radiation. For example, BMA with RMSE of 6.78, MAE of 5.25, and NSE of 0.96 had the best accuracy in the training stage of the Tabriz station. On the other hand, in the testing level of Tehran station, BMA (RMSE=7.89 MJ/ m2, MAE=6.89 MJ/ m2, NSE=0.95) gave the best accuracy, and the MLP model (RMSE= 14.12 MJ/ m2, MAE=12.23 MJ/ m2, and NSE=0.77) gave the worst performance, respectively.

There are some irregular and disordered noise points in large-scale point clouds, and the accuracy of existing large-scale point cloud classification methods still needs further improvement. This paper proposes a network named MFTR-Net, which considers the local point cloud’s eigenvalue calculation. The eigenvalues of 3D point cloud data and the 2D eigenvalues of projected point clouds on different planes are calculated to express the local feature relationship between adjacent point clouds. A regular point cloud feature image is constructed and inputs into the designed convolutional neural network. The network adds TargetDrop to be more robust. The experimental result shows that our methods can learn more high-dimensional feature information, further improving point cloud classification, and our approach can achieve 98.0% accuracy with the Oakland 3D dataset.

The accurate estimation of dew point temperature (Tdew) is important in climatological, agricultural, and agronomical studies. In this study, the feasibility of two soft computing methods, random forest (RF) and multivariate adaptive regression splines (MARS), is evaluated for predicting the long-term mean monthly Tdew. Various weather variables including air temperature, sunshine duration, relative humidity, and incoming solar radiation from 50 weather stations in Iran as well as their geographical information (or a subset of them) are used in RF and MARS as inputs. Three statistical indicators namely, root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) are used to assess the accuracy of Tdew estimates from both models for different input configurations. The results demonstrate the capability of the RF and MARS methods for predicting the long-term mean monthly Tdew. The combined scenarios in both the RF and MARS methods are found to produce the best Tdew estimates. The best Tdew estimates were obtained by the MARS model with the RMSE, MAE, and R of respectively 0.17°C, 0.14°C, and 1.000 in the training phase; 0.15°C, 0.12°C, and 1.000 in the validation phase; and 0.18°C, 0.14°C, and 0.999 in the testing phase.

The air kerma is a key parameter in medical diagnostic radiology. Radiologists use the air kerma parameter to evaluate organ doses and any associated patient hazards. The air kerma can be simply described as the deposited kinetic energy once a photon passes through the air, and it represents the intensity of the radiation beam. Due to the heel effect in the X-ray sources of medical imaging systems, the air kerma is not uniform within the X-ray beam’s field of view. Additionally, the X-ray tube voltage can also affect this nonuniformity. In this investigation, an intelligent technique based on the radial basis function neural network (RBFNN) is presented to predict the air kerma at every point within the fields of view of the X-ray beams of medical diagnostic imaging systems based on discrete and limited measured data. First, a diagnostic imaging system was modeled with the help of the Monte Carlo N Particle X version (MCNPX) code. It should be noted that a tungsten target and beryllium window with a thickness of 1 mm (no extra filter was applied) were used for modeling the X-ray tube. Second, the air kerma was calculated at various discrete positions within the conical X-ray beam for tube voltages of 40 kV, 60 kV, 80 kV, 100 kV, 120 kV, and 140 kV (this range covers most medical X-ray imaging applications) to provide the adequate dataset for training the network. The X-ray tube voltage and location of each point at which the air kerma was calculated were used as the RBFNN inputs. The calculated air kerma was also assigned as the output. The trained RBFNN model was capable of estimating the air kerma at any random position within the X-ray beam’s field of view for X-ray tube voltages within the range of medical diagnostic radiology (20–140 kV).

The rapid development of information technology and Internet technology has a far-reaching impact on vocational education. It is possible to accurately and objectively evaluate the training of learners by recording the process data of learners’ realization. The teaching evaluation of traditional vocational skill training requires time, workforce, and educational resources. Due to the limitations of experimental conditions, it is easy to ignore the procedural characteristics of skill training and difficult to implement the procedural evaluation. Based on the above problems, combined with virtual reality and the parts of vocationally skilled auto repair training specialty, using machine learning methods, engineering design of process evaluation method for skilled auto repair training, and takes the secondary vocational auto repair specialty as an example, constructs an evaluation index model based on KSA theoretical model, and evaluates three dimensions: knowledge acquisition, skill mastery, and ability cultivation (knowledge, skill, ability, KSA). The experimental verification of the process evaluator is carried out in the theoretical training evaluation auto repair system (TTE) based on virtual reality. The experimental results can effectively evaluate the practical training of students. The research results of this paper provide a new perspective and reference for the learning evaluation of skill-based training majors.

Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method.

Learning analysis provides a new opportunity for the development of online education, and has received extensive attention from scholars at home and abroad. How to use data and models to predict learners’ academic success or failure and give teaching feedback in a timely manner is a core problem in the field of learning analytics. At present, many scholars use key learning behaviors to improve the prediction effect by exploring the implicit relationship between learning behavior data and grades. At the same time, it is very important to explore the association between categories and prediction effects in learning behavior classification. This paper proposes a self-adaptive feature fusion strategy based on learning behavior classification, aiming to mine the effective E-learning behavior feature space and further improve the performance of the learning performance prediction model. First, a behavior classification model (E-learning Behavior Classification Model, EBC Model) based on interaction objects and learning process is constructed; second, the feature space is preliminarily reduced by entropy weight method and variance filtering method; finally, combined with EBC Model and a self-adaptive feature fusion strategy to build a learning performance predictor. The experiment uses the British Open University Learning Analysis Dataset (OULAD). Through the experimental analysis, an effective feature space is obtained, that is, the basic interactive behavior (BI) and knowledge interaction behavior (KI) of learning behavior category has the strongest correlation with learning performance.And it is proved that the self-adaptive feature fusion strategy proposed in this paper can effectively improve the performance of the learning performance predictor, and the performance index of accuracy(ACC), F1-score(F1) and kappa(K) reach 98.44%, 0.9893, 0.9600. This study constructs E-learning performance predictors and mines the effective feature space from a new perspective, and provides some auxiliary references for online learners and managers.

In clinical practice, the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications. Measuring the amount of each type of urine sediment allows for screening, diagnosis and evaluation of kidney and urinary tract disease, providing insight into the specific type and severity. However, manual urine sediment examination is labor-intensive, time-consuming, and subjective. Traditional machine learning based object detection methods require hand-crafted features for localization and classification, which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments. Deep learning based object detection methods have the potential to address the challenges mentioned above, but these methods require access to large urine sediment image datasets. Unfortunately, only a limited number of publicly available urine sediment datasets are currently available. To alleviate the lack of urine sediment datasets in medical image analysis, we propose a new dataset named UriSed2K, which contains 2465 high-quality images annotated with expert guidance. Two main challenges are associated with our dataset: a large number of small objects and the occlusion between these small objects. Our manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this dataset. Specifically, our goal is to improve the accuracy and efficiency of the detection algorithm and, in doing so, provide medical professionals with an automatic detector that saves time and effort. We propose an improved lightweight one-stage object detection algorithm called Discriminatory-YOLO. The proposed algorithm comprises a local context attention module and a global background suppression module, which aid the detector in distinguishing urine sediment features in the image. The local context attention module captures context information beyond the object region, while the global background suppression module emphasizes objects in uninformative backgrounds. We comprehensively evaluate our method on the UriSed2K dataset, which includes seven categories of urine sediments, such as erythrocytes (red blood cells), leukocytes (white blood cells), epithelial cells, crystals, mycetes, broken erythrocytes, and broken leukocytes, achieving the best average precision (AP) of 95.3% while taking only 10 ms per image. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles (UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The resultssuggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published. [ABSTRACT FROM AUTHOR]