Search Results (7,585)

Social Innovation is defined as both a concept and a tool for social change that, in recent decades, has formed the backbone of numerous policies implemented by the European Commission. However, part of the academic literature identifies several limitations in the discursive potentialities presented within the institutional framework. Accordingly, the aim of this article is to examine how European social policies on Social Innovation conceptualise the ‘being’ and ‘ought to be’ of citizenship, or the subject, from a critical Foucauldian perspective, with a view to problematising its implications for the analysis of the social reality represented in these policies. To this end, a qualitative strategy employing discourse analysis and the ‘logics of critical explanation’ approach is utilised, analysing 26 institutional documents from the European Commission issued between 2010 and 2024. The findings indicate that this discursive institutional framework construes citizenship as embodying a rational, active, capable, and conscious subject, committed to solving social problems. This conception of the ‘being’ of a citizen implies a significant transformation in the ‘ought to be’ of that citizen. If citizens are deemed capable of understanding and transforming their environment, they are thereby responsible for ensuring their own well-being and equipping themselves with the necessary skills to adapt to economic change, transforming them into “neoliberal subjects” within a Foucauldian framework. This new normativity appears to naturalise the functioning of social and economic structures and their dynamics, resulting in an undialectical analysis of social realities. Full article

Online media serves as the central platform for information dissemination; however, the rise of information disorders threatens the credibility and authenticity of public information. In China, the transformative impact of the digital age has intensified these disorders, significantly altering the media ecology. This study investigated the underlying mechanisms of information overload, algorithmic intervention, and audience polarization, which contribute to the phenomenon of information disorders. By analyzing the roles of content producers, distribution mediums, and audiences within the Chinese context, the study offers a comprehensive understanding of these dynamics. Furthermore, addressing information disorders within the digital media ecosystem has become a critical global challenge. Through a systematic literature review, this study explored potential strategies for mitigating these disorders in China, emphasizing the application of constructive journalism. The constructive journalism framework is proposed as a set of journalistic norms that can foster a healthier and more sustainable online media environment. By enhancing professional ethics, clarifying the instrumental rationality of technology, and employing positive psychology, this approach aims to reduce audience polarization and realize the public value of information dissemination. Full article

The colonial erosion of African traditional and cultural practices continues to dislocate the African identity in different ways. The changes and shifts made to burial practices have simply paved a way for the colonial agenda to bring about more Western ways of doing things, burial rites included. It is important to note that cremation, as another way of burying the dead, is slowly becoming a norm for black people. This paper intends to not only unveil the causes and contestations around cremation burials, but to also seek an African theological response as a way to guide black African culture moving forward. Full article

Progressive climate change (CC) forces people—particularly in the Global South—to adapt to its consequences, some of which include droughts, flooding, and new diseases. This study investigates the determinants of behaviors for adapting to these threats in a population from the region of Cusco (Peru). Data were gathered via a cross-sectional interview-based survey in 2016, using random-route sampling. For up to 542 cases, we regressed a scale combining performed behaviors and intentions on psychological constructs, for the entire and sub-samples (n > 179, allowing to detect an R² of 10% with a power of 80% at p = 0.05). Behavior-specific evaluations—particularly perceived feasibility (β = 0.355), descriptive norms (β = 0.267), and cost-benefit evaluations (β = 0.235)—can explain most of the variance (44% with a total R² = 61%). Furthermore, trust in specific sources (β = 0.106), general trust (β = 0.098), and negative attitudes toward nature (β = 0.077) are positively related to adaptation, particularly regarding public behaviors (supporting community projects and policies). However, evaluations directly related to CC, such as risk perception (β = 0.010) or how much a behavior helps prevent damage (adaptation efficacy, β = −0.042)), do not explain adaptation, except for an effect of adaptation efficacy on changing daily behaviors. Experiences with and expectations of CC consequences are mostly unrelated to adaptation. However, worries about such events are correlated with adaptation (r between 0.097 and 0.360). We conclude that, to promote adaptation behaviors in this region, the focus should be on the characteristics of the behavior performance (e.g., its costs or feasibility), not on the expected risks of extreme events because of CC. Full article

The implementation of cybersecurity standards and directives in the maritime sector plays a crucial role in protecting critical maritime infrastructures from cyber threats. The level of protection depends heavily on humans. However, the effectiveness of cybersecurity training and compliance programmes, an essential component of these standards, is often hindered by challenges related to the sector’s environment, including the established technologies, practices, and norms. This paper aims to identify these challenges through a literature review and set the basis for more effective human risk minimization, responses, and training. We identify 17 challenges and validate them with an online survey (N = 205) capturing real-world perspectives from maritime-related stakeholders. Our findings contribute to enhancing the effectiveness of maritime cybersecurity training and compliance programmes, ultimately strengthening the maritime cybersecurity posture. Full article

Power safety named entity recognition (NER) is essential for determining the cause of faults, assessing potential risks, and planning maintenance schedules, contributing to the comprehension and analysis of power safety documentation content and structure. Such analysis is crucial for the development of a knowledge graph within the power safety domain and the augmentation of the associated dataset. This paper introduces a power safety NER model using efficiently learning an encoder that classifies token replacements accurately (ELECTRA) model. This model employs root mean square layer normalization (RMSNorm) and the switched gated linear unit (SwiGLU) activation function, which substitutes the conventional layer normalization (LayerNorm) and the Gaussian error linear units (GeLU). This model also integrates bidirectional long short-term memory (BiLSTM) with conditional random fields (CRF) to bolster performance in NER tasks. Experimental results show that the improved ELECTRA model achieved an F1 value of 93% on the constructed power safety NER dataset. It outperforms the BERT-BiLSTM-CRF model, achieving a 3.3% performance improvement. Full article

The urgency of mitigating climate change and the challenges it poses to ecosystems and human systems are highlighted in the Intergovernmental Panel on Climate Change’s (IPCC) Sixth Assessment Report (AR6). In order to achieve sustainable development, it is imperative to adopt a series of adaptive measures to enhance the resilience of various sectors to climate change and reduce greenhouse gas emissions. This article analyzes the driving mechanism behind the reduction in electricity consumption by urban residents based on 302 valid questionnaires from 18 communities in nine districts in B City. Using a method that combines qualitative and empirical research, the study proposes policy recommendations aimed at guiding urban residents toward reducing their electricity consumption. These recommendations serve as a policy reference for cities striving to achieve sustainability and low-carbon targets. The primary innovations and conclusions of the study are as follows: (1) this study summarizes the primary factors and processes influencing the reduction in electricity consumption among urban residents, examined from the following three perspectives: residents’ characteristics, psychological understanding, and external environment. (2) On the basis of the research data, empirical analysis and hypothesis testing are conducted using a variety of mathematical and statistical methods. The results indicate significant differences in the electricity consumption reduction behavior of heterogeneous urban residents in both public and private areas. Subjective norms, perceived behavioral control, and knowledge of electricity conservation have significant direct influences on residents’ willingness to reduce their electricity consumption. Among these factors, subjective norms have the most significant impact, while the impact of attitude is negligible. Economic incentive policies have a significant positive regulatory effect on the relationship between “willingness (intention)” and “private area electricity consumption reduction behavior”. Full article

In-vehicle information system (IVIS) use while driving has raised concerns about driver distraction, especially for young drivers. To understand better their psychological factors, an extended theory of planned behavior (TPB) was employed to predict young drivers’ IVIS use intentions and behavior while driving. A two-wave longitudinal survey was conducted to explore the temporal effects of ‘intention–behavior’ causality. At Time 1, 236 qualified participants completed a main questionnaire assessing the standard TPB constructs (attitude, subjective norms, and perceived behavior control) and the extended constructs (descriptive norms, moral norms, and perceived risks). At Time 2, 145 follow-up questionnaires measuring self-reported behavior were successfully administered. The hierarchical multiple regression analyses showed that the standard constructs account for 36.5% of the intention variance and 41.2% of the behavior variance. The extended constructs additionally contributed 20.3% of intention variance. All variables were identified as significant predictors of intentions, except for perceived crash risks and perceived risks of being caught and fined. The sole significant predictor of prospective behavior was intention. Theoretically, the findings further support the efficacy of the TPB in explaining IVIS use while driving. Practically, it is helpful to design non-legal interventions that sustainably reduce young drivers’ engagement in IVIS-related distractions. Full article

Background/Objectives: Low back pain is thought to be caused by lumbar instability. To date, multiple definitions of radiological lumbar instability have been used without verifying the “normal range” of the lumbar segmental mobility. Ideally, normative data for lumbar mobility in healthy individuals are required to establish an acceptable threshold for lumbar instability. This study aims to elucidate (i) the prevalence of so-called radiological lumbar instability at each lumbar spine level in conventional criteria and (ii) a practical radiological threshold for lumbar instability in healthy individuals. Methods: Participants completed a questionnaire and underwent standard active dynamic radiography of the lumbar spine in the standing position. Intervertebral range of motion (IROM) and sagittal translation distance (ΔST) were measured at each intervertebral level. Nachemson’s criteria of radiological lumbar instability were applied. Results: This study involved four hundred and twenty participants (249 males and 171 females); 76% (320/420) met the criteria for radiological lumbar instability. The definition of lumbar instability based on IROM and ΔST was achieved by 0.2% and 1.7% of participants at the L5–sacrum (L5–S) level, respectively. Conclusions: The normative data of lumbar mobility were obtained from a large number of participants who had less LBP-related ADL disability. The widely accepted criteria for lumbar instability were not applicable except for the L5–S level. Further studies of lumbar mobility, including patients with severe LBP, might prove the relationship between hypermobility of the lumbar spine and LBP. Full article

This paper proposes the “ultimate compression” method as a solution to the expansive computation and high storage costs required by state-of-the-art neural network models in inference. Our approach uniquely combines tensor decomposition techniques with binary neural networks to create efficient deep neural network models optimized for edge inference. The process includes training floating-point models, applying tensor decomposition algorithms, binarizing the decomposed layers, and fine tuning the resulting models. We evaluated our approach in various state-of-the-art deep neural network architectures on multiple datasets, such as MNIST, CIFAR-10, CIFAR-100, and ImageNet. Our results demonstrate compression ratios of up to 169×, with only a small degradation in accuracy (1–2%) compared to binary models. We employed different optimizers for training and fine tuning, including Adam and AdamW, and used norm grad clipping to address the exploding gradient problem in decomposed binary models. A key contribution of this work is a novel layer sensitivity-based rank selection algorithm for tensor decomposition, which outperforms existing methods such as random selection and Variational Bayes Matrix Factorization (VBMF). We conducted comprehensive experiments using six different models and present a case study on crowd-counting applications, demonstrating the practical applicability of our method. The ultimate compression method outperforms binary neural networks and tensor decomposition when applied individually in terms of storage and computation costs. This positions it as one of the most effective options for deploying compact and efficient models in edge devices with limited computational resources and energy constraints. Full article

China’s agricultural sector faces significant challenges, including fragmented farming practices, limited farmer knowledge of sustainable production, and outdated pest control technologies. These issues result in improper fertilization, pesticide application, and disposal of agricultural inputs, contributing to agricultural non-point source pollution and hindering the transition to a green economy. Thus, promoting green production behavior among farmers is critical for achieving carbon peaking, carbon neutrality, and harmonious coexistence between humans and nature. However, the existing literature on this topic is still relatively scarce. This study aims to investigate the impact of farmers’ cognition on their green production behavior (GPB). Considering the role of policy, this study also examines the moderating effect of government regulation in this relationship. An analysis of 306 survey responses from tea farmers in Jingmai Mountain, Pu’er City, Yunnan Province, reveals that farmers’ cognition exerts a significant and positive impact on GPB. Social norms and personal norms serve as chain mediators in the relationship between farmers’ cognition and GPB. Moreover, government regulation moderates the influence of farmers’ cognition on social norms, further amplifying its impact on them. This study advances the theoretical understanding of farmers’ behavior and offers practical insights for fostering the sustainable development of the tea industry. Full article

Previous studies have generally concluded that public participation lacks substantive constraints and has weak environmental regulation effects. Using China’s environmental public interest litigation (EPIL), implemented in 2015, as a quasi-natural experiment to verify the environmental effects of public participation under judicial norms, the difference-in-differences (DID) estimates in this paper show that industrial wastewater and industrial sulfur dioxide (SO₂) emissions in the treated cities declined by an average of 2.76 million tons and 2.51 kilotons per year, respectively, which ultimately improved the city’s environmental quality. The results of the mechanism also show that the EPIL was able to mobilize all three parties: the public, government and enterprises. In the context of the environment as an externality product, where the interests of all the parties are difficult to coordinate, the EPIL has the advantage of overcoming conflicts of interest. Our study provides a quantitative justification for the environmental impact assessment of public litigation and contributes empirical references to overcome the weak binding defect of public participatory environmental regulation. Full article

The complexity of urban road scenes at night and the inadequacy of visible light imaging in such conditions pose significant challenges. To address the issues of insufficient color information, texture detail, and low spatial resolution in infrared imagery, we propose an enhanced infrared detection model called LFIR-YOLO, which is built upon the YOLOv8 architecture. The primary goal is to improve the accuracy of infrared target detection in nighttime traffic scenarios while meeting practical deployment requirements. First, to address challenges such as limited contrast and occlusion noise in infrared images, the C2f module in the high-level backbone network is augmented with a Dilation-wise Residual (DWR) module, incorporating multi-scale infrared contextual information to enhance feature extraction capabilities. Secondly, at the neck of the network, a Content-guided Attention (CGA) mechanism is applied to fuse features and re-modulate both initial and advanced features, catering to the low signal-to-noise ratio and sparse detail features characteristic of infrared images. Third, a shared convolution strategy is employed in the detection head, replacing the decoupled head strategy and utilizing shared Detail Enhancement Convolution (DEConv) and Group Norm (GN) operations to achieve lightweight yet precise improvements. Finally, loss functions, PIoU v2 and Adaptive Threshold Focal Loss (ATFL), are integrated into the model to better decouple infrared targets from the background and to enhance convergence speed. The experimental results on the FLIR and multispectral datasets show that the proposed LFIR-YOLO model achieves an improvement in detection accuracy of 4.3% and 2.6%, respectively, compared to the YOLOv8 model. Furthermore, the model demonstrates a reduction in parameters and computational complexity by 15.5% and 34%, respectively, enhancing its suitability for real-time deployment on resource-constrained edge devices. Full article

This research aims at investigating factors affecting entrepreneurial intention among university students in Selva Central-Peru. The study seeks to explore how perceptions of behavioural and social norms, as well as entrepreneurs’ self-efficacy, impact entrepreneurial intentions among students. The research method used is a quantitative one, which in turn praises data obtained through questionnaires applied to 114 active students and analysed by statistical methods. Confirmed the positive influences of self-efficacy on entrepreneurial intention are six times larger than those of attitude and subjective norms, with 79.2% explained variance, respectively, in relation to the proposal model’s findings. Results showed that although the majority of students demonstrated medium to high entrepreneurship intentions, much scope remains for increasing this. These findings confirm just how critical it is to provide an educational context that supports entrepreneurial locus of control-building and, in so doing, draws on broader conversation within the education-occupational landscape. With this, the study highlights avenues to better leverage entrepreneurship as a pathway toward sustainable livelihoods and economic inclusion in an economically divided region. Full article

This study explores the representation of female athletes cast in a South Korean reality show titled Sporty Sisters (Korean title: Nonun Unni). Though there have been attempts to understand the media representation of female athletes from diverse media landscapes, the analysis of Asian female athletes is limited. Such interpretations have been discussed through geopolitical relations or nationalistic representations, which lack in-depth understanding and exploration of Asian athletes. Therefore, this paper expands the narrative by analyzing the first season (54 episodes) of Sporty Sisters. It is observed that professional athletes who transition to the entertainment industry are identified as “spor-tainers” and implicitly follow norms and rules applied to public figures. In this reality show, female athletes express their opinions on marriage, menstruation, pregnancy, and domestic roles while displaying characteristics of a girl crush, manifesting a variety of the female attributes of sportswomen. Despite the social expectations to fulfill their roles as sports celebrities and Korean women, Sporty Sisters is an outlet for female athletes to an extent to freely expose and describe their experiences as athletes and individuals. Full article