Search Results (1,207)

Opioid use disorder (OUD) is a chronic disease that remains difficult to treat, even with significant improvements in available medications. While current treatments work well for some, they often do not account for the unique needs of individual patients, leading to less-than-ideal results. Precision medicine offers a new path forward by tailoring treatments to fit each person’s genetic, psychological, and social needs. This review takes a close look at medications for OUD, including methadone, buprenorphine, and naltrexone, as well as long-acting options that may improve adherence and convenience. Beyond medications, the review highlights the importance of addressing mental health co-morbidities, trauma histories, and social factors like housing or support systems to create personalized care plans. The review also explores how emerging technologies, including artificial intelligence and digital health tools, can enhance how care is delivered. By identifying research gaps and challenges in implementing precision medicine into practice, this review emphasizes the potential to transform OUD treatment. A more individualized approach could improve outcomes, reduce relapse, and establish a new standard of care focused on recovery and patient well-being. Full article

Energy-intensive industries (EIIs) face mounting pressure to reduce greenhouse gas emissions while maintaining international competitiveness—a balance that is central to achieving the EU’s 2030 and 2050 climate objectives. In this context, energy management (EM) emerges as a strategic instrument to decouple industrial growth from fossil energy consumption. This study proposes a redefinition of EM to support carbon-neutral supply chains within the European Union’s EIIs, addressing critical limitations of conventional EM frameworks under increasingly stringent carbon regulations. Using a modified systematic literature review based on PRISMA methodology, complemented by expert insights from EU Member States, this research identifies structural gaps in current EM practices and highlights opportunities for integrating sustainable innovations across the whole industrial value chain. The proposed EM concept is validated through an analysis of 24 EM definitions, over 170 scientific publications, and over 80 EU legal and strategic documents. The framework incorporates advanced digital technologies—including artificial intelligence (AI), the Internet of Things (IoT), and big data analytics—to enable real-time optimisation, predictive control, and greater system adaptability. Going beyond traditional energy efficiency, the redefined EM encompasses the entire energy lifecycle, including use, transformation, storage, and generation. It also incorporates social dimensions, such as corporate social responsibility (CSR) and stakeholder engagement, to cultivate a culture of environmental stewardship within EIIs. This holistic approach provides a strategic management tool for optimising energy use, reducing emissions, and strengthening resilience to regulatory, environmental, and market pressures, thereby promoting more sustainable, inclusive, and transparent supply chain operations. Full article

The adoption of artificial intelligence (AI) in marketing and social media is gaining scholarly interest. While AI technologies offer significant potential for enhancing customer engagement (CE), their effectiveness depends on an industry’s level of digital and AI readiness. This is especially relevant for people-centric sectors such as tourism and hospitality, where digital maturity remains relatively low. This study aims to understand how AI supports CE and social media marketing (SMM), and to identify the key antecedents and consequences of its use. Using the PRISMA approach, we conduct a systematic review of 55 peer-reviewed empirical studies on AI-based CE and SMM. Our analysis identifies the main contributing theories and AI technologies in the field, and uncovers four central themes: (1) AI in customer service and user experience design, (2) AI-based customer relationships with brands, (3) AI-driven development of customer trust, and (4) cultural differences and varying levels of AI readiness. We also develop a conceptual framework that outlines the determinants and outcomes of AI-based CE, including relevant moderators and mediators. The study concludes with directions for future research and provides theoretical and managerial implications, particularly for the tourism and hospitality industries. Full article

The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. Soil-borne mosaic wheat virus (SBWMV) is a soil-transmitted virus disease that poses a serious threat to wheat production across multiple ecological zones. Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. To address this, the problem is formulated as large-scale group decision-making process (LSGDM), where each planting plot is treated as an independent virtual decision maker, providing its own severity assessments. This modeling approach reflects the spatial heterogeneity of the disease and enables a structured mechanism to reconcile divergent evaluations. First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. Third, an enhanced spectral clustering method is employed to group plots with similar symptoms and assessment behaviors. Fourth, a feedback mechanism is introduced to iteratively adjust plot-level evaluations based on a set of defined agricultural decision indicators sets using a multi-granulation rough set (ADISs-MGRS). Once consensus is reached, final rankings of candidate plots are generated from indicators, providing an interpretable and evidence-based foundation for targeted prevention strategies. By using the WSBM dataset collected in 2017–2018 from Walla Walla Valley, Oregon/Washington State border, the United States of America, and performing data augmentation for validation, along with comparative experiments and sensitivity analysis, this study demonstrates that the AI-driven LSGDM model integrating enhanced spectral clustering and ADISs-MGRS feedback mechanisms outperforms traditional models in terms of consensus efficiency and decision robustness. This provides valuable support for multi-party decision making in complex agricultural contexts. Full article

Background: This systematic review examines artificial intelligence (AI) applications in neuroimaging for autism spectrum disorder (ASD), addressing six research questions regarding biomarker optimization, modality integration, social function prediction, developmental trajectories, clinical translation challenges, and multimodal data enhancement for earlier detection and improved outcomes. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search across 8 databases, yielding 146 studies from an initial 1872 records. These studies were systematically analyzed to address key questions regarding AI neuroimaging approaches in ASD detection and prognosis. Results: Neuroimaging combined with AI algorithms demonstrated significant potential for early ASD detection, with electroencephalography (EEG) showing promise. Machine learning classifiers achieved high diagnostic accuracy (85–99%) using features derived from neural oscillatory patterns, connectivity measures, and signal complexity metrics. Studies of infant populations have identified the 9–12-month developmental window as critical for biomarker detection and the onset of behavioral symptoms. Multimodal approaches that integrate various imaging techniques have substantially enhanced predictive capabilities, while longitudinal analyses have shown potential for tracking developmental trajectories and treatment responses. Conclusions: AI-driven neuroimaging biomarkers represent a promising frontier in ASD research, potentially enabling the detection of symptoms before they manifest behaviorally and providing objective measures of intervention efficacy. While technical and methodological challenges remain, advancements in standardization, diverse sampling, and clinical validation could facilitate the translation of findings into practice, ultimately supporting earlier intervention during critical developmental periods and improving outcomes for individuals with ASD. Future research should prioritize large-scale validation studies and standardized protocols to realize the full potential of precision medicine in ASD. Full article

Artificial intelligence (AI) is one of the most influential and rapidly developing phenomena of our time. New fields of study are being created at universities, and managers are constantly introducing new AI solutions for business management, marketing, and advertising new products. Unfortunately, AI is also used to promote dangerous political parties and ideologies. The research problem that is the focus of this work is expressed in the following question: How does the symbiotic relationship between artificial and natural intelligence manifest across three dimensions of human experience—philosophical understanding, educational practice, and pastoral care—and what hermeneutical, phenomenological, and critical realist insights can illuminate both the promises and perils of this emerging co-evolution? In order to address this issue, an interdisciplinary research team was established. This team comprised a philosopher, an educator, and a pastoral theologian. This study is grounded in a critical–hermeneutic meta-analysis of the existing literature, ecclesial documents, and empirical investigations on AI. The results of scientific research allow for a broader insight into the impact of AI on humans and on personal relationships in Christian communities. The authors are concerned not only with providing an in-depth understanding of the issue but also with taking into account the ecumenical perspective of religious, social, and cultural education of contemporary Christians. Our analysis reveals that cultivating a healthy symbiosis between artificial and natural intelligence requires specific competencies and ethical frameworks. We therefore conclude with practical recommendations for Christian formation that neither uncritically embrace nor fearfully reject AI, but rather foster wise discernment for navigating this unprecedented co-evolutionary moment in human history. Full article

Agriculture 5.0 represents a shift towards a more sustainable agricultural model, integrating Artificial Intelligence (AI), the Internet of Things (IoT), robotics, and blockchain technologies to enhance productivity and resource management, with an emphasis on social and environmental resilience. This article explores how the evolution of wireless technologies to sixth-generation networks (6G) can support innovation in Colombia’s agricultural sector and foster rural advancement. The study follows three main phases: search, analysis, and selection of information. In the search phase, key government policies, spectrum management strategies, and the relevant literature from 2020 to 2025 were reviewed. The analysis phase addresses challenges such as spectrum regulation and infrastructure deployment within the context of a developing country. Finally, the selection phase evaluates technological readiness and policy frameworks. Findings suggest that 6G could revolutionize Colombian agriculture by improving connectivity, enabling real-time monitoring, and facilitating precision farming, especially in rural areas with limited infrastructure. Successful 6G deployment could boost agricultural productivity, reduce socioeconomic disparities, and foster sustainable rural development, contingent on aligned public policies, infrastructure investments, and human capital development. Full article

This study examines the factors that influence individuals’ intentions to create socially oriented ventures, emphasizing the joint role of social and technical systems. Grounded in Socio-Technical Systems Theory, the research investigates how perceptions of social legitimacy and technological infrastructure shape social entrepreneurial intention (SEI) and how these effects are conditioned by generational cohort, familiarity and intent to use artificial intelligence (AI), and social proximity to entrepreneurial peers. Based on survey data from 388 respondents in China who expressed interest in both entrepreneurship and social problem-solving, the study applies a conditional process structural equation model to capture the complex interplay between external systems and individual-level readiness. The results show that both social and technical systems significantly and positively influence SEI, particularly among younger generations (Millennials and Generation Z). Furthermore, AI familiarity and social proximity operate as moderated mediators, differentially transmitting and shaping systemic influences on SEI. These findings advance the theoretical understanding of socio-technical determinants of social entrepreneurship and offer practical insights for fostering inclusive, generationally responsive entrepreneurial ecosystems. Full article

This paper examines the epistemological transformation prompted by the integration of generative artificial intelligence technologies into social science research, proposing the “adaptive epistemology” paradigm. In today’s post-digital era—characterized by pervasive infrastructures and non-human agents endowed with generative capabilities—traditional research approaches have become inadequate. Through a critical review of historical and discursive paradigms (positivism, interpretivism, critical realism, pragmatism, transformative paradigms, mixed and digital methods), here I show how the advent of digital platforms and large language models reconfigures the boundaries between data collection, analysis, and interpretation. Employing a theoretical–conceptual framework that draws on sociotechnical systems theory, platform studies, and the philosophy of action, the core features of adaptive epistemology are identified: dynamism, co-construction of meaning between researcher and system, and the capacity to generate methodological solutions in response to rapidly evolving contexts. The findings demonstrate the need for reasoning in terms of an adaptive epistemology that could offer a robust theoretical and methodological framework for guiding social science research in the post-digital society, emphasizing flexibility, reflexivity, and ethical sensitivity in the deployment of generative tools. Full article

This article explores the integration of artificial intelligence (AI) in digital marketing through social and mobile networks and its role in fostering sustainable consumer behavior. AI enhances personalization, sentiment analysis, and campaign automation, reshaping marketing dynamics and enabling brands to engage interactively with users. A quantitative study conducted on 501 social media users evaluates how perceived benefits, risks, trust, transparency, satisfaction, and social norms influence the acceptance of AI-driven marketing tools. Using structural equation modeling (SEM), the findings show that social norms and perceived transparency significantly enhance trust in AI, while perceived benefits and satisfaction drive user acceptance; conversely, perceived risks and negative emotions undermine trust. From a sustainability perspective, AI supports the efficient targeting and personalization of eco-conscious content, aligning marketing with environmentally responsible practices. This study contributes to ethical AI and sustainable digital strategies by offering empirical evidence and practical insights for responsible AI integration in marketing. Full article

This study investigates the impact of digital technology propagation, specifically artificial intelligence (AI) and the TikTok application, on enhancing student entrepreneurs’ entrepreneurial knowledge, business experience, and the performance of their ventures. This research employs a mixed-methods design, combining qualitative and quantitative elements, with the quantitative aspect analyzed through Structural Equation Modeling–Partial Least Squares (SEM–PLS) and the qualitative aspect analyzed through in-depth interviews with student entrepreneurs. The survey included participation from 125 students, with three additional students serving as key informants. Research findings suggest that AI directly enhances entrepreneurial knowledge and business performance, whereas TikTok indirectly influences business success by affecting the acquisition of entrepreneurial learning. The utilization of AI has a substantial direct impact on entrepreneurial expertise and business performance. In contrast, the utilization of TikTok has a moderate influence on entrepreneurial knowledge, which in turn mediates its effect on entrepreneurial success. Offer practical implications for higher education institutions to integrate AI-driven analytics and social media marketing strategies into entrepreneurship curricula. Future research should investigate the regulatory framework, long-term implications, and the inclusion of other digital platforms to refine the digital transformation of entrepreneurship education further. Full article

This study analyses algorithmic biases in AI-generated images, focusing on aesthetic violence, gender stereotypes, and weight discrimination. By examining images produced by the DALL-E Nature and Flux 1 systems, it becomes evident how these tools reproduce and amplify hegemonic beauty standards, excluding bodily diversity. Likewise, gender representations reinforce traditional roles, sexualising women and limiting the presence of non-normative bodies in positive contexts. The results show that training data and the algorithms used significantly influence these trends, perpetuating exclusionary visual narratives. The research highlights the need to develop more inclusive and ethical AI models, with diverse data that reflect the plurality of bodies and social realities. The study concludes that artificial intelligence (AI), far from being neutral, actively contributes to the reproduction of power structures and inequality, posing an urgent challenge for the development and regulation of these technologies. Full article

This study explores the role of public theology in addressing contemporary societal challenges, emphasizing ethical dialogue in response to secularization, pluralism, technological transformation, and social and environmental issues. It situates pastoral theology in the Christian tradition as an active social practice aimed at promoting justice, equality, and the common good. The study highlights the emergence of public theology as a response to the participation of religious discourse in the public arena, considering communication and digital technology, and articulating theological reflection with real-world social issues. Additionally, it examines the profound significance of dialogue within religious discourse and stresses the importance of ethical reflection in technological advancements, particularly concerning AI (Artificial Intelligence). Moreover, Catholic social thought and the concept of integral ecology are analyzed in dialogue with the SDGs (Sustainable Development Goals), underlining the potential of public theology to promote socio-environmental justice through a holistic approach. Full article

Cross-docking operations in Eastern and Central European markets face increasing complexity amid persistent uncertainty and inflationary pressures. This study provides the first comprehensive comparative analysis integrating economic efficiency with sustainability indicators across strategic locations. Using mixed-methods analysis of 40 bibliographical sources and quantitative modeling of cross-docking scenarios in Bratislava, Prague, and Budapest, we integrate environmental, social, and governance frameworks with activity-based costing and artificial intelligence analysis. Optimized cross-docking achieves statistically significant cost reductions of 10.61% for Eastern and Central European inbound logistics and 3.84% for Western European outbound logistics when utilizing Budapest location (p < 0.01). Activity-based costing reveals labor (35–40%), equipment utilization (25–30%), and facility operations (20–25%) as primary cost drivers. Budapest demonstrates superior integrated performance index incorporating operational efficiency (94.2% loading efficiency), economic impact (EUR 925,000 annual savings), and environmental performance (486 tons CO₂ reduction annually). This is the first empirically validated framework integrating activity-based costing–corporate social responsibility methodologies for an emerging market cross-docking, multi-dimensional performance assessment model transcending operational-sustainability dichotomy and location-specific contingency identification for emerging market implementation. Findings support targeted infrastructure investments, harmonized regulatory frameworks, and public–private partnerships for sustainable logistics development in emerging European markets, providing actionable roadmap for EUR 142,000–EUR 187,000 artificial intelligence implementation investments achieving a 14.6-month return on investment. Full article

This article presents a comparative study of content generated by artificial intelligence (AI) and articles authored by professional journalists, focusing on the perspective of a Kazakhstani audience. The analysis was conducted based on several key criteria, including the structure of the article, writing style, factual accuracy, citation of sources, and completeness of the information. The study spans a variety of topics, such as politics, economics, law, sports, education, and social issues. The results indicate that AI-generated articles tend to exhibit greater structural clarity and neutrality. On the other hand, articles written by journalists score higher in terms of factual accuracy, analytical depth, and the use of verified sources. Furthermore, the research explores the significance of journalistic ethics in ensuring transparency and information completeness in content production. Ultimately, the findings emphasize the importance of upholding rigorous journalistic standards when integrating AI into media practices. Full article