Search Results (2,065)

To address challenges such as temperature rise, operational instability, and premature failure in rolling bearings caused by retainer friction, this study designed and developed a high-performance polyimide (PI)-based composite self-lubricating retainer to enable “ultra-stable” bearing operation. Both solid and oil-porous self-lubricating retainers were fabricated through material composition and structural design. Systematic tests under controlled load and speed conditions were conducted to compare their temperature rise behavior and wear morphology. The results demonstrated that the temperature rise in the YSU-PI1 bearing with a solid retainer decreased by approximately 57% compared to a conventional bearing. The YSU-PA2 bearing with an oil-porous retainer exhibited a further improvement in thermal performance. Notably, under high-speed conditions, the equilibrium temperature of the YSU-PA2 bearing was lower than that under low-speed conditions, confirming a centrifugal-force-driven self-regulating oil-supply mechanism. Wear surface analysis revealed that the porous structure promoted the formation of a continuous and uniform transfer film, effectively mitigating wear and pitting. This study successfully integrates “material–structure–function” innovation. The oil-porous PI-based composite retainer transforms centrifugal force—typically considered detrimental—into a beneficial lubrication mechanism, effectively suppressing temperature rise and enabling “ultra-stable operation”. These findings provide crucial theoretical and technical support for developing bearings for high-end equipment. Full article

Within the ongoing scientific and disciplinary debate on the interplay between climate change and land-use governance, this paper highlights the critical role of urban planning and design in shaping environmental regeneration strategies for coastal urban areas vulnerable to flooding phenomena. These flood events—driven by the combined effects of sea-level rise (SLR) and riverine flood—represent one of the key challenges facing the “global risk society” given their increasing impact on urban areas and the tangible economic, social, and environmental damages they produce. In this context, this paper presents selected outcomes from the findings of the research project “Climate-proof planning and regeneration strategies for adaptation to sea-level rise. Experimentation and innovation in local urban planning in at-risk areas of the Lazio region”, conducted at Sapienza University of Rome. The project focuses on research and experimental planning in coastal areas of Lazio identified as being at risk of SLR-related flooding by 2100. It aims to define theoretical–methodological and operational references for urban regeneration with an ecosystemic approach within the framework of so-called climate-proof planning. This study examines three macro-areas, further subdivided into seven distinct sites, categorized by their prevailing urban functions. For each site, following a preliminary assessment of flood-prone zones, tailored design actions are proposed. These actions are framed within three overarching of urban resilience strategies, developed in previous research by the authors: “defence”, “adaptation”, and “relocation”. Full article

The COVID-19 pandemic catalyzed unprecedented innovation in indoor disinfection technologies, fundamentally transforming the patent landscape and commercial development in this sector. This comprehensive analysis examined patent filings from global databases and commercial market data spanning January 2020 to December 2025. Patent data were collected up to September 2022, while market data include both historical figures (2020–2023) and future projections (2024–2025) derived from industry research reports. A systematic review identified significant technological developments across five major categories: ultraviolet-C (UV-C) systems, ozone generators, photocatalytic oxidation systems, plasma disinfection technologies, and electromagnetic field applications. The analysis revealed that while patent activity surged dramatically during the pandemic period, commercial success rates varied significantly across technology categories. UV-C systems demonstrated the highest market penetration with established commercial viability, while emerging technologies such as electromagnetic disinfection faced substantial barriers to commercialization. Geographic analysis showed concentrated innovation in developed economies, with China leading in patent volume and South Korea achieving notable commercial success despite smaller patent portfolios. The study provides critical insights into the relationship between patent activity and commercial viability in emergency-driven innovation contexts. Full article

It has been recognized that Blockchain technology contributes to environmentally sustainable development goals (SDGs). It has emerged as a disruptive innovation capable of transforming various economic and social sectors significantly. This conceptual paper is driven by the need to explore how blockchain, specifically a consortium-based Ethereum architecture, can be integrated into higher education institutions to ensure data sovereignty, integrity, and verifiability while adhering to legal and ethical standards such as GDPR. We propose a multi-layered blockchain-based model for Kazakhstan’s Unified Platform of Higher Education (UPHE). This model employs hybrid on-chain/off-chain data storage, smart contract automation, and a Proof-of-Authority consensus mechanism to address system limitations, including data centralization and inadequate verification of academic credentials. Empirical simulations using Blockscout and Ethereum-compatible tools demonstrate the model’s feasibility and performance. This paper contributes to the growing discussion on educational blockchain applications by presenting a scalable, secure, and transparent architecture that aligns with institutional governance and Environmental, Social, and Governance (ESG) principles. It also supports the objectives of UN SDG 4 (i.e., Quality education) by fostering trust, transparency, and equitable access to verifiable educational credentials. Full article

Gear defect detection directly affects the operational reliability of critical equipment in fields such as automotive and aerospace. Gear defect detection technology based on machine vision, leveraging the advantages of non-contact measurement, high efficiency, and cost-effectiveness, has become a key support for quality control in intelligent manufacturing. However, it still faces challenges including difficulties in semantic alignment of multimodal data, the imbalance between real-time detection requirements and computational resources, and poor model generalization in few-shot scenarios. This paper takes the paradigm evolution of gear defect detection technology as the main line, systematically reviews its development from traditional image processing to deep learning, and focuses on the innovative application of intelligent algorithms. A research framework of “technical bottleneck-breakthrough path-application verification” is constructed: for the problem of multimodal fusion, the cross-modal feature alignment mechanism based on Transformer network is deeply analyzed, clarifying its technical path of realizing joint embedding of visual and vibration signals by establishing global correlation mapping; for resource constraints, the performance of lightweight models such as MobileNet and ShuffleNet is quantitatively compared, verifying that these models reduce Parameters by 40–60% while maintaining the mean Average Precision essentially unchanged; for small-sample scenarios, few-shot generation models based on contrastive learning are systematically organized, confirming that their accuracy in the 10-shot scenario can reach 90% of that of fully supervised models, thus enhancing generalization ability. Future research can focus on the collaboration between few-shot generation and physical simulation, edge-cloud dynamic scheduling, defect evolution modeling driven by multiphysics fields, and standardization of explainable artificial intelligence. It aims to construct a gear detection system with autonomous perception capabilities, promoting the development of industrial quality inspection toward high-precision, high-robustness, and low-cost intelligence. Full article

International building sustainability assessment tools (BSATs) offer a comprehensive framework for assessing environmental, economic, and social sustainability. However, these tools cannot fill the gap between their standards and the regional needs of developing countries such as Morocco. This paper presents a new framework to assess the sustainability of buildings in Morocco. The methodology proposed is the Fuzzy Delphi method to minimize the list of indicators with the help of 14 local experts and give an appropriate weight to the indicators and sub-indicators. The two-round analysis found a balanced weighting for the environmental, economic, and social dimensions, with the social pillar ranked highest in importance. A hierarchical framework of six consensus-based categories and 63 sub-indicators was developed. Consensus was measured using the dispersion threshold approach ≤ 0.2. The results show that waste and pollution (0.80), adaptability and resilience (0.78), and resources (0.75) are prioritized over the innovation category. Notably, sewage management, water reuse, and public infrastructure emerged as critical sub-indicators. A comparative evaluation against local BSATs from the region—Ethiopia, Sub-Saharan Africa, Saudi Arabia, and Oman—revealed convergence in core indicators like energy and water, yet divergence in economic and resilience criteria, reflecting regional specificities. This work contributes to the literature by presenting a validated, expert-driven assessment tool that aligns with local needs, offering a practical basis for national green certification and sustainable housing policy in Morocco and similar contexts. Full article

Large-scale production systems (LSPS) operate under growing complexity driven by digital transformation, tighter environmental regulations, and the demand for resilient and resource-efficient operation. Conventional control strategies, particularly PID and isodromic regulators, remain dominant in industrial automation due to their simplicity and robustness; however, their capability to achieve near-optimal performance is limited under constraints on control amplitude, rate, and energy consumption. This study develops an analytical–computational approach for the approximate realization of optimal nonlinear control using standard regulator architectures. The method determines switching moments analytically and incorporates practical feasibility conditions that account for nonlinearities, measurement noise, and actuator limitations. A comprehensive robustness analysis and simulation-based validation were conducted across four representative industrial scenarios—energy, chemical, logistics, and metallurgy. The results show that the proposed control strategy reduces transient duration by up to 20%, decreases overshoot by a factor of three, and lowers transient energy losses by 5–8% compared with baseline configurations, while maintaining bounded-input–bounded-output (BIBO) stability under parameter uncertainty and external disturbances. The framework provides a clear implementation pathway combining analytical tuning with observer-based derivative estimation, ensuring applicability in real industrial environments without requiring complex computational infrastructure. From a broader sustainability perspective, the proposed method contributes to the reliability, energy efficiency, and longevity of industrial systems. By reducing transient energy demand and mechanical wear, it supports sustainable production practices consistent with the following United Nations Sustainable Development Goals—SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). The presented results confirm both the theoretical soundness and practical feasibility of the approach, while experimental validation on physical setups is identified as a promising direction for future research. Full article

This study evaluates the innovation capacity of Egypt’s governorates to identify their potential for developing smart cities as innovation hubs. Smart cities represent essential instruments for tackling complicated urban issues like environmental degradation, regional economic disparities, and rapid urbanization. In the framework of Egypt Vision 2030, the establishment of fourteen fourth-generation smart cities is seen as an essential initiative to promote balanced, innovation-driven regional development. However, the absence of a thorough assessment of regional innovation capabilities during the planning phase poses significant concerns regarding the viability of attaining these objectives. A quantitative approach is employed to address this research gap, utilizing a composite Regional Innovation Capacity Index (RICI) as well as conducting cluster analysis and spatial autocorrelation analysis to assess the 27 governorates’ innovation capacities. The findings show significant gaps in innovation capacity among regions, with notable variances in knowledge creation, knowledge utilization, and supportive infrastructure. The findings demonstrate that new smart cities have been developed in some governorates with limited innovation capacity, while high-capacity governorates remain underutilized. These disparities underscore the need for specific policy actions to strengthen innovation ecosystems in lagging regions. The study offers actionable insights on how to match regional innovation capacities with Egypt’s smart city development policy. Full article

In post-disaster search and rescue (SAR) operations, unmanned aerial vehicles (UAVs) are essential tools, yet the large volume of raw visual data often overwhelms human operators by providing isolated, context-free information. This paper presents an innovative system with a novel cognitive–agentic architecture that transforms the UAV from an intelligent tool into a proactive reasoning partner. The core innovation lies in the LLM’s ability to perform high-level semantic reasoning, logical validation, and robust self-correction through internal feedback loops. A visual perception module based on a custom-trained YOLO11 model feeds the cognitive core, which performs contextual analysis and hazard assessment, enabling a complete perception–reasoning–action cycle. The system also incorporates a physical payload delivery module for first-aid supplies, which acts on prioritized, actionable recommendations to reduce operator cognitive load and accelerate victim assistance. This work, therefore, presents the first developed LLM-driven architecture of its kind, transforming a drone from a mere data-gathering tool into a proactive reasoning partner and demonstrating a viable path toward reducing operator cognitive load in critical missions. Full article

In response to the growing complexity of global exploration targets, traditional Euclidean geometric and linear statistical methods reveal inherent theoretical limitations in characterizing hydrocarbon reservoirs as complex geological bodies that exhibit simultaneous local disorder and global order. Fractal theory, with its core parameter systems such as fractal dimension and scaling exponents, provides an innovative mathematical–physics toolkit for quantifying spatial heterogeneity and resolving the multi-scale characteristics of reservoirs. This review systematically consolidates recent advancements in the application of fractal theory to oil and gas resource assessment, with the aim of elucidating its transition from a theoretical concept to a practical tool. We conclusively demonstrate that fractal theory has driven fundamental methodological progress across four critical dimensions: (1) In reservoir classification and evaluation, fractal dimension has emerged as a robust quantitative metric for heterogeneity and facies discrimination. (2) In pore structure characterization, the theory has successfully uncovered structural self-similarity across scales, from nanopores to macroscopic vugs, enabling precise modeling of complex pore networks. (3) In seepage behavior analysis, fractal-based models have significantly enhanced the predictive capacity for non-Darcy flow and preferential migration pathways. (4) In fracture network modeling, fractal geometry is proven pivotal for accurately characterizing the spatial distribution and connectivity of natural fractures. Despite significant progress, current research faces challenges, including insufficient correlation with dynamic geological processes and a scarcity of data for model validation. Future research should focus on the following directions: developing fractal parameter inversion methods integrated with artificial intelligence, constructing dynamic fractal–seepage coupling models based on digital twins, establishing a unified fractal theoretical framework from pore to basin scale, and expanding its application in low-carbon energy fields such as carbon dioxide sequestration and natural gas hydrate development. Through interdisciplinary integration and methodological innovation, fractal theory is expected to advance hydrocarbon resource assessment toward intelligent, precise, and systematic development, providing scientific support for the efficient exploitation of complex reservoirs and the transition to green, low-carbon energy. Full article

Background/Objectives: Gamification and game-based learning (GBL) have recently emerged as fresh and appealing ways of health education, and they have been shown to perform better in knowledge acquisition than traditional teaching approaches. Digital serious games are developing as innovative tools for cancer education and behaviour change, yet no review has systematically synthesized their use across key populations. This scoping review aimed to map evidence on serious games for cancer prevention, care, and survivorship among the public, patients, and healthcare professionals, framed through the Capability, Opportunity, Motivation-Behaviour (COM-B) model. Methods: Following Joanna Briggs Institute methodology, we searched Web of Science, MEDLINE, CINAHL, and PsycINFO. Eligible studies evaluated a serious game with a cancer focus and reported outcomes on knowledge, awareness, engagement, education, or behaviour. Data extraction and synthesis followed the PRISMA-ScR checklist. Results: Thirty-five studies met the inclusion criteria, covering diverse cancers, populations, and platforms. Most reported improvements in knowledge, engagement, self-efficacy, and communication. However, heterogeneity in study design and limited assessment of long-term behaviour change constrained comparability. Conclusions: Digital serious games show promise for enhancing cancer literacy and supporting behavioural outcomes across patients, professionals, and the public. By integrating multiple perspectives, this review highlights opportunities for theory-driven design, robust evaluation, and implementation strategies to maximize their impact in cancer education and awareness. Full article

In the context of deep AI–design integration, traditional methods struggle to translate multi-source requirements into sustainable engineering solutions while balancing innovation with practicality. This study proposes AQTA, an intelligent design framework that integrates Analytic Hierarchy Process (AHP), Quality Function Deployment (QFD), Theory of Inventive Problem Solving (TRIZ), and AI-Generated Content (AIGC) to enable sustainable product development. AQTA employs a four-stage closed-loop process: requirement analysis, contradiction resolution, solution generation, and validation. QFD and AHP quantify user and sustainability requirements to identify key contradictions, TRIZ resolves technical conflicts and stimulates innovative solutions, while AIGC generates eco-efficient visual concepts through prompt engineering. Multi-criteria decision-making supports evaluation and optimization based on environmental and economic indicators. Empirical studies demonstrate that AQTA significantly enhances innovation quality, design efficiency, and sustainability performance. The framework provides a replicable, hybrid ‘theory-driven + AI-generated’ methodology, which is validated through the case study of urban fire trucks, contributing to sustainable manufacturing practices in the intelligent era. Full article

Deep-sea mining has gradually emerged as a core domain in global resource exploitation. Underwater autonomous robots, characterized by low cost, high flexibility, and lightweight properties, demonstrate significant advantages in deep-sea mineral development. To address the limitations of traditional deep-sea mining equipment, such as large volume, high energy consumption, and insufficient flexibility, this paper proposes an innovative Underwater Vehicle Collector System (UVCS). Integrating bionic design with autonomous robotic technology, this system features a collection device mimicking the large opening–closing kinematics of the mouth of deep-sea dragonfish (Stomiidae). A dual-rocker mechanism is employed to realize the mouth opening-closing function, and the collection process is driven by the pitching motion of the vehicle without the need for additional motors, thus achieving the advantages of high flexibility, low energy consumption, and light weight. The system is capable of collecting seabed polymetallic nodules with diameters ranging from 1 to 12 cm, thus providing a new solution for sustainable deep-sea mining. Based on the dynamics of UVCS, this paper verifies its attitude stability and collection efficiency in planar motions through single-cycle and multi-cycle simulation analyses. The simulation results indicate that the system operates stably with reliable collection actions. Furthermore, water tank testings demonstrate the opening and closing functions of the UVCS collection device, fully confirming its design feasibility and application potential. In conclusion, the UVCS system, through the integration of bionic design, opens up a new path for practical applications in deep-sea resource exploitation. Full article

The increasing construction-driven growth in urbanization requires innovative and holistic design approaches for sustainable housing. This study examines the relationship between Minimalist Design Principles (MDPs) and the UN SDGs and develops a multi-stage decision-support model to operationalize these links. The research adopts a five-stage mixed-methods design. It includes content analysis based on a systematic literature review, conceptual mapping, a two-round Delphi method (N = 56), Fuzzy AHP for criteria weighting, and SEM for model validation. A total of 13 MDPs were mapped against 17 SDGs and 169 subtargets, revealing particularly strong linkages with SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production). The SEM results confirm the structural validity of the proposed model. Among the minimalist principles, those associated with “resource, material, and process simplification” and “user needs, functional flexibility, and quality of life” emerged as the most influential factors for the SDGs. This study proposes a measurable, multidimensional decision-support model in sustainable architecture that clarifies how MDPs influence the SDGs. Full article

Amid China’s economic transition toward high-quality development, the policy of additional deductions for R&D expenses, a key fiscal and taxation tool for incentivizing corporate innovation, has attracted considerable attention for its actual effects on the high-quality development of advanced manufacturing enterprises. Based on a sample of China’s A-share-listed advanced manufacturing enterprises from 2010 to 2024, this study empirically examines how the R&D extra deduction policy influences quality-driven development in high-end manufacturing enterprises, focusing on the mechanistic role of innovation input and output. The results indicate that the R&D tax deduction incentive policy substantially enhances quality-focused growth among high-tech manufacturing companies—a finding that persists after a series of robustness tests. Mechanism tests indicate that the policy promotes high-quality development by incentivizing enterprises to increase innovation input and output. Further analysis reveals a chain mediation effect between innovation input and output. Heterogeneity analysis reveals that the policy has a more significant promoting effect on large-scale advanced manufacturing enterprises and that, with the increasing policy intensity after 2018, its driving force for high-quality development intensified. This study provides micro-evidence for understanding the role of the additional deduction for R&D expenses in the high-quality development of advanced manufacturing enterprises, offering important implications for optimizing innovation incentive policies and promoting the transformation and upgrading of advanced manufacturing. Full article