Search Results (378)

Urban gardening, particularly through food-producing green spaces, is increasingly recognized as a key strategy for addressing the complex challenges of climate change, food insecurity, biodiversity loss, and social inequity in African cities. This systematic review synthesizes evidence from 47 peer-reviewed studies across sub-Saharan Africa between 2000–2025 to analyze how urban home gardens, rooftop farms, and agroforestry systems contribute to sustainable urban development. The protocol follows PRISMA guidelines and focuses on (i) plant species selection for ecological resilience, (ii) integration of modern technologies in urban gardens, and (iii) socio-economic benefits to communities. The findings emphasize the ecological multifunctionality of urban gardens, which support services such as pollination, soil fertility, and microclimate regulation. Biodiversity services are shaped by both ecological and socio-economic factors, highlighting the importance of mechanisms such as polyculture, shared labour and management of urban gardens, pollinator activity and socio-economic status, reflected in sub-Saharan urban gardens. Socioeconomically, urban gardening plays a crucial role in enhancing household food security, income generation, and psychosocial resilience, particularly benefiting women and low-income communities. However, barriers exist, including insecure land tenure, water scarcity, weak technical support, and limited policy integration. Although technologies such as climate-smart practices and digital tools for irrigation are emerging, their adoption remains uneven. Research gaps include regional underrepresentation, a lack of longitudinal data, and limited focus on governance and gender dynamics. To unlock urban gardening’s full potential, future research and policy must adopt participatory, equity-driven approaches that bridge ecological knowledge with socio-political realities. Full article

Sustainable innovation represents both a strategic priority and survival imperative for small- and medium-sized enterprises in resource-constrained economies. While digital transformation offers potential solutions, the synergistic effects of digital leadership, employee behaviors, and emerging technologies remain poorly understood. This study bridges this gap by developing and testing a behavioral-tech leadership framework grounded in the Job Demands-Resources (JD-R) model and Technology Acceptance Model. Analyzing survey data from 400 Iraqi SME employees using partial least squares structural equation modeling, we demonstrate that digital leadership directly enhances sustainable innovation while reducing counterproductive cyberloafing. Crucially, social cyberloafing, when properly managed, emerges as a positive mediator, improving employee well-being and creativity, particularly among mid-career and educated workers. Artificial Intelligence’s integration further amplifies these effects by optimizing operational efficiency and reducing human-resource strain. These findings challenge conventional perspectives by repositioning cyberloafing as a conditional resource within the JD-R framework and provide actionable insights for achieving sustainable innovation even in challenging environments. Practical implications include gender-inclusive digital leadership programs, ethical AI implementation guidelines and restorative cyberloafing policies. The study contributes to United Nations Sustainable Development Goals 8 (decent work), 9 (industry innovation) and 12 (responsible consumption) while highlighting the transformative potential of human-centric digital strategies in resource-constrained contexts. Full article

The paper examines a thin-walled steel road bridge that has been in service for over fifty years. Due to damage observed during detailed inspections, a comprehensive investigation was necessary to evaluate the structural safety of the superstructure. For this purpose, the new national guidelines for assessing the safety of existing road bridges were used for the first time. These guidelines are based on the new Eurocode prEN 1990-2, which provides the foundation for assessing existing structures. To enable reliable and rational decisions regarding repair or strengthening, a finite element analysis was performed considering the condition survey, NDT, and material testing. The analysis showed that the resistance of some superstructure elements was exceeded by over 600%, and about 180 elements are inadequately safe to carry the actual minimum traffic loads according to the safety standards mandated by the new national guidelines. A comparison between the analysis results and the condition survey identified the same elements where local plastic deformations were observed. Based on the experimental and numerical results within this new assessment framework, the final decision was made to close the bridge for service and replace the existing steel structure. Full article

Nickel-based alloys, including Inconel 718 and alloy 625, are indispensable in industries such as aerospace, marine, and nuclear energy due to their exceptional mechanical strength, high-temperature performance, and corrosion resistance. However, these very properties pose severe machining challenges, such as accelerated tool wear, poor surface finish, and high cutting forces. Although several studies have investigated coatings, lubrication strategies, and process optimization, a comprehensive and up-to-date integration of these advancements is still lacking. To address this gap, a systematic review was conducted using Web of Science and Scopus databases. The inclusion criteria focused on peer-reviewed journal and conference articles published in the last eleven years (2014–2025), written in English, and directly addressing machining of nickel-based alloys, with particular emphasis on tool coatings, lubrication/cooling technologies, and machinability optimization. Exclusion criteria included duplicate records, non-English documents, papers lacking experimental or modeling results, and studies unrelated to tool life or coating performance. Following this screening process, 101 high-quality articles were selected for detailed analysis. The novelty of this work lies in synthesizing comparative insights across TiAlN, TiSiN, and CrAlSiN coatings, alongside advanced lubrication methods such as HPC, MQL, nano-MQL, and cryogenic cooling. Results highlight that CrAlSiN coatings retain hardness up to 36 ± 2 GPa after exposure to 700 °C and extend tool life by 4.2× compared to TiAlN, while optimized cooling strategies reduce flank wear by over 30% and improve tool longevity by up to 133%. The integration of coating performance, thermal stability, and lubrication effects into a unified framework provides actionable guidelines for machining optimization. The study concludes by proposing future research directions, including hybrid coatings, real-time process monitoring, and sustainable lubrication technologies, to bridge the remaining gaps in machinability and promote industrial adoption. This integrative approach establishes a robust foundation for advancing machining strategies of nickel-based superalloys, ensuring improved productivity, reduced costs, and enhanced component reliability. Full article

Explosions, including those from war weapons, terrorist attacks, etc., can lead to damage and overall collapse of bridges. However, there are no clear guidelines for anti-blast design and protective measures for bridges under blast loading in current bridge design specifications. With advancements in intelligent construction, precast segmental bridge piers have become a major trend in social development. There is a lack of full understanding of the anti-blast performance of precast segmental bridge piers. To study the engineering calculation method for blast load acting on a typical precast segmental reinforced concrete (RC) pier in near-field explosions, an air explosion test of the precast segmental RC pier is firstly carried out, then a fluid–structure coupling numerical model of the precast segmental RC pier is established and the interaction between the explosion shock wave and the precast segmental RC pier is discussed. A numerical simulation of the precast segmental RC pier in a near-field explosion is conducted based on a reliable numerical model, and the distribution of the blast load acting on the precast segmental RC pier in the near-field explosion is analyzed. The results show that the reflected overpressure on the pier and the incident overpressure in the free field are reliable. The simulation results are basically consistent with the experimental results (with a relative error of less than 8%), and the fluid–structure coupling model is reasonable and reliable. The explosion shock wave has effects of reflection and circulation on the precast segmental RC pier. In the near-field explosion, the back and side blast loads acting on the precast segmental RC bridge pier can be ignored in the blast-resistant design. The front blast loads can be simplified and equalized, and a blast-resistant design load coefficient (1, 0.2, 0.03, 0.02, and 0.01) and a calculation formula of maximum equivalent overpressure peak value (applicable scaled distance [0.175 m/kg^1/3, 0.378 m/kg^1/3]) are proposed, which can be used as a reference for the blast-resistant design of precast segmental RC piers. Full article

Background: Hamstring tendon autografts are frequently used for anterior cruciate ligament reconstruction (ACLR), but they are associated with persistent hamstring strength deficits and delayed functional recovery. Current rehabilitation guidelines often delay open kinetic chain (OKC) hamstring exercises due to safety concerns, despite the limited supporting evidence. This uncontrolled, underpowered, and exploratory study aimed to evaluate the safety and effectiveness of introducing OKC hamstring strengthening exercises as early as three weeks after ACLR. Methods: An exploratory retrospective observational study was conducted at a single physiotherapy center on 13 patients (aged 18–35) who underwent primary ACLR with semitendinosus–gracilis grafts. Participants followed a standardized rehabilitation program including isometric leg curls at 60° and 90° knee flexion and long-lever glute bridges twice weekly, starting from postoperative week 3. Safety was assessed through predefined “safety flags” (pain > 4/10, hematoma, clinical hamstring strain). Strength outcomes, including isometric knee flexion strength at 60° and 90°, limb symmetry index (LSI), and endurance tests, were assessed at 6 and 12 weeks. Results: All participants completed the program without major adverse events. Pain remained consistently low (median 2.5/10), with only one transient episode exceeding the threshold. No other complications were recorded. Isometric knee flexion strength significantly improved between week 6 and week 12 at both 60° (p = 0.018) and 90° (p = 0.003), with large effect sizes. LSI at 90° also increased significantly (p = 0.006), whereas improvements at 60° did not reach significance. Endurance testing showed functional gains as early as 6 weeks. Conclusions: The early introduction of OKC hamstring strengthening exercises three weeks after ACLR with hamstring autografts appears safe and promotes clinically meaningful improvements in strength and endurance. These findings, while from a small uncontrolled study, challenge conservative rehabilitation protocols and support the reconsideration of early hamstring loading. Given the retrospective, uncontrolled, and underpowered design, these findings are hypothesis-generating and not generalizable beyond young adults with hamstring autografts; larger randomized trials are required. Full article

Supply chain financing offers advantages over traditional channels such as bank loans and equity financing, including greater flexibility, lower transaction costs, and simplified approval procedures. However, when a firm’s sustainability faces uncertainty, access to supply chain financing may become constrained by multiple factors, including the risk tolerance of supply chain partners, market transparency, and corporate reputation. ESG, representing Environmental, Social, and Governance standards, is a critical framework for assessing corporate sustainability performance. Given that divergent ESG evaluations reflect disparate market assessments of a firm’s sustainable development capabilities, such divergence may affect supply chain financing by altering stakeholder trust dynamics. This research examines A-share listed firms in China (2016–2022) and reveals that divergence in ESG evaluations significantly inhibits firms’ access to supply chain financing. Mechanism validation suggests that divergent ESG evaluations amplify informational opacity, operational risks, and negative reputation, thereby influencing supply chain partners’ risk perceptions and trust levels. Heterogeneity analysis shows that corporate governance quality, regional trust levels, and ESG awareness modulate the negative impact of divergent ESG evaluations on supply chain financing. The asymmetric effects of divergent ESG evaluations on supply chain financing are further confirmed, with distinct manifestations between upstream suppliers and downstream customers. By bridging gaps in existing research on divergent ESG evaluations and supply chain finance, this work offers regulatory guidelines, operational recommendations for firms, and investment decision frameworks. Full article

Objectives: The aim of this study was to synthesize current evidence on artificial intelligence (AI) adoption in cardiovascular imaging across low- and middle-income countries (LMICs), highlighting diagnostic performance, implementation barriers, and potential solutions. Methods: We conducted a systematic review of PubMed, Embase, Cochrane Library, Web of Science, and Scopus for studies evaluating AI-based echocardiography, cardiac CT, or cardiac MRI in LMICs. Articles were screened according to PRISMA guidelines, and data on diagnostic outcomes, challenges, and enabling factors were extracted and narratively synthesized. Results: Twelve studies met the inclusion criteria. AI-driven methods frequently surpassed 90% accuracy in detecting coronary artery disease, rheumatic heart disease, and left ventricular hypertrophy, often enabling task shifting to non-expert operators. Challenges included limited dataset diversity, operator dependence, infrastructure constraints, and ethical considerations. Insights from high-income countries, such as automated segmentation and accelerated imaging, suggest potential for broader AI integration in cardiac MRI and CT. Conclusions: AI holds promise for enhancing cardiovascular care in LMICs by improving diagnostic accuracy and workforce efficiency. However, multi-center data sharing, targeted training, reliable infrastructure, and robust governance are essential for sustainable adoption. This review underscores AI’s capacity to bridge resource gaps in LMICs, offering practical pathways for future research, clinical practice, and policy development in global cardiovascular imaging. Full article

As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions. Full article

Given the construction industry’s significant contribution of approximately 39% of global CO₂ emissions, implementing effective carbon reduction strategies is becoming increasingly critical. In this context, Internet of Things (IoT) technologies present promising solutions for monitoring and reducing emissions. However, there is a lack of comprehensive understanding regarding specific IoT applications, implementation barriers, and opportunities for carbon reduction in construction practices. This study investigates the role of IoT in reducing carbon emissions in the construction industry. Following PRISMA guidelines, this study analyzed bibliometric data from Scopus and Web of Science databases using VOSviewer for science mapping visualization. Content analysis was conducted on 17 carefully selected articles to identify key research topics and applications. The analysis identified four mainstream application areas: (1) IoT-based smart monitoring systems for carbon emissions, (2) energy efficiency and management applications, (3) sustainable construction implementation frameworks, and (4) smart cities and other built environment applications. Key findings highlight growing research interest in IoT applications for sustainable construction, with China, the United States, and the United Kingdom leading collaborative efforts. Despite demonstrated carbon reduction potential, significant implementation barriers exist, including technical limitations, organizational resistance, skill gaps, and economic constraints. Key opportunities include Artificial Intelligence (AI) integration, Building information modeling (BIM)-IoT synergies, energy prosumer models, and standardization frameworks. This study provides the first focused review of IoT applications specifically targeting carbon reduction in construction, highlighting a critical technology-practice gap where organizational factors frequently outweigh technological barriers. A proposed socio-technical integration framework in this study bridges technical and organizational elements to overcome adoption barriers. Full article

This study addresses the systematic optimization of Low-Rank Adaptation (LoRA) parameters for architectural knowledge integration in diffusion models, where existing AI research has provided limited guidance for establishing plausible parameter ranges in architectural massing applications. While diffusion models show increasing utilization in architectural design, general models lack domain-specific architectural knowledge, and previous studies have offered insufficient hyperparameter optimization frameworks for architectural massing studies—fundamental components for expressing architectural knowledge. This research establishes a comprehensive LoRA training framework specifically for architectural mass generation, systematically evaluating caption detail levels, optimizers, learning rates, schedulers, batch sizes, and training steps. Through analysis of 220 architectural mass images representing spatial transformation operations, the study recommends the following parameter settings: detailed captions, Adafactor optimizer, learning rate 0.0003, constant scheduler, and batch size 4, achieving significant improvements in prompt-to-output fidelity compared to baseline approaches. The contribution of this study is not in introducing a new algorithm, but in providing a systematic application of LoRA in the architectural domain, serving as a bridging milestone for both emerging architectural-AI researchers and advanced scholars. The findings provide practical guidelines for integrating AI technologies into architectural design workflows, while demonstrating how systematic parameter optimization can enhance the learning of architectural knowledge and support architects in early-stage massing and design decision-making. Full article

The persistence of a binary biomedical framework in healthcare has become increasingly inadequate to address the realities of human diversity. Recent literature highlights how this dichotomous model reinforces inequities for transgender and intersex populations, sustaining barriers to access, stigmatisation, and poorer health outcomes. In this Perspective, I critically reflect on the limitations of the binary paradigm and draw on developments in science, clinical practice, education, and policy to propose a future-oriented approach to health equity. Emerging evidence underscores the complexity of sexual development as a spectrum and the urgent need to move from pathological frameworks toward affirming care based on rights. Key advances include the adoption of affirmative care models, reforms in medical curriculum, and the rise of inclusive research methodologies that capture gender diversity beyond binaries. However, structural barriers—such as rigid clinical protocols, outdated educational content, and insufficient policy alignment—continue to hinder meaningful change. This article advocates for systemic transformation in healthcare education, practice, and research. I outline strategic priorities for the field are the implementation of gender diversity in medical training, the implementation of rights-based clinical guidelines, and the design of inclusive methodologies that remove structural discrimination. These actions are essential to build a more precise, ethical and universally inclusive health system. Ultimately, ensuring sustainable and equitable outcomes requires bridging scientific innovation with human rights principles and focussing on the lived experiences of transgender and intersex individuals. Full article

Framed within the lens of systems theory and sociotechnical systems thinking, this systematic review examines telehealth as a complex adaptive system and dynamic health system shaped by the interactions between interconnected technological, social, and institutional components. Recognizing telehealth as part of a complex adaptive system, the review identifies how interdependent factors, such as digital literacy, connectivity, and policy, evolve and influence access to and the emergent properties of care. A systematic review was conducted following the PRISMA 2020 guidelines and PROSPERO registration (CRD420251103608), analyzing 42 peer-reviewed articles published between January 2020 and June 2025, identified through the MEDLINE, Web of Science, EBSCOhost, ACM Digital Library, PsycINFO, and Scopus databases. Key findings include sustained but reduced telehealth use after the pandemic peak, as well as a small yet statistically significant positive effect of telehealth interventions on cognitive emergent properties, defined here as measurable outcomes like memory, attention, executive function, and processing speed (SMD = 0.29; 95% CI [0.04, 0.54]) with very low heterogeneity (I² = 0%). Significant system components such as digital illiteracy, poor internet connectivity, and complex technology interfaces disproportionately affected economically disadvantaged, minority, and rural older adults. Practical strategies rooted in systems thinking include digital literacy programs, simplified interfaces, caregiver support, improved broadband infrastructure, hybrid healthcare models, and supportive policies. Future research should focus on evidence-based, system-level interventions across diverse settings to bridge the digital divide and promote equitable access to telehealth for older adults. Full article

Phytoremediation has emerged as a sustainable and cost-effective strategy for mitigating contamination in soil and water systems, utilizing plants and their associated microbial consortia to uptake, degrade, or immobilize pollutants. This review synthesizes findings from over 100 peer-reviewed publications and case studies to identify key parameters influencing phytoremediation efficiency, including contaminant bioavailability, chemical speciation, concentration levels (ranging from trace to >100 mg/L), plant species suitability, hydraulic retention time, and temperature ranges (10–35 °C). Despite its proven potential, the absence of standardized design frameworks limits consistent implementation and cross-site performance comparability. To address this, the study proposes a conceptual system design framework supported by measurable performance metrics—such as pollutant removal efficiency (often >70% for heavy metals) and biomass uptake capacity. The review further examines regulatory and policy gaps that hinder the technology’s integration into national remediation strategies, particularly in low- and middle-income countries. It underscores the need for technical guidelines, regulatory benchmarks, and protocols for post-treatment biomass management to enable safe, effective, and scalable deployment. By advocating a multi-stakeholder, evidence-based approach, the study aims to bridge the gap between scientific innovation and environmental governance, positioning phytoremediation as a viable tool for pollution control, ecosystem restoration, and alignment with global sustainability targets. Full article

Given the tourism industry’s pivotal role in the global economy and its substantial environmental and socio-cultural impacts, advancing sustainable tourism development has become imperative. While existing research has largely focused on theoretical perspectives and sectoral organizations have issued best-practice guidelines, few studies have examined how industry professionals actively influence and implement sustainability measures. Consequently, research that bridges theoretical analysis with practical application remains scarce. This research addresses this gap by proposing a strategic action plan to guide Spain’s tourism sector toward a more sustainable model. Using a mixed-methods approach, it combines insights from a Delphi panel of experts with an in-depth review of scholarly and professional literature. The findings lead to a set of targeted initiatives aligned with the eight axes of Spain’s Sustainable Tourism Strategy 2030, aimed at strengthening the social, economic, environmental, and institutional dimensions of national tourism development. The expert panel emphasized the need for stronger regulatory standards and improved public–private collaboration as critical enablers of sustainability. This study also identifies key structural shortcomings within the sector and provides actionable policy recommendations to support stakeholders and policymakers in advancing sustainable tourism practices in Spain. Full article