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Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the total number of characteristics used. We evaluated the efficacy of the proposed approach in classifying eye-related glaucoma illnesses. In this study, we aim to assist professionals in identifying glaucoma by utilizing a proposed clinical decision support system that integrates image processing, soft-computing algorithms, and machine learning, and validates it on benchmark fundus images. Initially, we extract 65 features from the 646 retinal fundus images in the ORIGA benchmark dataset, from which a subset of features is created. For two-class classification, different machine learning classifiers receive the elected features. Employing 5-fold and 10-fold stratified cross-validation has enhanced the generalized performance of the proposed model. We assess performance using several well-established statistical criteria. The tests show that the suggested computer-aided diagnosis (CAD) model has an F1-score of 97.50%, an accuracy score of 96.50%, a precision score of 97%, a sensitivity score of 98.10%, a specificity score of 93.30%, and an AUC score of 94.2% on the ORIGA dataset. To demonstrate its excellence, we compared the suggested approach's performance with other current state-of-the-art models. The suggested approach shows promising results in predicting glaucoma, potentially aiding in the early diagnosis and treatment of the disease. Furthermore, real-time applications showcase the proposed approach's suitability, enabling its deployment in areas lacking expert medical practitioners. Overburdened expert ophthalmologists can use this approach as a second opinion, as it requires very little time for processing the retinal fundus images. The proposed model can also aid, after incorporating required modifications, in making clinical decisions for various diseases like lung infection and, diabetic retinopathy. [ABSTRACT FROM AUTHOR]

This paper addresses data privacy concerns and the need for a low computational framework in health sciences education. Introducing hybrid parametric splines as a novel approach in ECG signal modeling, the study explores three cases: parametric cubic spline, parametric quartic spline, and a hybrid approach combining both methods with second-order continuity. Accuracy is assessed using power spectrum analysis, root mean square error (RMSE), percent root mean square difference (PRD), Cross correlation, and Dynamic Time Warping (DTW) validated measures. Additionally, statistical analysis, including the Bland-Altman scatter plot, supports the hybrid approach. The hybrid approach achieves a harmonious blend of smoothness, heightened flexibility, and increased accuracy while ensuring computational simplicity. Efficient utilization of fewer data points optimizes storage and processing. Capable of generating diverse ECG signals, it allows flexibility in creating various scenarios. The hybrid approach demonstrates superior accuracy, as evidenced by the obtained RMSE, PRD, Cross correlation, and DTW values of 0.0410, 15.76, 0.98, and 0.49, respectively.Contributing to the advancement of ECG modeling, these findings provide enhanced visualization, analysis, and educational demonstrations in health science education, particularly in cardiovascular physiology. This research offers valuable insights for improving education in cardiovascular health sciences through the application of ECG modeling. By adopting the hybrid approach, educators and researchers can enhance their understanding and teaching of cardiovascular health, ultimately leading to improved education and advancements in the field. [ABSTRACT FROM AUTHOR]

Ensuring the security of communication is crucial in Vehicular Ad Hoc Networks (VANETs) to protect the integrity of information sharing among cars. To implement VANET communication as an answer for the different uses, secure communication is necessary. The unreliability of VANET environments is caused by message delays or tampering in VANET applications. Finding the sweet spot between VANET security and performance and dependability is the primary goal. This project's overarching goal is to fortify VANETs against Blackhole Routing Attacks and, by identifying and blocking harmful nodes, to mitigate the blackhole impact. This paper proposes a robust hybrid approach for the detection of black hole attacks in VANETs, leveraging the synergy between DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Decision Trees. DBSCAN, a density-based clustering algorithm, is employed to identify spatial clusters of vehicles, while Decision Trees are utilized to discern normal communication patterns from malicious ones within these clusters. The integration of these two techniques enhances the accuracy and efficiency of black hole attack detection in the dynamic and resource-constrained VANET environment. Experimental results demonstrate the effectiveness of the proposed hybrid approach, providing a promising solution for bolstering the security of VANETs against emerging threats. Here in result 73.89% improvement is received in Packet Drop Rate using DBSCAN, also minor improvement over Throughput and Average end to end delay and major improvement in terms of Network Routing Load. [ABSTRACT FROM AUTHOR]

The proliferation of social media platforms has revolutionized communication, but it has also given rise to social media bots that spread misinformation, manipulate public opinion, and compromise online discourse integrity. This study addresses the critical issue of detecting social media bots on Twitter, where traditional detection methods often fall short due to the evolving nature of these bots and the vast amount of data involved. To overcome these challenges, this research proposes a hybrid ensemble model that combines feature engineering and natural language processing techniques. The ensemble model is a meta-model that predicts the nature of a Twitter account based on the outputs of two base models. The first model uses a combination of engineered profile and content-based features, while the second model employs automatically extracted natural language processing features from posted tweets. By integrating these distinct features, the hybrid model captures a broader spectrum of bot behaviors and characteristics, leading to more accurate and robust detection. This combined approach allows the meta-model to identify bots that might evade detection when only one type of feature is used, offering a more holistic understanding of bot behavior across both structural and content dimensions. Extensive experiments demonstrate significant improvements in detection performance, achieving an impressive F1-score of 90.22% on the challenging Twibot-20 dataset, outperforming state-of-theart models. [ABSTRACT FROM AUTHOR]

Nowadays, smartphones seamlessly blend into every aspect of our lives, including as handheld assistants for individuals with disabilities. Therefore, this research addresses the need for a robust system that can classify Kazakh banknotes. By capitalizing on the availability of smartphones and the ability to integrate detectors with classifiers this study introduces classifiers of Kazakh banknote images specifically designed for banknotes ranging from 500 KZT to 20,000 KZT. It compares traditional and hybrid machine learning (ML) approaches, utilizing a dataset of diverse banknote images, aiming for both lightweight and high accuracy. Competitive performance is demonstrated by the traditional approach, enhanced by thoughtful feature engineering. The hybrid approach, utilizing features from a pre-trained ResNet-18 model, showcases remarkable accuracy and robustness. Evaluation metrics reveal significant achievements, with the traditional approach attaining 94.00% accuracy and the hybrid approach excelling at 99.11%. Model stacking, combining classifiers from both approaches, outperforms individual classifiers, achieving 95.00% and 99.55% accuracy for the traditional and hybrid ML approaches, respectively. Our methodology's comparable outcome in classifying Thai banknotes and coffee beans roasting levels demonstrates their versatility in image classification tasks that rely on color differentiation, showcasing the potential beyond banknote recognition. [ABSTRACT FROM AUTHOR]

Time series prediction of natural calamities is effectively solved with deep neural networks due to their ability to automatically assimilate the temporal linkages in time series data. This research develops a hybrid stacked deep learning with one-dimensional Convolution–Stacked Bidirectional Gated Recurrent Unit (Conv1D-SBiGRU) algorithm, unifying the predictive advantages of one-dimensional Convolution (Conv1D) and Bidirectional Gated Recurrent Unit (BiGRU) using hydro-meteorological and atmospheric data to build and evaluate a flood prediction model in forecasting the phenomenon of forthcoming flood events. The one-dimensional Convolution model effectively obtains valuable information and learns the time series cognitive representation. The stacked BiGRU model efficiently identifies and models the data sequence with temporal dependencies due to their ability to learn from past and future moments. The developed predictive model uses statistically significant predicted rainfall value to estimate the daily Relative Departure Index (RDI) which is used to predict floods. The proposed work was trained and evaluated for predicting floods on the real-world data of Alappuzha district, Kerala, India. The findings demonstrate the preeminence of the Conv1D-SBiGRU-based flood model with around 33% reduced MAE and RMSE and 9% improved R2 over the benchmark and some hybrid techniques. The outcomes showed the efficiency of Conv1D-SBiGRU in precisely forecasting floods during extreme weather events with an accuracy of 98.6%. [ABSTRACT FROM AUTHOR]

The utilization of computer simulators in university education is progressively being embraced to offer students a practical exposure to industrial bioprocesses. Bioreactor computer simulators hold various advantages over conventional laboratory experiments, such as cost‐effectiveness and enhanced safety. The research objective is to assess the effectiveness of integrating bioreactor computer simulators into hybrid teaching to promote active and collaborative learning experiences and evaluate their impact on student participation and understanding. A hybrid strategy combining synchronous, face‐to‐face, and online teaching has been implemented to enhance the teaching‐learning processes in the Industrial Bioprocesses course for Biochemistry students. The simulation software BIOSTAT®T Yeast was used. This software models the production of ethanol with Saccharomyces cerevisiae through batch cultivation and the determination of the kLa value of a bioreactor. In the first simulation activity, students analyzed the software response based on parameter values input by the instructor, while in the second simulation activity, students autonomously used the computer simulator under the primary oversight of the instructor. The survey results indicate that the pedagogical innovation was positively received and significantly motivating for the students. Comparing student satisfaction surveys between the two simulation activities suggests that fostering student autonomy and engagement through simulation technology can improve satisfaction and learning outcomes in bioprocess education. [ABSTRACT FROM AUTHOR]

Text summarisation reduces text length while retaining important information, helping individuals, especially students, in managing information overload during research or assignments. However, existing text summarisation methods often lose important details, generate irrelevant or redundant sentences, or produce incoherent summaries. This study introduces a hybrid approach, LongT5Rank (coined in this study), which combines TextRank, an extractive summarisation algorithm, with LongT5, an abstractive summarisation algorithm, to automate the summarisation process. TextRank utilizes GloVe, a pre-trained word embedding model, and PageRank, a graph-based ranking algorithm, to select representative sentences. LongT5, an encoder-decoder transformer model, condenses extracted sentences into a concise and coherent summary, handling input sequences up to 16,384 tokens, for long-range sequence-to-sequence tasks. The LongT5Rank approach has shown significant achievements, including a minimum 60% compression rate, a minimum 0.6 semantic textual similarity score, and an improved F-measure compared to employing TextRank alone. Furthermore, it received positive feedback from Human Level Performance (HLP), underlining the importance of evaluating results directly from human users. This emphasizes the belief that the performance of the proposed solution should be assessed natively by humans. By combining both extractive and abstractive methods, LongT5Rank excels in generating accurate and coherent summaries. [ABSTRACT FROM AUTHOR]

The binary decision diagram (BDD) method provides a means to calculate the exact probability of a fault tree, but cannot solve large fault trees for nuclear power plant PSAs. However, the BDD method can be used as a supplementary means to increase the accuracy of PSA quantification, especially for a seismic PSA which includes many non-rare events.Since it is difficult to solve a large PSA model using BDD method, several approaches have been developed that partially apply the BDD technique to small-sized branches of a large PSA model. This paper proposes an approach of partially applying BDD technique to large-sized branches. The results from the pilot application to a seismic PSA model shows that the partial BDD approach of this paper provides a relatively accurate way to quantify each sequence as well as the overall core damage in a seismic PSA model.

Abstract Background There are a variety of lethal infectious diseases that are seriously affecting people's lives worldwide, particularly in developing countries. Hepatitis B, a fatal liver disease, is a contagious disease spreading globally. In this paper, a new hybrid approach of feed forward neural networks is considered to investigate aspects of the SEACTR (susceptible, exposed, acutely infected, chronically infected, treated, and recovered) transmission model of hepatitis B virus disease (HBVD). The combination of genetic algorithms and sequential quadratic programming, namely CGASQP, is applied, where genetic algorithm (GA) is used as the main optimization algorithm and sequential quadratic programming (SQP) is used as a fast-searching algorithm to fine-tune the outcomes obtained by GA. Considering the nature of HBVD, the whole population is divided into six compartments. An activation function based on mean square errors (MSEs) is constructed for the best performance of CGASQP using proposed model. Results The solution's confidence is boosted through comparative analysis with reference to the Adam numerical approach. The results revealed that approximated results of CGASQP overlapped the reference approach up to 3–9 decimal places. The convergence, resilience, and stability characteristics are explored through mean absolute deviation (MAD), Theil’s coefficient (TIC), and root mean square error (RMSE), as well as minimum, semi-interquartile range, and median values with respect to time for the nonlinear proposed model. Most of these values lie around 10−10–10−4 for all classes of the model. Conclusion The results are extremely encouraging and indicate that the CGASQP framework is very effective and highly feasible for implementation. In addition to excellent reliability and level of precision, the developed CGASQP technique also stands out for its simplicity, wider applicability, and flexibility.

Advanced brain cancer is the deadliest type with just a few months survival rate. Existing technologies hinder the objective of forecasting cancer. This work aims to fulfil the pressing requirement for timely and precise identification of advanced-stage brain tumours, which are notorious for their markedly reduced life expectancy. It presents an innovative hybrid approach for predicting brain tumours and improves diagnostic capabilities. The Multiple Kernel K-Means Cluster Algorithm (MKKCA) is used to segment brain MRI images effectively, differentiating healthy and tumorous tissues. After segmentation, a hybrid approach with 3D-Convolutional Neural Network (CNN) and U-Net has been utilized for classification. The objective is to effectively and accurately distinguish normal and pathological brain images. To enhance the efficiency, we include the Improved Whale Optimization Algorithm (IWOA), which guarantees accurate and dependable performance via location updates. The methodology demonstrates outstanding precision with 98.5% accuracy rate, 98.56% specificity, 91% sensitivity, 87.45% precision and a recall rate of 96% with the F-Measure at 96.02%. These findings, obtained using MATLAB, demonstrate a substantial performance improvement compared to current approaches. This development not only represents a significant addition to diagnostic imaging but also a crucial role in the prediction and treatment of brain cancers.

Nephrolithiasis is a disease that occurs with the formation of stones in the urinary tract. The occurrence of such stones depends on geographical, climatic, ethnic, dietary, and genetic factors. Most often, they are seen in countries with warm and dry climates. The Balkans are an endemic area, and 2% of the population is estimated to have the condition annually. The frequency is the same in both sexes. It is relatively rarer in childhood. This study aimed to show the modern possibilities for surgical treatment of kidney stone disease and combinations of different surgical approaches to achieve mini-invasiveness, short-term hospital stay, and reduction of postoperative complications. We present a clinical case of a patient with kidney stone disease with multiple lithiasis with multiple localization found in the left kidney. A combined (hybrid) operative approach was used – combining the laparoscopic technique and flexible renoscopy. We combined the possibilities of the two operative procedures and achieved a one-act cure for the patient. The method was minimally invasive. The hospital stay was shorter, and no postoperative complications occurred.

This report describes a hybrid intervention addressing left circumflex artery occlusion during minimally invasive mitral valve repair. By using a radiopaque Cor-Knot device (LSI Solutions), targeted removal of occluding sutures was achieved, circumventing sternotomy and coronary artery bypass. Real-time coronary angiography provided assessment of procedural success during surgical revision in a hybrid operating room. [ABSTRACT FROM AUTHOR]

Advanced brain cancer is the deadliest type with just a few months survival rate. Existing technologies hinder the objective of forecasting cancer. This work aims to fulfil the pressing requirement for timely and precise identification of advanced-stage brain tumours, which are notorious for their markedly reduced life expectancy. It presents an innovative hybrid approach for predicting brain tumours and improves diagnostic capabilities. The Multiple Kernel K-Means Cluster Algorithm (MKKCA) is used to segment brain MRI images effectively, differentiating healthy and tumorous tissues. After segmentation, a hybrid approach with 3D-Convolutional Neural Network (CNN) and U-Net has been utilized for classification. The objective is to effectively and accurately distinguish normal and pathological brain images. To enhance the efficiency, we include the Improved Whale Optimization Algorithm (IWOA), which guarantees accurate and dependable performance via location updates. The methodology demonstrates outstanding precision with 98.5% accuracy rate, 98.56% specificity, 91% sensitivity, 87.45% precision and a recall rate of 96% with the F-Measure at 96.02%. These findings, obtained using MATLAB, demonstrate a substantial performance improvement compared to current approaches. This development not only represents a significant addition to diagnostic imaging but also a crucial role in the prediction and treatment of brain cancers. [ABSTRACT FROM AUTHOR]

Anomaly intrusion detection is a critical component of modern cybersecurity systems, aiming to identify and flag abnormal activities or behaviors that deviate from expected patterns within computer networks. Unlike signature-based intrusion detection systems that rely on known attack patterns, anomaly detection techniques focus on detecting unknown or novel attacks that lack predefined signatures. In recent years, machine learning and deep learning techniques have emerged as promising solutions to provide an additional layer of defense against emerging threats and zero-day attacks. This survey article provides a comprehensive review of the state of the art in network intrusion detection using ML and DL. We start by presenting an overview of the challenges and requirements associated with intrusion detection in today's dynamic network environments. We then delve into the fundamental concepts and methodologies of ML and DL, highlighting their strengths and limitations when applied to intrusion detection. We discuss the various types of network intrusion detection datasets commonly used in research, along with the preprocessing techniques employed to ensure data quality. We explore different feature selection and extraction methods that enable the effective representation of network traffic data, facilitating accurate intrusion detection. We review their architectural designs, training processes, and optimization techniques while discussing their performance in terms of detection accuracy. We highlight the current research trends and challenges in the field, including adversarial attacks, interpretability, scalability, and real-time processing. We conclude with potential future directions and recommendations for researchers and practitioners. [ABSTRACT FROM AUTHOR]

Background: There are a variety of lethal infectious diseases that are seriously affecting people's lives worldwide, particularly in developing countries. Hepatitis B, a fatal liver disease, is a contagious disease spreading globally. In this paper, a new hybrid approach of feed forward neural networks is considered to investigate aspects of the SEACTR (susceptible, exposed, acutely infected, chronically infected, treated, and recovered) transmission model of hepatitis B virus disease (HBVD). The combination of genetic algorithms and sequential quadratic programming, namely CGASQP, is applied, where genetic algorithm (GA) is used as the main optimization algorithm and sequential quadratic programming (SQP) is used as a fast-searching algorithm to fine-tune the outcomes obtained by GA. Considering the nature of HBVD, the whole population is divided into six compartments. An activation function based on mean square errors (MSEs) is constructed for the best performance of CGASQP using proposed model. Results: The solution's confidence is boosted through comparative analysis with reference to the Adam numerical approach. The results revealed that approximated results of CGASQP overlapped the reference approach up to 3–9 decimal places. The convergence, resilience, and stability characteristics are explored through mean absolute deviation (MAD), Theil's coefficient (TIC), and root mean square error (RMSE), as well as minimum, semi-interquartile range, and median values with respect to time for the nonlinear proposed model. Most of these values lie around 10−10–10−4 for all classes of the model. Conclusion: The results are extremely encouraging and indicate that the CGASQP framework is very effective and highly feasible for implementation. In addition to excellent reliability and level of precision, the developed CGASQP technique also stands out for its simplicity, wider applicability, and flexibility. [ABSTRACT FROM AUTHOR]

This paper introduces a novel formulation of the clustering problem, namely, the minimum sum-of-squares clustering of infinitely tall data (MSSC-ITD), and presents HPClust, an innovative set of hybrid parallel approaches for its effective solution. By utilizing modern high-performance computing techniques, HPClust enhances key clustering metrics: effectiveness, computational efficiency, and scalability. In contrast to vanilla data parallelism, which only accelerates processing time through the MapReduce framework, our approach unlocks superior performance by leveraging the multi-strategy competitive–cooperative parallelism and intricate properties of the objective function landscape. Unlike other available algorithms that struggle to scale, our algorithm is inherently parallel in nature, improving solution quality through increased scalability and parallelism and outperforming even advanced algorithms designed for small- and medium-sized datasets. Our evaluation of HPClust, featuring four parallel strategies, demonstrates its superiority over traditional and cutting-edge methods by offering better performance in the key metrics. These results also show that parallel processing not only enhances the clustering efficiency, but the accuracy as well. Additionally, we explore the balance between computational efficiency and clustering quality, providing insights into optimal parallel strategies based on dataset specifics and resource availability. This research advances our understanding of parallelism in clustering algorithms, demonstrating that a judicious hybridization of advanced parallel approaches yields optimal results for MSSC-ITD. Experiments on the synthetic data further confirm HPClust's exceptional scalability and robustness to noise. [ABSTRACT FROM AUTHOR]

Local morphometric variables (LMVs) are frequently found as weaker predictors than other environmental covariates in digital soil mapping. This study tested and evaluated the performance of a hybrid approach combining gradient boosted regression trees (GBRT) and regularized regression (RR) algorithms in predicting soil textural fractions using a set of LMVs in Khuzestan province, Iran. Here five LMVs (slope gradient, slope aspect, horizontal curvature, vertical curvature, and contour geodesic torsion) were derived from a spheroidal equal-angular DEM as original predictors. The results demonstrated that the hybrid approach improved prediction accuracy for sand, clay, and silt contents by an average of 56% compared to the GBRT models. The importance analysis revealed the significant contribution of tree-based variables obtained from decomposing GBRT models in predicting soil textural fractions. This approach could be recommended for digital soil mapping, particularly in situations of limited environmental covariates or geomorphometric techniques that cannot be easily applied. [ABSTRACT FROM AUTHOR]

AbstractIn the present paper are developed a new hybrid-spectral approach that deals with the dispersion of propagative, non-propagative and complex ultrasonic guided waves in different types of waveguides. First, a mathematical formulation of the eigenvalue and eigenvector problem has been presented. Then, the hybrid approach based on eigenvector processing is presented. A comparison is then made with the frequency and wavenumber approaches, to highlight the strengths of our method. The dispersion curves of propagative, non-propagative and complex modes are plotted in 2D and 3D. From these representations, several observations on mode behavior have been highlighted, notably the phenomenon of conversion of non-propagative modes into propagative modes, and also conversions specific to imaginary modes. The braiding phenomenon between symmetrical and antisymmetrical modes was also analyzed. Single and multi-layer planar and cylindrical geometries were treated with different degrees of anisotropy. The accuracy of the results was checked by comparison with analytical dispersion curves for planar structures and based on the thin plate limit for cylindrical structures. The error evaluated is of the order of  10−5. A computation time analysis has been carried out, showing that the approach developed is the fastest in comparison with those available in the literature. On the basis of the results obtained, the hybrid approach applied to eigenvalue problems resulting from a spectral formulation presents a very good alternative for obtaining dispersion curves. It is stable, convergent, easy to implement and requires very little computing time and it can generate all types of modes (propagative, non-propagative and complex), in contrast to existing approaches in the reference. [ABSTRACT FROM AUTHOR]

The "2024 ACC/AHA/AACVPR/APMA/ABC/SCAI/SVM/SVN/SVS/SIR/VESS Guideline for the Management of Lower Extremity Peripheral Artery Disease" provides recommendations to guide clinicians in the treatment of patients with lower extremity peripheral artery disease across its multiple clinical presentation subsets (ie, asymptomatic, chronic symptomatic, chronic limb-threatening ischemia, and acute limb ischemia). A comprehensive literature search was conducted from October 2020 to June 2022, encompassing studies, reviews, and other evidence conducted on human subjects that was published in English from PubMed, EMBASE, the Cochrane Library, CINHL Complete, and other selected databases relevant to this guideline. Additional relevant studies, published through May 2023 during the peer review process, were also considered by the writing committee and added to the evidence tables where appropriate. Recommendations from the "2016 AHA/ACC Guideline on the Management of Patients With Lower Extremity Peripheral Artery Disease" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing comprehensive care for patients with peripheral artery disease have been developed. [ABSTRACT FROM AUTHOR]

AIM: The "2024 ACC/AHA/AACVPR/APMA/ABC/SCAI/SVM/SVN/SVS/SIR/VESS Guideline for the Management of Lower Extremity Peripheral Artery Disease" provides recommendations to guide clinicians in the treatment of patients with lower extremity peripheral artery disease across its multiple clinical presentation subsets (ie, asymptomatic, chronic symptomatic, chronic limb-threatening ischemia, and acute limb ischemia). METHODS: A comprehensive literature search was conducted from October 2020 to June 2022, encompassing studies, reviews, and other evidence conducted on human subjects that was published in English from PubMed, EMBASE, the Cochrane Library, CINHL Complete, and other selected databases relevant to this guideline. Additional relevant studies, published through May 2023 during the peer review process, were also considered by the writing committee and added to the evidence tables where appropriate. STRUCTURE: Recommendations from the "2016 AHA/ACC Guideline on the Management of Patients With Lower Extremity Peripheral Artery Disease" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing comprehensive care for patients with peripheral artery disease have been developed. [ABSTRACT FROM AUTHOR]

Ensuring optimal load balancing is imperative for maintaining reliability and upholding quality of service as specified in service-level agreements (SLAs) for cloud computing providers. This research addresses the most common shortcomings of existing state-of-the-art methods, which often lack responsiveness and struggle to adapt to exponentially increasing demand, especially in the era of the internet of things (IoT). The proposed hybrid approach surpasses current literature approaches in performance metrics such as makespan, response time, number of cloudlet migrations, and SLA violations. It operates on two levels, initially employing a k-means clustering algorithm to group servers within each datacenter based on similar utilization rates. Subsequently, a round-robin method allocates task groups sequentially to non-overloaded clusters, and within each cluster, a genetic algorithm optimally assigns tasks to servers. This multilayered approach facilitates hot-deployment and scalability in operational cloud environments while promoting strong interoperability and decoupling of core mechanisms missions. Simulation experiments conducted on CloudSim Plus validate the superiority of our method, positioning it as a robust solution for enhancing load balancing and tasks scheduling in cloud environments, especially in the face of rapidly increasing IoT-related demands. [ABSTRACT FROM AUTHOR]

Background: The standard vascular surgical procedure (SV) for the treatment of distal aortic arch pathologies involves a hybrid approach using a left carotid-subclavian bypass and thoracic endovascular aortic repair. Considering the introduction of a thoracic side branch prosthesis (TBE), the aim of this study was to analyze the cost-revenue aspects of both procedures. Material and methods: A retrospective analysis was conducted on cases treated by SV from 2017 to 2022. To draw conclusions regarding the use of TBE, the main diagnoses and procedures of SV were recoded based on current classifications (ICD/OPS 2023) for revenue calculations and regrouped according to aG-DRG 2023. An OPS modification and regrouping were performed for modeling TBE revenues. Results: A total of 13 cases were identified (mean age 62.5 ± 13.8 years; 10 males). After regrouping, the following DRGs were obtained: F42Z in N = 5, F51A in N = 4, F08B in N = 2, and F07A and F36B each in N = 1. The total revenue after regrouping was € 666,514.13, including an additional payment (ZE) of € 132,729.14. With the modeled application of TBE, a total revenue of € 659,212.19 was achieved. Compared to SV, this represents a revenue decrease of € 16,886.71 (changed DRG), but with an increase in ZE revenue by € 65,559.78 (different ZE). The use of TBE resulted in a saving of 74 occupancy days, including 13.5 days in intensive care. Conclusion: A cost coverage seems probable with a change in the procedure, despite the yet to be determined pricing of TBE. This is highly dependent on the coding quality and the future development of ZE, given the annually changing DRG relative weights. Precise and transparent performance and cost documentation are essential for determining the pricing. [ABSTRACT FROM AUTHOR]

Negation plays an essential role in sentiment analysis within natural language processing (NLP). Its integration involves two key aspects: identifying the scope of negation and incorporating this information into the sentiment model. Before delving into scope detection, the specific negation cue must be identified, with explicit and implicit negation cues being the two main types. Various methodologies, such as rule-based, machine learning, and hybrid approaches, address the negation scope detection challenge. Strategies for leveraging negation information in sentiment models encompass heuristic polarity modification, feature space augmentation, end-to-end approach, and hierarchical multi-task learning. Notably, there is a need for more studies addressing implicit negation cue detection, even within the state-of-the-art bidirectional encoder representation for transformers (BERT) approach. Some studies have employed reinforcement learning and hybrid techniques to address the implicit negation problem. Further exploration, particularly through a hybrid and multi-task learning approach, is warranted to make potential contributions to the nuanced challenges of handling negation in sentiment analysis, especially in complex sentence structures. [ABSTRACT FROM AUTHOR]

The sustainable supply chains optimization is a high-dimensional multi-objective optimization problem. The involved costs can be categorized as economic, environmental, and social. Metaheuristics can be used for tackling this kind of problem efficiently. This short note deals with a comparative analysis of the main metaheuristics (according to recent surveys) for a sustainable supply chain model. The considered techniques are genetic algorithm, particle swarm optimization, simulated annealing, and non-dominated sorted genetic algorithm. Moreover, two hybrid models are also included, i.e. genetic algorithm combined with simulated annealing and genetic algorithm combined with particle swarm optimization. The latter provides the best result with a constraint satisfaction rate equal to 0.9968. [ABSTRACT FROM AUTHOR]

Recent progress in the realm of imaging technologies has swiftly disseminated fresh methodologies for representing objects, broadening the horizons for scholars such as art historians, archaeologists, conservators, and conservation scientists. Reflectance Transformation Imaging (RTI) and three-dimensional (3D) modeling via Structure from Motion (SfM) have rapidly gained popularity and are being employed by a wide range of users, expanding the possibilities for understanding objects of material cultural heritage from different perspectives. This paper explores the outcomes achieved through the application of these techniques to gain deeper insights into the surface of a bas-relief within a confined space. This methodology enhanced our understanding of the original sandstone surfaces of the bas-relief by integrating RTI and 3D visualization, thereby acquiring a novel investigative tool with enhanced illumination possibilities. A data fusion approach is proposed using photogrammetry to produce a Virtual RTI (V-RTI). In the case study reported in this paper, focusing on one inner wall of the chapel of Ellesiya at the Museo Egizio of Turin, V-RTI was employed to obtain useful surface information in a context where conventional RTI would have been impossible to achieve. Finally, the study compares the different techniques through direct qualitative and quantitative analysis, highlighting possible future developments from both instrumental and methodological points of view. [ABSTRACT FROM AUTHOR]

Twin screw granulation (TSG) has recently been emerged as a novel approach for the continuous wet granulation of fine particles (i.e., powders) in the pharmaceutical industry. The presence of brilliant advantages like the ability of operation at very low liquid concentrations and excellent product consistency has made this technique promising. Except positive points, the existence of major challenges like scalability and flexibility in the processing regimes has enhanced the importance of deeper investigations towards true recognition of this process. The central aim of this theoretical article is to develop the modeling process of TSG employing four machine learning models and one metaheuristic algorithms in a hybrid approach. Screw speeds, material throughputs, liquid binder (water)-to-solid ratios, and screw configurations are known as important parameters of TSG process, which were validated via their comparison with the obtained experimental data. GBR, SGD, and SVR were finally selected for 3 targets with their best combinations of hyper-parameters employing FA. The output is based on d-values (d10, d50, d90) for the granulate particle size distribution (PSD). Final models have R2 scores of 0.919, 0.960, and 0.877 for d10, d50, d90 outputs, respectively.

Abstract In this article, examine the performance of a physics informed neural networks (PINN) intelligent approach for predicting the solution of non-linear Lorenz differential equations. The main focus resides in the realm of leveraging unsupervised machine learning for the prediction of the Lorenz differential equation associated particle swarm optimization (PSO) hybridization with the neural networks algorithm (NNA) as ANN-PSO-NNA. In particular embark on a comprehensive comparative analysis employing the Lorenz differential equation for proposed approach as test case. The nonlinear Lorenz differential equations stand as a quintessential chaotic system, widely utilized in scientific investigations and behavior of dynamics system. The validation of physics informed neural network (PINN) methodology expands to via multiple independent runs, allowing evaluating the performance of the proposed ANN-PSO-NNA algorithms. Additionally, explore into a comprehensive statistical analysis inclusive metrics including minimum (min), maximum (max), average, standard deviation (S.D) values, and mean squared error (MSE). This evaluation provides found observation into the adeptness of proposed AN-PSO-NNA hybridization approach across multiple runs, ultimately improving the understanding of its utility and efficiency.

In this research paper, we propose a novel hybrid deep learning approach, SSA-CNN-LSTM, for forecasting solar power generation. The approach combines Singular Spectrum Analysis (SSA), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks to leverage temporal and spatial dependencies in real-time greenhouse solar power generation data. Through a comprehensive comparative analysis, SSA-CNN-LSTM is compared against three established models, CNN-LSTM, SSA-CNN, and SSA-LSTM, employing real solar power generation data over a two-year period. The findings prominently demonstrate SSA-CNN-LSTM's exceptional performance, particularly in the 1-hour ahead prediction horizon. With an hour-ahead Mean Absolute Error (MAE) of 0.1202, SSA-CNN-LSTM surpasses the forecast precision of CNN-LSTM (0.6269), SSA-CNN (0.2354), and SSA-LSTM (0.2049). This excellence extends to the 2-hour-ahead forecast, where SSA-CNN-LSTM maintains its superiority with an MAE of 0.1400. In the day-ahead forecast, SSA-CNN-LSTM upholds its competitiveness, demonstrating an MAE of 0.1774. These outcomes underscore the immense potential of SSA-CNN-LSTM as a formidable tool for precise solar power forecasting. The model's effectiveness empowers greenhouse operators and energy management systems to optimize resource allocation, ultimately fostering elevated energy efficiency and overall greenhouse productivity.

The financial performance of a listed company is a common concern for shareholders, creditors, employees, securities analysts, and the government. Measuring and forecasting financial performance informs stakeholders about a company's overall well-being. In this study, we propose a hybrid approach that combines grey relation analysis, Self-Organized Mapping (SOM) neural network, and convolutional neural network to assess the financial performance of listed companies. Grey relation analysis measures financial performance, SOM neural network clusters, and convolutional neural network forecasts. Compared to other models, the hybrid convolutional neural network model has a better predictive effect, accurately forecasting the financial status of listed companies. Findings also reveal that 70.93 percent of listed companies in agriculture, forestry, husbandry, and fisheries have a poor financial status.

Feature selection (FS) is crucial to transforming high-dimensional data into low-dimensional data. The FS approach selects influential traits and ignores the rest. This approach improves machine learning (ML) classifiers by reducing computational complexity and solution time. This empirical study presents a novel and effective methodology that uses two contemporary state-of-the-art soft-computing algorithms, the Grey Wolf Optimizer (GWO) and the Whale Optimization Algorithm (WOA). We have also created the hybrid version (hGWWO) of these two approaches as our novel, innovative scientific contribution. The baseline algorithms above have been used previously for feature selection across different domains. According to our understanding, these three algorithms are being used for the first time in glaucoma identification, particularly on the publicly available benchmark dataset, ORIGA. The rising global prevalence of glaucoma prompted this proposed methodology's focus on the illness. This illness is second only to cataracts in causing visual loss. Medical imaging professionals are examining retinal scans to diagnose glaucoma. Manual eye screening and retinal fundus imaging for confirmation of this infection require skilled ophthalmologists. The screening analysis method is time-consuming, requires experienced staff, and is subject to observational differences. In order to overcome these issues and to support the medical fraternity, an artificial intelligence-supported computer-aided clinical decision support system (CA-CDSS) is implemented in the present endeavor for confirmation of this disease from retinal fundus images. Nature-inspired computing strategies for feature selection and ML models for classification are employed to classify fundus retinal images under investigation. From the ORIGA dataset, sixty-five features were retrieved. A subset of most influential features is selected from the original dataset using three soft-computing-based FS methods. ML classifiers are trained using this portion of data and evaluated using a 70:30 technique. The suggested method yielded 96.8% accuracy, 0.981 specificity, 0.992 sensitivity, 0.969 precision, and a 0.982 F1-score. This study shows fresh initiatives with positive effects on ophthalmologists, researchers, and the public. [ABSTRACT FROM AUTHOR]