Search Results (7,936)

The development of the digital economy has become a significant driving force for the innovation of green technology in the manufacturing sectors. Green technology innovation in the manufacturing sectors is not only a key engine for realizing economic green transformation and achieving the goal of achieving peak carbon emissions by 2030 and carbon neutrality by 2060, but also an important path for cultivating new quality productivity. Based on Schumpeter’s endogenous growth theory, in this study, we constructed an analytical model with a unified framework of digital economic development and environmental regulation, systematically explored the mechanism of digital economic development with respect to green technological innovation in the manufacturing sectors and the moderating effect of environmental regulation, and carried out empirical research based on panel data at the provincial level and the level of the subdivided manufacturing sectors in China. We found that the development of the digital economy promotes green technology innovation in the manufacturing industry. However, according to the theory of increasing marginal information costs, it shows a significant nonlinear relationship. Absorptive capacity is the key means of support that manufacturing enterprises can leverage to improve their level of green technological innovation. Environmental regulation plays a crucial role in guiding green technological innovation in the manufacturing sectors. A further heterogeneity analysis showed that the development of the digital economy exerts a stronger positive impact on green technological innovation in cleaner-production-oriented manufacturing sectors and those located in regions with more advanced financial regions and in technology-intensive industries. This study provides theoretical support for understanding the driving mechanisms of green technological innovation in the manufacturing sector against the backdrop of the digital economy, offering practical implications for optimizing environmental regulation policies and enhancing the level of green development in manufacturing. Full article

Large-scale particle-based fluid simulations present significant computational challenges, particularly in achieving interactive frame rates while maintaining visual quality. Unity3D’s widespread adoption in game development, VR/AR applications, and scientific visualization creates a unique need for efficient fluid simulation within its ecosystem. This paper presents a GPU-accelerated Smoothed Particle Hydrodynamics (SPH) framework implemented in Unity3D that effectively addresses these challenges through several key innovations. Unlike previous GPU-accelerated SPH implementations that typically struggle with scaling beyond 100,000 particles while maintaining real-time performance, we introduce a novel fusion of Count Sort with Parallel Prefix Scan for spatial hashing that transforms the traditionally expensive O(n²) neighborhood search into an efficient O(n) operation, significantly outperforming traditional GPU sorting algorithms in particle-based simulations. Our implementation leverages a Structure of Arrays (SoA) memory layout, optimized for GPU compute shaders, achieving 30–45% improved computation throughput over traditional Array of Structures approaches. Performance evaluations demonstrate that our method achieves throughput rates up to 168,600 particles/ms while maintaining consistent 5.7–6.0 ms frame times across varying particle counts from 10,000 to 1,000,000. The framework maintains interactive frame rates (>30 FPS) with up to 500,000 particles and remains responsive even at 1 million particles. Collision rates approaching 1.0 indicate near-optimal hash distribution, while the adaptive time stepping mechanism adds minimal computational overhead (2–5%) while significantly improving simulation stability. These innovations enable real-time, large-scale fluid simulations with applications spanning visual effects, game development, and scientific visualization. Full article

Structured illumination microscopy (SIM) with π/2 phase-shift modulation traditionally relies on frequency-domain computation, which greatly limits processing efficiency. In addition, the illumination regime inherent in structured illumination techniques often results in poor visual quality of reconstructed images. To address these dual challenges, this study introduces DM-SIM-LLIE (Differential Low-Light Image Enhancement SIM), a novel framework that integrates two synergistic innovations. First, the study pioneers a spatial-domain computational paradigm for π/2 phase-shift SIM reconstruction. Through system differentiation, mathematical derivation, and algorithm simplification, an optimized spatial-domain model is established. Second, an adaptive local overexposure correction strategy is developed, combined with a zero-shot learning deep learning algorithm, RUAS, to enhance the image quality of structured light reconstructed images. Experimental validation using specimens such as fluorescent microspheres and bovine pulmonary artery endothelial cells demonstrates the advantages of this approach: compared with traditional frequency-domain methods, the reconstruction speed is accelerated by five times while maintaining equivalent lateral resolution and excellent axial resolution. The image quality of the low-light enhancement algorithm after local overexposure correction is superior to existing methods. These advances significantly increase the application potential of SIM technology in time-sensitive biomedical imaging scenarios that require high spatiotemporal resolution. Full article

Macro policy regulation centered on carbon emissions profoundly influences the path for enterprises to achieve low-carbon transformation. Using panel data from Chinese A-share listed companies over the period from 2014 to 2023, this study adopts the methods of panel regression, moderating effect and mediating effect. The empirical research finds that: (1) Policy uncertainty from carbon emission constraints significantly incentivizes industrial enterprises to adopt greener governance strategies. (2) The mechanism analysis indicates that the uncertainty posed by carbon emission constraints influences corporate green governance by enhancing regional green finance development, intensifying corporate financing constraints, and improving the quality of corporate green innovation. (3) Enterprises with substantial environmental protection investments and stronger reputations are less susceptible to changes in their green governance strategies triggered by carbon emission constraint policies. (4) The effects of carbon constraint policy uncertainty on green governance strategies of industrial enterprises exhibit heterogeneity. Specifically, these effects are relatively weaker for non-heavy-polluting enterprises located in carbon emission trading pilot cities, enterprises with higher information disclosure quality, and enterprises whose senior executives have backgrounds in environmental protection. Ultimately, to promote the sustainable development of industrial enterprises, this study provides three recommendations. Full article

Background: Digital self-efficacy is a crucial determinant of healthcare professionals’ ability to adapt to technological innovations. Understanding its predictors among nurses and nursing students is essential for workforce readiness. Objectives: To assess the level of digital self-efficacy and examine demographic, educational, and experiential factors associated with inadequate self-efficacy. Methods: This cross-sectional study involved 1081 Italian nurses and nursing students. The Digitech-S scale was used to measure digital self-efficacy, with ≥70/100 indicating adequacy. Logistic regression was performed to identify predictors of inadequate self-efficacy. Results: Only 47.1% of participants demonstrated adequate self-efficacy. Females had twice the odds of inadequate self-efficacy compared to males (OR = 2.038, p < 0.001). Nurses with bachelor’s degrees had 2.5 times higher odds than students (OR = 2.450, p < 0.001), while post-graduate education showed no effect. Early technology adoption before age 14 reduced the odds (OR = 0.675, p = 0.027). Each additional year of work experience decreased the odds by 4% (OR = 0.955, p < 0.001). Conclusions: Gender disparities persist in digital self-efficacy, and unexpectedly, students outperformed bachelor-level nurses. Findings highlight educational gaps and the importance of early exposure to technology. Tailored interventions are needed to strengthen digital readiness, which may improve care quality and healthcare system efficiency in the digital era. Full article

This study introduces and evaluates an innovative methodology for assessing constructability in the maintenance of university buildings, aiming to improve the quality of academic infrastructure. The proposed approach is based on four key criteria: functionality, usage, investment, and curricular planning. These criteria are derived from the principles established by the Chilean Construction Industry Council (CCI Chile, 2024) and were applied in a case study at Ricardo Palma University. A quasi-experimental research design was implemented in two physical spaces within the Faculty of Architecture and Urbanism, one of which underwent a maintenance intervention while the other remained unaltered. Data was collected through expert-validated instruments, administered to senior students and technical staff before and after the intervention. The results revealed significant improvements, with satisfaction levels increasing from 44% to 56% among students and a 10% rise in positive technical evaluations (p < 0.005) which reflected an improvement in the perceived quality of the academic environment, especially in areas related to maintenance planning, execution, control, safety, and user comfort. This study concludes that integrating constructability criteria into the maintenance phase can optimize infrastructure management, enhancing sustainability, operational efficiency, and user satisfaction. The developed methodology offers a practical and replicable tool for other academic units and universities, supporting continuous improvement and promoting evidence-based decision-making in the management of educational facilities. Full article

Background/Objectives: Existing research has highlighted the stress associated with the transition from student to practitioner among newly hired nurses, often resulting in diminished professional quality of life (ProQOL). However, there remains a dearth of understanding regarding the impact of the teaching methods during the COVID-19 pandemic on this transition period. This study aims to test a model assessing the mediating role of ProQOL in the association between structural empowerment and successful transition among newly hired nurses who underwent education during the COVID-19 pandemic. Methods: This study utilized a cross-sectional correlational design and was conducted in two university hospitals and four government hospitals in Saudi Arabia. The study sample was selected using purposive sampling. The Casey–Fink Graduate Nurse Experience Survey, the Arabic version of the ProQOL version 5, and the Conditions for Workplace Effectiveness Questionnaire Second Arabic version were used in the study. Data were analyzed using the Statistical Package for Social Sciences (SPSS) Version 28.0.1.1. The model was examined using Hayes’ process macro. Results: Structural empowerment significantly predicts successful transitions, both directly and indirectly through its impact on ProQOL. Conclusions: Nurse managers should employ optimal strategies and innovative structures within orientation programs to effectively facilitate the transition of Saudi graduate nurses. Moreover, nursing leaders and policymakers should leverage the increased attention garnered during the COVID-19 pandemic to enhance structural empowerment among newly hired nurses, thereby improving their transition and overall well-being. Structural empowerment was a direct and indirect predictor of successful transitions. Full article

Ensuring food quality and safety is a growing challenge in the food industry, where early detection of contamination or spoilage is crucial. Using gas sensors combined with Artificial Intelligence (AI) offers an innovative and effective approach to food identification, improving quality control and minimizing health risks. This study aims to evaluate food identification strategies using supervised learning techniques applied to data collected by the BME Development Kit, equipped with the BME688 sensor. The dataset includes measurements of temperature, pressure, humidity, and, particularly, gas composition, ensuring a comprehensive analysis of food characteristics. The methodology explores two strategies: a neural network model trained using Bosch BME AI-Studio software, and a more flexible, customizable approach that applies multiple predictive algorithms, including DT, LR, kNN, NB, and SVM. The experiments were conducted to analyze the effectiveness of both approaches in classifying different food samples based on gas emissions and environmental conditions. The results demonstrate that combining electronic noses (E-Noses) with machine learning (ML) provides high accuracy in food identification. While the neural network model from Bosch follows a structured and optimized learning approach, the second methodology enables a more adaptable exploration of various algorithms, offering greater interpretability and customization. Both approaches yielded high predictive performance, with strong classification accuracy across multiple food samples. However, performance variations depend on the characteristics of the dataset and the algorithm selection. A critical analysis suggests that optimizing sensor calibration, feature selection, and consideration of environmental parameters can further enhance accuracy. This study confirms the relevance of AI-driven gas analysis as a promising tool for food quality assessment. Full article

Targeting the classification and evaluation of chemical flooding well groups in medium-thick sandstone reservoirs (single-layer thickness: 5–15 m), this study proposes a multi-level classification model based on decision trees. Through the comprehensive analysis of key static factors influencing chemical flooding efficiency, a four-tier classification index system was established, comprising: interlayer/baffle development frequency (Level 1), thickness-weighted permeability rush coefficient (Level 2), reservoir rhythm characteristics (Level 3), and pore-throat radius-based reservoir connectivity quality (Level 4) as its core components. The model innovatively transforms common reservoir physical parameters (porosity and permeability) into pore-throat radius parameters to enhance guidance for polymer molecular weight design, while employing a thickness-weighted permeability rush coefficient to simultaneously characterize heterogeneity impacts from both permeability and thickness variations. Unlike existing classification methods primarily designed for thin-interbedded reservoirs—which consider only connectivity or apply fuzzy mathematics-based normalization—this model specifically addresses medium-thick reservoirs’ unique challenges of interlayer development and intra-layer heterogeneity. Furthermore, its decision tree architecture clarifies classification logic and significantly reduces data preprocessing complexity. In terms of engineering practicality, the classification results are directly linked to well-group development bottlenecks, as validated in the J16 field application. By implementing customized chemical flooding formulations tailored to the study area, the production performance in the expansion zone achieved comprehensive improvement: daily oil output dropped from 332 tons to 243 tons, then recovered to 316 tons with sustained stabilization. Concurrently, recognizing that interlayer barriers were underdeveloped in certain well groups during production layer realignment, coupled with strong vertical heterogeneity posing polymer channeling risks, targeted profile modification and zonal injection were implemented prior to flooding conversion. This intervention elevated industrial replacement flooding production in the study area from 69 tons to 145 tons daily post-conversion. This framework provides a theoretical foundation for optimizing chemical flooding pilot well-group selection, scheme design, and dynamic adjustments, offering significant implications for enhancing oil recovery in medium-thick sandstone reservoirs through chemical flooding. Full article

This paper contributes conceptually and empirically to a more rigorous understanding of the role of intellectual capital in the sustainable development of the BRICS+ bloc. We investigate the growing command of technical competencies over social competencies across the entire knowledge process. A range of factors, including the ever-increasing tension between AI and humans, the multidimensional nature of intellectual capital, and a focus on competency-based approaches, shape the theory of a knowledge economy. This study presents a spatial modeling approach to analyze the sustainable development of economic systems, reevaluates the importance of intellectual capital in the era of Industry 4.0, introduces the concept of scientific management of intellectual capital by categorizing it into the AI, individual, and collective human mind, and enhances the methodology of managing the knowledge economy to foster intellectual capital development. The primary finding of the research is that the advancement of the knowledge economy is driving digital communication and network-based collaboration on a larger scale within the BRICS+ bloc. Policy implications are intricately linked to the necessity for the holistic development of intellectual capital, encompassing both human and artificial intelligence. This development requires enhancements in quality of life and living standards, advancements in education and healthcare, optimization of the labor market, and reinforcing its connection with the educational sector. Concurrently, it is vital to stimulate research and development (R&D), support the commercialization of high-tech innovations, and accelerate the process of robotization. These combined efforts are essential to fostering economic growth effectively. Full article

Alzheimer’s disease is a growing global health concern, demanding innovative solutions for early detection, continuous monitoring, and patient support. This article reviews recent advances in Smart Wearable Medical Devices (SWMDs), Internet of Things (IoT) systems, and mobile applications used to monitor physiological, behavioral, and cognitive changes in Alzheimer’s patients. We highlight the role of wearable sensors in detecting vital signs, falls, and geolocation data, alongside IoT architectures that enable real-time alerts and remote caregiver access. Building on these technologies, we present LumiCare, a conceptual, context-aware mobile system that integrates multimodal sensor data, chatbot-based interaction, and emerging 6G network capabilities. LumiCare uses machine learning for behavioral analysis, delivers personalized cognitive prompts, and enables emergency response through adaptive alerts and caregiver notifications. The system includes the LumiCare Companion, an interactive mobile app designed to support daily routines, cognitive engagement, and safety monitoring. By combining local AI processing with scalable edge-cloud architectures, LumiCare balances latency, privacy, and computational load. While promising, this work remains at the design stage and has not yet undergone clinical validation. Our analysis underscores the potential of wearable, IoT, and mobile technologies to improve the quality of life for Alzheimer’s patients, support caregivers, and reduce healthcare burdens. Full article

Given the growing consumer demand for improved quality of life and health-promoting foods, replacing conventional fats in widely consumed products such as bread with oils derived from native Brazilian fruits represents a promising strategy. This study aimed to evaluate the bioactive and technological potential of buriti (Mauritia flexuosa) and acuri (Attalea phalerata) oils, extracted from palm fruits native to the Cerrado and Amazon biomes. Both oils proved to be rich sources of lipophilic bioactives, particularly carotenoids, tocopherols, and phenolic compounds, and exhibited excellent carotenoid bioaccessibility under in vitro digestion, with recovery rates of 74% for acuri oil and 54% for buriti oil. Notably, buriti oil showed a high β-carotene content (1476.5 µg/g). When incorporated into sandwich bread formulations, these oils enhanced antioxidant activity, improved texture, volume, and color, and maintained high sensory acceptance compared to bread made with soybean oil. Sensory evaluation scores averaged above 7 for all tested attributes. These findings underscore the industrial applicability of buriti and acuri oils as functional lipids aligned with sustainable development and nutritional innovation. Full article

Camouflaged object detection (COD) seeks to precisely detect and delineate objects that are concealed within complex and ambiguous backgrounds. However, due to subtle texture variations and semantic ambiguity, it remains a highly challenging task. Existing methods that rely solely on either convolutional neural network (CNN) or Transformer architectures often suffer from incomplete feature representations and the loss of boundary details. To address the aforementioned challenges, we propose an innovative hybrid architecture that synergistically leverages the strengths of CNNs and Transformers. In particular, we devise a Hybrid Feature Fusion Module (HFFM) that harmonizes hierarchical features extracted from CNN and Transformer pathways, ultimately boosting the representational quality of the combined features. Furthermore, we design a Combined Recursive Decoder (CRD) that adaptively aggregates hierarchical features through recursive pooling/upsampling operators and stage-wise mask-guided refinement, enabling precise structural detail capture across multiple scales. In addition, we propose a Foreground–Background Selection (FBS) module, which alternates attention between foreground objects and background boundary regions, progressively refining object contours while suppressing background interference. Evaluations on four widely used public COD datasets, CHAMELEON, CAMO, COD10K, and NC4K, demonstrate that our method achieves state-of-the-art performance. Full article

The rapid proliferation of Chat Generative Pre-trained Transformer (ChatGPT) marks a pivotal moment in artificial intelligence, eliciting responses from academic shock to industrial awe. As these technologies advance from passive tools toward proactive, agentic systems, their transformative potential and inherent risks are magnified globally. This paper presents a comprehensive, critical review of ChatGPT’s impact across five key domains: natural language understanding (NLU), content generation, knowledge discovery, education, and engineering. While ChatGPT demonstrates profound capabilities, significant challenges remain in factual accuracy, bias, and the inherent opacity of its reasoning—a core issue termed the “Black Box Conundrum”. To analyze these evolving dynamics and the implications of this shift toward autonomous agency, this review introduces a series of conceptual frameworks, each specifically designed to illuminate the complex interactions and trade-offs within these domains: the “Specialization vs. Generalization” tension in NLU; the “Quality–Scalability–Ethics Trilemma” in content creation; the “Pedagogical Adaptation Imperative” in education; and the emergence of “Human–LLM Cognitive Symbiosis” in engineering. The analysis reveals an urgent need for proactive adaptation across sectors. Educational paradigms must shift to cultivate higher-order cognitive skills, while professional practices (including practices within education sector) must evolve to treat AI as a cognitive partner, leveraging techniques like Retrieval-Augmented Generation (RAG) and sophisticated prompt engineering. Ultimately, this paper argues for an overarching “Ethical–Technical Co-evolution Imperative”, charting a forward-looking research agenda that intertwines technological innovation with vigorous ethical and methodological standards to ensure responsible AI development and integration. Ultimately, the analysis reveals that the challenges of factual accuracy, bias, and opacity are interconnected and acutely magnified by the emergence of agentic systems, demanding a unified, proactive approach to adaptation across all sectors. Full article