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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]

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]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the present scenario, Internet communities, forum and blogging sites play a very crucial role to present opinions, views and the comments on various events. As the reachability of sites are beyond the control of national boundaries, sometimes this leads to the conviction and persuasion of thoughts without considering any legal subsequences, and also influence the belief of others as well, thus finding or identifying the sentiments of public from the social media content is one of the major research issue. Analysis of the sentiments of social media data is very difficult to understand as this typically does not exhibit a suspicious pattern in the flow of information like individual's actual opinion on a specific occasion etc. With the advancement of methodology and easy to use, the users of social media sites are now expressing their opinions, sharing their views and experiences through images, text, animation, audio, video etc. Because of the complexity the conventional text based sentiment analysis procedure, a lot of methods have been evolved, however studies suggest mostly more complicated procedure and approaches. Twitter® (or X), is one of the most common platform used widely by individual to express their opinions and sentiments on various events. Twitter sentiment analysis basically deals with the analysis of twitter quotes to find the hidden pattern in the sentiments expressed by the users in past. This paper aims to takes the challenges regarding social media sentiments analysis and developed a hybrid approach (Text and visual sentiment) on twitter data for sentiment analysis by using NLP-based opinion clustering, textual mining, emotion API and some machine learning techniques for visual ontology. Simulation result shows the significance of work. [ABSTRACT FROM AUTHOR]

Organizations need to constantly improve their strategy, processes, and management practices to keep up with the ever-changing business environment. Over the past few years, there has been a significant surge in the popularity of lean and agile approaches. However, when we talk about a hybrid approach that combines both mindsets, more research is required to fully understand this hybrid concept. Based on the identified gap, a study will be conducted that will evaluate how lean and agile mindsets can be implemented in a private label product development process in an organization located in Portugal. The objective of this research project is to enhance the development process's efficiency and effectiveness by ensuring the product's speedy launch in the market while simultaneously improving its quality. This research project presented in this article is scheduled to take place over the course of the next two years. [ABSTRACT FROM AUTHOR]

The management of high-risk neonates with hypoplastic left heart syndrome or its variants remains a tremendous surgical and medical challenge for our specialty. The hybrid strategy has been shown to improve outcomes in high-risk patients. This article illustrates the specific technical details of bilateral branch pulmonary artery banding for such high-risk patients. [ABSTRACT FROM AUTHOR]

While the classical 1-stage Norwood procedure is still performed, there are several types of "hybrid" procedures for the management of hypoplastic left heart syndrome. These hybrid approaches consist of bilateral pulmonary artery banding with ductal stenting or prostaglandin infusion as the first-stage palliation, followed by a second-stage Norwood procedure or comprehensive stage II procedure. Since 2012, we have adopted a rapid 2-stage Norwood procedure as a routine strategy, where bilateral pulmonary artery banding is performed within 5 days of age with balloon atrial septectomy, if needed, before the development of hemodynamic instability. The second-stage Norwood procedure is performed within 1 month of age. The arterial duct is kept open by continuing prostaglandin administration. In addition, an important improvement of our Norwood procedure is the use of an auto-pericardial patch fixed on an arch-shaped metal mold. The pericardium is wrapped around the lesser curvature of the mold and treated with 0.6% glutaraldehyde for 15 min. This makes it easier to imagine the final shape of the arch and helps to enlarge the retroaortic space significantly, which could reduce the risk of bronchus or central pulmonary artery stenosis and facilitate hemostasis. These developments in strategy and procedure could improve our surgical results. [ABSTRACT FROM AUTHOR]

This research paper presents an integrated approach in dealing with the hydraulic models to investigate the impact of the reinforcement and maintenance work of the hydraulic structures, and presenting a case study of the El Bagoureya Regulator. The reinforcement works for El Bagoureya Regulator include the regulator's stilling basin, which will have to be filled with a reinforced concrete mattress, the extension of the central piers to the end of the winged wall, and finally the construction of a new concrete weir at the end of the winged wall downstream of the regulator. During the rehabilitation works, the flow will therefore pass through the navigation channel of the lock. The aim of the study is therefore to investigate the lock capacity to allow the maximum flows through at constant water levels and to meet the water demand. The study also examines the efficiency of the proposed solution and the impact on the hydraulic condition of the canal. The required protection and dredging both upstream and downstream of the lock were also studied. Therefore, A 2D numerical hydraulic model was integrated with a physical model at an appropriate scale as a near-field model. The results show a good correlation between the two models. The physical model confirmed the results of the numerical model and guaranteed the hydraulic efficiency of the proposed reinforcing structure. The results show that the proposed reinforcement works improved the flow pattern and the distribution of the flow velocity downstream of the pressure regulator. It also confirms that the navigation lock is capable of handling the high discharge during the rehabilitation works while maintaining the same operating water level. Finally, the physical model has ensured the stability of the proposed protection of the bottom upstream, downstream the navigation lock and downstream the regulator at the end of the stilling basin. [ABSTRACT FROM AUTHOR]

In today's rapidly evolving job market, qualification certificates play a crucial role in validating an individual's skills, knowledge, and abilities. This article explores the importance of qualification certificates in the modern workforce, their impact on employment prospects and professional development, and the emerging trends shaping their future. It highlights how qualification certificates bridge the skills gap and enhance employability by providing standardized evidence of an individual's proficiency in a specific field. Moreover, it emphasizes the benefits of certifications for professionals in staying relevant and competitive in rapidly changing industries. The article mostly discusses the significance of the printed form of qualification certificates, their role in authentication, credibility, and portability. It explores the emerging trend of digital alternatives to printed certificates and the challenges associated with verifying their authenticity. The proposed solution is a hybrid approach that combines electronic certificates with printed versions, allowing for easy verification of authenticity in both formats. The article presents a detailed description of this approach, focusing on secure QR codes with extended unique identifiers for efficient verification. It further examines the implementation of this approach within the context of the International Computer Driving License (ICDL) Certification Program in Poland. The study demonstrates the effectiveness of the hybrid certificate approach in simplifying the verification process while maintaining credibility and facilitating lifelong learning. [ABSTRACT FROM AUTHOR]

The advent of Industry 4.0 and propelled the application of Artificial Intelligence in different industrial fields and contexts, such as predictive maintenance (PdM). Through its ability to assess the condition of equipment to detect signs of failure and anticipate them, PdM brings several potential benefits in terms of reliability, safety and maintenance costs among many other benefits. Different approaches are proposed in the literature. They are based on data, physic models or knowledge but several problems and limits persist, in particular, to override this dependence on a particular context, to utilize data and business knowledge considering the challenges of applying existing solutions to another context, difficulties associated with data analysis, and uncertainty management. In this context, the goal of this paper is also to highlight the challenges faced in the area of PdM, both for implementation and use-case. PdM remains a hot topic in the context of Industry 4.0 but with several challenges to be better investigated in the area of machine learning, knowledge representation and semantic reasoning applications. [ABSTRACT FROM AUTHOR]

• A new hybrid framework that combines the Random Forest-based SHapley Additive exPlanations (RF-SHAP) method with a random parameter negative binomial regression model (RPNB) • Random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and Extreme Gradient Boosting (XGBoost), were compared for variable importance calculation. • The results indicate that the proposed framework demonstrates improved prediction accuracy and better factor interpretation in analyzing bicycle crash frequency. To effectively capture and explain complex, nonlinear relationships within bicycle crash frequency data and account for unobserved heterogeneity simultaneously, this study proposes a new hybrid framework that combines the Random Forest-based SHapley Additive exPlanations (RF-SHAP) method with a random parameter negative binomial regression model (RPNB). First, four machine learning algorithms, including random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and Extreme Gradient Boosting (XGBoost), were compared for variable importance calculation. The RF algorithm, demonstrating the best performance, was selected and integrated into an interpretable machine learning-based method (i.e., RF-SHAP) to provide an interpretable measure of each variable's impact, which is critical for understanding the model's predictions results. Finally, the RF-SHAP method was combined with the RPNB model to explore individual-specific variations that influence crash frequency predictions. Using 288 traffic analysis zones (TAZs) in Greater London and various regional risk factors for bicycle crash frequency, the proposed framework was validated. The results indicate that the proposed framework demonstrates improved prediction accuracy and better factor interpretation in analyzing bicycle crash frequency. The model exhibits consistent Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, indicating its reliable explanatory power. Furthermore, there is a significant improvement in the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This suggests that the proposed model effectively combines the explanatory power of statistical models with the forecasting powers of data-driven models. The interpretability of SHAP values, coupled with the causal insights from RPNB, provides policymakers with actionable information to develop targeted interventions. [ABSTRACT FROM AUTHOR]

The rapid growth of lifelog data, collected through smartphones and wearable devices, has driven the need for better Human Activity Recognition (HAR) solutions. However, lifelog data is complex and challenging to analyze due to its diverse sources of information. In response, we introduce an innovative hybrid data fusion framework for HAR. This framework comprises three key elements: a hybrid fusion mechanism, an attention-based classifier, and an ensemble-based recognition approach. Our hybrid fusion mechanism expertly combines the advantages of late and intermediate fusion, enhancing classification performance and improving the network's ability to learn connections between different data modalities. Additionally, our solution incorporates an attention-based classifier and an ensemble approach, ensuring robust and consistent performance in real-world scenarios. We evaluated our method across multiple public lifelog datasets, demonstrating that our hybrid fusion approach consistently surpasses existing fusion strategies in HAR, promising significant advancements in activity recognition. • The surge in lifelog data demands improved Human Activity Recognition (HAR). • Introducing an innovative hybrid data fusion framework to enhance HAR. • Validating on diverse public datasets, outperforming existing fusion strategies in HAR. [ABSTRACT FROM AUTHOR]

Transition of care is the planned movement of adolescents and young adults from pediatric to adult health care. Many studies have demonstrated the importance of an organized transition process. The purpose of this study is to determine the efficacy of a telemedicine intervention for improving transition readiness among adolescents with spina bifida. The present study is a randomized, controlled trial, including children 14 years of age and older with myelomeningocele from a multidisciplinary spina bifida clinic. Subjects were randomized to standard care or to an intervention, consisting of video telemedicine contacts at 3, 6, and 9 months from the clinic visit. The primary outcome measure was a change in the Transition Readiness Assessment Questionnaire score from baseline to 1-year follow-up. Twenty-four patients were enrolled in the study and underwent randomization. The mean age at enrollment was 15.8 years. Ten patients (40%) were female, and the majority were White, non-Hispanic (67%). Despite enrolling 24 patients, only 1 patient in the telemedicine group completed any of the planned telemedicine sessions. No other participant completed any telemedicine counseling sessions. The study was stopped early for lack of participation in the intervention. In a single-group, as-treated analysis, there was no significant change in the Transition Readiness Assessment Questionnaire score between enrollment and 1-year follow-up (Δ = 0.36, P = 0.46). However, there were significant improvements in subscores for Managing Medications, Appointment Keeping, and Managing Daily Activities. The primary finding from this study was very low participation in a telemedicine video follow-up intervention among adolescents with myelomeningocele. Based on these results, this strategy alone is unlikely to significantly improve readiness for transition to adult care. [ABSTRACT FROM AUTHOR]

In this work, we address a production and inventory routing problem for a liquid oxygen supply chain comprising production facilities, distribution network, and distribution resources. The key decisions of the problem involve production levels of production plants, delivery schedule and routing through heterogeneous vehicles, and inventory strategies for national stock-out prevention. Due to the problem complexity, we propose a two-level hybrid solution approach that solves the problem using both exact and metaheuristic methods. At the upper level, we develop a mixed-integer linear programming (MILP) model that determines production and inventory decisions and customer allocation. In the lower level, the original problem is reduced to several multi-trip heterogeneous vehicle routing problems by fixing the optimal production, inventory, and allocation decisions and clustering customers. A well-recognised metaheuristic, guided local search method, is adapted to solve the low-level routing problems. A real-world case study in the UK illustrates the applicability and effectiveness of the proposed optimisation framework. • Integrated production and inventory routing planning for liquid oxygen supply chains. • A hybrid approach consisting of an MILP and metaheuristics is proposed. • The proposed approach is tested on a real case of the oxygen supply chain in the UK. • Sensitivity analysis on a termination criterion of the metaheuristics is conducted. [ABSTRACT FROM AUTHOR]

Temperature control throughout the cold chain is of crucial importance in the preservation of the quality of cheese. As a result of cheese heat generation, both natural and forced convection need to be considered. This numerical study aimed to characterise the airflow and temperature fields within a ventilated pallet of heat-generating cheeses. An original computational fluid dynamics (CFD) hybrid approach was developed. This approach is based on a combination of a porous media approach for the contents of the boxes and a direct CFD approach for the outer cardboard walls, including vent size and position. The computational domain is limited to one pallet level. The simulations were conducted on a steady state for two upwind air velocities 0.31 m/s and 0.73 m/s and three generated heat fluxes 0.05 W, 0.15 W, and 0.3 W per product item (250 g). The model was validated by comparison with experimental results related to velocity and product temperature profiles obtained on a full-scale experimental set-up. The hybrid approach shows good accuracy while reducing the mesh size and the computational time in comparison with the direct CFD approach. • A heat-generating cheese pallet modelled with computational fluid dynamics (CFD). • Hybrid approach combining porous medium and direct CFD was developed. • Heat generation of cheese promotes natural convection in low-ventilated area. • Hybrid approach shows a good agreement with direct CFD and experimental results. • Hybrid approach reduces computational time by 12 compared to direct CFD approach. [ABSTRACT FROM AUTHOR]

DDoS attacks have impacted businesses financially and hit their market reputation. Entropy variation and machine learning are two popular measures of DDoS detection in the literature. The entropy-based detection takes fewer resources yet a longer time to detect the attack and produces high false positive rate. Meanwhile, traditional machine learning classifiers churn out more accurate classification, however, need ample resources for processing huge data. Since IoT devices generate large amounts of data generally; therefore training ML classifiers with all data is impractical. This paper presents an overview of practical merits and demerits of entropy-based detection approach and ML-based detection. In this paper, we have proposed a two-tier hybrid approach for IoT networks that employs entropy variation to filter the attack traffic from benign traffic in first tier. Further, the remaining and reduced volume of supposedly benign data is fed to the second tier which is ML-based detection approach. We have utilized the CICDDoS2019 dataset to illustrate our notions, perform evaluation and findings. The proposed approach has yielded 99.99% f1-score in the second cycle of training and prediction. The proposed approach gives the first response in comparatively less duration as compared to the ML classifiers and significantly reduces the false positive rate as compared to entropy-based detection. It is found that the proposed detection process takes fewer resources too. The findings of the analysis were validated on the CICIoT2023 dataset, which resulted in similar performance. The proposed approach is compared with peer IDSs and results indicate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Within the contemporary dairy industry, the effective monitoring of cheese ripeness constitutes a critical yet challenging task. This paper proposes the first public dataset encompassing images of cheese wheels that depict various products at distinct stages of ripening and introduces an innovative hybrid approach, integrating machine learning and computer vision techniques to automate the detection of cheese ripeness. By leveraging deep learning and shallow learning techniques, the proposed method endeavors to overcome the limitations associated with conventional assessment methodologies. It aims to provide automation, precision, and consistency in the evaluation of cheese ripeness, delving into a hierarchical classification for the simultaneous classification of distinct cheese types and ripeness levels and presenting a comprehensive solution to enhance the efficiency of the cheese production process. By employing a lightweight hierarchical feature aggregation methodology, this investigation navigates the intricate landscape of preprocessing steps, feature selection, and diverse classifiers. We report a noteworthy achievement, attaining a best F-measure score of 0.991 through the merging of features extracted from EfficientNet and DarkNet-53, opening the field to concretely address the complexity inherent in cheese quality assessment. [ABSTRACT FROM AUTHOR]

• The transition of two-vehicle collision to chain reaction crash (CRC) was analyzed. • A hybrid approach of machine learning and random parameter logit model was used. • Unobserved heterogeneity is tackled by prior latent class clustering. • Congestion, traffic variation, and curve combined with slope are risky. • CRC risk during night, adverse weather, and specific traffic patterns are explored. Chain reaction crashes (CRC) begin with a two-vehicle collision and rapidly intensify as more vehicles get directly involved. CRCs result in more extensive damage compared to two-vehicle crashes and understanding the progression of a two-vehicle collision into a CRC can unveil preventive strategies that have received less attention. In this study, to align with recent research direction and overcome the limitations of econometric and machine learning (ML) modelling, a hybrid approach is adopted. Moreover, to tackle the existing challenges in crash analysis, addressing unobserved heterogeneity in ML, and exploring random parameter effects and interactions more precisely, a new approach is proposed. To achieve this, a hybrid random parameter logit model and interpretable ML, joint with prior latent class clustering is implemented. Notably, this is the first attempt at using a clustering with hybrid modeling. The significant risk factors, their critical values, distinct effects, and interactions are interpreted using both marginal effects and the SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes crash, traffic, and geometric data from eleven suburban freeways in Iran collected over a 5-year period. The overall results indicate an increased risk of CRC in congested traffic, higher traffic variation, and on horizontal curves combined with longitudinal slopes. Some parameters exhibit distinct or fluctuating effects, which are discussed across different conditions or considering interactions. For instance, during nighttime, heightened congestion on 2-lane freeways, increased traffic variation in less congested conditions, and adverse weather combined with horizontal curves and slopes pose risks. During daytime, increased traffic variation within highly congested sections, higher proportion of heavy vehicle traffic in moderately congested sections, and two lanes in each direction coupled with curves, elevate the levels of risk. The results of this study provide a better understanding of risk factors impact across different conditions, which are usable for policy makers. [ABSTRACT FROM AUTHOR]

One of the elements of the modern trade and services market is a business chain solution (chain store/retail chain). An example of a business chain is, e.g., a restaurant chain, where each restaurant in the chain has the same decor, organization, menu, and delivery method. Although such solutions have been known for decades, the rapid development of IT technology, the widespread access to the Internet as well as the development of mobile technologies have changed and modernized their formula. Many customers place orders remotely with the option of delivery to their door. This method of ordering and fulfilling orders is becoming more and more popular and ever more common in the recent period due to the pandemic and the resulting restrictions and limitations on the functioning of trade and services. The following key questions arise in relation to customer order processing for chain business managers: How to allocate individual customer orders to selected branches so that the cost of their processing (production and delivery) is the lowest?, How to deliver on time ?, etc. To answer these questions, a decision support model has been developed, which combines routing, allocation and planning problems for restaurant/store chains. Two ways to implement the model have been proposed. The first one uses the methods of mathematical modeling and programming, and the other, which is a proprietary approach that integrates the mechanisms of evolution (specialized representations, repair mechanisms, genetic operators, etc.), uses constraint logic programming and dedicated heuristics. In addition, procedures for constraint handling and presolving have been developed. [ABSTRACT FROM AUTHOR]

Wind speed predictions are essential for wind power management and wind farm operation. However, due to the high volatility and nonstationarity of measured wind data, it is often difficult to achieve an accurate prediction. This study proposes a hybrid approach that consists of two stages, i.e., data preprocessing and wind speed predicting, to improve the accuracy of short-term wind speed prediction. A preprocessing algorithm for the transformation and standardization of hourly mean wind speed is utilized to remove the non-Gaussian distribution of wind data and diurnal nonstationarity. Several statistical models and artificial intelligence models are then adopted in the second stage of the prediction process, including a persistence model, autoregressive model, autoregressive moving average model and backpropagation neural network. The proposed approach is developed based on the weighted averaging of these models and error optimization theory. Finally, wind speed data for 12 months from two meteorological towers located in Yanan, China, are investigated to demonstrate the effectiveness and accuracy of the proposed approach for multistep wind speed predictions, and its performance is then compared with several existing prediction models. The results indicate that the prediction accuracy improves significantly after preprocessing with the proposed approach, outperforming all the existing aforementioned models. • Novel two-stage hybrid approach for predicting short-term wind speed. •Investigations on non-Gaussianarity and nonstationarity of wind speed series. •Evaluations of the multi-step wind speed predictions of the aforementioned models. •Detailed verification for the performance of the proposed hybrid model. [ABSTRACT FROM AUTHOR]

The present effort elaborates the fractional analyses for Darcy-Forchheimer hybrid nanoliquid flow over a porous spinning disk. Temperature and concentration slip conditions are utilized at the surface of the spinning disk. A specific type of nanoparticles known as Silver- Ag and Magnesium-oxide MgO is added to the base fluid, to synthesis the hybrid nanoliquid. By using Karman's approach, the system of partial differential equations is depleted into a dimensionless system of differential equations. The obtained equations are further diminished to the first-order differential equation via selecting variables. To develop the fractional solution, the proposed model has been set up by Matlab fractional code Fde12. For accuracy and validity of the resulting framework, the outputs are compared with the fast-approaching numerical Matlab scheme boundary value solver (bvp4c). The impact of several flow constraints versus velocity, mass and thermal energy profiles have been portrayed and discussed. Magnesium oxide MgO compound is consists of Mg 2+ and O 2− ions, together bonded by a strong ionic bond, which can be synthesized by pyrolysis of magnesium hydroxide Mg (OH) 2 and MgCO 3 (magnesium carbonate) at a very high temperature (700–1500 °C). It is more convenient for refractory and electrical applications. Similarly, the antibacterial upshots of silver Ag nano-size particles could be used to manage bacterial growth in several applications, such as dental work, burns and wound treatment, surgery applications and biomedical apparatus. [ABSTRACT FROM AUTHOR]

In the industrial flotation process, the operational adjustment is still manual, which mainly relies on the operator's observation of the flotation froth. Due to limited experience or operation lag, technical indexes such as concentrate grade are difficult to control within the qualified range. In the era of big data, case-based reasoning (CBR) and Bayesian network (BN) are two advanced technologies that can realize intelligent operational adjustment. Although CBR is highly reliable, it is rough and has poor generalization performance. Besides, BN is challenging in responding to multi-working conditions and strong nonlinearities. Inspired by the advantages of the integrated models, a two-step meticulous operational adjustment approach for the flotation process combining CBR and BN is proposed in this article. A case library is constructed in the offline stage, consisting of cases whose technical index has been improved by an operational adjustment in history. After introducing a new case, the rough operational adjustment solution is first determined by CBR. Based on this, a new incremental database is constructed and used for online training of the BN model. After receiving the evidence of the new case's problem attribute, the precise operational adjustment can be determined by BN reasoning. The final case solution to be performed is the sum of the rough and precise operational adjustment received in the two steps. Experiments in a real-world copper flotation process verify the performance and merit of the proposed hybrid approach. The results show that intelligent operational adjustment can significantly improve the copper concentrate grade index. • A hybrid approach integrating case-based reasoning and Bayesian network is proposed. • Proposed approach is applied to the operational adjustment of industrial flotation process. • Proposed integrated approach owns better performance compared with single CBR and BN. [ABSTRACT FROM AUTHOR]

Objectives: To assess feasibility, safety and efficacy of hybrid approach, consisting in a combination of modern drug-eluting stent (DES) and drug-eluting balloon (DCB), for treatment of de-novo diffuse coronary artery disease (CAD).Backgrounds: Contemporary DES are associated with a persistent risk of major cardiovascular events, due to in-stent thrombosis and restenosis. The hybrid approach, reducing the permanent metallic cage length, is supposed to mitigate the risk of device-related adverse events, especially in diffuse CAD.Methods: This is a prospective, non-randomized, observational, multicenter study intended to obtain data from 100 consecutive patients affected by de-novo diffuse CAD undergoing percutaneous coronary intervention with a hybrid approach, consisting in the combined use of DES and DCB in contiguous coronary segments. The study is recorded in ClinicalTrials.gov with the identifier: NCT03939468.Results: The primary endpoint is a device oriented composite end-point (DOCE) of cardiac death, target vessel myocardial infarction (TV-MI) and ischemia-driven target lesion revascularization (ID-TLR) in DES- and/or DCB-treated segment. DOCE will be assessed at 12-months follow-up.Conclusions: This will be the first study investigating the feasibility, safety and efficacy of hybrid DES/DCB approach for the treatment of de-novo diffuse CAD. Here we describe the rationale and the design of the study. [ABSTRACT FROM AUTHOR]

Reliable climate projections are essential for informed decision-making in the context of climate change. However, selecting suitable Global Climate Models (GCMs) for such projections remains challenging, especially when computational resources are limited. This study introduced an innovative approach to GCM selection, emphasizing the identification of models that exhibit consistency in projecting future climate changes and skill in representing current climate conditions, including average climate, seasonal patterns, and climatic variations. GCM performance in simulating these critical properties was evaluated for three major climatic variables: rainfall, maximum temperature, and minimum temperature. This assessment results in a structured 3 × 3 performance matrix for each GCM, encapsulating its ability to capture these essential climate features. The matrix distances, quantifying the disparities between each GCM's performance matrix and the ideal reference matrix, were used to collectively represent the overall model performance. Finally, GCMs were ranked based on these differences using the Jenks natural break classification method, a robust statistical technique, to aid in identifying the top-performing GCMs, making them ideal candidates for ensemble model construction. The newly developed method was tested by applying it to select GCMs for Nigeria from a pool of 19 CMIP6 GCMs. The results indicate that 15 of 19 GCMs consistently projected future climate within a 95% confidence interval. Further evaluation of matrix distances and natural classification reveals a subset of GCMs, ACCESS.ESM1.5, BCC.CSM2.MR, CMCC.ESM2 and MRI.ESM2.0 are the most suitable choice for simulating Nigeria's climate. The multimodel ensemble mean of the selected GCMs projected a notable increase in rainfall by 10–40% over most of the country and the maximum and minimum temperatures by 1.0–3.5 °C and 0.5–4.0 °C across the country. The method introduced in this study can be an effective tool for GCM selection to enhance climate projection reliability. • The method considered GCMs' consistency in projection and proficiency in historical simulations. • GCMs kill was evaluated in representing climatology, seasonality, and climatic variations. • The criterion was to simulate rainfall, maximum temperature, and minimum temperature. • This application in Nigeria identified models previously recognized as the best GCMs. [ABSTRACT FROM AUTHOR]

• Accurate prediction of aviation failure events is crucial for safety. • Proposed approach combines STL decomposition, transformer, and ARIMA. • STL decomposition isolates trend, seasonal, and remainder components. • Transformer trains and predicts trend component, improving efficiency. • ARIMA trains and predicts seasonal and remainder components, reducing complexity. Accurate prediction of aviation failure events helps to anticipate future safety situations and protect against further uncontrollable accidents. However, the large sample size, complex temporal characteristics, and significant long-term correlation of aviation failure events increase the operational cost of accurate prediction. To address these challenges, this paper proposes a novel approach involving seasonal-trend decomposition using Loess (STL) and a hybrid prediction model consisting of a transformer and autoregressive integrated moving average (ARIMA). First, STL decomposition is utilized to isolate trend, seasonal, and remainder components, contributing to a comprehensive understanding of the events sample characteristics. The trend component is then trained and predicted using transformer, solving the vanishing gradient problem and improving computational efficiency. ARIMA is employed to train and predict the seasonal and remainder components, maintaining accuracy while reducing complexity. Finally, a comparative evaluation between the proposed and multiple existing approaches is conducted using Aviation Safety Reporting System (ASRS) data. The results demonstrate that the STL-transformer-ARIMA provides more accurate predictions of failure events than single model. It also exhibits significant advantages in robustness and generalization capacity compared to single transformer-based predictors. This revealed that the proposed approach performed better in predicting aviation failure events. [ABSTRACT FROM AUTHOR]

• A hybrid approach is proposed for dynamic polymerization processes. • An enhanced steady-state Monte Carlo simulation method is designed for photoATRP. • Efficiency improved by adopting high-performance computing tool. • Evolutions of both macroscopic and microscopic properties of polymers are predicted. Among the various polymerization mechanisms available for producing polymers, photoinduced atom-transfer radical polymerization (photoATRP) stands out as a promising technique. By utilizing light as an external stimulus, photoATRP facilitates the production of well-defined polymers with precise distributions. The demand for advanced polymeric materials synthesis necessitates a deep understanding of polymer chains at the molecular level. As molecular weight distribution (MWD) is a key quality index of polymers, developing models with embedded MWD information holds significance for process design and optimization tasks. In this work, an accelerated hybrid deterministic and stochastic approach is proposed for simulating dynamic photoATRP processes. The proposed approach demonstrates computational efficiency and flexibility, enabling precise predictions for the evolution of both macroscopic and microscopic properties of polymers. An application to a batch photoATRP system model is presented to illustrate the validity and performance of the proposed hybrid approach. [ABSTRACT FROM AUTHOR]

In the realm of neuro-disorders, precise diagnosis and treatment rely heavily on objective imaging-based biomarker identification. This study employs a sparsity approach on resting-state fMRI to discern relevant brain region connectivity for predicting Autism. The proposed methodology involves four key steps: (1) Utilizing three probabilistic brain atlases to extract functionally homogeneous brain regions from fMRI data. (2) Employing a hybrid approach of Graphical Lasso and Akaike Information Criteria to optimize sparse inverse covariance matrices for representing the brain functional connectivity. (3) Employing statistical techniques to scrutinize functional brain structures in Autism and Control subjects. (4) Implementing both autoencoder-based feature extraction and entire feature-based approach coupled with AI-based learning classifiers to predict Autism. The ensemble classifier with the extracted feature set achieves a classification accuracy of 84.7% ± 0.3% using the MSDL atlas. Meanwhile, the 1D-CNN model, employing all features, exhibits superior classification accuracy of 88.6% ± 1.7% with the Smith 2009 (rsn70) atlas. The proposed methodology outperforms the conventional correlation-based functional connectivity approach with a notably high prediction accuracy of more than 88%, whereas considering all direct and noisy indirect region-based functional connectivity, the traditional methods bound the prediction accuracy within 70% to 79%. This study underscores the potential of sparsity-based FC analysis using rs-fMRI data as a prognostic biomarker for detecting Autism. [Display omitted] • Optimize sparse brain network to get the relevant connections among brain regions. • Predicting autism with the optimum sparse-based network. • Statistically discern autism vs. healthy sparse brain, sparse vs. correlation outcome. • Graph energy analysis showing energy patterns between autistic and healthy subject. [ABSTRACT FROM AUTHOR]

Aberrant subclavian artery (ASA), a well-described aortic arch anomaly, is frequently associated with dysphagia and development of Kommerell diverticulum (KD) with aneurysmal degeneration. Historically, open repair has been performed, which can be associated with significant morbidity. More recently, hybrid approaches using different arch vessel revascularization techniques in combination with thoracic endovascular aortic repair (hybrid TEVAR) have been described, but there is a paucity of literature describing outcomes. The objective of this analysis was to describe our experience with management of complicated ASAs using hybrid TEVAR, further adding to the literature describing approaches to and outcomes of hybrid ASA repair. A retrospective, single-institution review was performed of all patients treated for ASA complications using hybrid TEVAR (2002-2018). The primary end point was technical success, defined as absence of type I or type III endoleak intraoperatively and within 30 days postoperatively. Secondary end points included complications, reintervention, and survival. Centerline measurement of KD diameters (maximum diameter = opposing aortic outer wall to diverticulum apex) was employed. Kaplan-Meier methodology was used to estimate secondary end points. Eighteen patients (1.4% of 1240 total TEVAR procedures; male, 67%; age, 59 ± 13 years) were identified (left-sided arch and right ASA, 94% [n = 17]; right-sided arch and left ASA, n = 1 [6%]; retroesophageal location and associated KD, 100%); median preoperative KD diameter was 60 mm (interquartile range [IQR], 37-108 mm). Operative indications included diverticulum diameter (61%), dysphagia (17%), rupture (11%), rapid expansion (6%), and endoleak after TEVAR (6%). All procedures used some combination of supraclavicular revascularization and TEVAR (staged, 50% [n = 9]), whereas partial open arch reconstruction was used in 17% (n = 3). There were no perioperative deaths or spinal cord ischemic events. Major complications occurred in 22% (n = 4): nondisabling stroke, one; arm ischemia, one; upper extremity neuropathy, one; and iatrogenic descending thoracic aortic dissection, one. Technical success was 83%, but 44% (n = 8) had an endoleak (type I, n = 3; type II, n = 5 [intercostal, n = 2; aneurysmal subclavian artery origin, n = 3]) during follow-up (median, 4 months; IQR, 1-15 months). Two endoleaks resolved spontaneously, three were treated, and three were observed (1-year freedom from reintervention, 75% ± 10%). Median KD diameter decreased by 7 mm (IQR, 1-12 mm), and 78% (n = 14) experienced diameter reduction or stability in follow-up. The 1- and 3-year survival was 93% ± 6% and 84% ± 10%, respectively. Hybrid open brachiocephalic artery revascularization with TEVAR appears to be safe and reasonably effective in management of ASA complications as evidenced by a low perioperative complication risk and reasonable positive aortic remodeling. However, endoleak rates raise significant concerns about durability. Therefore, if this technique is employed, the mandatory need for surveillance and high rate of reintervention should be emphasized preoperatively. This analysis represents a relatively large series of a hybrid TEVAR technique to treat ASA complications, but greater patient numbers and longer follow-up are needed to further establish the role of this procedure. [ABSTRACT FROM AUTHOR]

Background: Percutaneous coronary interventions (PCI) of chronic total occlusions (CTO) are high risk procedures with low success rates compared to standard PCI. Recently the 'hybrid approach' method has been developed to increase success rate. In 2015 we set up a dedicated program to systematically treat CTOs by this hybrid approach. This retrospective, observational registry aims to report achieved results in a single PCI centre.Methods and Results: We reviewed all CTO procedures between January 2012 and December 2017. Procedures performed by dedicated operators after December 2014 were assigned to the hybrid cohort, procedures done before this time or performed by a non-CTO operator were assigned to the non-hybrid cohort. Procedural techniques, difficulty of lesions, J-CTO scores, outcomes and complications were analysed. In total 505 procedures were included. Average J-CTO score was 1.9 ± 1.1, which was significantly higher in the hybrid cohort (2.1 ± 1.2 vs. 1.6 ± 1.1; p < 0.001). Overall procedural success rate was 75.4% with significantly higher success rates in the hybrid cohort (81.2% vs. 68.2%; p < 0.001). Combining both cohorts, overall success rate increased over the years (2012-2017 respectively 65.2%, 60.0%, 71.7%, 83.2%, 77.9% and 81.4%). Complication rate was higher in the hybrid cohort compared to the non-hybrid cohort (4.6% vs 0.4%, respectively; p = 0.026).Conclusion: By introducing a systematic CTO program, including use of the hybrid approach, we observed higher success rates of PCI CTO, despite increased complexity of the lesions (higher J-CTO score). The occurrence of MACE was in accordance with current literature.Condensed Abstract: Our registry demonstrates that introduction of a dedicated CTO program increases success rates of CTO treatments despites increased lesions difficulty and with acceptable MACEs rates. [ABSTRACT FROM AUTHOR]

Iatrogenic coronary injury after mitral repair is related to blind annuloplasty suture ligation or kinking of the circumflex artery (CxA) and can present with early ST segment changes, malignant ventricular arrhythmias, and segmental wall motion abnormalities. Corrective treatment is imperative to avoid myocardial infarction and can include removal of the annuloplasty ring or CxA bypass. We present a novel hybrid approach for the rapid diagnosis and management of iatrogenic CxA injury after mitral repair. [ABSTRACT FROM AUTHOR]

• The paper deals with a real multi-compartment container loading problem. • The problem arises in the beverage distribution under several practical constraints. • A hybrid approach is proposed based on constructive heuristics and MILPP models. • Computational tests are performed with a variety of instances based on real data. • The approach obtains feasible solutions for all instances under all constraints. In this paper, we address a real problem of packing boxes into a multi-compartment container, in which the boxes must be delivered to customers in a predefined route. This problem arises, for example, in the distribution of beverages (packed in "boxes") by trucks whose container has multiple compartments. The objective is to find a feasible packing plan that minimizes the handling of boxes inside the container along the whole truck route. The following practical constraints must be met: orientation of the boxes, cargo stability, load bearing strength of the boxes and load balancing. To the best of our knowledge, this is the first study focusing on a vehicle packing problem with multi-compartment containers in the context of beverage distribution. To solve this problem, we present a hybrid approach which consists of a heuristic method based on the generation of horizontal layers and on the solution of mixed integer linear programming models. Computational tests were performed with this approach and a large variety of instances based on real data from a soft drink company. The results show that the approach is able to find feasible solutions for all instances considered under all constraints, contrary to what was observed in practice with the manual procedures available in the company. In practice, if a feasible solution is not obtained, it is necessary to change the predefined route plan and/or to consider an additional new delivery route and/or to use a solution that violates some of the constraints involved. In this sense, the proposed solution approach has good potential for being embedded into existing expert and intelligent systems for supporting the decision making process. [ABSTRACT FROM AUTHOR]

A huge amount of data is generated since the evolution in technology and the tremendous growth of social networks. In spite of the availability of data, there is a lack of tools and resources for analysis. Though Arabic is a popular language, there are too few dialectal Arabic analysis tools. This is because of the many challenges in Arabic due to its morphological complexity and dynamic nature. Sentiment Analysis (SA) is used by different organizations for many reasons as developing product quality, adjusting market strategy and improving customer services. This paper introduces a semi- automatic learning system for sentiment analysis that is capable of updating the lexicon in order to be up to date with language changes. HILATSA is a hybrid approach which combines both lexicon based and machine learning approaches in order to identify the tweets sentiments polarities. The proposed approach has been tested using different datasets. It achieved an accuracy of 73.67% for 3-class classification problem and 83.73% for 2-class classification problem. The semi-automatic learning component proved to be effective as it improved the accuracy by 17.55%. [ABSTRACT FROM AUTHOR]

Social media are becoming widely used in the healthcare field as a patients-caregivers communication tool giving birth to new sources of information rich with the knowledge that may improve this field. Therefore, social media data analysis becomes a real business requirement for healthcare industrials and data scientists. However, regarding their complexity and unstructured character, existing natural language processing tools cannot succeed their exploitation. In the literature, a wide range of approaches appeared based on dictionaries, linguistic patterns and machine learning having their strengths and weaknesses. In this work, we propose a hybrid system combining the above approaches by taking the advantage of each of them to extract structured and salient drug abuse information from health-related tweets. We improve the system accuracy by real time update of the domain dictionary. We collected 1000000 tweets and we conducted different experiments showing the advantage of hybridization on efficient information extraction from social media data. [ABSTRACT FROM AUTHOR]

• The hybrid algorithm for defects localization uses two criteria: energy and time of flight. • Approach employs fast discrete wavelet decomposition of output signals. • Hybrid approach is verified by laser based experimental technique. • An explicit FEM has been successfully applied for wave propagation modeling. • The numerical results are in full correspondence with the experimental results. In this paper, the hybrid algorithm for localization of damage and defects is implemented on the concrete plate-like structures for localizing the clay and gypsum inclusions. The hybrid approach employs fast discrete wavelet decomposition of sensor output signals, as well as energy and time of flight criteria. The applied localization algorithm is verified both experimentally and numerically on the concrete plates with one and two inclusions. The experiment is conducted in controlled laboratory conditions, using a piezoelectric actuator for excitation of the wave propagation in the structure, while the ultrasonic laser is used for measuring vibrations at the sensor locations. Numerical simulation of wave propagation is done using the explicit finite element method on 3D models. The numerically obtained results are in full correspondence with the experimental results. The images of material defects positions obtained by the hybrid approach show a good agreement with the actual positions, which indicates a good potential of the used approach in localization of various types of material defects in plate-like concrete structures. [ABSTRACT FROM AUTHOR]

Emergence of social media invokes social actors to share their information digitally. Moreover, it enables them to maintain their links with other people worldwide. Due to its massive popularity, it has become a fascinating test bed to initiate various attacks. Attackers form Sybil nodes to disseminate malicious content in order to infect legitimate users aiming to steal sensitive information, such as user's credentials. Therefore, the focus of this paper is to detect malicious profiles, specifically on the Twitter microblogging platform. We propose a hybrid approach which leverages the capabilities of machine learning techniques to identify malicious profiles. Initially, Petri net structure analyzes the user's profile and various features, and these features are then used to train classifier. This is achieved to identify malicious profiles from legitimate users. Finally, to prove the efficiency, a comparative analysis of our approach is conducted with existing state-of-the-art techniques. The experimental results reveal that our approach achieves a high detection rate of 99.16% which is better than other techniques. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has been widely used in the last decades for agricultural applications, both for assessing vegetation condition and for subsequent yield prediction. Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for an advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 and Landsat 8 optical remote sensing data and machine learning methods in order to estimate crop GPP. With this approach, we by-pass the need for an intermediate step to retrieve the set of vegetation biophysical parameters needed to accurately model photosynthesis, while still accounting for the complex processes of the original physically-based model. Several implementations of the machine learning models are tested and validated using simulated and flux tower-based GPP data. Our final neural network model is able to estimate GPP at the tested flux tower sites with r 2 of 0.92 and RMSE of 1.38 gC d−1 m−2, which outperforms empirical models based on vegetation indices. The first test of applicability of this model to Landsat 8 data showed good results (r 2 of 0.82 and RMSE of 1.97 gC d−1 m−2), which suggests that our approach can be further applied to other sensors. Modeling and testing is restricted to C3 crops in this study, but can be extended to C4 crops by producing a new training dataset with SCOPE that accounts for the different photosynthetic pathways. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms. • Sentinel-2 offers a great potential for the monitoring of agricultural productivity. • Machine learning applied to a process-based model to estimate GPP from satellite data. • Our hybrid approach accurately estimates GPP without any local information. • The use of red edge bands of Sentinel-2 improves GPP modeling. • Global application to multiple satellites using on the same model [ABSTRACT FROM AUTHOR]

• Simulated cases were compared with available experimental data. • Empirical correlations for liquid jet primary breakup height were gauged numerically. • Different secondary breakup models were evaluated and compared. • Two-way and four-way coupling and their influence were analyzed for LJIC cases. • Domain reduction and refinement enhancement techniques were applied in meshing. Spray formation in a liquid jet in air crossflow is investigated numerically using a hybrid approach for two cases: W e = 11 and W e = 53 , where We is the air Weber number. The VOF method was used to solve the interaction between the liquid jet column and the air flow up to the primary breakup. The Eulerian liquid portion is converted to discrete Lagrangian drops by the use of source terms of mass and momentum and according to empirical correlations. The effects of two secondary breakup models, as well as two-way coupling with droplet-to-droplet collisions were evaluated in this work, in order to establish an improved hybrid model suitable to solve liquid jet in crossflow at low computational cost. The results were very satisfactory relating droplet velocity and mass fraction distribution for the lower Weber number case. For the higher Weber number case, the deviation of the numerical mass fraction distribution in comparison with the experimental one was less than 14 × 10 − 2 . Improvement to the droplet size distribution was observed by using a new secondary breakup model, specially for the higher Weber number case. [ABSTRACT FROM AUTHOR]

The cutting process is a complex nonlinear system. Predicting such a system with conventional regression models is inefficient. In this paper, a hybrid approach using deep neural networks (DNN) is proposed to predict the specific cutting forces. With the aim of obtaining the hybrid training data, orthogonal cutting tests and 2D FEM chip formation simulations have been performed under diverse cutting parameters, tool geometries and tool wear conditions. Predictive models using a DNN and a conventional linear regression method were established. In comparison with the conventional linear regression method, the hybrid model using the machining learning is more accurate. [ABSTRACT FROM AUTHOR]

Highlights • Real logistics problem including constraints and requirements is addressed. • Hybrid approach ILSP is proposed for planning the high-quality logistic solution. • Quickly finding initial solutions by partition, routing and insertion strategies. • Iteratively improving the solution quality by ACO -based strategies. • The performance of ILSP is evaluated based on real logistics data. Abstract With the busy life of modern people, more and more consumers are preferring to shop online. This change on shopping behavior results in large volumes of packages must be transported, and thus research on logistics planning considering real constraints has increased. To solve this problem, several heuristics or evolutionary methods with expert knowledge were proposed previously, but they are usually inefficient or need a large amount of memory. In this paper, we propose a hybrid approach called Iterative Logistics Solution Planner (ILSP) for not only quickly finding a nice logistics solution but also iteratively improving the solution quality while meeting the real logistics constraints. ILSP contains two main phases including initial logistics solution generation and iterative logistics solution improvement based on the intelligence and knowledge from domain experts. Several algorithms and strategies are designed in ILSP for package partitioning, route planning and quality improvement. From the view of expert systems, the significance and impact of ILSP are simultaneously taking both computational efficiency and iterative quality improvement based on the expert knowledge into account on logistics planning problem with pickup and delivery. Through the rigorous experimental evaluations of real logistics data, the results demonstrated the excellent performance of ILSP. [ABSTRACT FROM AUTHOR]

Abstract Landslides, which could cause huge losses of lives or property damages, result from several different environmental factors whose influences on landslides are very complex. Therefore, it is essential to understand the relationships between these environmental factors and landslides. Thus, the integration of the analytical hierarchy process (AHP) with the normalized frequency ratio (NFR) is evaluated for landslide susceptibility analyses. However, in addition to these complex relationships, the randomness and fuzziness always affect landslide susceptibility mapping. This study introduces the cloud model (CM) to improve the integrated AHP-NFR method, and proposes a novel hybrid AHP-NFR-CM method for landslide susceptibility analyses, which can better address issues of the randomness and fuzziness. Firstly, ten environmental parameters are selected as landslide impact factors, and their values for all the landslides identified in the study area are obtained through the remote sensing (RS) and geographical information system (GIS) technologies. The AHP method is used to obtain the weight of each landslide impact factor, and the NFR method is used to obtain the weight of each subclass in each landslide impact factor, which can reflect the relationship between the landslide impact factor and landslide occurrence. After applying an appropriate compositional operation between the weights of the landslide impact factors and the weights of the subclasses of the impact factors, a landslide susceptibility index (LSI) for each grid divided via the attribution-based spatial information multi-grid method (ASIMG) can be computed. To solve the inevitable issues of randomness and fuzziness in landslide susceptibility analyses, a cloud model that uses three numerical features (expectation, entropy and hyper-entropy) to represent the intension of the concept, is adopted to improve the methods of AHP and NFR. The relative importance of two landslide impact factors is scaled with the cloud model rather than the Saaty criteria. Pair-wise comparison matrixes of landslide impact factors given by each expert are described by the normal cloud model, and the floating cloud model is used to aggregate all experts' judgments. The weight of each landslide impact factor is also expressed with the cloud model rather than a certain value. In improving the NFR, the weight of each subclass of each landslide impact factor is expressed with the cloud model rather than a certain value. In the improvement of the landslide susceptibility results, the domain of landslide risk assessment results is also displayed with the cloud model instead of a series of definite intervals. As the study area examined is large, several grids would need to be divided, meaning that it would take a considerable amount time to subject the entire study area to landslide susceptibility mapping. Thus, we propose a new attribute-based spatial information multi-grid (ASIMG) division method and introduce grid-computing technology to improve the calculation efficiency during the process. Finally, the proposed hybrid AHP-NFR-CM-ASIMG approach is validated and applied in the study area. It's concluded that the new integration of AHP and NFR methods with the cloud model can consider both randomness and fuzziness and therefore can increase the robustness of landslide susceptibility analyses, while the ASIMG technology can enhance the calculation efficiency in regional landslide susceptibility mapping. Highlights • Cloud model is used to reduce subjectivity of experts in analytical hierarchy process method. • Cloud model is used to express the randomness and fuzziness in normalized frequency ratio method. • Cloud model is used to describe the results of landslide susceptibility index. • Spatial information multi-grid method and grid computing technology are introduced to increase the computing efficiency. [ABSTRACT FROM AUTHOR]

Proper equipment maintenance can significantly reduce overall operating costs by boosting productivity. Management personnel often consider maintenance an expense; however, a more positive approach is to view maintenance as a profit center. Considering this new perspective, the requirements for maintenance management have changed drastically from the traditional concept of fix-it-when-broken to a more complex approach that entails adopting a maintenance strategy for a more integrated approach and alignment. This paper presents a Goal Programming (GP) approach to define the most cost-effective method for maintaining some critical pumps in the paper industry. The failure mode and effects analysis (FMEA) criteria consider each machine failure's occurrence, severity, and detection. After determining the weights, the optimal strategy for each failure is calculated by solving the goal programming. Thus, FMEA can be used to design a network structure for each pump failure mode. For this purpose, the limitations and goals affecting the model are obtained from research literature and interviews with experts. Regarding resource utilization and failure reduction, the results demonstrate that predictive and preventive maintenance strategies are superior to corrective strategies. Therefore, these strategies can predict failures, provide helpful information to maintenance managers, and limit the negative aspects of a failure regarding safety and cost. [ABSTRACT FROM AUTHOR]

Canopy water content (CWC) is an important indicator of crop growth. Currently, the hybrid approach, which combines the physical model and machine learning (ML) method, is commonly used for inversing CWC at the regional scale via optical imagery. Although this approach has good inversion accuracy, it suffers from ill-posed issues from the physical model due to the difference between actual and simulated scenes. Meanwhile, the ability of existing ML methods to resolve this difference remains unclear. To fill the above study gaps, we added different degrees of Gaussian noise into the simulated dataset to reduce the differences in spectral reflectance between that simulated by the PROSAIL-5B model and that measured from optimal imagery. Furthermore, this study also compared the performance of different ML approaches (neural network, NN; Gauss process regression, GPR; and kernel ridge regression, KRR) in the retrieval of wheat (Triticum aestivum L.) CWC based on PROSAIL-simulated datasets. Additionally, we also compared the inversion results for the CWC generated by the SNAP Toolbox with those estimated by the hybrid approach. The results showed that the aforementioned hybrid approaches performed well in the retrieval of wheat CWC when Gaussian noise with a mean of zero and a standard deviation of 0.06 (η: 0, 0.06) was added to the simulated dataset. The hybrid model based on GPR had the highest accuracy of estimation for the CWC (RMSE = 0.0096 g/cm2, RRMSE = 22.2%), when compared with NN or KRR (NN: RMSE = 0.0105 g/cm2, RRMSE = 26.9%; KRR: RMSE = 0.0099 g/cm2, RRMSE = 24.3%). It also outperformed the existing CWC-retrieval algorithms among the SNAP Toolbox (RMSE = 0.027 g/cm2, RRMSE=44.1%). Our results demonstrate that the hybrid approach in combination with GPR and the physical model can accurately retrieve the CWC of crops, thus confirming that the approach is suitable for estimating the CWC. • Hybrid approach coupling machine learning and physical model had better efficiency and universality. • The accuracy of all hybrid models was improved when certain Gaussian noise was added. • CWC model combining gauss process regression and PROSAIL-5B had the highest accuracy. • Our proposed hybrid approach outperformed SNAP Toolbox for wheat CWC estimation. [ABSTRACT FROM AUTHOR]

Abstract Adequate separation of regional-residual components from observed gravity anomaly is always a challenging task in gravity interpretation. Several techniques have been developed for effective regional-residual separation, however, no single approach can perfectly accomplish the job. In this work, a hybrid approach has been proposed with an objective to enhance the performance of Bi-dimensional Empirical Mode Decomposition (BEMD) in decomposing gravity anomaly by jointly employing low pass filter and BEMD. The paper discusses the efficacy of this hybrid approach in gravity anomaly separation from noisy synthetic and field data. Synthetic studies involving forward modelling of asymmetrically placed shallow and deeper spherical bodies with added Gaussian noises of different levels demonstrate that the proposed approach is more efficient in separating the regional-residual components from observed gravity anomaly compared to the individual application of filtering or BEMD. It has an added advantage of suppressing the unwanted noises significantly. After successful test with synthetic data the proposed approach has been applied to field data and satisfactory results are obtained. Thus, the proposed hybrid approach is more effective in delineating gravity signature related to complex near surface features even from noisy gravity data. Highlights • A new hybrid approach has been proposed for regional-residual separation from gravity anomaly. • Feasibility and robustness of the proposed approach has been tested with more realistic synthetic data. • It can significantly reduce the unwanted noises in the separated anomaly maps. • Provides an alternative but effective way for regional-residual separation from gravity anomaly. [ABSTRACT FROM AUTHOR]

Performance of shallow geothermal systems such as borehole thermal energy storage (BTES) and ground source heat pump (GSHP) mainly depends on the thermal properties of the subsurface and proper design of borehole heat exchangers (BHE). This paper introduces a novel hybrid approach for measuring the effectiveness of BHEs and surrounding subsurface thermal properties, which combines traditional thermal response test (TRT) with the borehole temperature relaxation method (BTR), based on two dimensional radial conductive heat transfer. The new method allows for: (1) evaluation of how convective heat loss at groundwater layers influence estimation of subsurface thermal properties; (2) examination of non-uniform heat transfer through a BHE to stratified subsurface layers; and, (3) calculation of depth-dependency of thermal properties of unsaturated subsurface layers. The hybrid approach was tested using a 50 m U-type BHE, the results of which indicated that convective heat transfer at the groundwater level altered the real value of effective thermal conductivity from 0.45 to 1.56 W/m K. The non-uniformity of heat transfer along the BHE was confirmed by calculations that showed subsurface thermal conductivities were depth dependent, varying between 0.34 and 0.61 W/m K. [ABSTRACT FROM AUTHOR]

Abstract In this paper, laser cutting of Aluminum alloy (Al 6061-T6) thin sheet has been carried out using pulsed Nd-YAG laser to investigate the dimensional accuracy of the kerf geometry and zone of the metallurgical changes in the sheetmetal. These responses are quantified in terms of kerf taper and heat affected zone for the study. The taper in the kerf and heat affected zone are the functions of four laser cutting parameters namely: lamp current, pulse width, pulse frequency and cutting speed and different settings of these laser cutting parameters are utilized to analyze the responses. In order to analyze the experimental results, a systematic design of experiments namely Box Behnken design (BBD) has been employed to conduct the experiments. Further, the experimental results have been optimized using hybrid approach of grey relational analysis and fuzzy logic. The analysis of variance (ANOVA) has been carried out to determine the significance of laser cutting parameters for the process where pulse frequency is found as the most leading parameter in the study. The application of hybrid approach is capable to reduce the kerf taper and HAZ of laser cut kerf by 2.52% and 42.32%, respectively. Highlights • Nd-YAG laser cutting of reflective sheetmetal has been investigated. • Quality of the laser cut kerf geometry has been quantified in terms of kerf taper and heat affected zone. • Application of hybrid approach has reduced taper and heat affected zone significantly. [ABSTRACT FROM AUTHOR]

Highlights • Adrenal tumors can be adherent to liver, spleen, spinal cord, kidney, and this situation prevents an accurate segmentation of adrenal tumors. • In addition, low contrast, non-standardized shape and size, homogeneity, and heterogeneity of the tumors are considered as problems in segmentation. • This study proposes a computer-aided diagnosis (CAD) system to segment adrenal tumors by eliminating the above problems. • The performance of the proposed method was assessed on 113 Magnetic Resonance (MR) images using seven statistical metrics. • As a result, an efficient framework is presented on segmentation of adrenal tumors for MR images, especially for cyst-based tumors. Abstract Background and objective Adrenal tumors, which occur on adrenal glands, are incidentally determined. The liver, spleen, spinal cord, and kidney surround the adrenal glands. Therefore, tumors on the adrenal glands can be adherent to other organs. This is a problem in adrenal tumor segmentation. In addition, low contrast, non-standardized shape and size, homogeneity, and heterogeneity of the tumors are considered as problems in segmentation. Methods This study proposes a computer-aided diagnosis (CAD) system to segment adrenal tumors by eliminating the above problems. The proposed hybrid method incorporates many image processing methods, which include active contour, adaptive thresholding, contrast limited adaptive histogram equalization (CLAHE), image erosion, and region growing. Results The performance of the proposed method was assessed on 113 Magnetic Resonance (MR) images using seven metrics: sensitivity, specificity, accuracy, precision, Dice Coefficient, Jaccard Rate, and structural similarity index (SSIM). The proposed method eliminates some of the discussed problems with success rates of 74.84%, 99.99%, 99.84%, 93.49%, 82.09%, 71.24%, 99.48% for the metrics, respectively. Conclusions This study presents a new method for adrenal tumor segmentation, and avoids some of the problems preventing accurate segmentation, especially for cyst-based tumors. [ABSTRACT FROM AUTHOR]

Malware detection is an important factor in the security of the smart devices. However, currently utilized signature-based methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. In this context, an efficient hybrid framework is presented for detection of malware in Android Apps. The proposed framework considers both signature and heuristic-based analysis for Android Apps. We have reverse engineered the Android Apps to extract manifest files, and binaries, and employed state-of-the-art machine learning algorithms to efficiently detect malwares. For this purpose, a rigorous set of experiments are performed using various classifiers such as SVM, Decision Tree, W-J48 and KNN. It has been observed that SVM in case of binaries and KNN in case of manifest.xml files are the most suitable options in robustly detecting the malware in Android devices. The proposed framework is tested on benchmark datasets and results show improved accuracy in malware detection. [ABSTRACT FROM AUTHOR]

Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed. [ABSTRACT FROM AUTHOR]