EEG assessment of artificial intelligence-generated content impact on student creative performance and neurophysiological states in product design.

Objectives: The purpose of this study has been to evaluate the use of Artificial Intelligence-Generated Content (AIGC) tools in design education, in terms of their effects on creative performance, concentration, and relaxation levels, for university students enrolled in an undergraduate design program.

Methods: An experimental design was implemented, using two groups differentiated by their design tool usage (AIGC tools versus traditional software). The sample consisted of 64 third-year undergraduate design students from a public university in Eastern China.

Hippocampal subfield volumes in mild cognitive impairment and alzheimer's disease: a systematic review and meta-analysis.

The hippocampus is a complex structure that consists of several subfields with distinct and specialized functions. Although numerous studies have been performed to explore hippocampal atrophy at the sub-regional level in mild cognitive impairment (MCI) and Alzheimer's disease (AD), the results have been inconsistent especially for whether and which subfields can be served as the most potential biomarkers in MCI and AD. Herein, we used a meta-analytic approach to synthesize the extant literatures on hippocampal subfields in MCI and AD through PubMed, Web of Science, and Embase (PROSPERO CRD42021257586).

Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects.

Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e.

Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States.

Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and other morphological characteristics, and thus its calculation is a highly complex engineering problem. In this research, three relatively explored machine learning (ML) models, including Random Forest (RF), Gradient Boosting Decision Tree (GTB), and XGboost-Grid, were proposed for the Kx determination.

Capturing potential impact of challenge-based gamification on gamified quizzing in the classroom.

Challenges in education have continuously been addressed by integrating gamification, but a gap remains for game design principles that support user engagement. This paper outlines results obtained from integrating challenge-based gamification into an elementary school classroom to examine the emergence of student engagement and learning-related behavior. The approach was applied to logical puzzle quizzes where different gamification adjustments were captured and examined using physics' analogy (called the motion in mind concept).

On the prediction of methane fluxes from pristine tropical peatland in Sarawak: application of a denitrification-decomposition (DNDC) model.

Tropical peatlands have high potential function as a major source of atmospheric methane (CH) and can contribute to global warming due to their large soil carbon stock, high groundwater level (GWL), high humidity and high temperature. In this study, a process-based denitrification-decomposition (DNDC) model was used to simulate CH fluxes in a pristine tropical peatland in Sarawak. To test the accuracy of the model, eddy covariance tower datasets were compared.

Frictional Pressure Drop and Cost Savings for Graphene Nanoplatelets Nanofluids in Turbulent Flow Environments.

Covalent-functionalized graphene nanoplatelets (CF-GNPs) inside a circular heated-pipe and the subsequent pressure decrease loss within a fully developed turbulent flow were discussed in this research. Four samples of nanofluids were prepared and investigated in the ranges of 0.025 wt.

Reinforcing bar development length modeling using integrative support vector regression model with response surface method: New approach.

Due to the non-uniformity of bond stress distribution, the full bar development length should be tested to validate the development length of the reinforcing bar embedded in concretes. The current study proposed the design of a hybrid artificial intelligence (AI) model using integration of support vector regression (SVR) coupled with response surface method (RSM) for prediction of reinforcement bar development length. Two nonlinear calibrating processes are conducted, RSM is used to connect the input data on the hardening dataset in first stage while SVR is used for determining the nonlinear relation between the hardening dataset and the output development bar stress.

A new insight for real-time wastewater quality prediction using hybridized kernel-based extreme learning machines with advanced optimization algorithms.

Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values.

The assessment of emerging data-intelligence technologies for modeling Mg and SO surface water quality.

The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health.

Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks.

Streamflow (Q) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Q (short-term) at Brisbane River and Teewah Creek, Australia.

Art Therapy: A Complementary Treatment for Mental Disorders.

Art therapy, as a non-pharmacological medical complementary and alternative therapy, has been used as one of medical interventions with good clinical effects on mental disorders. However, systematically reviewed in detail in clinical situations is lacking. Here, we searched on PubMed for art therapy in an attempt to explore its theoretical basis, clinical applications, and future perspectives to summary its global pictures.

Designing Low-Cost Capacitive-Based Soil Moisture Sensor and Smart Monitoring Unit Operated by Solar Cells for Greenhouse Irrigation Management.

Precise and quick estimates of soil moisture content for the purpose of irrigation scheduling are fundamentally important. They can be accomplished through the continuous monitoring of moisture content in the root zone area, which can be accomplished through automatic soil moisture sensors. Commercial soil moisture sensors are still expensive to be used by famers, particularly in developing countries, such as Egypt.

Heat Transfer and Hydrodynamic Properties Using Different Metal-Oxide Nanostructures in Horizontal Concentric Annular Tube: An Optimization Study.

Numerical studies were performed to estimate the heat transfer and hydrodynamic properties of a forced convection turbulent flow using three-dimensional horizontal concentric annuli. This paper applied the standard k-ε turbulence model for the flow range 1 × 10 ≤ Re ≥ 24 × 10. A wide range of parameters like different nanomaterials (AlO, CuO, SiO and ZnO), different particle nanoshapes (spherical, cylindrical, blades, platelets and bricks), different heat flux ratio (HFR) (0, 0.

Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: Development of hybrid tree-based artificial intelligence models.

Dissolved oxygen (DO) is an important indicator of river health for environmental engineers and ecological scientists to understand the state of river health. This study aims to evaluate the reliability of four feature selector algorithms i.e.

Using the D-DANP-mV Model to Explore the Continuous System Improvement Strategy for Sustainable Development of Creative Communities.

With globalization, the notion of "creative city" has become a core concept of many cities in the world development policies, with real properties being upgraded or used to change, renewal is being conducted, and creative industries are emerging. This trend has reached its peak in the past decade, with different forms and scales gathering global development momentum among the creative communities to promote the development of creative economies. In recent years, however, there was still skepticism about the sustainability of the current creative communities.