Search Results (55)

This study investigates the critical capabilities and integration mechanisms that enable organizations to achieve substantive sustainability transformations. Using a mixed-methods approach combining survey data (n = 234), in-depth interviews (n = 42), and comparative case studies (n = 6), the research identifies how organizations effectively navigate sustainability paradoxes while developing integration practices that embed sustainability throughout organizational systems. Our research is primarily grounded in paradox theory, complemented by insights from organizational learning theory, institutional logics, and power dynamics perspectives to develop a comprehensive theoretical framework. Statistical analysis reveals strong relationships between paradox navigation capabilities and transformation outcomes (β = 0.31, p < 0.01), with integration practices emerging as the strongest predictor of sustainability success (β = 0.42, p < 0.01). Qualitative findings illuminate four essential integration mechanisms—governance integration, strategic integration, operational integration, and performance integration—and their temporal development. The significant interaction between power mobilization and integration practices (β = 0.19, p < 0.01) demonstrates that structural interventions are insufficient without attention to power relationships. The research contributes to sustainability science by advancing theory on paradoxical tensions in transformation processes, demonstrating how organizations can transcend the gap between sustainability rhetoric and substantive action through both structural integration and power-conscious approaches. By identifying contextual contingencies across sectors and organizational types, the study challenges universal prescriptions for sustainability transformation, offering instead a nuanced framework for creating organizational conditions conducive to context-specific transformation toward more sustainable futures. Our findings offer practical guidance for organizations navigating the complex landscape of sustainability transformation and contribute to the implementation of UN Sustainable Development Goals, particularly SDG 12 (Responsible Consumption and Production) and SDG 17 (Partnerships for the Goals). 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

Uniquely rooted in regenerative leadership and complemented by Complexity Leadership Theory (CLT), this conceptual essay offers a theoretical exploration of how educational institutions can act as dynamic systems that catalyze adaptive, community-led responses to anthropocentric socio-environmental crises. Rather than sustaining existing structures, educational leadership for regeneration seeks to restore ecological balance and nurture emergent capacities for long-term resilience. Positioned as key sites of influence, educational institutions are explored as engines of innovation capable of mobilizing students, educators, and communities toward collective environmental action. CLT offers a valuable lens for understanding how leadership emerges from nonlinear, adaptive processes within schools, enabling the development of innovative, collaborative, and responsive strategies required for navigating complexity and leading planetary-positive change. Drawing on a synthesis of the recent global literature, this paper begins by outlining the need to go beyond sustainability in envisioning regenerative futures, followed by an introduction to regenerative principles. It then examines the current and evolving role of educational leadership, the relevance in enabling whole-institution transformation, and how this relates to regenerative practices. The theoretical frameworks of systems thinking and CLT are introduced before noting their application within regenerative educational leadership. The final sections identify implementation challenges and offer practical recommendations, including curriculum innovation, professional development, and youth-led advocacy, before concluding with a call for education as a vehicle for cultivating planetary-conscious citizens and systemic change. This work contributes a timely and theoretically grounded model for reimagining educational leadership in an era of global turbulence. Full article

Background: Antimicrobial resistance (AMR) is a growing global concern in poultry production, where antibiotic use can disrupt gut microbiota and enrich antimicrobial resistance genes (ARGs). Objectives: This study aimed to assess the in vivo effects of a veterinary-approved amoxicillin formulation on gut microbiome composition and ARG profiles in broiler chickens. Methods: A total of 120 Ross-308 broiler chickens were randomly allocated into 12 experimental groups (n = 10 per group), with three replicates per treatment. Birds received either full-dose (1×), a subtherapeutic quarter-dose (¼×) of amoxicillin, a placebo (starch), or no treatment. Cloacal swabs were collected on days 0, 14, and 28 for shotgun metagenomic sequencing. One-way ANOVA was used to evaluate treatment effects on body weight, with significant differences observed from day 14 onward (p < 0.0001). Results: The ¼× dose caused a more pronounced microbiome shift than the 1× dose, with a marked reduction in Pseudomonadota and increase in Bacillota and Bacteroidota. ARG abundance declined in the ¼× group (from 1386 to 1012). While TEM-type ESBL genes were ubiquitous, CTX-M-1 emerged only after ¼× treatment. Worryingly, 20 types of vancomycin resistance genes were detected across all samples. Plasmid-borne ARGs and mobile genetic elements decreased in the ¼× group. Conclusions: Even subtherapeutic antibiotic exposure significantly reshapes the gut microbiota composition and ARG landscape, highlighting the need for refined risk assessments and microbiome-conscious antimicrobial policies in poultry farming. Full article

Background/Objectives: Aortic stenosis is a common heart disease, particularly among elderly patients. Transcatheter aortic valve implantation (TAVI) offers a minimally invasive alternative method to surgical valve replacement for high-risk patients. Although clinical guidelines for TAVI are well established, standardized nursing care pathways are lacking. This systematic review aims to clarify the nursing role in the pre-, peri-, and postoperative phases of TAVI. Methods: This review was conducted in accordance with the PRISMA guidelines. After applying the eligibility criteria, ten studies were selected from five databases: PubMed, Scopus, CINAHL, Web of Science, and the Cochrane Library. The work was registered in the PROSPERO database with the ID number CRD420251061863. Results: The analysis revealed the following: (1) a strong emphasis on preoperative patient education, often led by nurse coordinators; (2) perioperative nursing roles in conscious sedation protocols and early mobilization; (3) a lack of standardized rehabilitative protocols, especially in the preoperative phase; and (4) an emerging but insufficiently evaluated role of the TAVI nurse coordinator in multidisciplinary care. Most studies concentrated on postoperative care, outcomes, follow-up, and rehabilitation, but the small sample sizes limited the strength of the conclusions. Conclusions: Nurses play a vital role in multidisciplinary TAVI teams. There is an urgent need for evidence-based nursing guidelines to standardize care, improve clinical outcomes, and address the needs of TAVI patients. This review highlights the pivotal contribution of nursing to the success of TAVI. Full article

Accurate fish species identification is crucial for marine biodiversity conservation, environmental monitoring, and sustainable fishery management, particularly as marine ecosystems face increasing pressures from human activities and climate change. Traditional morphological identification methods are inherently labor-intensive and resource-demanding, while contemporary automated approaches, particularly deep learning models, often suffer from significant computational overhead and struggle with the pervasive issue of class imbalance inherent in ecological datasets. Addressing these limitations, this research introduces a novel computationally parsimonious fish classification framework leveraging the hybrid Mobile–Former neural network architecture. This architecture strategically combines the local feature extraction strengths of convolutional layers with the global context modeling capabilities of transformers, optimized for efficiency. To specifically mitigate the detrimental effects of the skewed data distributions frequently observed in real-world fish surveys, the framework incorporates a sophisticated robust asymmetric loss function designed to enhance model focus on under-represented categories and improve resilience against noisy labels. The proposed system was rigorously evaluated using the comprehensive FishNet dataset, comprising 74,935 images distributed across a detailed taxonomic hierarchy including eight classes, seventy-two orders, and three-hundred-forty-eight families, reflecting realistic ecological diversity. Our model demonstrates superior classification accuracy, achieving 93.97 percent at the class level, 88.28 percent at the order level, and 84.02 percent at the family level. Crucially, these high accuracies are attained with remarkable computational efficiency, requiring merely 508 million floating-point operations, significantly outperforming comparable state-of-the-art models in balancing performance and resource utilization. This advancement provides a streamlined, effective, and resource-conscious methodology for automated fish species identification, thereby strengthening ecological monitoring capabilities and contributing significantly to the informed conservation and management of vital marine ecosystems. Full article

With the global emphasis on sustainable growth and development, the depletion of natural energy reserves due to reliance on fossil fuels and non-renewable sources remains a critical concern. Despite strides in transitioning to electrical mobility, rural and agricultural communities depend heavily on liquefied petroleum gas and firewood for cooking, lacking viable, sustainable alternatives. This study focuses on community-led efforts to advance biogas adoption, providing an eco-friendly and reliable energy alternative for rural and farming households. By designing and developing balloon-type anaerobic biodigesters, this initiative provides a robust, cost-effective, and scalable method to convert farm waste into biogas for household cooking. This approach reduces reliance on traditional fuels, mitigating deforestation and improving air quality, and generates organic biofertilizer as a byproduct, enhancing agricultural productivity through organic farming. The study focuses on optimizing critical parameters, including the input feed rate, gas production patterns, holding time, biodigester health, gas quality, and liquid manure yield. Statistical tools, such as descriptive analysis, regression analysis, and ANOVA, were employed to validate and predict biogas output data based on experimental and industrial-scale data. Artificial neural networks (ANNs) were also utilized to model and predict outputs, inspired by the information processing mechanisms of biological neural systems. A comprehensive database was developed from experimental and literary data to enhance model accuracy. The results demonstrate significant improvements in cooking practices, health outcomes, economic stability, and solid waste management among beneficiaries. The integration of statistical analysis and ANN modeling validated the biodigester system’s effectiveness and scalability. This research highlights the potential to harness renewable energy to address socio-economic challenges in rural areas, paving the way for a sustainable, equitable future by fostering environmentally conscious practices, clean energy access, and enhanced agricultural productivity. Full article

This study explores the complex relationship between innovation and carbon dioxide (CO₂) emissions across the primary, secondary, and tertiary sectors within the 27 European Union (EU) member states over the period 2017–2023. Drawing on a comprehensive dataset and grounded in theoretical frameworks, the research investigates how different innovation indicators—including broadband penetration, digital skills, public and business R&D expenditure, ICT training, and SME-driven innovations—affect sectoral CO₂ emissions. Using robust regression, the findings reveal a nuanced landscape: while ICT skills training, human resource mobility in science and technology, and SME business process innovations are associated with significant reductions in emissions, certain R&D investments and broadband penetration display positive correlations with emissions in specific service-oriented sectors. These results suggest that the environmental impacts of innovation are highly context-dependent and not uniformly positive. This study highlights the importance of strategically aligning innovation policies with sustainability objectives. Policymakers are encouraged to promote targeted digital training, support environmentally conscious R&D, and foster SME-led innovation practices. The results contribute to the growing discourse on sustainable innovation and provide actionable insights to advance the EU’s green transition. Full article

Cities are looking for an approach to affordable, integrated and sustainable transport systems across all transport modes and services. Automated vehicle (AV) technologies use emerging technologies to integrate multimodal transport systems and ensure sustainable mobility in a city. Vehicle automation has entered the public conscious with several auto companies leading recent developments in legislation and affordable cars. Governments support AVs through policies and legal frameworks, and it is the responsibility of AV dealers to comply with legal and policy provisions so that the benefits of this new and promising industry can be felt. Despite the growing interest in AVs as a potential solution for sustainable transportation, several research gaps remain in relation to technology and infrastructure readiness, policy and regulation, equity and accessibility concerns, public acceptance and behaviour, and integration with public transport. This paper discusses the challenges and dilemmas of adopting AVs within the existing urban transportation system and within existing design standards in the United Kingdom and explores the progress and opportunities related to policies of transportation that may stem from the emergence of AV technologies in the UK. The potential of AVs is still limited by cyber insecurity, incompetent infrastructure, social acceptance, and public awareness. However, AVs are crucial to a city’s efficiency and prosperity and will become essential components for the provision of more flexible, convenient, integrated and sustainable travel options. Full article

Background: Disorders of Consciousness (DoC) following acute brain injuries, such as intracerebral hemorrhage, present significant clinical challenges in intensive care and rehabilitation settings. Early multidisciplinary interventions, including physiatric care, are critical in optimizing recovery trajectories. However, evidence regarding the timing and intensity of rehabilitation interventions remains limited. This case report highlights the role of physiatrists in managing a critically ill patient with a DoC in an Intensive Care Unit (ICU), focusing on early rehabilitation strategies and individualized care planning. Case presentation: A 63-year-old male with a history of hypertension and cardiac disease presented with a left hemispheric hemorrhage and quadriventricular intraventricular hemorrhage. The patient was admitted to the ICU in a comatose state (Glasgow Coma Scale [GCS] 5). Initial physiatric evaluation revealed a critical condition precluding immediate initiation of an Individual Rehabilitation Project (IRP). Over subsequent weeks, clinical improvements were observed, including an increased GCS and Coma Recovery Scale-Revised (CRS-R) score. A tailored IRP was implemented, emphasizing passive mobilization to prevent complications such as muscle atrophy, joint contractures, and pressure ulcers. The patient demonstrated gradual progress, transitioning to a Minimally Conscious State (MCS) and achieving improved joint mobility and reduced peripheral edema. Discussion and Conclusions: This case underscores the pivotal role of physiatrists in ICU settings, particularly for patients with DoC. Early physiatric interventions, even in critically ill patients, can prevent secondary complications and facilitate functional recovery. Close collaboration with ICU teams and infectious disease specialists ensured the safe implementation of rehabilitation strategies despite the patient’s severe condition. The observed clinical improvements highlight the potential benefits of early mobilization and individualized care plans, both in terms of survival (quoad vitam) and quality of life (quoad valetudinem). This report emphasizes the need for further research to refine rehabilitation practices for patients with DoC, bridging gaps between acute care and neurorehabilitation. Full article

Lumbar range of motion (ROM) is essential to develop effective movements during the underwater undulatory swimming technique. Core exercises are used to improve the strength of the muscles that participate in that technique, and variations in sensory input and attentional focus may modulate neuromuscular responses and impact training outcomes. The aim of this study was to investigate the impact of an 11-week program of repeated maximal lumbar movements with closed eyes and without focused attention on lumbar sagittal ROM in elite swimmers versus executing them solely with proper exercise technique with controlled breathing. Methods: A sample of 57 professional swimmers, including 34 males (20.2 ± 4.2 years) and 23 females (20.7 ± 3.3 yrs), volunteered to complete this study. They were randomly divided into two experimental groups (EG1 and EG2) and one control group (CG). All subjects underwent the same type of training program in parallel with the EG intervention. EG1 and EG2 performed three sets of ten repetitions of lumbar flexion and extension exercises at breathing pace, 6 days a week for 11 weeks. EG1 performed the core workout with closed eyes and focused attention on the lumbar movement, while EG2 only followed the technique of the exercises at a controlled breathing pace. Lumbar flexion (F), extension (E), and total ROM (TROM) were assessed by an electrogoniometer in a seated, relaxed position over a Swiss ball. Results: Repeated measures ANOVA showed significant differences in the multivariate profiles across groups and over time. F (8, 48) = 3.495, p = 0.002. EG1 had non-significant increases in lumbar ROM, EG2 had significant increases in TROM and extension ROM, and CG had no changes. Conclusions: The results suggest that repeating maximal lumbar movement at a controlled breathing pace, with opened eyes and non-focusing attention on the movement, increases lumbar ROM in the sagittal plane. Full article

The proliferation of wearable sensors and mobile devices has fueled advancements in human activity recognition (HAR), with growing importance placed on both accuracy and privacy preservation. In this paper, the author proposes a federated learning framework for HAR, leveraging a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model to enhance feature extraction and classification in decentralized environments. Utilizing three public datasets—UCI-HAR, HARTH, and HAR7+—which contain diverse sensor data collected from free-living activities, the proposed system is designed to address the inherent privacy risks associated with centralized data processing by deploying Federated Averaging for local model training. To optimize recognition accuracy, the author introduces a dual-feature extraction mechanism, combining convolutional blocks for capturing local patterns and a hybrid LSTM-GRU structure to detect complex temporal dependencies. Furthermore, the author integrates an attention mechanism to focus on significant global relationships within the data. The proposed system is evaluated on the three public datasets—UCI-HAR, HARTH, and HAR7+—achieving superior performance compared to recent works in terms of F1-score and recognition accuracy. The results demonstrate that the proposed approach not only provides high classification accuracy but also ensures privacy preservation, making it a scalable and reliable solution for real-world HAR applications in decentralized and privacy-conscious environments. This work showcases the potential of federated learning in transforming human activity recognition, combining advanced feature extraction methodologies and privacy-respecting frameworks to deliver robust, real-time activity classification. Full article

Despite the advancement in autonomous vehicles, public trust and acceptance are crucial for AV’s widespread adoption. This study examines how different collaboration levels between human drivers and artificial intelligence influence users’ trust and acceptance of AVs. Using an extended Technology Acceptance Model, this study incorporates psychological factors and technological attitudes such as perceived safety, perceived risk, AI literacy, and AI technophobia. Data collected from 392 vehicle owners across 11 Chinese cities were analyzed using Partial Least Squares Structural Equation Modeling and Multi-Group Analysis. The findings reveal that at the fully manual level, perceived ease of use significantly influences perceived usefulness, while trust remains grounded in mechanical reliability rather than AI systems. In contrast, as AI assumes driving responsibilities at collaborative automation levels, the findings show that AI literacy significantly increases perceived trust and ease of use, while AI technophobia decreases them, with these effects varying across different driving automation levels. As AI takes on greater driving responsibilities, perceived ease of use becomes less critical, and perceived trust increasingly influences users’ acceptance. These findings highlight the need for targeted public education and phased automation strategies, offering guidance for AV developers to address user concerns and build trust in autonomous technologies. By enhancing public trust and acceptance, this study contributes to sustainable development by promoting safer roads and enabling more efficient, resource-conscious transportation systems. Gradually integrating AVs into urban mobility also supports smart city initiatives, fostering more sustainable urban environments. Full article

Low-color-temperature candlelight organic light-emitting diodes (OLEDs) offer a healthier lighting alternative by minimizing blue light exposure, which is known to disrupt circadian rhythms, suppress melatonin, and potentially harm the retina with prolonged use. In this study, we explore the integration of transition metal dichalcogenides (TMDs), specifically molybdenum disulfide (MoS₂) and tungsten disulfide (WS₂), into the hole injection layers (HILs) of OLEDs to enhance their performance. The TMDs, which are known for their superior carrier mobility, optical properties, and 2D layered structure, were doped at levels of 0%, 5%, 10%, and 15% in PEDOT:PSS-based HILs. Our findings reveal that OLEDs doped with 10% MoS₂ exhibit notable enhancements in power efficacy (PE), current efficacy (CE), and external quantum efficiency (EQE) of approximately 39%, 21%, and 40%, respectively. In comparison, OLEDs incorporating 10% of WS₂ achieve a PE of 28%, a CE of 20%, and an EQE of 35%. The enhanced performance of the MoS₂-doped devices is attributed to their superior hole injection and balanced carrier transport properties, resulting in more efficient operation. These results highlight the potential of incorporating 2D TMDs, especially MoS₂, into OLED technology as a promising strategy to enhance energy efficiency. This approach aligns with environmental, social, and governance (ESG) goals by emphasizing reduced environmental impact and promoting ethical practices in technology development. The improved performance metrics of these TMD-doped OLEDs suggest a viable path towards creating more energy-efficient and health-conscious lighting solutions. Full article

This research explores the perspectives of kindergarten through to Grade 12 (K-12) teachers on incorporating information and communication technology (ICT) into the environmental education (EE) curriculum. In the context of the increasing influence of ICT in education, this study examines both the potential enhancements ICT offers to EE and the challenges it poses. Using data from an online survey and an in-person focus group, the investigation addresses the capacity of ICT to promote environmental stewardship and personal growth, alongside concerns regarding technology’s potential to alienate students from nature and the divided opinions among educators regarding optimal technology use. Attention is given to systemic barriers that complicate EE integration and the variability of its implementation in Ontario, Canada, where EE is mandated across K-12 curricula. The findings illuminate educators’ concerns about digital dependencies among their students and the difficulty they face in striking a balance between the use of ICT and non-technical pedagogical approaches when engaging students in environmental lessons. Importantly, study participants identified limited contemporary and timely technological tools to support EE delivery that deemphasize using personal mobile devices (e.g., smartphones and tablets). In response, we recommend three forms of technology (and accompanying lesson ideas) that are affordable, easy to integrate into classrooms, and do not require off-site trips, thereby enhancing accessibility and equity. This study’s implications are aimed at educators, policymakers, and stakeholders seeking to enhance EE delivery within a technologically evolving educational framework and ensure the development of environmentally conscious students. Full article