Search Results (946)

The concept of sustainable development has emerged as a global consensus, forged in response to environmental constraints and critical reflection on conventional growth-oriented paradigms. It now serves as the overarching framework for addressing climate, ecological, and socio-economic crises. In the period after the adoption of the Sustainable Development Goals (SDGs) in 2016, there was an observable trend of increased integration of these objectives into the strategic frameworks of national and subnational entities. However, global assessments have indicated a divergence between the progress achieved and the trajectory delineated by the SDGs. The Earth system is demonstrating signs of decreased resilience, with widening inequalities and the emergence of multiple crises, thereby hindering the implementation of the 2030 Agenda for Sustainable Development. As the 2030 deadline approaches, a fundamental question arises for global development governance: what should be the future of the SDGs beyond 2030? While insufficient progress has prompted debates over the adequacy of the SDG framework, fundamentally revising or replacing the SDGs would risk undermining a hard-won international consensus forged through decades of negotiation and institutional investment. Based on a comprehensive review of the historical evolution of the sustainable development concept, this study argues that the SDGs represent a rare and fragile achievement in global governance. While insufficient progress has sparked debates about their effectiveness, fundamentally revising or replacing the SDGs would jeopardize the hard-won international consensus forged through decades of negotiations and institutional investments. This study further analyzes the latest progress on the SDGs and identifies emerging risks, aiming to explore how to accelerate and optimize sustainable development pathways within the existing SDG framework rather than propose a new global goal system. Based on both global experience and practice in China, four interconnected strategic priorities—namely, economic reform, social equity, environmental justice, and technology sharing—are proposed as a comprehensive framework to accelerate SDG implementation and guide the transformation of development pathways towards a more just, low-carbon, and resilient future. Full article

Background: The global increase in orthopedic implant use—both for trauma fixation and arthroplasty—has profoundly transformed musculoskeletal surgery. As a consequence, fractures occurring in the presence of implants have become more frequent and clinically relevant. Yet, these injuries are currently described using highly heterogeneous terminology, including periprosthetic (fracture occurring in the presence of a prosthetic joint replacement) peri-implant (fracture occurring around an osteosynthesis or fixation device), implant-related, and hardware-related fractures (umbrella terms encompassing both prosthetic and fixation devices, used descriptively rather than classificatorily). This coexistence of multiple, context-specific terminologies hinders clinical communication, complicates registry documentation, and limits research comparability across orthopedic subspecialties. Because fractures occurring in the presence of orthopedic implants significantly alter load transfer, muscle force distribution, and musculoskeletal biomechanics, a clear and unified terminology is also relevant for muscle-focused research addressing implant–tissue interaction and functional recovery. Objective: This systematic review aimed to critically analyze the terminology used to describe fractures influenced by orthopedic implants, quantify the heterogeneity of current usage across anatomical regions and publication periods, and explore the rationale for adopting a unified umbrella term—“artificial fracture.” Methods: A systematic search was performed in PubMed, Scopus, and Web of Science from January 2000 to December 2024, following PRISMA guidelines. Eligible studies included clinical investigations, reviews, registry analyses, and consensus statements explicitly employing or discussing terminology related to implant-associated fractures. Data were extracted on publication characteristics, anatomical site, terminology employed, and classification systems used. Quantitative bibliometric and qualitative thematic analyses were conducted to assess frequency patterns and conceptual trends. Results: Of 1142 records identified, 184 studies met the inclusion criteria. The most frequent descriptor in the literature was periprosthetic fracture (68%), reflecting its predominance in arthroplasty-focused studies, whereas broader and more practical terms such as implant-related and peri-implant fracture were more commonly used in musculoskeletal and fixation-related research. Terminological preferences varied according to anatomical site and implant type, and no universally accepted, cross-anatomical terminology was identified despite multiple consensus efforts. Discussion and Conclusions: The findings highlight persistent heterogeneity in terminology describing fractures influenced by orthopedic implants. A transversal, descriptive framework may facilitate communication across subspecialties and support registry-level harmonization. Beyond orthopedic traumatology, this approach may also benefit muscle and musculoskeletal research by enabling more consistent interpretation of data related to muscle–bone–implant interactions, rehabilitation strategies, and biomechanical adaptation. Full article

The growing reliance on solar energy necessitates robust and interpretable forecasting models for stable grid management. Current research frequently employs Explainable AI (XAI) to glean insights from complex black-box models, yet the reliability and consistency of these explanations remain largely unvalidated. Inconsistent feature attributions can mislead grid operators by incorrectly identifying the dominant drivers of solar generation, thereby affecting operational planning, reserve allocation, and trust in AI-assisted decision-making. This study addresses this critical gap by conducting a systematic statistical evaluation of feature rankings generated by multiple XAI methods, including model-agnostic (SHAP, PDP, PFI, ALE) and model-specific (Split- and Gain-based) techniques, within a time-series regression context. Using a LightGBM model for one-day-ahead solar power forecasting across four sites in Calgary, Canada, we evaluate consensus and divergence using the Friedman test, Kendall’s W, and Spearman’s rank correlation. To ensure the generalizability of our findings, we further validate the results using a CatBoost model. Our results show a strong overall agreement across methods (Kendall’s W: 0.90–0.94), with no statistically significant difference in ranking (p > 0.05). However, pairwise analysis reveals that the “Split” method frequently diverges from other techniques, exhibiting lower correlation scores. These findings suggest that while XAI consensus is high, relying on a single method—particularly the split count—poses risks. We recommend employing multi-method XAI and using agreement as an explicit diagnostic to ensure transparent and reliable solar energy predictions. Full article

Large multi-UAV mission systems operate over time-varying communication graphs with heterogeneous platforms, where classical distributed task assignment may incur excessive message passing and suboptimal task–resource matching. To address these challenges, this paper proposes CLAC-CBBA (Centrality-Driven and Load-Aware Adaptive Clustering CBBA), an enhanced variant of the Consensus-Based Bundle Algorithm (CBBA) for large heterogeneous swarms. The proposed method is biomimetic in the sense that it integrates swarm-inspired self-organization and load-aware self-regulation to improve scalability and robustness, resembling decentralized role emergence and negative-feedback workload balancing in natural swarms. Specifically, CLAC-CBBA first identifies key nodes via a centrality-based adaptive cluster-reconfiguration mechanism (CenCluster) and partitions the network into cooperation domains to reduce redundant communication. It then applies a load-aware cluster self-regulation mechanism (LCSR), which combines resource attributes and spatial information, uses K-medoids clustering, and triggers split/merge reconfiguration based on real-time load imbalance. CBBA bidding is executed locally within clusters, while anchors and cluster representatives synchronize winners/bids to ensure globally consistent, conflict-free assignments. Simulations across diverse network densities and swarm sizes show that CLAC-CBBA reduces communication overhead and runtime while improving total task score compared with CBBA and several advanced variants, with statistically significant gains. These results demonstrate that CLAC-CBBA is scalable and robust for large-scale heterogeneous UAV task allocation. Full article

Understanding how moral and emotional language operates in paid social advertising is essential for evaluating persuasion and its ethical contours. We provide a descriptive map of Moral Foundations Theory (MFT) language in Meta ad copy (Facebook/Instagram) drawn from seven global beverage brands across eight English-speaking markets. Using the moralstrength toolkit, we implement a two-channel pipeline that combines an unsupervised semantic estimator (SIMON) with supervised classifiers, enforces a strict cross-channel consensus rule, and adds a non-overriding purity diagnostic to reduce attribute-based false positives. The corpus comprises 758 text units, of which only 25 ads (3.3%) exhibit strong consensus, indicating that much of the copy is either non-moral or linguistically ambiguous. Within this high-consensus subset, the distribution of moral cues varies systematically by brand and category, with loyalty, fairness, and purity emerging as the most prominent frames. A valence pass (VADER) indicates that moralized copy tends toward negative valence, yet it may still yield a constructive overall tone when advertisers follow a crisis–resolution structure in which high-intensity moral cues set the stakes while surrounding copy positions the brand as the solution. We caution that text-only models undercapture multimodal signaling and that platform policies and algorithmic recombination shape which moral cues appear in copy. Overall, the study demonstrates both the promise and the limits of current text-based MFT estimators for advertising: they support transparent, reproducible mapping of moral rhetoric, but future progress requires multimodal, domain-sensitive pipelines, policy-aware sampling, and (where available) impression/spend weighting to contextualize descriptive labels. Full article

Introduction: The appropriate use of antibiotic prophylaxis (AP) in surgical procedures is an ongoing debate. There is a lack of evidence, and urological guidelines provide limited, procedure-specific recommendations. Our aim was to develop a generic model of an audit to define the need for AP in urological procedures, as well as in other surgical specialties. Material and Methods: Based on our experience with the Global Prevalence of Infections in Urology (GPIU) study and a literature review, we defined benchmark standards for 30-day infection rates, including sepsis, and estimated the number of patients needed to be included in a comparative study of AP versus no AP for a surgical procedure within one year. The generic study model was developed during a modified consensus process within the UTISOLVE research group. Urology departments giving and not giving AP were invited to join our development project as an extension of GPIU. Results: Radical prostatectomy was used as a model procedure. Ca. 60 urology centers performing more than 50 radical prostatectomies per year signed up. There was variation in AP practice among sites. Our own review showed that infection rates were ca. 5%, with severe infections, including sepsis, occurring in <0.5% of cases. A sample of 1825 patients would be required to achieve a 95% confidence interval half-width of ±1.0% for general infections. For sepsis, assuming an incidence of 0.5%, a sample of 2124 patients would be needed to reach a 95% confidence interval precision of ±0.30%. Enrollment of 2070 consecutive procedures would be needed to yield precisions of ±0.94% for infection and ±0.30% for sepsis. Based on the number of procedures performed and the number of interested study sites, we agreed on a prospective, multi-center, non-interventional service evaluation, expected to collect standardized data over a 3-month period. The primary outcome was defined as the 30-day incidence of infectious complications. All patients will undergo 30-day post-procedure follow-up through routine clinical care pathways. Conclusions: Our audit model is based on benchmarking of relevant outcomes. It defines how to assess AP in surgical procedures and clarifies a series of issues necessary to defend the status of a generic study model. We regard DEEP-URO to be a comprehensive, multi-center-based initiative that will help balance infection prevention with antimicrobial stewardship and improve the quality of clinical practice and personalized medicine. Full article

Background: Irisin, an exercise-induced myokine, has emerged as a potent neuroprotective factor, though a systematic synthesis of its role across neurological disorders is lacking. This review systematically evaluates clinical and preclinical evidence on irisin’s association with neurological diseases and its underlying mechanisms. Methods: Following PRISMA 2020 guidelines, a systematic search of PubMed/MEDLINE, Scopus, Web of Science, Embase, and Cochrane Library was conducted. The review protocol was prospectively registered in PROSPERO. Twenty-one studies were included, comprising predominantly preclinical evidence (n = 14), alongside clinical observational studies (n = 6), and a single randomized controlled trial (RCT) investigating irisin in cerebrovascular diseases, Parkinson’s disease (PD), Alzheimer’s disease (AD), and other neurological conditions. Eligible studies were original English-language research on irisin or FNDC5 and their neuroprotective effects, excluding reviews and studies without direct neuronal outcomes. Risk of bias was independently assessed using SYRCLE, the Newcastle–Ottawa Scale, and RoB 2, where disagreements between reviewers were resolved through discussion and consensus. Results were synthesized narratively, integrating mechanistic, pre-clinical, and clinical evidence to highlight consistent neuroprotective patterns of irisin across disease categories. Results: Clinical studies consistently demonstrated that reduced circulating irisin levels predict poorer outcomes. Lower serum irisin was associated with worse functional recovery and post-stroke depression after ischemic stroke, while decreased plasma irisin in PD correlated with greater motor severity, higher α-synuclein, and reduced dopamine uptake. In AD, cerebrospinal fluid irisin levels were significantly correlated with global cognitive efficiency and specific domain performance, and correlation analyses within studies suggested a closer association with amyloid-β pathology than with markers of general neurodegeneration. However, diagnostic accuracy metrics (e.g., AUC, sensitivity, specificity) for irisin as a standalone biomarker are not yet established. Preclinical findings revealed that irisin exerts neuroprotection through multiple mechanisms: modulating microglial polarization from pro-inflammatory M1 to anti-inflammatory M2 phenotype, suppressing NLRP3 inflammasome activation, enhancing autophagy, activating integrin αVβ5/AMPK/SIRT1 signaling, improving mitochondrial function, and reducing neuronal apoptosis. Irisin administration improved outcomes across models of stroke, PD, AD, postoperative cognitive dysfunction, and epilepsy. Conclusions: Irisin represents a critical mediator linking exercise to brain health, with consistent neuroprotective effects across diverse neurological conditions. Its dual ability to combat neuroinflammation and directly protect neurons, demonstrated in preclinical models, positions it as a promising therapeutic candidate for future investigation. Future research must prioritize the resolution of fundamental methodological challenges in irisin measurement, alongside investigating pharmacokinetics and sex-specific effects, to advance irisin toward rigorous clinical evaluation. Full article

Background: Computer-guided static implant surgery (CGSIS) is widely adopted to enhance the precision of dental implant placement. However, significant heterogeneity in reported accuracy values complicates evidence-based clinical decision-making. This variance is likely attributable to a fundamental lack of standardization in the methodologies used to assess dimensional accuracy. Objective: This scoping review aimed to systematically map, synthesize, and analyze the clinical methodologies used to quantify the dimensional accuracy of CGSIS. Methods: The review was conducted in accordance with the PRISMA-ScR guidelines. A systematic search of PubMed/MEDLINE, Scopus, and Embase was performed from inception to October 2025. Clinical studies quantitatively comparing planned versus achieved implant positions in human patients were included. Data were charted on study design, guide support type, data acquisition methods, reference systems for superimposition, measurement software, and accuracy metrics. Results: The analysis of 21 included studies revealed extensive methodological heterogeneity. Key findings included the predominant use of two distinct reference systems: post-operative CBCT (n = 12) and intraoral scanning with scan bodies (n = 6). A variety of proprietary and third-party software packages (e.g., coDiagnostiX, Geomagic, Mimics) were employed for superimposition, utilizing different alignment algorithms. Critically, this heterogeneity in measurement approach directly manifests in widely varying reported values for core accuracy metrics. In addition, the definitions and reporting of core accuracy metrics—specifically global coronal deviation (range of reported means: 0.55–1.70 mm), global apical deviation (0.76–2.50 mm), and angular deviation (2.11–7.14°)—were inconsistent. For example, these metrics were also reported using different statistical summaries (e.g., means with standard deviations or medians with interquartile ranges). Conclusions: The comparability and synthesis of evidence on CGSIS accuracy are significantly limited by non-standardized measurement approaches. The reported ranges of deviation values are a direct consequence of this methodological heterogeneity, not a comparison of implant system performance. Our findings highlight an urgent need for a consensus-based minimum reporting standard for future clinical research in this field to ensure reliable and translatable evidence. Full article

Traditional Human Resource Management (HRM) systems are criticized for lacking transparency, being inefficient, and offering ample opportunities for fraud because of their centralized design and reliance on manual processes. This work proposes a blockchain-enabled framework for HRM that enhances the transparency, trust, and global mobility of talents by integrating distributed ledgers, consensus protocols, and smart contract networks into Human Resources (HR) functions. A four-layer theoretical model—data, consensus, smart contract, and application layers—is developed and comparatively examined against traditional HR systems to show how blockchain principles can be systematically mapped into HR processes. This study shows how blockchain-driven HRM can ensure tamper-evident employee records, automate contractual and payroll operations, and enhance auditability and compliance. By informing the framework with established technology adoption perspectives, this paper extends both the theoretical and managerial understanding of blockchain in HR. In comparison with previous studies that were limited to either recruitment or credential verification, this article presents an overarching, cross-layer synthesis that connects blockchain architectures with end-to-end HR functions, thus providing a clear conceptual foundation for its future enterprise adoption in the digital economy. Full article

The convergence of artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics is transforming the governance, sustainability, and resilience of modern banking ecosystems. This study provides a multivariate bibliometric analysis using Principal Component Analysis (PCA) of research indexed in Scopus and Web of Science to explore how decentralized digital infrastructures and AI-driven analytical capabilities contribute to sustainable financial development, transparent governance, and climate-resilient digital societies. Findings indicate a rapid increase in interdisciplinary work integrating Distributed Ledger Technology (DLT) with large-scale data processing, federated learning, privacy-preserving computation, and intelligent automation—tools that can enhance financial inclusion, regulatory integrity, and environmental risk management. Keyword network analyses reveal blockchain’s growing role in improving data provenance, security, and trust—key governance dimensions for sustainable and resilient financial systems—while AI/ML and big data analytics dominate research on predictive intelligence, ESG-related risk modeling, customer well-being analytics, and real-time decision support for sustainable finance. Comparative analyses show distinct emphases: Web of Science highlights decentralized architectures, consensus mechanisms, and smart contracts relevant to transparent financial governance, whereas Scopus emphasizes customer-centered analytics, natural language processing, and high-throughput data environments supporting inclusive and equitable financial services. Patterns of global collaboration demonstrate strong internationalization, with Europe, China, and the United States emerging as key hubs in shaping sustainable and digitally resilient banking infrastructures. By mapping intellectual, technological, and collaborative structures, this study clarifies how decentralized intelligence—enabled by the fusion of AI/ML, blockchain, and big data—supports secure, scalable, and sustainability-driven financial ecosystems. The results identify critical research pathways for strengthening financial governance, enhancing climate and social resilience, and advancing digital transformation, which contributes to more inclusive, equitable, and sustainable societies. Full article

To address the challenges of inefficient convergence in UAV swarms under complex environments due to static position allocation (SPA), as well as the tendency of traditional artificial potential field (APF) obstacle avoidance to get stuck in local optima, this paper proposes a formation control method (DPAF-SA) based on dynamic position allocation (DPA) and APF-SA fusion, grounded in the principle of consensus and the simulated annealing (SA) algorithm. First, the formation position allocation is formulated as an online combinatorial optimization problem. Based on this framework, a dynamic position allocation and dynamic virtual center mechanism is designed to solve the optimal “UAV-position point” mapping in real time, minimizing the total convergence cost of the swarm. Second, to address the local optimum trap and decoupling issues in APF, the global search capability and probabilistic jump mechanism of SA are integrated into APF. This enables optimization of the consistency control input, ensuring tight coupling between efficient obstacle avoidance and formation maintenance. Finally, a high-fidelity HIL simulation platform based on Unity3D 2022.3.2. was established to validate the engineering feasibility and real-time robustness of the proposed algorithm. Simulation results demonstrate that, compared with the representative baseline model, the proposed method achieves improvements of approximately 46.1%, 24.5%, and 39.6% in formation accuracy, convergence performance, and safety margin, respectively, validating its effectiveness. Full article

As the goal of carbon peak and carbon neutrality becomes a global consensus, the circular economy is gradually evolving from an environmental concept to a core lever for national strategy and industrial transformation. To achieve green and low-carbon development, China is accelerating the construction of a circular economy system, particularly in the fields of resource recycling and utilization. Industrial waste heat, a strategically critical supplementary energy resource, performs a pivotal role in advancing the circular economy. Based on an energy technology coupling model, this study assesses the waste heat utilization potential in China and quantitatively measures its impact on energy conservation and carbon reduction. The results show that: (1) The potential of industrial waste heat in China is characterized by an inverted U-shaped trajectory. Over the near-to-medium term, the steel and power industries remain the primary contributors to waste heat utilization potential. (2) Low-grade waste heat represents the majority of utilization potential in China’s industrial sector, mainly from power generation, fuel processing, and steel manufacturing. The model results indicate that the proportion of low temperature waste heat will increase from approximately 66% in 2025 to 83% in 2060. (3) Waste heat utilization significantly influences the energy transition pathway. The findings of this study demonstrate that energy-intensive industries have the potential to reduce primary energy consumption by more than 13%. Moreover, making full use of waste heat could accelerate China’s carbon peaking target to 2028, and reduce peak carbon emissions by an estimated 5.1%. Full article

Dynamic heterogeneous redundancy (DHR) architectures combine heterogeneity, redundancy, and dynamism to create security-centric frameworks that can be used to mitigate network attacks that exploit unknown vulnerabilities. However, conventional DHR architectures rely on centralized control modules for scheduling and adjudication, leading to significant single-point failure risks and trust bottlenecks that severely limit their deployment in security-critical scenarios. To address these challenges, this paper proposes a decentralized DHR architecture based on the Raft consensus algorithm. It deeply integrates the Raft consensus mechanism with the DHR execution layer to build a consensus-centric control plane and designs a dual-log pipeline to ensure all security-critical decisions are executed only after global consistency via Raft. Furthermore, we define a multi-dimensional attacker model—covering external, internal executor, internal node, and collaborative Byzantine adversaries—to analyze the security properties and explicit defense boundaries of the architecture under Raft’s crash-fault-tolerant assumptions. To assess the effectiveness of the proposed architecture, a prototype consisting of five heterogeneous nodes was developed for thorough evaluation. The experimental results show that, for non-Byzantine external and internal attacks, the architecture achieves high detection and isolation rates, maintains high availability, and ensures state consistency among non-malicious nodes. For stress tests in which a minority of nodes exhibit Byzantine-like behavior, our prototype preserves log consistency and prevents incorrect state commitments; however, we explicitly treat these as empirical observations under a restricted adversary rather than a general Byzantine fault tolerance guarantee. Performance testing revealed that the system exhibits strong security resilience in attack scenarios, with manageable performance overhead. Instead of turning Raft into a Byzantine-fault-tolerant consensus protocol, the proposed architecture preserves Raft’s crash-fault-tolerant guarantees at the consensus layer and achieves Byzantine-resilient behavior at the execution layer through heterogeneous redundant executors and majority-hash validation. To support evaluation during peer review, we provide a runnable prototype package containing Docker-based deployment scripts, pre-built heterogeneous executors, and Raft control-plane images, enabling reviewers to observe and assess the representative architectural behaviors of the system under controlled configurations without exposing the internal source code. The complete implementation will be made available after acceptance in accordance with institutional IP requirements, without affecting the scope or validity of the current evaluation. Full article

This paper presents a distributed smoothing neurodynamic approach for solving the $L_1$-$L_p$ minimization problem, with application to robust and collaborative multi-view three-dimensional (3D) space localization. To handle the non-Lipschitz continuity gradients, a smooth approximation technique is introduced, yielding a distributed neurodynamic model that integrates classical smoothing neural networks with multi-agents consensus theory. Theoretical analysis guarantees the global convergence of each agent’s state to the optimal solution. The stability and convergence of the proposed approaches are rigorously proved using Lyapunov theory. Numerical experiments on multi-view 3D space localization in the presence of measurement noise demonstrate the method’s effectiveness and practical value for distributed visual computing. Full article

Background: In common clinical practice, cardiac overload is still often overlooked in patients with chronic obstructive pulmonary disease (COPD) despite its substantial impact on clinical outcomes and mortality. This study aimed to assess the prevalence of cardiac overload and heart failure (HF) risk, using N-terminal pro-B-type natriuretic peptide (NT-proBNP), in older COPD patients eligible for single-inhaler triple therapy (SITT) and without history of overt HF. We also evaluated changes in NT-proBNP after 3 months of SITT. Methods: This multicenter observational study included 165 older outpatients with a recent moderate-to-severe acute exacerbation of COPD (AECOPD), categorized as ‘Group E’ according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD). Patients were stratified for the presence of cardiac overload and HF risk using age- and comorbidity-adjusted NT-proBNP thresholds, as recommended by the 2023 Clinical Consensus Statement of the Heart Failure Association (HFA) of the European Society of Cardiology (ESC). NT-proBNP was measured at baseline and after three months of SITT (116 patients with available test at three months). Results: Mean age was 80.7 ± 9.7 years. Patients with NT-proBNP levels indicative of “HF likely” and “HF very high-risk” were 43.0% and 24.2%, respectively. After 3 months of SITT, NT-proBNP significantly decreased by 7.2% (95%CI 9.0–5.4%, p < 0.001), with the largest reductions observed in younger patients [11.0% (95% CI 14.1–7.2%) ≤ 76 years old, 8.4% (95% CI −11.3–5.5%) in 77–87 years old, −3.0% (95% CI −6.1–0.0%) in ≥88 years old, p for interaction = 0.007]. Conclusions: In real-life clinical practice, a substantial proportion of older patients with GOLD Group E COPD had elevated NT-proBNP, suggestive of cardiac overload and high risk of HF. The early identification of these patients may prompt further cardiologic evaluation and management. After SITT and before cardiology evaluation, a significant NT-proBNP reduction has been observed, suggesting potential cardiovascular benefit of SITT. Full article