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The compared results show that the inverse regression model gives the best forecasting performance.
Open accessJournal ArticleDOI
Marie Tremblay, Daniel Wallach 
01 Sep 2004-Agronomie
55 Citations
Ridge regression is found to be superior to this best method of model selection.
The best models were created using artificial neural networks and logistic regression.
Open accessJournal ArticleDOI
01 Jun 2006
36 Citations
Despite this, results indicate that the best regression model improves the capability of predicting the ...
Linear regression helps to find

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What are the game theory methods used for university course timetabling?
4 answers
Game theory methods are not explicitly mentioned in the provided contexts. However, various metaheuristic approaches have been applied to university course timetabling problems. These methods include the Bat Algorithm (BA), Improved Parallel Genetic Algorithm and Local Search (IPGALS), artificial bee colonies, cloud theory-based simulated annealing, and genetic algorithms. These metaheuristic methods aim to efficiently allocate events into time slots and rooms while satisfying predefined constraints in the University Course Timetabling Problem (UCTP). While game theory methods are not specifically discussed, the utilization of metaheuristic algorithms showcases the diverse range of optimization techniques employed in addressing the complexities of course timetabling in academic institutions.
What are the key arguments in push-pull model In student mobility?
4 answers
The push-pull model in student mobility highlights various factors influencing international students' decisions to study abroad. Factors such as aspirations for new experiences, economic outlook in the home country, and job prospects act as significant push factors. Conversely, bright economic prospects in the destination country, university reputation, and specific academic programs serve as crucial pull factors attracting international students. Additionally, the push-pull model has been extended to understand Chinese students' motivations to study abroad, emphasizing how social networks and organizations shape decisions, challenging the traditional view of a well-planned decision-making process. Moreover, determinants like population size, distance between home and host countries, and enrollment significantly influence international student movements in higher education.
Is there a cement scarcity in nigeria?
4 answers
Yes, there was a cement scarcity issue in Nigeria, leading to the country relying on cement imports to meet domestic construction needs. However, after the implementation of the backward integration policy (BIP) in 2002, Nigeria significantly increased its cement production capacity, becoming self-sufficient and even exceeding domestic demand. The study also highlighted the challenges faced by the cement industry in Nigeria, such as environmental concerns due to air pollution and solid waste generation. Strategies like carbon capture technologies are being explored to mitigate the negative environmental impact of cement production. Overall, Nigeria's cement industry has undergone significant transformation from scarcity to surplus, emphasizing the importance of effective policies and investments in the sector.
What are the negative impact of Advanced airport infrastructure and ground handling technologies in terms of Cost for Passengers?
5 answers
Advanced airport infrastructure and ground handling technologies can potentially lead to increased costs for passengers. While digital technologies in airport ground operations aim to enhance efficiency and cut costs, the implementation of such technologies may require significant investments, which could be passed on to passengers in the form of higher ticket prices or additional fees. Moreover, inefficiencies in ground handling operations are a major cause of flight delays, indicating that improvements in this area may involve additional expenses that could impact passengers. Therefore, despite the benefits these technologies offer in terms of process efficiency and revenue generation for ground handling agents, passengers may experience negative cost implications due to the adoption of advanced airport infrastructure and ground handling technologies.
What are the negative impact of Advanced airport infrastructure and ground handling technologies in terms of Accessibility for Passengers?
4 answers
Advanced airport infrastructure and ground handling technologies can have negative impacts on accessibility for passengers with special assistance needs. The complexity of airport ground handling operations, modeled as a multiple-fleet vehicle routing problem, can lead to challenges in providing efficient and high-quality solutions for passenger access. Additionally, the rapid growth in air passenger traffic puts pressure on ground access networks and airports, impacting accessibility. Passenger queue congestion in airport public transport systems can further exacerbate accessibility issues, leading to poor travel experiences and unexpected time costs. These factors collectively highlight the need for innovative strategies to address accessibility challenges posed by advanced airport infrastructure and ground handling technologies.
How does the use of multi-objective technology impact the optimization process in electrical machines?
4 answers
The utilization of multi-objective technology in the optimization process of electrical machines significantly enhances predictive performance and computational efficiency. By leveraging correlated sub-objectives through methods like chained regression or multi-output artificial neural networks (ANNs), higher accuracy in predicting targets is achieved compared to separate predictions. Additionally, the application of a variational auto-encoder (VAE) allows for concurrent parametric multi-objective technology optimization for different machine technologies simultaneously, leading to efficient design optimization in a high-dimensional space. Furthermore, employing a hybrid data-and physics-driven model with deep neural networks (DNNs) as meta-models enables the prediction of key performance indicators (KPIs) while maintaining or even improving the quality of results compared to conventional finite element (FE) simulation-based optimization, all at a reduced computational cost.
How does the shopping behavior of Generation X differ from other generations?
5 answers
Generation X's shopping behavior differs from other generations in various aspects. Research indicates that Gen X consumers are impacted by factors like mobile shopping efficiency, effort expectancy, and the perceived severity of COVID-19, influencing their mobile commerce buying intentions. Additionally, Gen X consumers do not prioritize brands when purchasing clothing products, focusing more on the benefits, unlike Generation Y who use clothing brands to showcase social status. Furthermore, a study comparing shopping habits across generations found that Gen X shows the lowest frequency in purchasing fashion products online, preferring a combination of online and traditional shopping methods. These findings highlight the unique characteristics of Generation X in terms of shopping behavior compared to other generations.
How AI is used in the wood furniture industry, with particular focus on wood panels?
5 answers
Artificial Intelligence (AI) plays a crucial role in the wood furniture industry, especially in tasks like furniture assembly, predicting material properties, surface roughness control, disruption detection, and digitalization of production plants. In the context of wood panels, AI is utilized for tasks such as predicting the bending strength of laminated wood products, controlling surface roughness values of densified wood materials, and detecting production disruptions in wood-based panel industries. For instance, AI algorithms are employed to recognize and interpret assembly instructions for furniture assembly tasks, predict the modulus of rupture of laminated wood products, control surface roughness values of densified wood materials, and detect and predict production disruptions in wood-based panel industries. This integration of AI enhances efficiency, accuracy, and productivity in the wood furniture industry.
How to checking Multicollinearity in smart pls?
4 answers
To check for multicollinearity in Smart PLS, one can utilize diagnostic tools like the variance inflation factor (VIF), condition index, and variance decomposition proportion (VDP). Multicollinearity, which can lead to incorrect regression results, is indicated by high VIF values (typically above 5 to 10) or condition indices (usually above 10 to 30). Additionally, VDPs obtained from eigenvectors can help identify multicollinear variables by showing the inflation of regression coefficient variances according to each condition index. Excluding multicollinear variables can lead to more stable regression models. Smart PLS, a structural equation modeling technique, can benefit from these methods to detect and address multicollinearity issues effectively, ensuring the reliability of the analysis.
What are environmental influences on adult learning motivation?
4 answers
Environmental influences on adult learning motivation encompass various factors. The learning environment, including family, community, school, and friends, significantly impacts students' motivation and development. Additionally, the university environment and student satisfaction play crucial roles in shaping student learning motivation. Factors such as needs, rewards, interests, and the learning environment are identified as key influencers of learning motivation. Adult learners in online or blended learning environments value face-to-face interactions for enhanced learning but also appreciate the flexibility of distance learning moments. Moreover, motivation theorists highlight the environment's diverse roles in motivation, from providing external cues to shaping motivation through rewards and punishments or cultural scripts, impacting the design of motivating learning environments.
What are applications of LLMs in business?
4 answers
Large Language Models (LLMs) find applications in various business domains. They excel in tasks like natural language querying for process mining, financial information extraction from reports, and improving accuracy and efficiency in tasks such as translation, sentiment analysis, chatbots, and text classification. LLMs, like GPT-4, demonstrate human-level performance in answering questions and analyzing business processes, showcasing their potential to revolutionize natural language processing. Specifically, LLMs enhance the comprehension of hybrid text and tabular data, enabling automated extraction of critical information from complex documents like financial reports. These advancements highlight the versatility and effectiveness of LLMs in enhancing various business processes through natural language understanding and data extraction capabilities.