Search Results (10,168)

Remaining Useful Life (RUL) prediction is a key component of prognostics and health management for modern industrial systems. While deep learning methods have significantly improved prediction accuracy, many existing approaches rely on large neural networks that are difficult to deploy on resource-constrained edge devices. This study presents a deployment-oriented evaluation of compact neural networks for RUL prediction using the NASA C-MAPSS turbofan engine benchmark. Two lightweight hybrid architectures, CNN–GRU and CNN–TCN, were developed with approximately 28k–32k parameters to represent realistic models for CPU-based edge inference. A systematic experimental analysis was conducted across all four C-MAPSS subsets (FD001–FD004), which represent increasing levels of operational and fault complexity. In addition to baseline performance, two post-training compression techniques (i.e., global unstructured magnitude pruning and dynamic INT8 quantization) were evaluated. To assess real deployment behavior, inference latency was measured on both a high-performance Intel x86 workstation and a resource-constrained ARM platform. Results show that CNN–GRU generally achieves higher predictive accuracy, whereas CNN–TCN provides more consistent and lower inference latency due to its convolution-only temporal modeling. Unstructured pruning can yield modest improvements in prediction accuracy, suggesting a regularization effect, but it does not reliably reduce model size or latency on standard CPUs due to the overhead associated with pruning masks. Dynamic quantization substantially reduces model size (particularly for CNN–GRU) while preserving predictive accuracy; however, it increases runtime latency because of additional quantization and dequantization operations. These findings demonstrate that compression techniques commonly used for large models do not necessarily translate into deployment benefits for already compact RUL architectures and highlight the importance of hardware-aware evaluation when designing edge prognostics systems. Full article

Background: In multicultural healthcare systems such as the Italian one, migrant children may experience dental care as particularly stressful because linguistic and cultural barriers can limit communication, emotional expression, and understanding of the clinical setting. Aim: Understanding the emotional experience of migrant children during dental visits is essential for improving clinical management in pediatric dentistry and orthodontics within multicultural contexts. Because linguistic barriers often limit verbal communication, this study aimed to explore children’s mental representations, emotional states, and perceptions of the dental environment through drawing and to evaluate the clinical implications for communication and therapeutic collaboration. Methods: This qualitative study was conducted in Italy between 2016 and 2025 and analyzed 50 drawings produced by 50 foreign-born migrant children aged 6–13 years, recruited through an educational cooperative in Piacenza. Most participants originated from developing countries and had limited proficiency in Italian, frequently showing a marked “experience gap” in drawing ability that interfered with normative developmental stages described by Lowenfeld. The analysis focused on spatial organization, line quality, color use, posture, interpersonal distance, and representation of the clinical environment, integrating graphic competence assessment with emotional interpretation. Results: Younger children commonly depicted rigid lines, essential settings, and oversized dental unit lamps, whereas older children increasingly represented threatening or disproportionate instruments, aggressive dentists, and omission of the patient figure. Around age 10, drawings became more detailed and colorful, although symbols of closure, such as locked doors, persisted. In adolescents, representations polarized between rich, coherent scenes and extremely essential drawings dominated by fear, rigidity, minimal environments, and symbols of constraint. The findings suggest that drawing may represent a valuable non-verbal clinical and communicative resource for exploring migrant children’s emotional experience of dental care and for identifying signs of anxiety and vulnerability that may not emerge through verbal interaction alone. Conclusions: These findings support the value of a culturally sensitive dental approach integrating drawing, visual aids, multilingual educational materials, and play-based strategies to reduce communication barriers and improve cooperation in migrant children receiving pediatric dental and orthodontic care. Full article

Sheep welfare outcomes vary depending on production systems, breeds, and environmental conditions. This study examined the effects of extensive, semi-extensive, and semi-intensive sheep production systems on animal welfare in Serbia, using the AWIN Welfare Protocol to evaluate 30 farms. Welfare indicators were categorised into resource-based, management-based, and animal-based metrics. The results indicated that there was no significant difference in space allowance among the production systems (p > 0.05). This suggests that the space provided was adequate for semi-intensive farms and suitable for both semi-extensive and extensive farms. However, management practices showed significant variations (p < 0.05), indicating diverse impacts on sheep welfare. No ocular discharge or stereotypic behaviours were observed, while respiratory issues, social withdrawal, and excessive itching were found to have a very low prevalence across all farms. The primary welfare concern identified in the extensive farms was the use of painful mutilations. Semi-extensive and semi-intensive farms had significantly higher rates of tail docking (p < 0.05) and poorer fleece cleanliness. These findings highlight the necessity of addressing the root causes of poor welfare to improve sheep welfare standards. Therefore, achieving sustainable welfare outcomes requires an integrated approach that combines genetic suitability, adequate housing, and effective management practices. Full article

Transitioning from traditional to organic production is gaining popularity worldwide with significant challenges including weed management. We evaluated how legumes sown as cover crops in a synchronous intercropping (SI) system with organic oat (Avena sativa) as the main crop impacted weed communities. A split-plot design was set up on a farm in Poularies (Quebec, Canada) to compare Melilotus officinalis, Trifolium incarnatum, Trifolium repens and a control without legumes for two years (2019–2020). We determined the botanical composition, calculated diversity indices, and measured plant functional traits. Species richness was similar (S = 5.5 ± 0.4) across treatments in 2019, but higher in the control (S = 12.2 ± 2.6) and lower (S = 6.0 ± 1.2) under T. incarnatum in 2020. Shannon diversity was lower in 2019 (H′ = 1.49 ± 0.07) than in 2020 (H′ = 1.99 ± 0.04), and higher under the control (H′ = 1.87 ± 0.05) than under T. incarnatum (H′ = 1.46 ± 0.04). Weeds under T. incarnatum had a high specific leaf area and a resource-acquisition strategy, while those in the control had a higher leaf dry matter content and a resource-conservation strategy. Our study brings novel results on the use of legumes in SI systems to control weeds. Using T. incarnatum in a SI system with oat had the greatest capacity to cover the ground, control weeds and reduce their diversity, but this species and the acquisitive weeds in this treatment could compete with the main crop. Future research should evaluate the quantity and quality of yields to complete this ecological study and give appropriate agronomic recommendations. Our results could provide agronomists and farmers with indications on the level of competition weeds exert on the cropping system depending on the SI treatment. Full article

Spatiotemporal heterogeneity of land-use multifunction (LUMF) is crucial for the preservation and management of large-scale national cultural parks in alleviating potential human-land conflicts. Using 28 multidimensional indicators across economic, social, and environmental dimensions, this study established an LUMF index system for the Long March National Cultural Park of China (CLMNCP). LUMF values for 77 prefecture-level cities were quantified from 2008 to 2023, and their spatiotemporal heterogeneity was examined using a spatial autocorrelation model. Subsequently, the Optimal Parameters-based GeoDetector (OPGD) model was applied to identify key driving factors. The main findings are as follows: (1) From 2008 to 2023, the total, economic (EF), social (SF), and environmental (EnF) functions in the CLMNCP exhibited a consistent upward trend. (2) Significant spatial heterogeneity characterized the trade-offs and synergies among these functions. The EF-EnF interaction displayed a concave synergistic relationship, while the EF-SF and SF-EnF interactions showed convex, fluctuating patterns during their transitions between trade-off and synergy. (3) The primary drivers varied across function pairs. The EF-SF synergy was predominantly influenced by agricultural production, resource supply, and cultural service factors. The EF-EnF interaction was mainly shaped by natural conditions and environmental improvement factors. In contrast, the SF-EnF interaction was primarily driven by economic development, cultural services, and resource supply. These findings support functional zoning and targeted management of large-scale national cultural park to balance development and conservation while reducing human-land conflicts. Full article

As complex system engineering involving multiple stakeholders, multi-objective collaboration, and multi-spatiotemporal scales, the components, logical structure, and functional mechanisms of production equipment operation management and control (PEOMC) can be generalized through functional modelling to support dynamic analysis and intelligent decision-making of PEOMC in the industrial internet environment. To address the diversity of scenarios and objectives of PEOMC, a hierarchical construction method for the functional model of PEOMC based on IDEF0 is proposed. By analysing relevant international standards, such as ISO 55010, ISO/IEC 62264, and OSA-CBM, the generic functional modules for the first and second layers of the functional model are identified and defined. On the basis of semi-supervised machine learning, topic clustering is used to extract the components, functional mechanisms, and logical relationships of production equipment operation management and control from approximately 200 standard texts and to construct a reference resource pool for the third-layer functional module. On this basis, an interface matching and recursive traversal algorithm for functional modules is designed, and a composition and orchestration strategy of functional modules for specific scenarios is provided to support the flexible construction of diversified and personalized PEOMC scenarios. The proposed construction and application method was validated through an engineering case study in an aero-engine transmission unit manufacturing workshop: the average process capability index of the enterprise’s production equipment steadily increased from 1.28 to approximately 1.60, the mean time to repair (MTTR) of production equipment failures significantly decreased from 8 h to 3 h, and the average overall equipment effectiveness (OEE) increased from 56.43% to a stable 68.57%, demonstrating its effectiveness and practicality. Full article

We explored the application of spatial information technology in the assessment and optimization of cultural heritage resources within traditional settlements in Meizhou City, a core area of Hakka culture in China. By integrating methods such as geographic information systems and Kernel density estimation, it systematically evaluates the spatial distribution and socioeconomic conditions of these settlements. A multi-criteria evaluation model is constructed to quantify resource endowment across cultural, historical, and ecological dimensions, with particular emphasis on key factors influencing conservation effectiveness, such as infrastructure and economic vitality. Combining field investigations and literature review, we propose adaptive reuse strategies and policy recommendations to enhance settlement resilience and balance cultural preservation with regional development. Their expected outcomes include the engineering of a multidimensional geographic database for traditional settlements, the establishment of a spatial decision-support framework for heritage infrastructure conservation, and the development of systematic optimization protocols integrated with China’s rural revitalization technical policies. These results provide a computational and methodological foundation for interdisciplinary research in sustainable cultural heritage management and smart rural engineering. Full article

This study presents a systematic literature review on the environmental impact of industrial applications of Rare Earth Elements (REEs), particularly those classified as Critical Raw Materials (CRMs), such as Neodymium alloys. These materials are key components of permanent magnets (PMs) used in electrical machines, including automotive applications, wind turbine generators, and various consumer electronics. A structured methodology began with a comprehensive search across multiple scientific databases utilizing primary and secondary keywords. Studies were selected through a multi-step process, including screening by title, abstract, and full-text review, ensuring the inclusion of relevant and high-quality research. This approach allowed for a rigorous and reproducible assessment of the literature. The review was conducted to address two central issues: the main environmental impacts of using rare earths in permanent magnets for electric motors, and the role of recycling and reuse strategies in reducing them. The review summarizes current knowledge on the life cycle environmental impacts of REEs, from extraction to end-of-life management, highlighting opportunities and challenges in recycling and reuse. While recycling can partially reduce environmental impact, significant gaps remain in efficiency and large-scale feasibility. The literature also emphasizes the substantial impacts of REEs in permanent magnets, including resource depletion, energy use, and emissions. Overall, the study highlights the need to integrate environmental considerations into the design and management of REE-containing systems and identifies research gaps to support more sustainable and efficient materials management. Full article

Background/Objectives: Nursing turnover represents an increasing threat to the sustainability of healthcare systems. Burnout, a syndrome of chronic work-related stress, is one of the primary predictors of intention to leave work; however, certain organizational factors may be associated with variations in its impact. Among these, the leadership of the Unit Coordinator may represent a potential resource, but its association with the relationship between burnout and intention to leave remains poorly explored. This study investigates the role of coordinators’ leadership in the relationship between burnout and intention to leave the profession. Methods: A cross-sectional study was conducted among 668 nurses providing direct patient care in various Italian healthcare settings. Data were collected through an online questionnaire comprising validated scales reported in the literature. A structural equation modeling approach was used for the analysis. Results: More than 30% of the variance in burnout is explained by interpersonal conflicts, workload, and organizational constraints. Burnout accounts for 24.4% of the variance in nurses’ intention to leave their jobs. The leadership of the nurse coordinator partially mediates the relationship between burnout and nurses’ intention to leave their job (total effect β = 0.532; p < 0.001; indirect effect β = 0.139; p = 0.007; direct effect β = 0.393; p < 0.001). Conclusions: Burnout is a key predictor of nurses’ intention to leave the profession, while ethical leadership of nurse coordinators emerges as a potential organizational resource associated with this relationship. Nursing implications: These findings highlight the importance of promoting ethical leadership within nursing management as part of broader organizational strategies to improve staff well-being and potentially support efforts aimed at reducing nurses’ intention to leave the profession. Full article

Although artificial intelligence (AI) capabilities have emerged as a critical driver of corporate innovation in the contemporary business landscape, how they facilitate ambidextrous green innovation (AGI) during the manufacturing sector’s green transition—and under what conditions these benefits are most pronounced—remains unclear. Drawing on the Resource-Based View (RBV) and Knowledge-Based View (KBV), this study investigates the mechanism by which AICs foster AGI through the mediating role of green knowledge management (GKM), while further examining how Human–Organization–Technology (HOT) fit moderates these pathways. An analysis of survey data from 238 Chinese manufacturing firms using PLS-SEM reveals that AICs significantly drive AGI, with GKM playing a pivotal mediating role. Furthermore, the study confirms that Human–Organization–Technology (HOT) fit acts as a boundary condition, moderating the impact of AICs on GKM. These findings clarify the underlying mechanisms and boundary conditions of AICs, offering actionable insights for manufacturers seeking to boost green innovation capabilities by optimizing HOT alignment and leveraging green knowledge management systems. Full article

In recent years, optimization algorithms have emerged as powerful computational tools for addressing complex and dynamic challenges across diverse domains. These domains include engineering, technology, management, and decision-making. Their growing importance is motivated by (a) the increasing complexity of modern systems, (b) the need for efficient resource utilization, and (c) the demand for scalable algorithmic solutions. These algorithms enable the systematic and computational exploration of large solution spaces, supporting decision-making and design under uncertainty, large-scale data, and evolving requirements. This study provides a structured review and comparative scientometric analysis of optimization algorithms, covering: (a) exact methods, (b) approximation techniques, (c) metaheuristics, and (d) emerging physics-informed frameworks. The analysis highlights algorithmic trends, performance-oriented research directions, and the increasing integration of mathematical programming, machine learning, and numerical methods. The results show a renewed focus on classical algorithmic paradigms. Moreover, rapid growth in hybrid and physics-informed optimization approaches is observed. These findings confirm the central role of optimization algorithms in modern algorithm engineering and interdisciplinary computational research. Full article

Despite significant progress in general-purpose diffusion-based models capable of producing high-quality media, this approach is still too difficult to implement on consumer/gamer hardware. We present here a memory-optimized DreamBooth framework designed for consumer-grade GPUs with 16 GB of VRAM, that allows for end-to-end image personalization and addresses some of the limitations of existing solutions. Our system reduces peak GPU memory from 22 GB (baseline DreamBooth) to 14.2 GB through novel hierarchical memory management, including attention slicing, Variational Autoencoder (VAE) tiling, gradient accumulation, and gradient checkpointing integrated within the Hugging Face Accelerate ecosystem. The framework further incorporates state-of-the-art techniques for preserving facial features and a comprehensive automated quality management system. The result is a complete end-to-end pipeline achieving a peak memory of 14.2 GB, with quantitative performance (LPIPS: 0.139, SSIM: 0.879, identity: 0.852, and FID: 23.1) competitive with methods requiring significantly more hardware resources. Full article

Escalating climate volatility, particularly the El Niño/Southern Oscillation (ENSO), poses severe operational and financial risks to corporate sustainability in the energy sector. However, quantitative evidence regarding how macro-level climate shocks transmit to micro-level operational performance remains scarce. Integrating dynamic capability and social network theories, this study analyzes a panel of 103 Chinese listed energy firms (2005–2022) using System GMM, mediation, and moderation models. The results indicate that ENSO intensity significantly impairs performance; specifically, a 1 °C rise in sea surface temperature anomalies decreases firms’ return on assets (ROAs) by 0.142%. We identify supply chain resilience as a critical strategic mechanism for climate adaptation, where response capacity acts as the dominant mediating channel, while recovery capacity functions as an independent compensatory mechanism. Conversely, supply network complexity—across horizontal, vertical, and spatial dimensions—amplifies the negative impact of climate disruptions by hindering resource mobility. Heterogeneity analysis reveals that state-owned enterprises exhibit stronger institutional resilience, and firms in southern regions partially offset impacts through hydropower advantages. This study bridges climate science with operations management, offering strategic guidance for managers to configure resilient, sustainable supply chains capable of withstanding environmental turbulence. Full article

Islands often suffer from relatively limited freshwater resources, and the effective utilization and distribution of water resources are a key issues for the sustainable development of island-based economies and societies. While island water security has been widely discussed, few studies trace the socio-technical construction of island water-supply systems across the stages of planning, construction, and operation. Integrating Actor-Network Theory with political ecology, this study investigates the water-supply infrastructure of Kinmen. Drawing on official archives, participant observation, and in-depth interviews, this research analyzes the collective actions mobilized to address Kinmen’s water scarcity following the lifting of martial law in 1992. These efforts jointly reshaped both water-supply practices and the infrastructural network. Over the past three decades, Kinmen’s water-supply system has transformed into a sophisticated technological network, integrating reservoirs, desalination plants, and advanced sewage infrastructure. The introduction of these technologies, which function as critical non-human actors within the system, marks a clear shift in how water is managed and distributed. However, the rapid expansion of water-intensive industries, especially tourism, liquor distilling, and cattle farming, has outpaced local ecological limits, precipitating the current water crisis. The study concludes that this shortage has been mitigated through the strategic integration of water sources, most notably the cross-strait pipeline from mainland China, which now provides more than 80 percent of the island’s water. This transition marks a profound shift in the island’s socio-technical and geopolitical network. Full article

As private landownership becomes increasingly fragmented and socially diverse, understanding how new types of landowners interact with wildlife and engage in management practices is essential for achieving landscape-scale conservation outcomes. This issue has taken on renewed urgency in the post-pandemic era, as amenity migration expanded beyond the traditionally studied ultra-wealthy purchasers of large ranches to include a broader socioeconomic spectrum acquiring small-acreage properties. This shift has introduced a more heterogeneous population of land stewards, many operating with limited financial, technical, and institutional resources in highly fragmented landscapes. This paper examines the intersection of sociodemographic change and private land conservation, focusing on the implications of amenity migration for wildlife management in rural private ecosystems. Through an integrative review of the amenity migration literature informed by a PRISMA-based search and screening protocol, we show that although wildlife is frequently referenced in this literature, wildlife management is rarely examined as an intentional and coordinated social–ecological practice. We argue that the implications of contemporary amenity migration for wildlife management extend beyond individual landowners to include institutional systems, shifting community dynamics, and cross-boundary governance challenges that shape wildlife outcomes across private landscapes. Recognizing the post-pandemic transformation of rural landownership is therefore essential for aligning conservation resources and institutional support systems with emerging patterns of private land stewardship. Full article