Search Results (2,619)

With the rapid growth of the digital economy, the application of artificial intelligence (AI) technology has injected new momentum into persistent green innovation. Using data on Chinese A-share listed companies from 2010 to 2023, this article aims to investigate whether senior executives’ adoption of AI technology influences companies’ persistent green innovation and to identify the specific mechanisms underlying this relationship. To improve measurement accuracy, this paper employs the BERT model to conduct an in-depth analysis of corporate annual report texts to construct an executive AI adoption metric. The findings reveal that executive AI adoption significantly promotes corporate persistent green innovation, and this effect is primarily achieved through enhanced data factor allocation capabilities. Moreover, strategic agility positively moderates the relationship between executive AI adoption and corporate persistent green innovation. Specifically, the higher the level of strategic agility, the stronger the mediating role of data factor allocation in the relationship between executive AI adoption and corporate persistent green innovation. In particular, executive AI adoption plays a more significant role in fostering persistent green innovation among firms with higher total factor productivity and those facing intense market competition. Full article

In contemporary organizations, digital learning environments and AI-supported instructional modalities play an increasingly important role in workforce upskilling and operational efficiency. Despite growing investments in video-based learning and AI-generated instructional agents, empirical evidence on their effectiveness remains inconclusive. This study examines whether different digital learning modalities influence skill acquisition, task performance, retention, and user perceptions in a simulated work-related context. An experimental study was conducted with 65 participants assigned to one of three learning conditions: static instructional material, video-based instruction with human narration, and video-based instruction with an AI-generated avatar. Performance was assessed through a pretest–posttest design, a practical task simulating a typical data-processing activity, and a delayed retention test after seven days. Participants also evaluated the learning experience in terms of clarity, engagement, and overall effectiveness. The results revealed no statistically significant differences between instructional modalities in knowledge acquisition, task performance, or retention. Similarly, no statistically significant differences were observed in participants’ self-reported ratings. However, qualitative findings suggested that some participants perceived the AI-generated avatar as somewhat distracting, despite generally positive evaluations of the video-based formats. These findings did not provide evidence that more technologically advanced and resource-intensive learning formats led to superior performance outcomes in the present sample. The findings highlight the importance of instructional design quality over technological complexity and point to a potential mismatch between user preferences and actual performance. From a management perspective, the results raise relevant questions regarding the cost-effectiveness of AI-supported learning solutions and provide evidence-based insights for decision-making in organizational learning and digital transformation strategies. Full article

For thousands of years glass packaging for wine has traditionally been associated with quality and remains used today as an inert and recyclable container. However, alternative containers such as aluminum cans and polyethylene terephthalate (PET) bottles have been gaining traction over the last several years because of their lower cost, increased recyclability, and increasing consumer acceptance. Advancements in can-liner technology further support aluminum cans as a realistic option for wineries; however, data on how different packaging types influence the quality of packaged wine remains sparse. This study evaluated the physiochemical properties of carbonated blueberry wine stored in glass bottles, aluminum cans, and polyethylene terephthalate (PET) bottles under accelerated conditions (35 °C). Across the three packaging types, the wine quality parameters of total acidity, sugar, and pH did not differ significantly. There were, however, measurable statistical differences that emerged in color, anthocyanin content, and volatile organic compound (VOC) profiles. Pearson’s correlation analysis revealed a strong linear relationship between the degradation of color (intensity and hue) and anthocyanin concentration over time for all packaging types, with the loss being dependent upon packaging type. These findings indicate that while certain quality attributes vary with container, the overall chemical changes in blueberry wine are comparable across glass, aluminum, and PET bottles. Consequently, aluminum can packaging stands as a viable, cost-effective alternative packaging for blueberry wine producers. Full article

Increasing external uncertainty, supply disruptions, and market volatility have made resilience enhancement increasingly important for sustainable agricultural supply chains. While existing studies mainly examine agricultural supply chain resilience from macro or operational perspectives, limited attention has been paid to how firms’ strategic AI investment reshapes organizational resilience under external shocks. Using panel data on Chinese agricultural-related listed firms from 2010 to 2024, this study examines whether and how strategic AI investment enhances supply chain resilience. Empirical results show that strategic AI investment significantly improves both dimensions of supply chain resilience, namely resistance capacity and recovery capacity. Mechanism analyses indicate that this effect mainly operates through supply diversification, technological innovation, and information transparency. Further analyses reveal heterogeneous effects across supply chain positions, ownership structures, and regional digital development environments. In addition, compatibility analyses show that strategic AI investment not only strengthens supply chain resilience but also improves operational efficiency, R&D investment intensity, and financial stability. Overall, this study highlights strategic AI investment as an important organizational capability for strengthening agricultural supply chain resilience under increasing external uncertainty. Full article

The cement industry is one of the most energy- and emission-intensive sectors and plays a crucial role in achieving climate neutrality and sustainability objectives. This study examines waste management practices in cement production within the framework of the circular economy and low-carbon transition, with particular emphasis on Polish cement plants operating under EU environmental regulations. Particular attention is given to the use of waste as alternative fuels and secondary raw materials, as well as to the economic and environmental implications of EU climate policy instruments. The research methodology includes an analysis of key emission sources such as clinker production, fuel combustion, and raw material transport and an evaluation of technological and organizational measures aimed at improving energy efficiency and reducing emissions. The empirical analysis is based primarily on operational observations from selected Polish cement plants operating under EU ETS conditions and combines plant-level operational evidence with comparative sectoral data and scenario-based techno-economic assessments related to selected low-carbon technologies. The results suggest that increasing the use of waste-derived fuels and materials may contribute to emission reduction, lower reliance on non-renewable resources, and improved circularity in cement production systems operating under advanced regulatory conditions. Furthermore, the findings highlight the potential for synergies between environmental performance and economic competitiveness. The study underscores the importance of coherent regulatory frameworks and continued investment in low-emission and circular technologies to ensure the long-term sustainability and viability of the cement industry. Full article

Digital technologies are increasingly integrated into elderly care systems and have important implications for sustainable social development. This study investigates whether China’s Smart Health and Elderly Care (SHEC) Pilot Program enhances elderly care service provision and improves the spatial equity of resource distribution. Using prefecture-level data on elderly care institutions from 2010 to 2021, this paper employs a staggered difference-in-differences (DID) approach to identify the impact of SHEC on elderly care service provision. SHEC is treated as a digitally oriented policy initiative rather than a direct measure of digital technology adoption intensity. The results show the following: First, the pilot program significantly expands the service capacity of the elderly care system, as reflected in increases in both the number of elderly care institutions and bed capacity. Second, the policy has stronger effects on service capacity expansion in less-developed and high-aging regions, whereas the estimated effects are limited in more-developed and low-aging regions. Third, the analysis also provides exploratory evidence on potential supply-side and demand-side mechanisms. Finally, the equity analysis based on the Theil index suggests that participation in SHEC improves allocative equity, thereby supporting sustainable social development. This paper contributes to the literature by highlighting how digital empowerment-oriented policy interventions in the elderly care sector promote the sustainable expansion and equitable allocation of public service resources. Full article

This paper investigates the impact of atmospheric conditions on distance determination accuracy in the VDES R-Mode system, based on system development and long-term analytical work conducted within the ORMOBASS project. A dedicated VDES R-Mode transmitter and monitoring station were developed and deployed in Poland, in the Port of Gdynia and at the boatswain’s office in the port of Jastarnia, respectively. Both stations were synchronized in time and frequency using a fiber-optic link and White Rabbit technology, ensuring high-precision and stable operation during long-term measurements. Based on a one-year stationary measurement campaign, a comprehensive dataset combining ranging results and meteorological observations was collected and analyzed. Statistical evaluation demonstrated that atmospheric conditions—particularly rainfall intensity and water vapor density—have a measurable impact on ranging accuracy. These findings motivated the development of a mathematical model describing the relationship between atmospheric conditions and distance measurement errors. The proposed logarithmic regression-based approach was validated using real measurement data; the authors also demonstrated its ability to reduce error variability during changing weather conditions, indicating its potential for future implementation in VDES R-Mode receivers. Full article

Recycled aggregates (RAs) from construction and demolition waste (CDW) provide substantial circular-economy benefits, yet their elevated porosity, adhered mortar, and heterogeneity typically impair the mechanical performance and durability of recycled aggregate concrete (RAC). This PRISMA 2020-compliant systematic review synthesises 2180 records (2015–2026) to evaluate advanced strategies for enhancing RA quality prior to structural use. This paper critically compares removal-based treatments (mechanical, thermal, acid cleaning) with strengthening and densification approaches, including accelerated carbonation, pozzolanic and nano-silica coatings, polymer impregnation, microbial-induced calcium carbonate precipitation (MICP), and modified mixing methods such as triple-stage mixing (TSMA). Evidence shows that while all RA types (including recycled fine aggregate (RFA), recycled coarse aggregate (RCA), and their combination (RFCA)) can slightly reduce compressive strength and 30% replacement serves as a critical threshold, beyond this, strength loss accelerates, particularly in RCA and RFCA mixes. However, accelerated carbonation and TSMA consistently refine the interfacial transition zone, reduce water absorption by 17–30%, and recover 85–94% of natural aggregate concrete strength. Bio-deposition reduces water absorption by 13–21%, while acid/silica fume treatments improve late-age strength but carry environmental trade-offs. This review formulates a practice-oriented implementation framework for structural-grade RAC. Sustainability analyses indicate that carbonated RA can achieve net-positive CO₂ abatement when under low-carbon energy supply. A mechanistic schematic is presented to synthesise treatment-to-pore-structure/durability pathways across the four principal treatment routes, and a quantitative synthesis plot compares water absorption reductions across all treatment types using 13 data points drawn from included studies. A structured treatment comparison evaluates the energy intensity, industrial scalability, CO₂ footprint, and technology readiness level for each strategy. The remaining challenges include a lack of hybrid treatment studies, limited real-scale durability data, and insufficient mechanistic models linking treatment to pore structure evolution. This review recommends harmonised durability-based criteria and updates to standards (e.g., BS 8500, EN 12620) to support the scalable deployment of treated RA. Full article

This study assesses export concentration risk in four Central Asian economies (Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan) by examining trade specialization patterns in 31 mineral, chemical, textile, and metallurgical product groups over 2017–2024. Using a multi-index framework based on Revealed Symmetric Comparative Advantage (RSCA), Relative Trade Advantage (RTA), and the Lafay Index (LI), the paper distinguishes structurally embedded competitive advantages from export signals that are weak, import-dependent, or potentially transient. The revised analysis adds explicit data consistency checks, a clarified classification rule, and robustness tests based on sign concordance, majority-index rules, and RSCA-only thresholds. The results show that Central Asia’s risk profile is highly persistent but heterogeneous: Tajikistan is exposed to extreme single-commodity risk in aluminium and cotton-related segments; Kazakhstan remains vulnerable to mineral-fuel concentration and energy-price volatility; Uzbekistan has broader but still labour-intensive textile specialization; and Kyrgyzstan shows ambiguous competitiveness that may partly reflect re-export and transit-related trade. Fully competitive product groups are confined mainly to resource- and labour-intensive activities, while chemicals and technologically complex manufacturing remain non-competitive across the region. The findings support risk-differentiated policy responses, including commodity-price hedging, counter-cyclical stabilization tools, downstream processing, textile upgrading, and regional value-chain development. Full article

This research paper examines the environmental impact of natural and synthetic fur coats, focusing exclusively on the processing and manufacturing stages. Using one coat weighing approximately 5 kg as the functional unit, a comparative Life Cycle Assessment (LCA) is conducted from raw material processing to final garment production, explicitly excluding animal farming. The analysis includes key processes such as cleaning, tanning, dyeing, and sewing for natural fur, and polymer production, fabric formation, dyeing, and finishing for synthetic fur. Data from international academic literature (Google Scholar and Scopus) are used to evaluate CO₂ emissions, energy and water consumption, chemical inputs, and waste generation. Results indicate that synthetic fur production is energy-intensive but requires relatively low water use, whereas natural fur processing involves high water consumption and chemical treatments, resulting in significantly higher emissions—often reaching hundreds to thousands of kg CO₂e per coat. The study further investigates the role of embedded IoT systems in improving efficiency within tanneries and textile manufacturing. Real-time monitoring and automated dosing systems can reduce emissions and chemical use by approximately 10–20%. Case studies of a smart tannery and an IoT-enabled synthetic fur production line illustrate potential implementation pathways. Although such optimizations can reduce environmental impacts, the findings clearly show that natural fur processing remains considerably more carbon-intensive than synthetic alternatives. This research highlights the importance of integrating digital technologies into industrial processes and suggests directions for future work based on real-world operational data. Full article

Asymmetry is a prevalent phenomenon in low-power and lossy networks (LLNs) due to resource constraints and unstable links. The routing protocol for the low power and lossy network (RPL), standardized by the Internet Engineering Task Force (IETF), is specifically designed for LLNs with characteristics of resource constraints, lossy links, and complex communication environments. However, its performance is fundamentally limited by node capabilities and unstable links, a contradiction exacerbated by the stringent QoS demands of emerging applications like IIoT or precision agriculture. Consequently, new RPL routing technologies based on the digital twin-enabled computing power network, called RPL-DTCP, were designed to improve network QoS and support practical applications. First, a low-power and lossy network architecture based on twin-enabled computing network was proposed, considering LLN requirements and computing twin services. Second, in response to the requirements of the digital twin, computing power network and LLNs for low synchronization latency, high data accuracy, efficient computing resource utilization, and energy conservation, several routing metrics were designed, including the data processing model, model deployment rate, end-to-end delay, node remaining energy, and ETX. Then an initial matrix and a comprehensive objective function were formulated to comprehensively evaluate these metrics. Third, to solve the multi-objective optimization problem, an enhanced whale optimization algorithm (E-WOA) was developed. E-WOA improved upon the standard version by using improved Tent chaotic mapping for population initialization, nonlinear adaptive convergence factor, and Cauchy variation mutation operator for solution perturbation, thereby enhancing its global search capability and convergence speed, enabling it to effectively identify the optimal routing path. Simulations confirmed that RPL-DTCP outperforms benchmark algorithms, achieving significant reductions in end-to-end delay, higher packet delivery ratios, extended network lifetime, etc. These findings demonstrate that RPL-DTCP effectively addresses the resource-performance contradiction in LLNs, providing a reliable and efficient routing framework for emerging compute-intensive IoT applications. Full article

Electromagnetic signal waveform recognition (ESWR) constitutes a fundamental enabling technology for modern spectrum management, cognitive radio, and electronic warfare applications. Among various ESWR subtasks, automatic modulation recognition (AMR) has attracted the most intensive research efforts and serves as the primary focus of this survey. Over the past decade, deep learning (DL) has fundamentally transformed ESWR by replacing hand-crafted feature engineering with data-driven end-to-end learning paradigms. However, the rapid proliferation of DL-based approaches has resulted in a fragmented research landscape. This paper addresses this gap by proposing a unified system-component framework that decomposes any DL-ESWR system into four foundational modules: (i) dataset construction and data augmentation, (ii) signal representation and preprocessing, (iii) core network architecture, and (iv) training and optimization strategy. Through this systematic lens, we provide a comprehensive review that catalogs the state of the art across recent publications and precisely attributes each innovation to specific modules within our framework. Furthermore, we identify eight core challenges confronting the practical deployment of DL-ESWR systems and systematically analyze how targeted modular innovations address each challenge. A critical analysis of prevalent benchmark datasets reveals significant limitations in channel diversity, modulation coverage, and ecological validity. Finally, we outline seven promising future research directions, including foundation models for wireless signals, physics-informed neural networks, and waveform recognition for emerging communication paradigms, such as semantic communications and integrated sensing and communication (ISAC). This survey aims to provide researchers and practitioners with a structured roadmap for understanding, evaluating, and advancing the field of AI-enabled electromagnetic signal waveform recognition. Full article

Artificial intelligence is transforming agriculture from a mechanized, labor-intensive sector into a data-driven, perception-enabled, and increasingly autonomous production system. In this review, AI serves as an umbrella term encompassing machine learning, computer vision, and robotic control, among other technologies. We synthesize recent advances across the tillage–sowing–management–harvesting (TSMH) workflow, covering intelligent tillage, precision sowing, field management, and robotic harvesting. The literature shows that AI has significantly improved agricultural perception, prediction, and task-level decision-making. However, large-scale adoption remains constrained by data heterogeneity, limited cross-scene generalization, environmental uncertainty, and insufficient integration across operational stages. Future progress will depend on multimodal data fusion, lightweight and interpretable models, cloud-edge collaboration, and full-chain decision architectures. By framing current research within the TSMH pipeline, this review highlights both technical advances and the critical bottlenecks that must be addressed to move smart agriculture from stage-specific intelligence toward system-level autonomy. Representative studies indicate that AI models can improve soil-property prediction and reduce sowing miss-detection rates to below 3% under controlled or bench-top conditions. However, field deployment may be affected by environmental variability, including illumination changes, dust, vibration, occlusion, and hardware constraints. These limitations highlight the need for robust and edge-compatible architectures. Full article

This work presents a noninvasive imaging method to locate veins using a tuned microwave loop resonator. It offers a low-cost, fast, and effective solution to the challenges in venipuncture. The sensor features a loop resonator with a 5.2 mm radius, incorporating a self-tuning mechanism, and operates at 2.408 GHz with a reflection coefficient of −48.77 dB. It generates localized high-intensity electric fields that penetrate tissues to sufficient depths, enabling the detection of veins based on shifts in resonant frequencies that are induced by the varied dielectric properties of blood vessels. Two-dimensional raster scan simulations of the cephalic and median cubital veins yielded a ∼25 MHz downward resonant-frequency shift between vein and non-vein positions, with the median cubital vein still detectable at depths up to 6 mm. To quantify generalization to real tissues, a decision tree classifier trained on 63 simulation samples and evaluated on 335 in vivo measurements achieved 82.09% classification accuracy (sensitivity 81.25%, specificity 83.02%), demonstrating that the simulation-derived frequency contrast transfers reliably to experimental data despite inter-subject tissue variability. Extensive tests conducted demonstrate the sensor’s effectiveness, producing consistent and distinguishable frequency shifts when the sensor moves on the skin across veins. This technology holds significant promise for improving venipuncture accuracy, minimizing complications, and enhancing patient comfort. Full article

Green technology innovation is widely recognized as a crucial driver for combating environmental pollution and achieving carbon reduction goals. Based on panel data from 30 provinces in China spanning 2006 to 2021, this study aims to examine the impact of green technology innovation (GTI) on carbon emission performance (CEP). The results indicate that (1) a significant U-shaped relationship exists between green technology innovation and carbon emission performance. (2) Heterogeneity analyses reveal that the effect is more pronounced in regions with higher levels of human capital, stronger macro-control, and a smaller urban–rural income gap. (3) Mechanism tests reveal that green technology innovation significantly improves carbon emission performance by driving the decarbonization of energy consumption structure. Furthermore, energy intensity negatively moderates the U-shaped relationship, leading to an “energy rebound effect”. (4) Spatial spillover analysis indicates that green technology innovation has a U-shaped impact on the carbon emission performance of adjacent regions. The findings of this study provide empirical evidence of and new perspectives on the crucial role of green innovation in achieving low-carbon sustainable development. Full article