Search Results (218)

This study aims to investigate the role of artificial intelligence (AI) for strategically steering corporate environmental sustainability, which remains underexplored in the context of emerging economies. Drawing on the resource-based perspective and Dynamic Capabilities Theory, we argue that the adoption of AI also represents an aspect associated with an organizational capability on a higher rung that can enhance performance towards environmental goals. We further examine a mediating framework through which the effect of AI on environmental sustainability is transmitted through firms’ green governance structures. Using a longitudinal panel dataset of 75 publicly listed industrial firms operating in six Gulf Cooperation Council (GCC) countries from 2018 to 2025, we used fixed-effects regression analysis alongside bootstrapped mediation analysis. In fact, the empirical evidence suggests that AI adoption is positively and significantly associated with environmental sustainability. We also show that green governance partially mediates this relationship implying that AI-based strategic investment is better realized in terms of measurable environmental impacts when it is embedded within sound board-level ESG governance systems. As such, the findings provide an important empirical perspective on the AI–sustainability nexus in the GCC industrial landscape and also explain the empirical role played by green governance in implementing technology, constituting technological enablers for the transformation of technological capabilities to concrete environmental outcomes. The study will also provide policymakers and managers with actionable insights on the potential for digital transformation to act as a strategic enabler of sustainable development in resource-intensive industries. Full article

The electro-hydraulic track tensioning system of a tracked vehicle directly affects track engagement stability, vibration response, and energy utilization efficiency under complex terrain and time-varying loads. Accurate and robust control is therefore of great engineering significance. This paper focuses on an electro-hydraulic tensioning system with a composite actuation structure consisting of a proportional main valve and two 2/2 on–off valves and proposes a learning-enhanced nonlinear model predictive control (L-NMPC) method. Residual learning, adaptive weight/constraint scheduling, and execution-layer mode coordination are integrated into a unified predictive control framework. The study is carried out on a strongly coupled Simulink–AMESim–RecurDyn co-simulation model and an LF1352 prototype-vehicle test platform. Comparative evaluations are conducted under steady step-and-ramp tracking, random rough terrain, sudden steering/braking pulses, supply-pressure limitation, and parameter drift/sudden-change conditions. The evaluation indices include track-tension tracking error, peak overshoot, settling time, energy consumption, and stability under parameter mismatch. Compared with conventional nonlinear model predictive control (NMPC), the proposed L-NMPC reduces the root-mean-square error of track tension by 42–58%, decreases peak overshoot by 30–40%, shortens settling time by 25–35%, and achieves a 12–17% reduction in energy consumption at the simulation level. Under ±20% parameter perturbation, the fluctuation in track tension can be constrained within ±1.1 kN. The simulation and real-vehicle results remain consistent in terms of the dominant dynamic trends and performance ranking. This study provides a verifiable implementation path for model–data-fusion control of strongly coupled electro-hydraulic actuation systems and offers an engineering reference for intelligent, energy-efficient, and highly reliable control of tracked-vehicle chassis systems. Full article

The transition toward environmentally sustainable 6G networks requires mitigating the high-power consumption of traditional active base stations and relay nodes currently used to overcome signal path loss. This paper introduces Reconfigurable Intelligent Surfaces (RIS) as a paradigm-shifting, inherently passive alternative that alters the wireless propagation environment without requiring power-intensive radio frequency (RF) chains. Rather than focusing solely on spectral efficiency, this research aims to maximize Energy Efficiency (EE) to achieve a critical equilibrium between network performance and power consumption. MATLAB-based analytical models demonstrate that received signal power scales quadratically with the number of reflecting elements via constructive interference. Furthermore, systematic evaluations reveal that a 64-element RIS panel imposes a negligible hardware load consuming merely 0.005 Watts per element, offering a highly sustainable alternative to the massive transmit power (up to 40 dBm) frequently required by unassisted networks in noisy environments. By defining a mathematical “Green Operating Point,” this study demonstrates that integrating lightweight RIS panels significantly enhances Signal-to-Noise Ratio (SNR) and data rates, steering next-generation telecommunications toward highly sustainable, low-power operations. Full article

Context: Automatic Speech Recognition (ASR) in healthcare is undergoing a significant shift driven by the integration of Large Language Models (LLMs). While traditional ASR focused on transcription fidelity, LLM-based systems extend this capability to intelligently reason, summarize, and structure clinical data. This scoping review maps the emerging landscape of LLM-based ASR in healthcare, examining its applications, technical foundations, evaluation practices, and reported challenges. Methods: Following PRISMA-ScR guidelines, we searched different databases for peer-reviewed, open-access studies published between January 2022 and December 2025 to ensure reproducibility and accessibility. Results: Nineteen studies met the inclusion criteria from 384 screened records. Administrative documentation was the most common application (42.1%), followed by diagnosis, therapy, and doctor–patient communication. Whisper dominated ASR (52.6%), typically paired with GPT-family or LLaMA-family LLMs in frozen configurations steered through prompting. LLMs served as the primary component in 68.4% of studies. ASR evaluation within the reviewed studies predominantly relied on word error rate, while LLM evaluation remains fragmented with no standard metric. Studies reported documentation time reductions of 30–90%, though privacy reporting was inconsistent, equity concerns were rarely tested systematically, and only five studies provided replication packages. Conclusions: LLM-based ASR shows potential for reducing documentation burden and supporting clinical workflows, but gaps in evaluation standardization, equity testing, and reproducibility must be addressed before safe clinical deployment. Full article

The steer-by-wire (SBW) system represents an optimal solution for achieving intelligent vehicle steering. However, the current reliability of SBW motors and electronic control units remains limited. Disturbances, including variations in the external road environment and time-varying parameters, can significantly impact vehicle stability. To address these challenges, a hierarchical control strategy is proposed in this paper. In the upper layer, model predictive control (MPC) is employed to optimize the sideslip angle and yaw rate by tracking their reference values, thereby enhancing the stability of the SBW system. In the lower layer, a composite reaching law sliding mode control based on an extended state observer (ESO-CRLSMC) is developed to address dual-motor parameter mismatch and speed synchronization issues, thereby ensuring the reliability of the dual-motor system. Finally, hardware-in-the-loop experiments demonstrate that under time-varying disturbances and parameter mismatches, the proposed controller not only ensures vehicle handling stability but also improves steering response speed, robustness, and synchronization performance. Full article

Artificial intelligence (AI) is increasingly embedded in how cities are governed, shaping decisions on mobility, land use, public services, and environmental management. Yet urban AI is predominantly governed through fragmented frameworks designed at national or corporate scales, offering limited guidance for municipal decision-making and overlooking place-specific social and ecological consequences. As the level of government closest to everyday urban life, cities are uniquely positioned to steer AI toward public value, but face persistent tensions between efficiency, equity, accountability, and sustainability. This paper argues that responsible urban AI cannot be governed through top-down or one-size-fits-all approaches. To address this, the study aims to conceptualise and advance a ground-up model of responsible urban AI governance that places cities and local governments at the centre of decision-making. It addresses the following research question: How can municipal authorities translate high-level ethical principles into practical, context-sensitive governance arrangements that respond to local capacities, risks, and public values? Drawing on global governance principles and illustrative city experiences, we propose a locally grounded, stage-based framework for municipal AI governance. The framework addresses institutional capacity gaps, fragmented responsibilities, and algorithmic externalities, advancing a participatory, place-sensitive, and adaptive model that aligns urban AI innovation with democratic legitimacy, social justice, and sustainable urban futures. Full article

This article examines the requirements for the successful deployment of business analytics in industry and uses this as a framework to provide a business intelligence perspective on the demise of a case study company, drinks manufacturer HP Bulmer Ltd., resulting in the collapse and takeover of the company in 2003. Based on a scoping literature review and a qualitative interpretivist approach, the article investigates the critical success factors for business analytics software projects and classifies these into five main organisational pillars that are required for successful analytics deployment. Then, using documents available in the public domain, the article examines the case study of HP Bulmer Ltd., which used analytics software in the 1990s and early 2000s as the company attempted to establish itself as a global drinks manufacturer. The article reports on how the company struggled to put the necessary pillars in place for successful use of their analytics systems, but having finally achieved this, then failed to take the necessary decisions to steer the company towards profitability as opposed to rapid growth in turnover. The article uses the case study to reflect on the key aspects of analytics technology deployment and the wider field of digitalisation and digital transformation, and points to the critical importance of political will to formulate and steer data-informed strategy. The research contributes to the development of theory regarding analytics deployment and will be of value to practitioners faced with the challenges of implementing analytics systems in industry. Full article

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control. Full article

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining. Full article

Artificial intelligence (AI) governance is becoming increasingly important due to technological development, widespread adoption, and the lack of boundaries. This triggers many opportunities for innovation but also increases risks. By conducting 23 subject-matter expert interviews with global Chief Data Officers and performing a workshop with 31 European Chief Data Officers, we explored five AI governance mechanisms identified in the literature, and we discussed the impact and developments regarding future legislation in a global context and the impact of data and operational sovereignty on (agentic) AI governance. Our data suggests that installing an AI governance steering committee is currently the most important mechanism and that stakeholder management and model ownership are prerequisite mechanisms; moreover, audit and impact assessments, as well as staff training, are hygiene mechanisms. In the context of agentic AI governance, we found that the mechanisms identified for AI governance must be applied with greater scrutiny, rigor, and consistency; in addition, in this second round of our research, we find that stronger AI tooling is required to effectively support governance practices. Our research resulted in an AI governance framework with six governance mechanisms and six AI governance good practices that provide a starting point for organizations to implement AI governance. Full article

Driven by the development of next-generation wireless networks and the widespread adoption of sensing, communication is shifting from traditional bit-level transmission to intelligent, rich interactions within our digital social system. However, existing speech semantic communication frameworks predominantly focus on textual accuracy, neglecting the critical affective information (e.g., tone and emotion) that is essential for natural human-centric interactions in the real world. To address this limitation, we propose the Heterogeneous Affective Speech Semantic Communication (HASCom) framework, designed for the robust transmission of highly expressive speech over complex wireless channels. Specifically, we design a heterogeneous dual-stream transmission architecture that decouples discrete phoneme-level linguistic content from continuous emotional embeddings. For discrete semantic information, we use reliable digital coding protected by Low-Density Parity-Check (LDPC) to guarantee strict recoverability. Conversely, for emotional features, we employ Deep Joint Source-Channel Coding (JSCC) analog transmission to prevent irreversible quantization errors and the cliff effect. Additionally, we develop a prior-guided diffusion reconstruction module at the receiving end. This module leverages a structural prior network to align the decoded semantics, which then steers the reverse diffusion process conditioned on the recovered affective features. Extensive experiments under both AWGN and Rayleigh fading channels demonstrate that HASCom significantly outperforms state-of-the-art baselines. Specifically, it achieves superior objective semantic similarity and subjective Mean Opinion Score (MOS) at low Signal-to-Noise Ratios (SNRs), while the JSCC transmission modules maintain an ultra-low inference latency of less than 0.1 ms, validating its high efficiency and robustness for practical deployments. Full article

Artificial intelligence (AI) is increasingly reshaping corporate production and governance, raising the question of how policy can steer corporations toward sustainable development. This study treats the staggered implementation of China’s National Artificial Intelligence Innovation and Development Pilot Zone policy (AI Pilot Zone policy) as a quasi-natural experiment. Using data from Chinese listed companies from 2014 to 2024, we employ a multi-period difference-in-differences approach to identify the impact of the policy on corporate sustainable development performance (SDP) and to explore the underlying mechanisms. The results show that the AI Pilot Zone policy significantly improves corporate SDP, and this finding remains robust to a series of checks, including parallel trend tests, placebo tests, PSM-DID estimations, and tests addressing potential biases under staggered policy adoption. Heterogeneity analysis based on the TOE framework indicates that the policy effect is more pronounced among firms with higher R&D intensity, stronger internal control, and those located in regions with higher levels of digital inclusive finance. Mechanism analysis further suggests that dynamic capabilities, including innovation capability, adaptation capability, and absorptive capability, play important mediating roles in the relationship between the policy and corporate SDP. Overall, this study provides micro-level evidence on the sustainability effects of AI-oriented public policies and offers insights for improving policy design and corporate capability development. Full article

The agri-food sector is undergoing a profound transformation driven by the combined pressures of climate change, resource scarcity, policy frameworks, and evolving consumer expectations. In this context, digital and green technologies have emerged as key enablers of more sustainable, transparent, and resilient food systems. This review provides a comprehensive overview of the conceptual foundations, technological drivers, and policy frameworks shaping the digital and green transition of the agri-food sector. Digital technologies—including precision agriculture, sensing and data acquisition systems, artificial intelligence, blockchain, and data platforms—are examined in relation to their role in improving resource-use efficiency, traceability, and decision-making across the food value chain. In parallel, green technologies and sustainable practices in food production, processing, and waste management are discussed, with emphasis on resource optimization, circular economy approaches, and environmental impact reduction. This review further highlights the role of European and global policy frameworks, such as the European Green Deal and the Farm to Fork strategy, in steering technological adoption and aligning innovation with sustainability objectives. By synthesizing technological, environmental, and policy perspectives, this work underscores the importance of integrated digital–green strategies for achieving long-term sustainability, competitiveness, and resilience in agri-food systems. Full article

Accurate acquisition of low-observable targets with a minimal radar cross-section (RCS) poses a significant challenge for multi-source remote sensing systems, such as integrated radar–electro-optical (REO) platforms, particularly in complex electromagnetic environments characterized by strong noise interference and a high false-alarm rate. Conventional methods, which often treat data association and fusion from heterogeneous sensors as separate, offline processes, struggle with the dynamic uncertainties and real-time decision requirements of such scenarios. To address these limitations, this paper proposes a novel Evidence–Reinforcement Learning-based Decision and Control (ERL-DC) framework. It operates through a closed-loop architecture consisting of three core modules: A static assessment model for initial target prioritization, a Dempster–Shafer (D–S) evidence-based multi-source data decision generator for dynamic information fusion and uncertainty-aware target selection, and a Deep Reinforcement Learning (DRL) controller for noise-robust sensor steering. A high-fidelity simulation environment was developed to model the multi-source data stream, encompassing radar detection with clutter and false targets, as well as the physical constraints of the electro-optical (EO) servo system. Based on the averaged results from multiple Monte Carlo simulations, the proposed ERL-DC framework reduced the Average Decision Time (ADT) from 7.51 s to 4.53 s, corresponding to an absolute reduction of 2.98 s when compared to the conventional method integrating threshold logic with Model Predictive Control (MPC). Furthermore, the Net Discrimination Accuracy (NDA), derived from the statistical outcomes across all the simulation runs, exhibited an absolute increase of 37.8 percentage points, rising from 57.8% to 95.6%. These results indicate that ERL-DC achieves a more favorable trade-off in terms of scheduling efficiency, decision robustness, and resource utilization. The primary contribution is an intelligent, closed-loop architecture that tightly couples high-level evidential reasoning for multi-source data fusion with low-level adaptive control. Within the simulated environment characterized by clutter, false targets, and angular measurement noise, ERL-DC demonstrates improved target discrimination accuracy and decision efficiency compared to conventional methods. Future work will focus on online parameter adaptation and validation on physical platforms. Full article

Developing new quality productive forces represents a core strategy for steering China’s path to modernization and shaping new competitive advantages for the nation. As a leading technology in the new round of technological revolution and industrial transformation, artificial intelligence (AI) serves as a key engine for fostering new quality productive forces. Utilizing panel data from China’s A-share listed manufacturing firms (2012–2024), this study employs the penetration rate of industrial robots to proxy for AI development levels and the entropy method to measure new quality productive forces. From the perspective of supply chain resilience, ordinary least squares (OLS) and instrumental variable (IV) methods are employed to examine the impact of AI on enterprise new quality productive forces and its underlying mechanisms. The findings indicate that AI significantly enhances corporate new quality productive forces, a conclusion that remains robust after addressing potential endogeneity and conducting robustness checks. Mediation analysis reveals that AI reinforces corporate supply chain resilience by improving supply chain efficiency and strengthening supply chain discourse power, which in turn drives the enhancement of corporate new quality productive forces. Heterogeneity analysis indicates that the impact of AI on corporate new quality productive forces is heterogeneous, with particularly pronounced effects observed in firms with higher innovation levels, state-owned enterprises, and firms located in western China. This study contributes new evidence from a supply chain resilience perspective to understand the micro-level pathways through which AI empowers new quality productive forces, and offers targeted policy and managerial recommendations to foster the sustainable development of the manufacturing sector. Full article