Search Results (7,617)

Land degradation issues and declining fertility are driving the need for agroecological practices. This research analysed the determinants of acceptance and actual use of five main agroecological practices (contour farming techniques, organic fertiliser, crop association, improved seeds and integrated crop management practices) by farmers in Mali. The extended Unified Theory of Acceptance and Use of Technology (UTAUT) was used to develop the conceptual model. Data were collected from 505 randomly selected farming households in the cotton and cereal production zones in Mali. Partial Least Square–Structural Equation Modelling (PLS-SEM) was used to estimate technology acceptance and use. The findings revealed that behavioural intention is significantly and positively influenced by the expected performance and social influence. The expected effort is a key influential factor of the behavioural intention to adopt organic fertiliser. Experience has a mediating effect on the relationship between social influence and behavioural intention to adopt improved seeds adapted to the agroecological conditions. The actual use behaviour is directly and positively affected by the behavioural intention, facilitating conditions and expected net benefit. These findings align with the UTAUT model, have useful implications for both farmers and decision-makers and offer directions for technical approaches to agroecological practices’ development. Full article

Magnolia officinalis Rehder & E.H. Wilson is a deciduous tree in the Magnoliaceae family with extensive medicinal uses in China and Japan, being used to treat symptoms such as indigestion, insomnia, and anxiety. In this study, we used the MaxEnt model to (1) simulate the suitable spatial distribution areas of M. officinalis in China in the current and future periods (2050s and 2090s) and, (2) identify the key environmental variables affecting its spatial distributions by comparing the changes in the center of mass of the suitable areas under the current and projected future climate. The research results show that the current distribution range of M. officinalis is mainly between east longitude 102.2° to 122.2° and north latitude 23.7° to 33.9°, and it is located in the subtropical region of China. In the future, only the high-suitability area under scenario SSP1-2.6 and the low-suitability area under scenario SSP5-8.5 decreased in the 2050s, while the area increased under all other conditions. In the 2050s, the high- and medium-suitability areas under the SSP5-8.5 scenario increased the most, by 54.76% and 20.90%, respectively. Most of the key bio-climatic variables affecting the spatial distributions of M. officinalis are related to temperature and precipitation, and soil, terrain, chemical, and human variables that are also key environmental variables affecting the spatial distributions of M. officinalis. Currently, the suitable spatial distribution centroid of M. officinalis is at (111.71° E, 28.52° N), but it will change in the future climate; although, it will still be located in Hunan Province. This study predicts the spatial distribution areas that are favorable for the cultivation of M. officinalis with the intention of offering an objectively informed identification of suitable areas for the current and future development of this tree crop’s industry. Full article

In this paper, we analyse the attitudes and sentiments of Romanian smallholders towards mole infestations, as expressed in online contexts. A corpus of texts on the topic of ground moles and how to get rid of them was collected from social media and blog thread discussions. The texts were analysed using topic modelling, clustering, and sentiment analysis, revealing both negative and positive sentiments and attitudes. The methods used by farmers when dealing with ground moles involve both eco-friendly repellent solutions and toxic substances and pesticides. Even well-intentioned farmers are discouraged by crop and lawn damage, resorting to environmentally aggressive solutions. The study shows that the relationship between humans and moles could be improved by active education on effective ecological agricultural approaches. Full article

This study examines how the green marketing mix—comprising green product, price, place, and promotion—affects consumers’ environmental attitudes and green purchase intentions, incorporating environmental knowledge as a moderating factor, focusing on a sample of 334 environmentally conscious consumers from second- and third-tier cities in China. Structural equation modeling (SEM) was used to analyze the data collected through SPSS 24 and AMOS 26. The results indicate that green promotion, price, and place significantly influenced environmental attitude, while green products did not show a significant effect. Both green products and promotions positively affected green purchase intention, whereas price and place had no notable impact. Environmental attitude strongly influenced green purchase intention, emphasizing its critical role in shaping consumer behavior. Interestingly, higher levels of environmental knowledge weakened the link between environmental attitude and green purchase intention, potentially due to increased skepticism toward environmental claims and the onset of “green fatigue”. By analyzing data from consumers in China’s second- and third-tier cities, this study provides valuable insights into the unique dynamics of green consumer behavior in emerging markets, offering strategic guidance for companies to develop more effective and environmentally responsible marketing approaches. It offers insights for policymakers to promote a sustainable, eco-conscious society. Full article

Based on social cognitive theory and the theory of planned behaviour, this study explores the influence of green self-efficacy on consumers’ willingness to buy forest-certified products. Despite extensive research being conducted on forest-certified products, there has been limited investigation into the personal characteristics and intrinsic decision-making factors of consumers. Therefore, this study focuses on the mediating role of consumers’ perceived behavioural control and perceived scarcity, as well as the moderating roles of product involvement. To examine these effects, a total of 560 questionnaires were distributed, and 511 valid responses were collected, resulting in a valid response rate of 91.25%. The data were analysed using SPSS 26.0 and the Process macro to test the mediating and moderating effects. The results indicate that green self-efficacy has a significant positive effect on purchasing intention through direct influence and a significant indirect positive effect on purchasing intention through perceived behavioural control and perceived scarcity. Furthermore, the positive effects of perceived behavioural control and perceived scarcity on purchase intention were more pronounced in high-product-involvement contexts. Based on these findings, it is advised that both enterprises and the government collaborate in conducting green consumption educational initiatives to boost consumers’ green self-efficacy. Businesses can adopt marketing strategies such as limited sales and time-limited offers to create a sense of product scarcity. In addition, enterprises should focus on product design and brand building to increase product engagement and take multiple initiatives to promote green consumption. Full article

As older adults age, changes in their physiological, psychological, and cognitive functions often lead to inherent anxiety and fear regarding the use of digital technologies. Cognitive age, reflecting an older adult’s mental perception of their chronological age, is a crucial moderating factor in shaping their willingness and behavior towards technology adoption. However, the mechanisms through which cognitive age impacts the behavior of older adults within the realm of digital technology utilization remain unclear. Thus, grounded in the Extended Technology Acceptance Model (ETAM) and employing structural equation modeling, this study intends to investigate mechanisms which influence older adults’ behavioral intentions towards the use of museum digital displays. Consequently, it confirms that attitudes mediate the correlation between perceived usefulness, subjective norm, perceived self-efficacy, and the behavioral intention towards museum digital display utilization. Perceived ease of use does not directly affect attitudes, but instead exerts an indirect impact on attitude through perceived usefulness. Cognitive age negatively moderates the relationship between attitudes and the behavioral intention to use digital technologies. Additionally, the mediating role of attitudes in the correlation between older adults’ perception of museum digital displays and behavioral intention is moderated by cognitive age. Specifically, older adults with a higher cognitive age value (who perceive themselves to be younger than their chronological age) exhibit a weaker mediating effect of attitudes on the relationship between perceived usefulness and behavioral intention compared to those with a lower cognitive age value (who perceive themselves to be older than their chronological age). The findings seek to unbox the “black box” of how cognitive age mediates the correlation between attitudes and behavioral intention towards the use of museum digital displays among older adults, providing valuable theoretical insights for the bidirectional enhancement of digital technology utilization, the overall well-being of older adults, and high-quality development in museums. Full article

This scoping literature review explores parent interactions with teachers and school environments at the middle level, particularly how parents and teachers perceive middle school environments and parent–teacher relationships, the strategies that schools use to foster parent–teacher collaboration, and the impacts of these interactions on students. The research team identified 14 peer-reviewed studies from 2015 to 2024 that focused on parent interactions with teachers and school environments at the middle level, including parent and teacher perceptions of school environments and parent–teacher relationships and collaborations. The findings revealed a common disconnect between how parents and teachers view school environments, but also showed that intentional, structured communication can help to bridge these gaps. The review also highlights that research on strategies to enhance parent–teacher partnerships is limited and context-specific. Based on this review of the literature, more research is needed on how home–school relationships benefit diverse middle school communities and how collaboration can support student success, particularly during the critical middle school years. Full article

This study proposes an optimization method based on Rough Set Theory (RST) and Particle Swarm Optimization–Support Vector Regression (PSO-SVR), aimed at enhancing the emotional dimension of outdoor micro-space (OMS) design, thereby improving users’ outdoor activity duration preferences and emotional experiences. OMS, as a key element in modern urban design, significantly enhances residents’ quality of life and promotes public health. Accurately understanding and predicting users’ emotional needs is the core challenge in optimizing OMS. In this study, the Kansei Engineering (KE) framework is applied, using fuzzy clustering to reduce the dimensionality of emotional descriptors, while RST is employed for attribute reduction to select five key design features that influence users’ emotions. Subsequently, the PSO-SVR model is applied to establish the nonlinear mapping relationship between these design features and users’ emotions, predicting the optimal configuration of OMS design. The results indicate that the optimized OMS design significantly enhances users’ intention to stay in the space, as reflected by higher ratings for emotional descriptors and increased preferences for longer outdoor activity duration, all exceeding the median score of the scale. Additionally, comparative analysis shows that the PSO-SVR model outperforms traditional methods (e.g., BPNN, RF, and SVR) in terms of accuracy and generalization for predictions. These findings demonstrate that the proposed method effectively improves the emotional performance of OMS design and offers a solid optimization framework along with practical guidance for future urban public space design. The innovative contribution of this study lies in the proposed data-driven optimization method that integrates machine learning and KE. This method not only offers a new theoretical perspective for OMS design but also establishes a scientific framework to accurately incorporate users’ emotional needs into the design process. The method contributes new knowledge to the field of urban design, promotes public health and well-being, and provides a solid foundation for future applications in different urban environments. Full article

Dog and cat faeces are a globally neglected issue that demands proper management. The poor handling of pet waste not only impacts environmental health but also contributes to community conflicts due to insufficient waste management practices. The objectives were to investigate the properties of pet wastes compared to livestock wastes (pigs, hens, and cattle) with the intention of formulating an efficient compost product suitable for agricultural applications. Faeces from dogs and cats were collected from the community, while faeces from livestock (pigs, cattle, and hens) were collected from farms. Faeces were mixed with probiotics, rice bran, and rice husk to make compost and used to grow sweet corn plants. The nutrient content was compared between fresh and composted faeces. Composted manure from different animal sources was compared for its effectiveness in promoting sweet corn growth. The results showed that fresh and composted dog and cat manures contained higher levels of nutrients (p < 0.05) compared to livestock manures. Additionally, composted pet manure accelerated (p < 0.05) the growth of the plants compared to composted livestock manures and control groups. This is the first study to conclude that composted pet faeces surpass livestock manure in their higher nutrients and enhance plant growth. The findings could help reduce pet waste and transform it into a valuable recycled resource. However, the safety of composted manure, especially concerning toxoplasmosis from cat faeces, remains a significant concern and requires further investigation. Full article

Dialogue systems must understand children’s utterance intentions by considering their unique linguistic characteristics, such as syntactic incompleteness, pronunciation inaccuracies, and creative expressions, to enable natural conversational engagement in child–robot interactions. Even state-of-the-art large language models (LLMs) for language understanding and contextual awareness cannot comprehend children’s intent as accurately as humans because of their distinctive features. An LLM-based dialogue system should acquire the manner by which humans understand children’s speech to enhance its intention reasoning performance in verbal interactions with children. To this end, we propose a fine-tuning methodology that utilizes the LLM–human judgment discrepancy and interactive response data. The former data represent cases in which the LLM and human judgments of the contextual appropriateness of a child’s answer to a robot’s question diverge. The latter data involve robot responses suitable for children’s utterance intentions, generated by the LLM. We developed a fine-tuned dialogue system using these datasets to achieve human-like interpretations of children’s utterances and to respond adaptively. Our system was evaluated through human assessment using the Robotic Social Attributes Scale (RoSAS) and Sensibleness and Specificity Average (SSA) metrics. Consequently, it supports the effective interpretation of children’s utterance intentions and enables natural verbal interactions, even in cases with syntactic incompleteness and mispronunciations. Full article

People who inject drugs (PWIDs) experience high rates of hepatitis C virus (HCV) infection, primarily due to needle sharing and limited healthcare access, resulting in a disproportionate disease burden within this population. This prospective study evaluated treatment outcomes in 432 adult patients with chronic hepatitis C (CHC) treated with direct-acting antivirals (DAAs) at the University Clinical Center of Serbia. Patients were categorized into two groups based on a history of drug addiction: PWIDs (163, 37.7%) and non-PWIDs (269, 62.3%). The PWID group was further categorized into subpopulations of problematic PWIDs (39, 23.9%), ex-PWIDs (124, 76.1%), and PWIDs on OST (96, 58.9%). The PWID group demonstrated significantly lower treatment adherence, with an intention-to-treat (ITT) rate of 82.8%, compared to 96.3% in the control group (p < 0.001). In contrast, no significant differences were observed in per-protocol (PP) outcomes between the two groups. Additionally, PWIDs were significantly younger (p < 0.001) and had higher rates of psychiatric disorders (p < 0.001), alcohol abuse (p < 0.001), and HCV genotype 1a (p < 0.001). Advanced fibrosis was predictor of PP treatment failure among PWIDs, while mood disorders and alcohol use disorder were associated with interruptions before the scheduled completion time. For non-PWIDs, older age and advanced fibrosis emerged as key predictors of PP treatment failure. The loss to follow-up was most commonly observed in the problematic PWID subgroup (p = 0.001). These findings highlight the importance of addressing barriers in PWIDs through integrated care strategies that concurrently manage addiction and HCV. Full article

The increasing adoption of healthcare devices necessitates a deeper understanding of the factors that influence user acceptance in this rapidly evolving area. Therefore, this study examined the factors influencing the technology acceptance of healthcare devices, focusing on radar sensors and wearable devices. A total of 1158 valid responses were used to test hypotheses, mediation, and moderation effects using SmartPLS 4.0. The results highlighted the significant role of performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived risk in shaping user attitudes and trust, which in turn influence behavioral intention. The findings suggested that attitudes fully mediate the effects of performance expectancy and effort expectancy on behavioral intention, while social influence, facilitating conditions, and perceived risk exhibit partial mediation. Moderation analysis revealed significant effects of generation on the relationship between attitude, trust, and behavioral intention. Additionally, device type moderated the effect of trust on behavioral intention, showing a different influence between radar sensors and wearable devices. These findings provide theoretical contributions by extending the unified theory of acceptance and use of technology (UTAUT) model and offering practical implications for manufacturers and policymakers to tailor strategies that foster positive attitudes, enhance trust, and address generational and device-specific differences in healthcare technology adoption. Full article

With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade. Most previous efforts have focused on identifying distinctive information within electroencephalogram (EEG) recordings. In this study, an EEG-based user authentication scheme is presented, employing a multi-layer perceptron feedforward neural network (MLP FFNN). The scheme utilizes P300 potentials derived from EEG signals, focusing on the user’s intent to select specific characters. This approach involves two phases: user identification and user authentication. Both phases utilize EEG recordings of brain signals, data preprocessing, a database to store and manage these recordings for efficient retrieval and organization, and feature extraction using mutual information (MI) from selected EEG data segments, specifically targeting power spectral density (PSD) across five frequency bands. The user identification phase employs multi-class classifiers to predict the identity of a user from a set of enrolled users. The user authentication phase associates the predicted user identities with user labels using probability assessments, verifying the claimed identity as either genuine or an impostor. This scheme combines EEG data segments with user mapping, confidence calculations, and claimed user verification for robust authentication. It also accommodates new users by transforming EEG data into feature vectors without the need for retraining. The model extracts selected features to identify users and to classify the input based on these features to authenticate the user. The experiments show that the proposed scheme can achieve 97% accuracy in EEG-based user identification and authentication. Full article

Room reverberation can affect oral/aural communication and is especially critical in computer analysis of voice. High levels of reverberation can distort voice recordings, impacting the accuracy of quantifying voice production quality and vocal health evaluations. This study quantifies the impact of additive simulated reverberation on otherwise clean voice recordings as reflected in voice metrics commonly used for voice quality evaluation. From a larger database of voice recordings collected in a low-noise, low-reverberation environment, voice samples of a sustained [a:] vowel produced at two different speaker intents (comfortable and clear) by five healthy voice college-age female native English speakers were used. Using the reverb effect in Audacity, eight reverberation situations indicating a range of reverberation times (T20 between 0.004 and 1.82 s) were simulated and convolved with the original recordings. All voice samples, both original and reverberation-affected, were analyzed using freely available PRAAT software (version 6.0.13) to calculate five common voice parameters: jitter, shimmer, harmonic-to-noise ratio (HNR), alpha ratio, and smoothed cepstral peak prominence (CPPs). Statistical analyses assessed the sensitivity and variations in voice metrics to a range of simulated room reverberation conditions. Results showed that jitter, HNR, and alpha ratio were stable at simulated reverberation times below T20 of 1 s, with HNR and jitter more stable in the clear vocal style. Shimmer was highly sensitive even at T20 of 0.53 s, which would reflect a common room, while CPPs remained stable across all simulated reverberation conditions. Understanding the sensitivity and stability of these voice metrics to a range of room acoustics effects allows for targeted use of certain metrics even in less controlled environments, enabling selective application of stable measures like CPPs and cautious interpretation of shimmer, ensuring more reliable and accurate voice assessments. Full article

Although the potential of the blockchain has been extensively recognized by scholars and practitioners across multiple fields, research on its adoption in the framework of the circular economy (CE) is still scarce. In this context, this study extends the technology acceptance model (TAM) by integrating the technology–organization–environment (TOE) framework to holistically understand how technological perception factors (perceived usefulness and perceived ease of use) interact with organizational and environmental factors in influencing the intention to adopt the blockchain in the CE within the context of the Chinese automotive supply chain. Based on survey data from 305 respondents from Chinese automotive companies, the proposed hybrid TOE-TAM conceptual model was validated. The results indicate that, except for the effects of the knowledge management capability on the perceived ease of use and regulatory support on blockchain adoption intention, all of the other hypotheses are deemed significant. Moreover, by conducting an in-depth analysis of the evolution of blockchain adoption intention in the CE, this study not only deepens the understanding of how the technology is disseminated but also provides valuable insights to theory and practice within the Chinese automotive value chain. Full article