Search Results (2,362)

Improving carbon emission efficiency (CEE) is crucial for coordinating economic development and reducing carbon emissions. Drawing on panel data for 30 provinces in China from 2013 to 2022, this paper selects six key antecedent conditions guided by the Technology–Organization–Environment (TOE) framework. Then the dynamic qualitative comparative analysis (DQCA) is employed to explore CEE improvement pathways from a configurational perspective, and regression analysis is used to compare the driving effects of different pathways. The findings reveal that (1) single factors cannot independently achieve high CEE; instead, multiple factors must work synergistically to form various improvement pathways, including “technology–organization dual-driven”, “environment-dominated”, and “multi-equilibrium” pathways, with industrial structure upgrading as the core factor for improving CEE; (2) temporally, these improvement pathways demonstrate universality, while, spatially, they exhibit significant provincial heterogeneity; and (3) in terms of marginal effects, the “multi-equilibrium” pathway has the strongest driving effect on CEE. The findings provide valuable policy implications for developing targeted CEE enhancement strategies across provinces at different developmental stages. Full article

Optical coordinate measurement is a universal technique that aligns with the rapid development of industrial technologies and new materials. Nevertheless, can this technique be consistently effective when applied to the precise measurement of all types of materials? As shown in this article, an analysis of optical measurement systems reveals that some materials cause difficulties during the scanning process. This article details the matting process, resulting, as demonstrated, in lower measurement uncertainty values compared to the pre-matting state, and identifies materials for which applying a matting spray significantly improves the measurement quality. The authors propose a classification of materials into easy-to-scan and hard-to-scan groups, along with specific procedures to improve measurements, especially for the latter. Tests were conducted in an accredited Laboratory of Coordinate Metrology using an articulated arm with a laser probe. Measured objects included spheres made of ceramic, tungsten carbide (including a matte finish), aluminum oxide, titanium nitride-coated steel, and photopolymer resin, with reference diameters established by a high-precision Leitz PMM 12106 coordinate measuring machine. Diameters were determined from point clouds obtained via optical measurements using the best-fit method, both before and after matting. Color measurements using a spectrocolorimeter supplemented this study to assess the effect of matting on surface color. The results revealed correlations between the material type and measurement accuracy. Full article

Fault diagnosis is of great significance for the maintenance of rotating machinery. Deep learning is an intelligent diagnostic technique that is receiving increasing attention. To address the issues of industrial data with small samples and varying working conditions, a residual convolutional neural network based on the attention mechanism is put forward for the fault diagnosis of rotating machinery. The method incorporates channel attention and spatial attention simultaneously, implementing channel-wise recalibration for frequency-dependent feature adjustment and performing spatial context aggregation across receptive fields. Subsequently, a residual module is introduced to address the vanishing gradient problem of the model in deep network structures. In addition, LSTM is used to realize spatiotemporal feature fusion. Finally, label smoothing regularization (LSR) is proposed to balance the distributional disparities among labeled samples. The effectiveness of the method is evaluated by its application to the vibration signal data from the safe injection pump and the Case Western Reserve University (CWRU). The results show that the method has superb diagnostic accuracy and strong robustness. Full article

Dynamic wireless charging systems have emerged as a promising solution to extend the driving range of electric vehicles by enabling energy transfer while the vehicle is in motion. However, the segment-based charging lane structure introduces challenges such as pulsation of the output power and the need for precise switching control of the transmitting segments. This paper proposes a position-sensorless control method for managing transmitting lines in a dynamic wireless charging system. The proposed approach uses a segmented charging lane structure combined with two receiving coils and LCC compensation circuits on both the transmitting and receiving sides. Based on theoretical analysis, the study determines the optimal switching positions and signals to reduce the current fluctuation. To validate the proposed method, a dynamic wireless charging system prototype with a power rating of $3kW$ was designed, constructed, and tested in a laboratory environment. The results demonstrate that the proposed position-sensorless control method effectively mitigates power fluctuations and enhances the stability and efficiency of the wireless charging process. Full article

This study presents the first investigation into the application of ultrasonic vibration-assisted electrical discharge machining (UV-EDM) using graphite electrodes for external cylindrical surface machining—an essential surface in the production of tablet punches and sheet metal-forming dies. A custom ultrasonic horn was designed and fabricated using 90CrSi material to operate effectively at a resonant frequency of 20 kHz, ensuring stable vibration transmission throughout the machining process. A Box–Behnken experimental design was employed to explore the effects of five process parameters—vibration amplitude (A), pulse-on time (Ton), pulse-off time (Toff), discharge current (Ip), and servo voltage (SV)—on two key performance indicators: material removal rate (MRR) and surface roughness (Ra). The optimization process was conducted in two stages: single-objective analysis to maximize MRR while ensuring Ra < 4 µm, followed by a hybrid multi-objective approach combining NSGA-II and the Analytic Hierarchy Process (AHP). The optimal solution achieved a high MRR of 9.28 g/h while maintaining Ra below the critical surface finish threshold, thus meeting the practical requirements for punch surface quality. The findings confirm the effectiveness of the proposed horn design and hybrid optimization strategy, offering a new direction for enhancing productivity and surface integrity in cylindrical EDM applications using graphite electrodes. Full article

Improving the green innovation quality (GIQ) of center cities is crucial to achieve sustainable urban agglomeration development. Utilizing data on green patent citations and economic indicators across cities in the Yangtze River Delta urban agglomeration (YRD) from 2003 to 2022, this research examines the influence of center cities’ GIQ on the economic performance of peripheral municipalities. The results show the following: (1) Center cities’ GIQ exerts a significant suppressive effect on peripheral cities’ economic growth overall. Heterogeneity analysis uncovers a distance-dependent duality. GIQ stimulates growth in proximate cities (within 300 km) but suppresses it beyond this threshold. This spatial siphoning effect is notably amplified in national-level center cities. (2) Mechanisms suggest that GIQ accelerates the outflow of skilled labor in peripheral cities through factor agglomeration and industry transfer mechanisms. Concurrently, it impedes the gradient diffusion of urban services, collectively hindering peripheral development. (3) Increased government green attention (GGA) and industry–university–research cooperation (IURC) in centers can mitigate these negative impacts. This paper contributes to the theoretical discourse on center cities’ spatial externalities within agglomerations and offers empirical support and policy insights for the exertion of spillover effects of high-quality green innovation from center cities and the sustainable development of urban agglomeration. Full article

This study endeavors to elucidate the mechanisms and pathways through which the digital economy shapes agricultural green development, providing theoretical underpinnings and practical guidance for the green transformation of regional agriculture. (1) Using panel data from 18 prefecture-level cities in Sichuan Province (2013–2022), a comprehensive evaluation index system for agricultural green development was formulated. Fixed-effects, mediating-effects, and threshold-effects models were employed to systematically analyze the direct effects, transmission pathways, and nonlinear characteristics of the digital economy on agricultural green development. (2) The fixed-effects model shows that the digital economy markedly propels agricultural green development in Sichuan Province. The mediating-effects model verifies two transmission pathways: “digital economy → technological progression → agricultural green development” and “digital economy → industrial structure upgrading → agricultural green development”. The threshold-effects model suggests that when the digital economy is in the low-threshold interval, it exerts a suppressive impact on agricultural green development; however, once the threshold is surpassed, its promoting effect strengthens significantly. (3) The results demonstrate the following findings: First, the digital economy exerts a significant positive effect on agricultural green development. Second, this promoting effect exhibits significant nonlinear characteristics that vary with the level of digital economy development. Third, the impact manifests remarkable regional heterogeneity, necessitating context-specific development strategies. (4) Five optimization recommendations are proposed: promote the categorized development of agricultural digital technologies and industrial upgrading; advance digital infrastructure and technology adaptation in phases; design differentiated regional policies; establish a hierarchical and classified long-term guarantee mechanism; and strengthen the “industry-university-research-application” collaborative innovation and dynamic monitoring system. Full article

Innovation is a strategic driver of sustainable competitive advantage and long-term economic growth. This study proposes an empirical framework to support the sustained development of national innovation capacity by examining key enabling factors. Drawing on an extensive review of the literature, the research investigates the interrelationships among governmental support (GS), innovation agents (IA), university–industry R&D collaborations (UIRD), and innovation cluster development (ICD), and their influence on two critical innovation outcomes, knowledge creation (KC) and knowledge diffusion (KD). Using panel data from G7 countries spanning 2008 to 2018, sourced from international organizations such as the World Bank, the World Intellectual Property Organization, and the World Economic Forum, the study applies regression analysis to test the proposed conceptual model. Results highlight the foundational role of GS in providing a balanced framework to foster collaborative networks among IA and enhancing the effectiveness of UIRD. Furthermore, IA emerges as a pivotal actor in advancing innovation efforts, while the development of innovation clusters is shown to selectively enhance specific innovation outcomes. These findings offer theoretical and practical contributions for policymakers, researchers, and stakeholders aiming to design supportive ecosystems that strengthen sustainable national innovation capacity. Full article

This study aims to develop a preliminary list of indicators suitable for green restaurants in Taiwan. The research methodology includes expert consensus (Delphi method) and incorporates interviews with field experts. An analysis of the responses provided by these industry experts led to the identification of five dimensions of evaluation indicators for green restaurants. The K–S test involves using a z-test on ordinal variables for single samples to determine whether the sample distribution diverges from the frequency distribution. This study analyzed the responses provided by the interviewed experts to identify and extract evaluation indicators for green restaurants. The extracted indicators comprise five dimensions (resource management, ingredient and product selection, environmental and indoor quality, green certification and management, and customer awareness and participation), 15 sub-dimensions, and 70 detailed indicators. The research results can serve as a reference for course planning in related programs at universities and colleges, as well as for industry planning of green restaurants, and as a reference for the promotion of national sustainable environmental policies in Taiwan. Therefore, based on the results of this study, recommendations are provided for educational institutions related to green restaurants, official organizations related to green restaurants, the industry related to green restaurants, and future researchers. Full article

This study, based on engineering education accreditation standards, addresses the supply–demand imbalance in China’s cybersecurity talent cultivation by constructing a sustainable “education-industry-society” collaborative model. Through case studies at Huaihua University and other institutions, employing methods such as literature analysis, field research, and empirical investigation, we systematically explore reform pathways for an innovative cybersecurity talent development system. The research proposes a “three-platform, four-module” practical teaching framework, where the coordinated operation of the basic skills training platform, comprehensive ability development platform, and innovation enhancement platform significantly improves students’ engineering competencies (practical courses account for 41.6% of the curriculum). Findings demonstrate that eight industry-academia practice bases established through deep collaboration effectively align teaching content with industry needs, substantially enhancing students’ innovative and practical abilities (172 national awards, 649 provincial awards). Additionally, the multi-dimensional evaluation mechanism developed in this study enables a comprehensive assessment of students’ professional skills, practical capabilities, and innovative thinking. These reforms have increased the employment rate of cybersecurity graduates to over 90%, providing a replicable solution to China’s talent shortage. The research outcomes offer valuable insights for discipline development under engineering education accreditation and contribute to implementing sustainable development concepts in higher education. Full article

Recent advancements in deep learning have spurred significant research interest in fault diagnosis for elevator systems. However, conventional approaches typically require substantial labeled datasets that are often impractical to obtain in real-world industrial environments. This limitation poses a fundamental challenge for developing robust diagnostic models capable of performing reliably under data-scarce conditions. To address this critical gap, we propose MetaRes-DMT-AS (Meta-ResNet with Dynamic Meta-Training and Adaptive Scheduling), a novel meta-learning framework for few-shot fault diagnosis. Our methodology employs Gramian Angular Fields to transform 1D raw sensor data into 2D image representations, followed by episodic task construction through stochastic sampling. During meta-training, the system acquires transferable prior knowledge through optimized parameter initialization, while an adaptive scheduling module dynamically configures support/query sets. Subsequent regularization via prototype networks ensures stable feature extraction. Comprehensive validation using the Case Western Reserve University bearing dataset and proprietary elevator acceleration data demonstrates the framework’s superiority: MetaRes-DMT-AS achieves state-of-the-art few-shot classification performance, surpassing benchmark models by 0.94–1.78% in overall accuracy. For critical few-shot fault categories—particularly emergency stops and severe vibrations—the method delivers significant accuracy improvements of 3–16% and 17–29%, respectively. Full article

This study developed a full solid waste-based cementitious material (ISWs-CM) using steel slag (SS), ground granulated blast furnace slag (GGBFS), phosphorus slag (PS), carbide slag (CS), and desulfurized gypsum (DG) to completely replace cement. A two-layer optimization strategy, combining three chemical moduli and simplex lattice experiments, was employed to determine the proportion and to investigate the impact of proportions on the uniaxial compressive strength of mortar. As an application case, the ISWs-CM with the optimal proportion was employed to stabilize aeolian sand, and its effectiveness as a cement substitute and the underlying mechanisms were investigated. The results indicated that the ISW proportion that maximized the strength of the mortar was SS:GGBFS:PS:CS = 5:20:20:40. The strength of the mortar was enhanced when the proportion of GGBFS exhibiting the highest reactivity was increased and also increased initially and then decreased with an increase in CS when the dosage of GGBFS was fixed. The aeolian sand stabilized by ISW-CM exhibited higher strength than that stabilized with cement. The greater number and variety of hydration products resulted in denser connections and encapsulation of sand particles, which highlights the synergistic effect of ISWs and the potential of ISW-CM as a cement replacement across diverse applications including aeolian sand stabilization. Full article

Hydrogen produced from renewable sources has the potential to tackle various energy challenges, from allowing cost-effective transportation of renewable energy from production to consumption regions to decarbonizing intensive energy consumption industries. Due to its application versatility and non-greenhouse gaseous emissions characteristics, it is expected that hydrogen will play an important role in the decarbonization strategies set out for 2050. Currently, there are some barriers and challenges that need to be addressed to fully take advantage of the opportunities associated with hydrogen. The present work aims to characterize the state of the art of different hydrogen production, storage, transport, and distribution technologies, which compose the hydrogen value chain. Based on the information collected it was possible to conclude the following: (i) Electrolysis is the frontrunner to produce green hydrogen at a large scale (efficiency up to 80%) since some of the production technologies under this category have already achieved a commercially available state; (ii) in the storage phase, various technologies may be suitable based on specific conditions and purposes. Technologies of the physical-based type are the ones mostly used in real applications; (iii) transportation and distribution options should be viewed as complementary rather than competitive, as the most suitable option varies based on transportation distance and hydrogen quantity; and (iv) a single value chain configuration cannot be universally applied. Therefore, each case requires a comprehensive analysis of the entire value chain. Methodologies, like life cycle assessment, should be utilized to support the decision-making process. Full article

This work was conducted within the framework of French university reforms undertaken since 2022. Regardless of learning level and target audience, project-based learning has proved its effectiveness as a teaching strategy for many years. The novelty of the present contribution lies in the gamification of this learning method. A popular game, Trivial Pursuit, was adapted to enable students to acquire knowledge in a playful manner while preparing for upcoming technical challenges. Various technical subjects were chosen to create new cards for the game. A total of 180 questions and their answers were created. The colored tokens were then used to trace manufactured products. This teaching experiment was conducted as part of a project-based learning program with third-year Bachelor students (Electrical Engineering and Industrial Computing Department). The game components associated with the challenge proposed to the students comprised six key elements: objectives, challenges, mechanics, components, rules, and environment. Within the framework of the Industry 4.0 concept, this pedagogical activity focused on the knowledge, understanding, development, and application of an RFID (Radio Frequency Identification) system demonstrating the capabilities of this technology. This contribution outlines the various stages of the work assigned to the students. An industrial partner was also involved in this work. Full article

Background/Objectives: Swine influenza A virus (swIAV), a prevalent respiratory pathogen in porcine populations, poses substantial economic losses to global livestock industries and represents a potential threat to public health security. Neuraminidase (NA) has been proposed as an important component for universal influenza vaccine development. NA has potential advantages as a vaccine antigen in providing cross-protection, with specific antibodies that have a broad binding capacity for heterologous viruses. In this study, we evaluated the immunogenicity and protective efficacy of a tetrameric recombinant NA subunit vaccine in a swine model. Methods: We constructed and expressed structurally stable soluble tetrameric recombinant NA (rNA) and prepared subunit vaccines by mixing with ISA 201 VG adjuvant. The protective efficacy of rNA-ISA 201 VG was compared to that of a commercial whole inactivated virus vaccine. Pigs received a prime-boost immunization (14-day interval) followed by homologous viral challenge 14 days post-boost. Results: Both rNA-ISA 201 VG and commercial vaccine stimulated robust humoral responses. Notably, the commercial vaccine group exhibited high viral-binding antibody titers but very weak NA-specific antibodies, whereas rNA-ISA 201 VG immunization elicited high NA-specific antibody titers alongside substantial viral-binding antibodies. Post-challenge, both immunization with rNA-ISA 201 VG and the commercial vaccine were effective in inhibiting viral replication, reducing viral load in porcine respiratory tissues, and effectively mitigating virus-induced histopathological damage, as compared to the PBS negative control. Conclusions: These findings found that the anti-NA immune response generated by rNA-ISA 201 VG vaccination provided protection comparable to that of a commercial inactivated vaccine that primarily induces an anti-HA response. Given that the data are derived from one pig per group, there is a requisite to increase the sample size for more in-depth validation. This work establishes a novel strategy for developing next-generation SIV subunit vaccines leveraging NA as a key immunogen. Full article