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A cheap, easy-to-build basic module and multiplatform configurations obtained by connecting basic modules with dimensions corresponding to typical rail containers (10-foot, 20-foot, or larger) can be used to support intermodal rail-road transport of wooden and metal logs, including pipes, products transported on pallets and loose materials. This paper presents assumptions of the hybrid numerical research method and selected aspects of numerical strength tests of platform-containers in the basic 10-foot configuration. Multibody analyses (MBS), the finite element method (FEM), the original methodology, and various class mathematical models of the tested platforms were used appropriately in numerical tests. In the analysis of the results of multi-variant tests with the use of maximum operating loads, special attention was paid to the stress exerted on the joints between the movable components of the intermodal platform. [ABSTRACT FROM AUTHOR]

The growing economic influence of cryptocurrency and the development of the associated infrastructure has brought about an interest in the new areas of its application. Payment of compensation in cryptocurrency is one such evolving application of cryptocurrency. Using a qualitative research design, we attempt to identify the factors and ecosystem constituents that play an important role in the diffusion, adoption and use of cryptocurrency as a compensation medium. A hybrid methodology involving both inductive and deductive methods of qualitative inquiry was used to develop the themes. The study identified nine main factors that influenced a participant's decision to accept compensation in cryptocurrency. They are: (1) the prospects of high return; (2) ease of transaction; (3) faster international transactions; (4) price volatility of cryptocurrencies; (5) threat of hacking; (6) taxation policies of the country; (7) lack of recognition as legal tender; (8) influence of peer groups; and (9) influence of celebrities. The inductive part of the study enabled the mapping of the major stakeholders involved in the decision, namely, the government, the employer, the peer groups of the employee, celebrities and the cryptocurrency-issuing company. [ABSTRACT FROM AUTHOR]

A cheap, easy-to-build basic module and multiplatform configurations obtained by connecting basic modules with dimensions corresponding to typical rail containers (10-foot, 20-foot, or larger) can be used to support intermodal rail-road transport of wooden and metal logs, including pipes, products transported on pallets and loose materials. This paper presents assumptions of the hybrid numerical research method and selected aspects of numerical strength tests of platform-containers in the basic 10-foot configuration. Multibody analyses (MBS), the finite element method (FEM), the original methodology, and various class mathematical models of the tested platforms were used appropriately in numerical tests. In the analysis of the results of multi-variant tests with the use of maximum operating loads, special attention was paid to the stress exerted on the joints between the movable components of the intermodal platform.

Copyright of Dilemas Contemporáneos: Educación, Política y Valores is the property of Dilemas Contemporaneos: Educacion, Politica y Valores and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction LDL is the acronym used to denote low-density lipoproteins. When the level of LDL is high, it leads to the accumulation of cholesterol in the arteries, and as a result, it is commonly known as "bad" cholesterol. Recognizing the significance, a study is conducted on computational and statistical modelling for parameter optimization of LDL-cholesterol levels. In recent years, there has been an increase in the application of precise statistical analysis methodologies. Consequently, scientists are more focused on producing reliable results and are more determined to provide credible findings. Objective This study aims to develop and validate a proposed hybrid method that combines Multilayer Feedforward Neural Network (MLFFNN), and Multiple Linear Model (MLR) and to present the R-syntax applications of the proposed hybrid method with clinical study data. Material and Methods The proposed hybrid model will be built using the generalized mixture model, which is formulated using an MLFFNN framework and Linear Model (LM). The performance of the hybrid model will be assessed utilizing the predicted mean square error neural network (MSE.net) and the predicted mean square error (P.MSE), which will serve as a benchmark for precision and efficacy. Results: The result indicates that hybrid method modelling is superior, with the highest R-squared value and the lowest MSE.net value. The hybrid model technique was found to produce a more precise forecast of the outcome when the data is separated into training and testing datasets. The R-square score in this report demonstrates that the LM model is a good fit (84.97%), and the MSE.net value of 0.00529 indicates that the model is both accurate and predictive. Conclusion The research concludes that the hybrid model method proposed is preferable. This critical conclusion helps us comprehend the hybrid method's proportional contribution to this illustration's result. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Production Engineering / Revista Brasileira de Engenharia de Produção is the property of Brazilian Journal of Production Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The global drive toward renewable energy is imposing challenging operating requirements on power turbines. Flexible load-leveling applications must accept more frequent and demanding start-stop cycles. Full transient analyses are too computationally expensive for real-time simulation across all operating regimes so monitoring relies on sparse physical measurements. Alone, these sparse data lack the fidelity for real-time prediction of a complex thermal field. A novel hybrid methodology is proposed, coupling data across a range of fidelities to bridge the limitations in the individual analyses. Combining several fidelity methods in parallel; low-order models, corrected by real-time physical measurements, are calibrated with high-fidelity simulations. A newly developed low-order thermal network code is used to predict the thermal field in real-time. High-fidelity flow characteristics are routinely transferred to the decoupled low-order solution. A critical enabling feature of this hybrid approach is the fast data interpolation between differing fidelity numerical simulations. This paper evaluates a spatial Kriging method for robust data transfer between two different fidelity mesh, tested in the case of thermal profile prediction of a power turbine. Additionally, a novel coordinate-based hash mapping process is demonstrated for the fast high-to-low fidelity data transfer. Localized hashing allows independent, parallel, nearest neighbor search at significantly reduced computational cost. The demonstrated method facilitates fast mesh pairing, necessary to support the real-time hybrid method for thermal field prediction during turbine transient operation. [ABSTRACT FROM AUTHOR]

The global drive toward renewable energy is imposing challenging operating requirements on power turbines. Flexible load-leveling applications must accept more frequent and demanding start-stop cycles. Full transient analyses are too computationally expensive for real-time simulation across all operating regimes so monitoring relies on sparse physical measurements. Alone, these sparse data lack the fidelity for real-time prediction of a complex thermal field. A new hybrid methodology is proposed, coupling data across a range of fidelities to bridge the limitations in the individual analyses. Combining several fidelity methods in parallel, low-order models, corrected by real-time physical measurements, is calibrated with high-fidelity simulations. The multifaceted hybrid approach enables the real-time speed of low-order analysis at high resolution. This paper series develops the critical enabling features of the hybrid method. Real-time cross-fidelity data transition is fundamental to the hybrid methodology. A novel neural network auto-encoder method is presented, facilitating complex thermal profile reconstruction. Uncovering a compressed latent space, auto-encoders leverage underlying data features for fast simulation. Coupled with a dynamic mask and top-k selection, thermal probe placement can be automatically optimized. The auto-encoder method is demonstrated on a turbine casing, reconstructing over 500 h of transient operation in real-time, whilst reducing the required number of measurements by half. [ABSTRACT FROM AUTHOR]

Forecasting air pollutant levels is essential in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter. Focusing the forecast on air pollution peaks is challenging and complex since the pollutant time series behavior is not regular and is affected by several environmental and urban factors. In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM2.5 pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at different monitoring stations, with a set of multilayer perceptron to forecast extreme values of PM2.5 for each cluster. The proposed model was applied to analyze five-year pollution data obtained from nine weather stations in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves performance metrics when forecasting daily extreme values of PM2.5.

The application process for the Medical Assistance Fund (MAF) to help patients receive medical interventions at public hospitals has been plagued with delays due to the increasing number of applications, inefficient management, and manual tasks. This study aims to redesign the current application process through Business Process Reengineering (BPR) and to evaluate the effectiveness of a new process to be carried out by the Medical Social Work Unit (MSWU). A new methodology, B2Lean which is based on a combination of the BPR and Lean Thinking was proposed to help identify the processes requiring improvement and to remove inefficient ones. A new online application system, the Patient Assistance Management System was proposed as an IT enabler for this BPR initiative. This study was based on a qualitative research methodology involving a case study in which interviews and observations were used to collect data. The sample study consisted of seven MSWU officers and several supporting staff selected from a government hospital. In the interviews, the '5W 1H' technique was used to elicit and analyze their feedback on issues relating to the existing processes. The findings of the analysis revealed that several manual tasks were inefficient, which had to be either removed or replaced, effectively helping to reduce the overall processing time by 52%. With the new and efficient processes, practitioners could certainly expedite patients' applications for medical assistance, thus helping them to receive prompt medical interventions. The B2Lean methodology could be used as a guideline for organizations seeking improvement in their current practices. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forecasting air pollutant levels is essential in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter. Focusing the forecast on air pollution peaks is challenging and complex since the pollutant time series behavior is not regular and is affected by several environmental and urban factors. In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM 2.5 pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at different monitoring stations, with a set of multilayer perceptron to forecast extreme values of PM 2.5 for each cluster. The proposed model was applied to analyze five-year pollution data obtained from nine weather stations in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves performance metrics when forecasting daily extreme values of PM 2.5 . [ABSTRACT FROM AUTHOR]

Safety is one of the most important issues in aviation. Aviation regulations provide significant information pertinent to the safety design and operation of aircraft; however, this information has not been effectively used. It is difficult to precisely identify and obtain the necessary information due to massive and unstructured provisions. In this study, a hybrid methodology is proposed to realize knowledge system construction using Chinese Civil Aviation Regulations as the object of study. To realize structured knowledge organization and intelligent knowledge application for aviation regulations, the hybrid methodology integrates a semantic cohesion model, a knowledge recognition model, a knowledge organization model, and a knowledge application model. A knowledge system of aviation regulations is built using the hybrid methodology comprising all knowledge necessary for aviation safety. The system provides intelligent knowledge support for the safety analysis of aircraft from the perspective of control system, including the accurate positioning of control elements and thorough acquisition of control conditions. Experiments were conducted to confirm the accuracy of the proposed method, and the 56,853 knowledge triples contained in the knowledge system supported its reliability. A few examples of knowledge retrieval are provided, focusing on the interaction processes of socio-technical elements during aircraft missions. It takes only a few seconds to acquire the knowledge required for safety analysis. The examples show how the hybrid methodology and knowledge system can be utilized to increase the efficiency of safety analysis for socio-technical systems while advancing intelligent knowledge applications in the aviation domain. [ABSTRACT FROM AUTHOR]

ABSTRACTMaps published for tourism promotion and information constitute a particular sub-group of tourist maps made using Geographic Information Systems (GIS). This paper proposes a methodological protocol for the systematic elaboration of tourism mapping. This procedure is applied to the design of the tourist map of the province of Huesca. The cartographic tool was designed based on the needs of tourism promotion by the Public Administration, seeking the halfway point between persuasion and precision when representing elements on the map. Given that data would have to be updated and modified in the future, open-source software was used so that the administration can then run, modify and update it. In addition, the project was validated through surveys to two different audiences (general and expert).

The concept of collaborative R&D has been increasing interest among scholars and policy-makers, making collaboration a pivotal determinant to innovate nowadays. The availability of reliable data is a necessary condition to obtain valuable results. Specifically, in a collaborative environment, we must avoid mistaken identities among organizations. In many datasets, indeed, the same organization can appear in a non-univocal way. Thus its information is shared among multiple entities. In this work, we propose a novel methodology to disambiguate organization names. In particular, we combine supervised and unsupervised techniques to design a "hybrid" methodology that is neither fully automated nor completely manual, and easy to adapt to many different datasets. Thus, the flexibility and potential scalability of the methodology make this paper a worthwhile contribution to different research fields. We provide an empirical application of the methodology to the dataset of participants in projects funded by the first three European Framework Programmes. This choice is because we can test the quality of our procedure by comparing the refined dataset it returns to a well-recognized benchmark (i.e., the EUPRO database) in terms of the connection structure of the collaborative networks. Our results show the advantages of our approach based on the quality of the obtained dataset, and the efficiency of the designed methodology, leaving space for the integration of affiliation hierarchies in the future. [ABSTRACT FROM AUTHOR]

Project management plays significant role in modern economy and public administration. Project management trends have a great impact on the performance of projects and enterprises, methods and tools of management, training programs for managers. The ≪Pulse of the Profession ≫ survey, regularly published by the Project Management Institute, allows tracking current trends in project management practice. In 2021, the ≪Pulse of the Profession≫ was dedicated to a new concept — the ≪gymnastic≫ enterprise. In this article, the authors analyze the text of ≪Pulse of the Profession≫, 2021 using mixed research methods including qualitative and quantitative content analysis tools. The results show the closeness of the ≪gymnastic≫ enterprise concept to the concept of organizational ambidexterity. A content analysis helped to identify the key organizational practices of ≪gymnastic≫ enterprise, such as the situational use of hybrid project management methodologies and methodological pluralism, the combination of structural coherence and flexibility in project management processes, the active use of entrepreneurial and innovative methods and approaches, as well as tools and techniques for talent management, organizational change and learning. The indicated practices allow ≪gymnastic≫ enterprises to achieve better results in comparison with ≪traditional≫ companies. The results also indicate the ability of the ≪gymnastic≫ enterprise concept to influence project management practice, educational and training programs and consulting services in the field of project management.

Sludge management account for high economic costs and energy consumption in wastewater treatment. Accurate forecasting of sewage sludge generation thus can be important for the planning, operation and optimization of processes at wastewater treatment plant (WWTP). In this study data from a municipal treatment plant with a capacity of 88 thousand cubic meters of sewage per day located in south part of Poland were used to find a good forecasting model for sludge amount prediction. Among models an autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting. Since the ARIMA model cannot capture the non-linear structure of the data research activities in forecasting suggest using neural networks. Long-short term memory (LSTM) recurrent neural network proved its usability in time series forecasting. Looking at the curve representing data of sludge amount generated in the previous years at WWTP the linear and non-linear patterns could be distinguished. To address these issue a hybrid methodology that combines advantage of ARIMA and LSTM was proposed and used for forecasting purpose. Experimental results showed that the combined model can be an effective way to improve the forecasting accuracy of sludge amount generated at this WWTP. [ABSTRACT FROM AUTHOR]

The research focuses on children with autism, who cannot access education either because they do not have a school near their homes or because they are misinformed by their parents that they can access education regardless of their disability. By not receiving an education, children are deprived of their right to education. The objective is to design a prototype of an educational platform taking advantage of technological tools for the learning of children with special educational needs. A hybrid methodology and the balsamiq tool were used, as they were appropriate for developing the project. The result was that children with disabilities can learn by interacting with technology and in the same way their parents can visualize some exercises and crafts that they can do at home with their children, since it is based on their educational needs. It is concluded that teachers are in total agreement with the use of technology for children with autism, since this will help to improve their learning. The research contributes to society, teachers and parents with children with autism problems. [ABSTRACT FROM AUTHOR]

Project management plays an important role in the modern economy and public administration. Trends in project management have a significant impact on the performance of companies, public organizations, and programs for the socio-economic development in Russia. One of the interesting developments in project management is the expansion of hybrid methodologies, which combine tools and approaches of flexible and predictive approaches.This article aims to study the forms, results, and types of hybrid project management methodologies through the lens of the concept of organizational ambidexterity. Methodologically, the research is based on a systematic review of the Russian-language scientific literature on project management hybrid methodologies and organizational ambidexterity, as well as a qualitative content analysis of selected papers.The results of the study indicate that there are significant similarities between key aspects of hybrid methodologies and organizational ambidexterity. The authors conclude that hybrid project management methodologies can be considered as the instruments of organizational ambidexterity. As practical recommendations, the authors propose to expand the use of hybrid methodologies in the management of not only project-based firms, but also in other organizations, as well as in the management of socio-economic programs, especially in conditions of high uncertainty. The potential of hybrid methodologies to improve organizational ambidexterity and thereby simultaneously ensure the stability and flexibility organizations makes them invaluable tools to increase the resistance organizations and strategic programs to technological disruptions, socio-economic crises, and global shocks.

Maps published for tourism promotion and information constitute a particular sub-group of tourist maps made using Geographic Information Systems (GIS). This paper proposes a methodological protocol for the systematic elaboration of tourism mapping. This procedure is applied to the design of the tourist map of the province of Huesca. The cartographic tool was designed based on the needs of tourism promotion by the Public Administration, seeking the halfway point between persuasion and precision when representing elements on the map. Given that data would have to be updated and modified in the future, open-source software was used so that the administration can then run, modify and update it. In addition, the project was validated through surveys to two different audiences (general and expert). [ABSTRACT FROM AUTHOR]

The present investigation aimed at a sustainable green turning operation of Ti-6Al-4V alloy using multi-objective optimization of surface roughness, tool flank wear, material removal rate, and tool wear loss with cutting speed, feed rate, and depth of cut as input parameters. The experiments were performed on a CNC lathe machine using an alumina-zirconia (Al2O3-ZrO2) ceramic insert under a dry machining environment. The experiments were performed to examine the influence of input parameters with three levels and their interactions on the output responses. The experiment trials were designed according to Taguchi's L25 orthogonal array. A hybrid approach of grey Taguchi-based response surface methodology (GT-RSM) was exposed for estimating the optimal integration of turning parameters. The weights to the output characteristics were assigned by the analytical hierarchy process (AHP). According to the ANOVA, cutting speed was the most influencing factor in the grey relation grade (GRG) succeeded by the depth of cut and feed rate. The optimized turning settings improved the surface roughness, tool flank wear, and tool wear loss by 10.31%, 6.51%, and 7.69% respectively with a 2.21% decrease in material removal rate. The result indicated that the GT-RSM methodology provided an excellent database that was effective to enhance the output quality responses. [ABSTRACT FROM AUTHOR]

Risk management in software development has always been one of the necessities of software project management. The logical nature of software projects and products has caused several challenges and risks in these projects. On the other hand, with the emergence of agile methodologies, especially Scrum, and extreme programming (XP) methodologies, in recent years, this issue has become more serious. This is mainly because emphasizing limited documentation in these methodologies has caused these methods to pay little attention to some aspects of project management, particularly risk management. Concentrating on this challenge, the current study has proposed a risk management model in the hybrid methodology, combining Scrum and XP. Using this model in a case study shows this model's success in achieving risk management purposes. The results of this study indicate an appropriate reduction in the number of reworks, change requests, identified risks, and occurred risks. Moreover, the number of eliminated risks and team productivity have increased. [ABSTRACT FROM AUTHOR]

An analysis of trends in the development of infrastructure management systems shows that the key factors are reliability of implementation, efficiency in creating values and environmental harmonization. At the same time, increasing the level of efficiency of infrastructure programs are strategic areas of development for most countries of the world. A key role in successfully solving pressing problems of infrastructure projects and programs, including the satisfaction of certain sectors of the economy with an improvement in the environment, is to determine innovative technologies aimed at the development of “intelligent” technologies. The analysis of the problems of systems of regions, cities, towns and consumers determined the relevance and practical importance of research on the implementation of infrastructure projects in turbulence, the construction of a hybrid multi-level methodology for managing infrastructure programs based on a convergent balanced approach. At the same time, problems and challenges were identified regarding the implementation of infrastructure projects and programs as development drivers. A holistic model for solving the problems of implementing infrastructure projects and programs on the basis of multi-level dual management within the framework of hybrid methodologies combining different models according to the principles of management has been built. The experimental studies of the proposed approaches, models and methods for managing infrastructure programs have confirmed their adequacy and effectiveness. The methodology of multi-level proactive dual management of infrastructure projects based on adaptive technologies is based on three interconnected adaptive systems: planning and formation, monitoring and change management, and regulates the formation of resilient to disturbances in the management process, which helps to prevent a significant decrease in the quality of management and loss of controllability of project activities.

Copyright of International Journal of Organizations / Revista Internacional de Organizaciones (RIO) is the property of Analisi Social i Organitzativa grup de recerca and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Model updating issues with high-dimensional and strong-nonlinear optimization processes are still unsolved by most optimization methods. In this study, a hybrid methodology that combines the Gaussian-white-noise-mutation particle swarm optimization (GMPSO), back-propagation neural network (BPNN) and Latin hypercube sampling (LHS) technique is proposed. In this approach, as a meta-heuristic algorithm with the least modification to the standard PSO, GMPSO simultaneously offers convenient programming and good performance in optimization. The BPNN with LHS establishes the meta-models for FEM to accelerate efficiency during the updating process. A case study of the model updating of an actual bridge with no distribution but bounded parameters was carried out using this methodology with two different objective functions. One considers only the frequencies of the main girder and the other considers both the frequencies and vertical displacements of typical points. The updating results show that the methodology is a sound approach to solve an actual complex bridge structure and offers good agreement in the frequencies and mode shapes of the updated model and test data. Based on the shape comparison of the main girder at the finished state with different objective functions, it is emphasized that both the dynamic and static responses should be taken into consideration during the model updating process. [ABSTRACT FROM AUTHOR]

This study presents a multi‐objective based on a hybrid methodology to apply the problem of economic emission dispatch that is incorporated with hydro, thermal and wind power units. The hybrid method is the combination of both the modified quantum‐behaviour lightning search algorithm (MQLSA) and artificial intelligence technique with the help of PSNN. To determine the optimal amounts of the generated powers from the thermal, wind farms and hydro units, the proposed approach is introduced by minimising the cost of generation and the emission level simultaneously. MQLSA is utilised to generate the optimal combination of thermal power with the objective of the minimum error function to minimise the fuel and emission cost of the system with wind speed factor. To capture the optimised wind power with the objective of minimum speed factor, the PSO‐artificial neural network technique is utilised. The algorithm is integrated with the feasible solution constraint handling techniques. To validate the effectiveness of the proposed method, the six and ten generating thermal system with wind power is studied. The proposed hybrid technique is implemented in MATLAB/Simulink platform and the simulation result demonstrates the superiority of the proposed method compared to the various existing techniques. [ABSTRACT FROM AUTHOR]

A hybrid methodology for continuous performance improvement (CPI) is presented, based on the joint recourse to business process re-engineering (BPR) and continuous quality improvement (CQI) principles and tools. The methodology (named HY-CHANGE) is conceived as a logical and technical support to the decision maker. It results in a number of recursive phases, where the rational and synchronous use of several tools and techniques, borrowed from the two methodologies mentioned above, is structured into a unique approach. HY-CHANGE thus offers a structured guide for performance improvement processes more suited to the enterprise reality and its dynamical evolution. The value of the methodology is to provide a structured frame to use several methodologies and tools (here referred to as 'decisional tool-kit') with the scope of effectively managing change processes at any level within the enterprise. The practical implications of the idea are discussed based on a case study conducted in an Italian SME producing sofas. [ABSTRACT FROM AUTHOR]

Hybrid methodologies have become popular in many fields of research as they allow researchers to explore various methods, understand their strengths and weaknesses and combine them into new frameworks. Thus, the combination of different methods into a hybrid methodology allows to overcome the shortcomings of each singular method. This paper presents the methodology for two hybrid methods that can be used for time series forecasting. The first combines singular spectrum analysis with linear recurrent formula (SSA-LRF) and neural networks (NN), while the second combines the SSA-LRF and weighted fuzzy time series (WFTS). Some of the highlights of these proposed methodologies are: • The two hybrid methods proposed here are applicable to load data series and other time series data. • The two hybrid methods handle the deterministic and the nonlinear stochastic pattern in the data. • The two hybrid methods show a significant improvement to the single methods used separately and to other hybrid methods.

Background In applied sciences, statistical models are pivotal for uncovering relationships in complex datasets. The applied linear model establishes associative links between variables. While qualitative predictors are essential, their integration into linear models poses challenges. The dummy variable approach transforms qualitative variables into binary ones for regression analysis. Multilayer Feedforward Neural Networks (MLFFNN) offer validation of regression models, and fuzzy regression offers alternative methods to address the ambiguity of qualitative predictors. This study aims to enhance the integration of qualitative predictors in applied linear models through statistical methodologies. Material and methods This study design involves the transformation of qualitative predictors into dummy variables, the bootstrapping technique to improve the parameter estimates, the Multilayer Feedforward Neural Network, and fuzzy regression. This study uses the programming language R as an analysis tool. Results The multiple linear regression model demonstrates precision and a significant fit (p<0.05), with an R-squared value of 0.95 and mean square error (MSE) of 9.97. Comparing actual and predicted values, fuzzy regression exhibits superior predictability over linear regression. The MLFFNN yields a reduced MSE net of 0.362, indicating enhanced prediction precision for derived models. Conclusion This study presents a precise methodology for integrating qualitative variables into linear regression, supported by the combination of specific statistical methodologies to enhance predictive modeling. By integrating fuzzy linear regression, MLFF neural networks, and bootstrapping, the proposed technique emerges as the most effective approach for modeling and prediction. These findings underscore the efficacy of this method in seamlessly integrating qualitative variables into linear models, ultimately enhancing accuracy and prediction capabilities., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Ahmad et al.)

In this paper, a hybrid wind power forecasting approach based on Bayesian model averaging and Ensemble learning (BMA-EL) is proposed. Firstly, SOM clustering and K-fold cross-validation are used to generate multiple sets of the training subsets with the same distribution from the training set of meteorological data to increase the difference of the input samples of the base learners. These training subsets are imported into three base learners, i.e. BPNN, RBFNN, and SVM, to train the model. Then, the BMA combining strategy is trained based on the outputs of the three base learners on the validation set. Finally, the test set is combined by the BMA through the outputs of the three base learners to obtain the WPF results. By comparing the simulation error and curve between the base learner and other literature approaches, our proposed method can accurately and reliably forecast the wind power outputs under different meteorological conditions, with higher precision and reliability. • SOM clustering and k-fold cross-validation are combined on the training set. • Different base learners and diverse training subsets improve EL-model's performance. • The wind power forecasting approach based on BMA-EL has better prediction results. [ABSTRACT FROM AUTHOR]

Constraint normalization ensures consistency in scaling for each constraint in an optimization problem. Most constraint handling studies only address the issue to deal with constraints and use problem information to scale the constraints. In this paper, we propose a hybrid evolutionary algorithm—Constraint Handling with Individual Penalty Approach (CHIP)—which scales all constraints adaptively without any problem specific information from the user. Penalty parameters for all constraints are estimated adaptively by considering overall constraint violation as a helper objective for minimization and as a result any number of constraints can be dealt without incurring proportional computational cost. The efficiency of the proposed method is demonstrated using 23 test problems and two problems from engineering optimization. The constrained optimum and function evaluations of CHIP method are inspected with five recently developed evolutionary-based constraint handling methods. The simulation results show that the proposed CHIP mechanism is very efficient, faster and comparable in the aspect of accuracy against other recently developed methods. [ABSTRACT FROM AUTHOR]

Although South‐East Asia's trading networks have existed for millennia, recent decades have seen markets dramatically intensify in the region's frontiers, bringing social and environmental upheaval. Within political ecology, such transitions are framed as frontier incorporation into global capitalism – a process that has been ongoing since European colonisation. This paper, however, responds to recent calls for commodity network studies to better account for specific material and social variances in "actually existing capitalism." My analysis focuses on Mondulkiri province in north‐eastern Cambodia, where a boom in cassava cultivation has produced two distinct commodity networks. The first network supplies dried cassava chips to trans‐border markets that serve bioethanol and livestock production. The second network supplies fresh cassava to Vietnamese starch and processed food factories. In order to understand why two market networks have evolved from one crop, I analyse the social, material and spatial relationships in each network. Comparative analysis shows that both networks involve land and labour commodification and respond to global demand. Yet subtle geographical variations in transport networks, migration patterns and the availability of uncleared land, support dried cassava production in some areas and fresh cassava in others. The cassava case shows that although frontier markets are propelled by globally connected processes of commodification, they ultimately take form through co‐productive networks that mould to and shape frontier landscapes. Furthermore, market networks are not only mobilised, but also can be demobilised by environmental, economic and social pressures – a point that frontier incorporation perspectives may overlook. The paper therefore argues for an understanding of frontier geographies as dynamic and constitutive in market formation. [ABSTRACT FROM AUTHOR]

Abstract The aim of the present paper is to understand the behavior of high capacity helical piles subjected to ground movement. It was found that the plates of the high capacity helical piles placed near the slip surface move with the pile due to soil mass movement. The plates movement mobilizes the soil reactions beneath and above the plates, this creates a resisting moment, increasing eventually the resisting force offered by high capacity helical piles. The presence of plates does not only lead to an increase in the resisting force, but also to a diminution in the pile head deflection. [ABSTRACT FROM AUTHOR]