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By greatly increasing automation, accuracy, and flexibility at every step of the additive manufacturing process, from design and production to quality assurance, artificial intelligence (AI) is revolutionizing the 3D printing industry. The integration of AI algorithms into 3D printing systems enables real-time optimization of print parameters, accurate prediction of material behavior, and early defect detection using computer vision and sensor data. Machine learning (ML) techniques further streamline the design-to-production pipeline by generating complex geometries, automating slicing processes, and enabling adaptive, self-correcting control during printing—functions that align directly with the principles of Industry 4.0/5.0, where cyber-physical integration, autonomous decision-making, and human–machine collaboration drive intelligent manufacturing systems. Along with improving operational effectiveness and product uniformity, this potent combination of AI and 3D printing also propels the creation of intelligent manufacturing systems that are capable of self-learning. This confluence has the potential to completely transform sectors including consumer products, healthcare, construction, and aerospace as it develops. This comprehensive review explores how AI enhances the capabilities of 3D printing, with a focus on process optimization, defect detection, and intelligent control mechanisms. Moreover, unresolved challenges are highlighted—including data scarcity, limited generalizability across printers and materials, certification barriers in safety-critical domains, computational costs, and the need for explainable AI. Full article

Stricter requirements on nutrient removal in wastewater treatment are being imposed by rapid urbanization and tightening water-quality standards. Despite their excellent solid–liquid separation and effective biological treatment, MBRs in conventional operation remain hindered by membrane fouling, limited robustness to influent variability, and elevated energy consumption. In recent years, precise process control and resource-oriented operation have been enabled by the integration of artificial intelligence (AI) with MBRs. Advances in four areas are synthesized in this review: optimization of MBR control architectures, intelligent adaptation to multi-source wastewater, regulation of membrane operating parameters, and enhancement of nitrogen and phosphorus removal. According to reported studies, increases in total nitrogen and total phosphorus removal have been achieved by AI-driven strategies while energy use and operating costs have been reduced; under heterogeneous influent and dynamic operating conditions, stronger generalization and more effective real-time regulation have been demonstrated relative to traditional approaches. For large-scale deployment, key challenges are identified as improvements in model interpretability and applicability, the overcoming of data silos, and the realization of multi-objective collaborative optimization. Addressing these challenges is regarded as central to the realization of robust, scalable, and low-carbon intelligent wastewater treatment. Full article

In the knowledge economy era, optimizing R&D team size is crucial for breakthrough innovation. Breakthrough technologies rely more on knowledge restructuring and technological leaps than general technologies do. However, it remains unclear whether breakthrough technology formation follows a simple “more people, more power” logic within technological systems. This work examines 35,955 patents in recommendation system technology to propose a relationship model between collaboration scale and breakthrough technological innovation based on patent data from the recommendation system field. It aims to elucidate how collaboration scale influences breakthrough technological innovation through knowledge restructuring, thereby providing theoretical support and practical guidance for enterprises, institutions, and governments in innovation activities to advance technological innovation. The findings reveal three key points: (1) The relationship between collaboration scale and breakthrough innovation is not linear but follows an inverted U-shaped curve; (2) Knowledge recombination significantly mediates this relationship, also exhibiting an inverted U-shaped pattern with collaboration scale; (3) The inverted U-shaped effect of collaboration scale on breakthrough innovation varies by country. The optimal thresholds are 14.058 entities for China, 57.151 entities for the United States, and 4.801 entities for Russia. This work breaks through the limitations of the traditional theoretical framework and constructs a three-dimensional analysis framework of “collaboration scale → knowledge recombination → breakthrough technological innovation”. By introducing the mediating variable of knowledge recombination, this paper reveals the mechanism of R&D team size on radical innovation. It provides a theoretical basis for the construction of an innovation team and provides a theoretical basis for enterprises, governments, and institutions. Full article

In large-scale e-commerce, recommendation systems must overcome the shortcomings of conventional models, which often struggle to convert user interest into purchases. This study proposes a revenue-driven recommendation approach that explicitly incorporates user price sensitivity. This study introduces a hybrid recommendation engine that combines collaborative filtering (CF), best match 25 (BM25) for textual relevance, and a price-similarity algorithm. The system is deployed within a scalable three-tier architecture using Elasticsearch and Redis to maintain stability under high-traffic conditions. The system’s performance was evaluated through a large-scale A/B test against both a CF-only model and a popular-item baseline. Results showed that while the CF-only model reduced revenue by 5.10%, our hybrid system increased revenue by 5.55% and improved click-through rate (CTR) by 2.55%. These findings demonstrate that integrating price similarity is an effective strategy for developing commercially viable recommendation systems that enhance both user engagement and revenue growth on large online platforms. Full article

Improving green total factor productivity (GTFP) is critical for balancing economic benefits and ecological constraints. While most existing studies emphasize the pivotal role of governments in GTFP enhancement, they predominantly treat governments as homogeneous entities, overlooking the fundamental premise of local government attention allocation. Analyzing 2010–2020 data from 285 Chinese cities, this study reveals that increased local government green attention significantly stimulates GTFP through three channels: fostering green technology collaboration among firms, deepening green involvement of public research institutions, and elevating green innovation quality. Heterogeneity analyses demonstrate amplified effects in cities characterized by intense intergovernmental competition, stringent intellectual property protection, robust fiscal capacity, and advanced technological infrastructure, but attenuated impacts in resource-dependent regions with heavy reliance on extractive industries. Full article

Background: Neuroimaging-based diagnostic approaches are of critical importance for the accurate diagnosis and treatment of major depressive disorder (MDD). However, multisite neuroimaging data often exhibit substantial heterogeneity in terms of scanner protocols and population characteristics. Moreover, concerns over data ownership, security, and privacy make raw MRI datasets from multiple sites inaccessible, posing significant challenges to the development of robust diagnostic models. Federated learning (FL) offers a privacy-preserving solution to facilitate collaborative model training across sites without sharing raw data. Methods: In this study, we propose the personalized Federated Gradient Matching and Contrastive Optimization (pF-GMCO) algorithm to address domain shift and support scalable MDD classification using multimodal MRI. Our method incorporates gradient matching based on cosine similarity to weight contributions from different sites adaptively, contrastive learning to promote client-specific model optimization, and multimodal compact bilinear (MCB) pooling to effectively integrate structural MRI (sMRI) and functional MRI (fMRI) features. Results and Conclusions: Evaluated on the Rest-Meta-MDD dataset with 2293 subjects from 23 sites, pF-GMCO achieved accuracy of 79.07%, demonstrating superior performance and interpretability. This work provides an effective and privacy-aware framework for multisite MDD diagnosis using federated learning. Full article

In the context of digital school development, the leadership practices of school leadership teams play a significant role. If leadership teams want to enact leadership practices effectively, they require strong connections to the entire teaching staff as well as close contact with other key actors in the digital process. Since little is known about these connection patterns of school leadership teams, this study aims to uncover them. The aim is to provide practical advice to school administrators and schools regarding digital school development, and to derive concrete recommendations for action concerning their relationships and management. To this end, we examined the social networks of the teaching staff of 13 German secondary schools (N = 817 teachers) by asking all the teachers to complete a questionnaire about their contacts in relation to digital school development. We conducted a social network analysis and extracted various network metrics pertaining to the school leadership teams of these institutions, considering not only their integration within the overall network but also their connections with a pivotal stakeholder: the digital coordinator. To contextualize our findings, we compared the network metrics of the two different professional target groups using t-tests. The results reveal significant variability in the connection patterns of school leadership teams across different schools. Furthermore, our analysis indicates that digital coordinators consistently exhibit higher levels of connectedness within the realm of digital school development than the members of the school leadership teams. These findings highlight the importance of close collaboration between school leadership teams and the digital coordinator in order to advance digital school development. It is also suggested that school leadership teams should consider delegating more responsibilities to the digital coordinator, particularly those necessitating close collaboration with the teaching staff. Full article

To counter the high unmet need for family planning in urban areas of Sindh province, Pakistan, Greenstar Social Marketing began implementation of The Challenge Initiative (TCI) in collaboration with the government departments of Population Welfare and Health in eight urban districts of Sindh province. This study aimed to assess the effectiveness of TCI’s Community Health Volunteers (CHVs) on public sector service provision of family planning services in eight urban districts of Sindh province, Pakistan. The Contraceptive Logistics Management Information System (cLMIS) and District Health Information System 2 (DHIS2) were used to obtain monthly contraceptive data from June 2022 to December 2024. CHVs began implementation at different time points in each district, starting from January 2023 to October 2023, when CHVs became operational in all eight districts. Descriptive statistics and two-sample t-tests were used for data analysis. CHVs significantly improved family planning service provision, particularly for short- and long-acting methods at the facility level, with greater change observed in Department of Health facilities. This study provides preliminary evidence of the effectiveness of CHVs in increasing public sector service provision of contraceptives, particularly for Department of Health facilities. CHVs bridge the gap between the community and the facility, particularly in areas uncovered by the government’s existing mobilization staff. Full article

Competitive programming has emerged as a critical benchmark for evaluating large language models (LLMs) in solving algorithmic problems under competitive conditions. Existing methods, such as the Sequential One-Agent Pipeline (SOP) approach, suffer from significant limitations, including the inability to effectively manage semantic drift across multiple stages, a lack of coordinated adversarial testing, and suboptimal final solutions. These issues lead to high rates of wrong answer (WA) and time-limit exceeded (TLE) errors, especially on complex problems. In this paper, we propose the Cross-Verification Collaboration Protocol (CVCP), a multi-agent framework that integrates symmetry detection, symmetry-guided adversarial testing, Round-Trip Review Protocol (RTRP), and Asynchronous Voting Resolution (AVR) to address these shortcomings. We evaluate our method on the CodeELO dataset, showing significant improvements in performance, with Elo Ratings increasing by up to 7.1% and Pass Rates for hard problems improving by as much as 1.8 times compared to the SOP baseline. Full article

Coronary artery disease (CAD), a major contributor to healthcare burdens worldwide, is closely linked with chronic inflammation. Probiotic supplementation has been investigated for its potential to modulate inflammatory responses, yet its role in patients with CAD remains unclear. To address this, we conducted a systematic review and meta-analysis following PRISMA guidelines, with literature searches performed across PubMed, EMBASE, and Cochrane CENTRAL up to 19 March 2025. Eligible studies included randomized controlled trials that examined the effects of probiotics, prebiotics, or synbiotics in patients with CAD or ischemic heart disease. Study quality was assessed using the Cochrane Collaboration tool, and standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated for pooled outcomes. A total of five randomized controlled trials involving 256 patients with CAD were included. The meta-analysis demonstrated significant improvements in inflammatory biomarkers among participants receiving probiotics compared with those in the placebo group. Specifically, probiotic supplementation led to greater reductions in high-sensitivity C-reactive protein (pooled SMD [pSMD] = −0.61; 95% confidence interval [CI]: −0.87 to −0.36) and malondialdehyde (pSMD = −0.52; 95% CI: −0.91 to −0.12). No significant increase was observed in nitric oxide (pSMD = 0.91; 95% CI: −3.72 to 5.54) or total antioxidant capacity (pSMD = 0.35; 95% CI: −2.16 to 2.86) in the intervention group over control. No significant difference was found in glutathione levels between the two groups (pSMD = 0.01; 95% CI: −0.51 to 0.53). Overall, these findings suggest that probiotic supplementation exerts a beneficial effect on inflammatory status in patients with CAD. The evidence highlights its potential in reducing systemic inflammation and oxidative stress, as reflected by improvements in high-sensitivity C-reactive protein and malondialdehyde. Full article

The aim of this study is to examine the role and use of augmented reality in engineering education by examining the existing literature. A total of 235 studies from Scopus and Web of Science published during 2011–2025 were examined. The study focused on analyzing the main characteristics of the studies, identifying the main topics, and exploring the use of augmented reality in engineering education. The study also highlighted current challenges and limitations and suggested future research directions. Based on the results, 7 main topics arose which were related to (i) Immersive technologies in engineering education, (ii) Gamified learning experiences, (iii) Remote and virtual laboratories, (iv) Visualization and 3D modeling, (v) Student motivation, (vi) Collaborative and interactive learning environments, and (vii) User-centered design and user experience. Augmented reality emerged as an effective educational tool that can positively impact engineering education and support both students and teachers. Specifically, physical, remote, and virtual laboratories that can improve students’ learning performance, motivation, creativity, engagement, and satisfaction can be created through augmented reality. Using augmented reality, students can develop their practical skills and knowledge within low-risk and secure learning environments. Additionally, via the realistic and interactive visualization, students’ knowledge acquisition and understanding can be enhanced. Finally, its ability to effectively support collaborative learning and experiential learning arose. Full article

Corporate greenwashing has emerged as a pervasive and systemic threat to global sustainability efforts, undermining regulatory effectiveness and obstructing progress toward multiple United Nations Sustainable Development Goals. As environmental opportunism increasingly diffuses across interconnected industrial supply networks, it evolves from isolated corporate misconduct into a chain-level governance challenge with significant systemic risks. Traditional governance mechanisms—whether market-based self-regulation or top-down administrative control—have proven insufficient, while the effectiveness of hybrid approaches integrating administrative coordination with market dynamics remains largely unexplored. This study investigates China’s Chain Leader Policy, a novel hybrid governance model that combines formal administrative authority with market coordination mechanisms to systematically address environmental opportunism across industrial supply networks, and its impact on mitigating greenwashing. Employing a multi-period difference-in-differences design on 12,334 firm-year observations of Chinese A-share listed companies from 2011 to 2023, we find that the policy reduces corporate greenwashing by 10.8% through four pathways: stabilizing supply–demand relationships, reducing coordination costs, fostering green collaborative innovation, and enhancing external scrutiny via social networks. Coercive isomorphism strengthens these effects, while mimetic isomorphism weakens them; impacts are more pronounced in state-owned enterprises, firms with stronger green awareness and higher levels of internationalization, and in more concentrated industries. By operationalizing embedded autonomy theory in an environmental governance context, this research extends theoretical understanding of hybrid governance mechanisms, offers robust empirical evidence for designing policies to curb greenwashing, and provides a replicable framework for achieving corporate environmental sustainability worldwide. Full article

Background/Objectives: Psychosocial care for siblings and caregivers of youth with cancer (SCYC) is a critical yet under-implemented component of comprehensive pediatric oncology care, as outlined by the Standards for Psychosocial Care for Children with Cancer and Their Families. Despite evidence supporting psychosocial interventions, such as integrative care interventions, as effective for stress mitigation and coping, barriers to implementation include revenue-generating funding models and siloed psychosocial disciplines, which hinder accessibility for adult caregivers within pediatric institutions and geographically dispersed families. This manuscript describes the relevant extant literature as well as a model for leveraging short-term funding opportunities and interdisciplinary collaboration to develop integrative care programs for these underserved groups. Methods: Philanthropic funding supported part-time child life specialist and creative arts therapist deployment to develop and implement integrative virtual group programs, as well as interdisciplinary integrative programs, to serve SCYC. Attendance, engagement, and qualitative feedback were used for program iteration and supported the transition to institutional funding. Results: Integrative programs provided 331 caregiver and sibling encounters during the two-year pilot. Qualitative feedback from caregivers highlighted the value of virtual services in reaching geographically dispersed families and addressing feelings of isolation among SCYC at the universal and targeted levels of care. Communication about these key outcomes led to operational funding and sustained integrated care programs. Conclusions: This manuscript illustrates a successful model of leveraging philanthropic funding to support the development of integrative care programs to serve SCYC. Future research should focus on refining the clinical and financial feasibility of such models and assessing their impact on family well-being. Full article

1,3-butadiene (BD) is a volatile organic pollutant. Upon inhalation, it is metabolically activated to reactive epoxides which alkylate genomic DNA and form potentially mutagenic monoadducts and DNA–DNA crosslinks including N7-(1-hydroxyl-3-buten-1-yl)guanine (EB-GII) and 1,4-bis-(guan-7-yl)-2,3-butanediol (bis-N7G-BD). While metabolic activation resulting in mutagenicity is a well-established mode of action for 1,3-butadiene, characterization of the extent of inter-individual variability in response to BD exposure is a gap in our knowledge. Previous studies showed that population-wide mouse models can be used to evaluate variability in 1,3-butadiene DNA adducts; therefore, we hypothesized that this approach can be used to also study variability in the formation and loss of BD DNA adducts across tissues and between sexes. To test this hypothesis, female and male mice from five genetically diverse Collaborative Cross (CC) strains were exposed to filtered air or 1,3-butadiene (600 ppm, 6 h/day, 5 days/week for 2 weeks) by inhalation. Some animals were kept for two additional weeks after exposure to study DNA adduct persistence. EB-GII and bis-N7G-BD adducts were quantified in liver, lungs and kidney using established isotope dilution ESI-MS/MS methods. We observed strain- and sex-specific effects on both the accumulation and loss of both DNA adducts, indicating that both factors play important roles in the mutagenicity of 1,3-butadiene. In addition, we quantified the intra-species variability for each adduct and found that for most tissues/adducts, variability values across strains were modest compared to default uncertainty factors. Full article

People with disabilities often face challenges like moving around independently and depending on personal caregivers for daily life activities. Traditional assistive devices are universally accepted by these communities, but they are designed with one-size-fits-all approaches that cannot adjust to individual human sizes, are not easily customized, and are made from rigid materials that do not adapt as a person’s condition changes over time. This systematic review examines the integration of smart materials, sensors, actuators, and 3D/4D printing technologies in advancing assistive devices, with a particular emphasis on mobility aids. In this work, the authors conducted a comparative analysis of traditional devices with commercially available innovative prototypes and research stage assistive devices by focusing on smart adaptable materials and sustainable additive manufacturing techniques. The results demonstrate how artificial intelligence drives smart assistive devices in hospital decentralized additive manufacturing, and policy frameworks agree with the Sustainable Development Goals, representing the future direction for adaptive assistive technology. Also, by combining 3D/4D printing and AI, it is possible to produce adaptive, affordable, and patient centered rehabilitation with feedback and can also provide predictive and preventive healthcare strategies. The successful commercialization of adaptive assistive devices relies on cost effective manufacturing techniques clinically aligned development supported by cross disciplinary collaboration to ensure scalable, sustainable, and universally accessible smart solutions. Ultimately, it paves the way for smart, sustainable, and clinically viable assistive devices that outperform conventional solutions and promote equitable access for all users. Full article