Search Results (189)

Exploring the influencing factors and mechanisms of willingness to adopt GAI for collaborative decision-making in the generative artificial intelligence context is of significant importance for advancing the application of collaborative decision-making between human intelligence and generative AI. This study builds upon the traditional Technology Acceptance Model (TAM) and the Task–Technology Fit (TTF) models by introducing factors of human–GAI trust and collaborative efficacy to construct a theoretical model of the influencing factors of willingness to adopt GAI for collaborative decision-making. Empirical analysis is conducted using Structural Equation Modeling (SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). The results show that perceived usefulness and collaborative efficacy emerge as key determinants of willingness to adopt GAI for collaborative decision-making. Attitude and human–GAI trust exert significant direct positive effects, while perceived ease of use and task–technology fit demonstrate significant indirect positive influences. The fsQCA results further identify three distinct configuration pathways: perceived value-driven, functional compensation-driven, trust in technology-driven. Full article

Automated food item recognition and recipe recommendation systems have gained increasing importance in dietary management and culinary applications. Recent advancements in Computer Vision, particularly in object detection, classification, and image segmentation, have facilitated progress in these areas. However, many existing systems remain inefficient and lack seamless integration, resulting in limited solutions capable of both identifying food items and providing relevant recipe recommendations. Furthermore, modern neural network architectures have yet to be extensively applied to food recognition and recipe recommendation tasks. This study aims to address these limitations by developing a system based on the MobileNetV2 architecture for accurate food item recognition, paired with a recipe recommendation module. The system was trained on a diverse dataset of fruits and vegetables, achieving high classification accuracy (97.2%) and demonstrating robustness under various conditions. Our findings indicate that the modified model, the MobileNetV2 model, can effectively recognize different food items, making it suitable for real-time applications. The significance of this research lies in its potential to improve dietary tracking, offer valuable culinary insights, and serve as a practical tool for both personal and professional use. Ultimately, this work advances food recognition technology, contributing to enhanced health management and fostering culinary creativity. Some potential applications of this work include personalized dietary management, automated meal logging for fitness apps, smart kitchen assistants, restaurant ordering systems, and enhanced food analysis for nutrition tracking. Full article

As global food security challenges escalate and post-harvest losses in perishable crops remain a critical pressure point, on-farm transport robots have emerged as a promising sustainable solution for transforming farm-to-storage logistics systems and reducing agricultural waste. However, farmer acceptance of robotic transport technologies remains heterogeneous and represents a critical barrier to achieving widespread adoption of these sustainable agricultural innovations. Existing research has yet to integrate task-technology fit (TTF), anticipated emotions, and anthropomorphism into a unified theoretical framework for understanding sustainable agricultural technology adoption. Drawing on TTF theory and the model of goal-directed behavior, this study proposes a comprehensive model integrating anticipated emotions as mediators and robot anthropomorphism as a moderator. We surveyed 320 farmers and employed PLS-SEM to test our hypotheses. Results indicate that farm transport task complexity, farmer technology readiness, and robot transport functionality significantly strengthen TTF ($\beta = 0.136$, 0.358, 0.382, respectively; all $p < 0.01$). TTF drives acceptance intention through a dual-path emotional mechanism: directly enhancing positive expectancy emotions ($\beta = 0.411$, $p < 0.001$) while reducing negative expectancy emotions ($\beta = -0.150$, $p < 0.05$). Crucially, higher anthropomorphism levels diminish both emotional mediation paths ($\beta = -0.053$ and $\beta = -0.027$, both $p < 0.01$), establishing important boundary conditions for sustainable agricultural technology design. These findings suggest that reducing post-harvest losses requires prioritizing functional consistency over overly anthropomorphic designs in agricultural robots, thereby promoting the development of agricultural technologies that are both emotionally resonant and highly functional. Full article

Multi-threshold image segmentation plays an irreplaceable role in extracting discriminative structural information from complex images. It is one of the core technologies for achieving accurate target detection and regional analysis, and its segmentation accuracy directly affects the analysis quality and decision reliability in key fields such as medical imaging, remote sensing interpretation, and industrial inspection. However, most existing image segmentation algorithms suffer from slow convergence speeds and low solution accuracy. Therefore, this paper proposes an Adaptive Cooperative Pelican Optimization Algorithm (ACPOA), an improved version of the Pelican Optimization Algorithm (POA), and applies it to global optimization and multilevel threshold image segmentation tasks. ACPOA integrates three innovative strategies: the elite pool mutation strategy guides the population toward high-quality regions by constructing an elite pool composed of the three individuals with the best fitness, effectively preventing the premature loss of population diversity; the adaptive cooperative mechanism enhances search efficiency in high-dimensional spaces by dynamically allocating subgroups and dimensions and performing specialized updates to achieve division of labor and global information sharing; and the hybrid boundary handling technique adopts a probabilistic hybrid approach to deal with boundary violations, balancing exploitation, exploration, and diversity while retaining more useful search information. Comparative experiments with eight advanced algorithms on the CEC2017 and CEC2022 benchmark test suites validate the superior optimization performance of ACPOA. Moreover, when applied to multilevel threshold image segmentation tasks, ACPOA demonstrates better accuracy, stability, and efficiency in solving practical problems, providing an effective solution for complex optimization challenges. Full article

The rapid and widespread development of technology is in line with global trends of population growth and increasing demand for food. Significant breakthroughs in science have not yet fully met the needs of agriculture for increased food production and higher yields. The aim of this work is to discuss the current advancements in the application of beneficial microorganisms for crop cultivation and their integration into speed breeding technology to create optimal growing conditions and achieve the ultimate goal of developing new plant varieties. New breeding techniques, such as speed breeding—now a critical component of the breeding process—allow multiple plant generations to be produced in a much shorter time, facilitating the development of new plant varieties. By reducing the time required to obtain new generations, breeders and geneticists can optimize their efforts to obtain the required crop genotypes for both agriculture and industry. This helps to meet the demand for food, animal feed and plant raw materials for industrial use. One potential aspect of speed breeding technology is the incorporation of effective beneficial microorganisms that inhabit both the above-ground and below-ground parts of plants. These microorganisms have the potential to enhance the speed breeding method. Microorganisms can stimulate growth and development, promote overall fitness and rapid maturation, prevent disease, and impart stress resistance in speed breeding plants. Utilizing the positive effects of beneficial microorganisms offers a pathway to enhance speed breeding technology, an approach not yet explored in the literature. The controlled practical use of microorganisms under speed breeding conditions should contribute to producing programmable results. The use of beneficial microorganisms in speed breeding technology is considered an indispensable part of future precision agriculture. Drawing attention to their practical and effective utilization is an urgent task in modern research. Full article

Sustainability has an ever-increasing importance in our lives, mainly due to climate changes, finite resources, and a growing population, where each of us is called to make a change. Although climate change is a global phenomenon, our individual choices can make the difference. The transportation sector is one of the largest contributors to global carbon emissions, making the transition toward sustainable mobility a critical priority. The adoption of electric vehicles is widely recognized as a key solution to reduce the environmental impact of transportation. However, their widespread acceptance depends on various technological, behavioral, and economical factors. Within this research we use as an artifact the CO₂ Emission Management Gauge (CEMG) devices to better understand how the manufacturers, with integrated features on vehicles, could significantly enhance sales and drive the movement towards electric vehicle adoption. This study proposes an innovative new theoretical model based on Task-Technology Fit, Technology Acceptance, and the Theory of Planned Behavior to understand the main drivers that may foster electric vehicle adoption, tested in a quantitative study with structural equation modelling (SEM), and conducted in a South European country. Our findings, not without some limitations, reveal that while technological innovations like CEMG provide consumers with valuable transparency regarding emissions, its influence on the intention of adoption is dependent on the attitude towards electric vehicles and subjective norm. Our results also support the influence of task-technology fit on perceived usefulness and perceived ease-of-use, the influence of perceived usefulness on consumer attitude towards electric vehicles, and the influence of perceived ease-of-use on perceived usefulness. A challenge is also presented within our work to expand CEMG usage in the future to more intrinsic urban contexts, combined with smart city algorithms, collecting and proving CO₂ emission information to citizens in locations such as traffic lights, illumination posts, streets, and public areas, allowing the needed information to better manage the city’s quality of air and traffic. Full article

Establishing a high-precision passenger flow prediction model is a critical and complex task for the optimization of urban rail transit systems. With the development of artificial intelligence technology, the data-driven technology has been widely studied in the intelligent transportation system. In this study, a neural network model based on the data-driven technology is established for the prediction of passenger flow in multiple urban rail transit stations to enable smart perception for optimizing urban railway transportation. The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. Experiments based on the actual passenger flow data of Beijing Metro Line 13 are conducted to compare the prediction performance of the proposed data-driven model with the other baseline models. The experimental results demonstrate that the proposed prediction model achieves lower MAE and RMSE in passenger flow prediction, and its fitted curve more closely aligns with the actual passenger flow data. This demonstrates the model’s practical potential to enhance intelligent transportation system management through more accurate passenger flow forecasting. Full article

VR technology is increasingly being integrated into daily life, with virtual book communities emerging as novel platforms for immersive reading and interaction. This study investigates how internal and external factors jointly influence users’ usage intention from psychological and behavioral science perspectives. A multivariate structural equation model based on three-dimensional perception theory was developed and tested through a survey of individuals with prior VR reading experience. The model examines the roles of task–technology fit, privacy and security risks, emotional resonance, self-expression, and the sense of belonging. The results reveal that task–technology fit positively influences usage intention, while privacy and security risk has a negative effect. Internally, emotional resonance and a sense of belonging significantly enhance usage intention. Furthermore, emotional resonance mediates the relationship between self-expression and both sense of belonging and usage intention, while sense of belonging also mediates between emotional resonance and usage intention. These findings underscore the critical interplay between technical attributes and affective factors in shaping engagement with VR-based reading platforms. This study offers new insights into user acceptance mechanisms in virtual book communities, and provides a theoretical foundation and practical implications for enhancing user experience and adoption in digital library systems. Full article

Narrative mapping, an advanced geographic information visualization technology, presents spatial information episodically, enhancing readers’ spatial understanding and event cognition. However, during 3D scene construction, viewpoint selection is heavily reliant on the cartographer’s subjective interpretation of the event. Even with fixed-angle settings, the task of ensuring that selected viewpoints align with the narrative theme remains challenging. To address this, an automated viewpoint selection method constrained by narrative relevance and visual information is proposed. Narrative relevance is determined by calculating spatial distances between each element and the thematic element within the scene. Visual information is quantified by assessing the visual salience of elements as the ratio of their projected area on the view window to their total area. Pearson’s correlation coefficient is used to evaluate the relationship between visual salience and narrative relevance, serving as a constraint to construct a viewpoint fitness function that integrates the visual salience of the convex polyhedron enclosing the scene. The chaotic particle swarm optimization (CPSO) algorithm is utilized to locate the viewpoint position while maximizing the fitness function, identifying a viewpoint meeting narrative and visual salience requirements. Experimental results indicate that, compared to the maximum projected area method and fixed-value method, a higher viewpoint fitness is achieved by this approach. The narrative views generated by this method were positively recognized by approximately two-thirds of invited professionals. This process aligns effectively with narrative visualization needs, enhances 3D narrative map creation efficiency, and offers a robust strategy for viewpoint selection in 3D scene-based narrative mapping. Full article

The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology is automated traffic accident detection and prediction, which may help in saving precious human lives. This study aims to explore critical features related to traffic accident detection and prevention. A public US traffic accident dataset was used for the aforementioned task, where various machine learning (ML) models were applied to predict traffic accidents. These ML models included Random Forest, AdaBoost, KNN, and SVM. The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. Owing to the black box nature of ML models, this best-fit ML model was executed with explainable AI (XAI) methods such as LIME and permutation importance to understand its decision-making for the given classification task. The unique aspect of this study is the introduction of explainable artificial intelligence which enables us to have human-interpretable awareness of how ML models operate. It provides information about the inner workings of the model and directs the improvement of feature engineering for traffic accident detection, which is more accurate and dependable. The analysis identified critical features, including sources, descriptions of weather conditions, time of day (weather timestamp, start time, end time), distance, crossing, and traffic signals, as significant predictors of the probability of an accident occurring. Future ADAS technology development is anticipated to be greatly impacted by the study’s conclusions. A model can be adjusted for different driving scenarios by identifying the most important features and comprehending their dynamics to make sure that ADAS systems are precise, reliable, and suitable for real-world circumstances. Full article

The increasing demands in high-performance sports have led to the integration of technological solutions for training optimization. This study aimed to develop and validate an algorithm-based system for analyzing three critical phases in canoe training: initial acceleration, steady-state cruising, and final sprint. Using inertial measurement units (WIMU PRO™) sampling at 10 Hz, we collected performance data from 12 young canoeists at the Mar Menor High-Performance Sports Center. The custom-developed algorithm processed velocity–time data through polynomial fitting and phase detection methods. Results showed distinctive patterns in the acceleration phase, with initial rapid acceleration (5 s to stabilization) deteriorating in subsequent trials (9–10 s). Athletes maintained consistent stabilized speeds (14.62–14.98 km/h) but required increasing space for stabilization (13.49 to 31.70 m), with slope values decreasing from 2.58% to 0.74% across trials. Performance deterioration was evident through decreasing maximum speeds (18.58 to 17.30 km/h) and minimum speeds (11.17 to 10.17 km/h) across series. The algorithm successfully identified phase transitions and provided real-time feedback on key performance indicators. This technological approach enables automated detection of training phases and provides quantitative metrics for technique assessment, offering coaches and athletes an objective tool for performance optimization in canoeing. Our aim is to automate the analysis task that is currently performed manually by providing an algorithm that the coaches can understand, using very basic mathematical tools, and that saves time for them. Full article

The rapid development of generative artificial intelligence (GenAI) technology has triggered extensive discussions about its potential applications in sustainable higher education. Based on the technology acceptance model (TAM) and task–technology fit (TTF) theory, this research aimed to investigate the current situations and challenges of Chinese university students using GenAI in four typical task scenarios. This was performed using a cross-sectional research design. The data were collected via questionnaire, with 486 undergraduates from a Chinese university participating. The data analysis methods include descriptive statistics, inferential statistics, and content analysis. The results show that more than 70% of university students actively use GenAI, but nearly half of them are not very proficient in its use. Doubao and ERNIE Bot are the GenAI tools they prefer most. The primary functions they use are text production and information retrieval. They mainly learn the relevant knowledge and skills through self-media and knowledge-sharing platforms. Among the four typical task scenarios, GenAI is widely used in course learning and research activities, while its application in daily life and job search is relatively limited. The analysis of demographic variables shows that grade and major have a significant impact on university students’ use of GenAI. In addition, university students suggest that universities should offer relevant courses or lectures and provide comprehensive technical support to improve the popularity and operability of GenAI. This study provides suggestions for universities, education administration departments, and technology development departments to improve GenAI services. It will help universities optimize the allocation of educational resources and promote educational equity for sustainability. Full article

While generative artificial intelligence (Gen AI) is accelerating digital transformation and innovation in corporate product design (CPD), limited research has explored how designers adopt this technology. This study aims to identify the key factors and causal configurations that influence designers’ intentions to adopt Gen AI in CPD. This study involved 327 in-service designers as participants, employed semi-structured interviews and a questionnaire to collect data, and applied the grounded theory and fsQCA to analyze the data. The findings indicate the following: (1) Personal innovativeness, AI technological anxiety, perceived usefulness, task–technology fit, perceived risk, social influence, and organizational support are the key factors influencing designers’ adoption of Gen AI. (2) None of these factors constitute a necessary condition for designers to adopt Gen AI. (3) High adoption intention results from the interaction of multiple factors, which can be categorized into three driving logics: “task demand-driven”, “organizational environment-driven”, and “individual characteristics-driven”. It is recommended that corporate managers establish an AI training framework, foster a supportive organizational environment, and implement tailored strategies to facilitate the integration of new technologies. This study clarifies the factors influencing designers’ adoption of Gen AI in CPD and provides a framework for companies to effectively integrate AI systems into product design. Full article

The multimodal knowledge graph link prediction model integrates entity features from multiple modalities, such as text and images, and uses these fused features to infer potential entity links in the knowledge graph. This process is highly dependent on the fitting and generalization capabilities of deep learning models, enabling the models to accurately capture complex semantic and relational patterns. However, it is this deep reliance on the fitting and generalization capabilities of deep learning models that leads to the black-box nature of the decision-making mechanisms and prediction bases within the multimodal knowledge graph link prediction models, which are difficult to understand intuitively. This black-box nature not only restricts the promotion and popularization of multimodal knowledge graph link prediction technology in practical applications but also hinders our understanding and exploration of the internal working mechanism of the model. Therefore, the purpose of this paper is to deeply explore the explainability problem of multimodal knowledge graph link prediction models and propose a multimodal post hoc model-independent multi-granularity explanation method (MMExplainer) for multimodal link prediction tasks. We learn the importance of each modality through modal separation, use textual semantics to guide a heuristic search to filter candidate explanation triples, and use textual masks to obtain explanation phrases that play an important role in prediction. Experimental results show that MMExplainer can provide coarse-grained explanations at the modal level and fine-grained explanations in structural and textual modalities, and the relevance index of the explanations in model decision-making is better than that of the baseline model. Full article

Background/Objectives: Advances in cochlear implant (CI) technology have led to the expansion of the implantation criteria. As a result, more CI candidates may have greater residual hearing in one or two ears. Many of these candidates will perform better with a CI in one ear and a hearing aid (HA) in the other ear, the so-called bimodal solution. The bimodal solution often requires patients to switch to HAs that are compatible with the CI. However, this can be a challenging decision, not least because it remains unclear whether this impacts hearing performance. Our aim is to determine whether speech perception in noise remains unchanged or improves with new replacement HAs compared to original HAs in CI candidates with residual hearing. Methods: Fifty bilateral HA users (mean age 63.4; range 23–82) referred for CI were recruited. All participants received new replacement HAs. The new HAs were optimally fitted and verified using Real Ear Measurement (REM). Participants were tested with the Hearing in Noise Test (HINT), which aimed at determining the signal-to-noise ratio (SNR) required for a 70% correct word recognition score at a speech sound pressure level (SPL) of 65 dB. HINT testing was performed with both their original and new replacement HAs. During HINT, pupillometry was used to control for task engagement. Results: Replacing the original HAs with new replacement HAs after one month was not statistically significant with a mean change of SRT70 by −1.90 (95% CI: −4.69;0.89, p = 0.182) dB SNR. Conclusions: New replacement HAs do not impact speech perception scores in CI candidates prior to the decision of cochlear implantation. Full article