Search Results (9,699)

Three-dimensional printed concrete (3DPC) has emerged as an innovative construction technology for extreme environments, offering advantages in thermal insulation, reduced labor requirements, and rapid construction. However, this layer-by-layer deposition process brings interlayer effects that affect mechanical anisotropy, permeability, and thermal performance, posing challenges for structural reliability. This review systematically examines current methods for characterizing and mitigating interlayer effects in 3DPC. Material-related factors—including admixtures, aggregates, recycled materials, fibers, and geopolymer incorporation—alongside process parameters such as printing speed, nozzle geometry, layer height, interlayer time, and environmental conditions, are analyzed for their influence on interlayer quality. State-of-the-art techniques for evaluating interlayer voids, mechanical behavior, and thermal performance are summarized. Moreover, results from micro-imaging, mechanical testing, and heat transfer assessments are also introduced. Ultimately, strategies for optimizing material composition and printing parameters to improve interlayer bonding and overall performance are highlighted. Overall, this paper provides a methodological framework to guide the design, testing, and practical implementation of 3DPC in demanding engineering applications. Full article

As artificial intelligence (AI) systems are increasingly adopted in recruitment practices, applicants’ responses to AI-mediated interviews have become an important issue for organizations. Understanding how applicants interpret these systems is relevant for organizational attractiveness and employer branding. Drawing on social exchange theory and signaling theory, this study examines the role of AI interview explainability in shaping applicants’ evaluations of organizations. It proposes that explainability influences organizational attractiveness through two parallel mechanisms: perceived organizational support and perceived innovativeness. Survey data were collected from 196 job applicants with experience in AI-based interviews. The results show that higher perceived explainability of AI interviews is associated with stronger perceptions of organizational support and organizational innovativeness. Both perceptions are positively related to organizational attractiveness. These findings support a dual-mediation model and suggest that explainable AI interview systems communicate both supportive intentions and technological capability to applicants. By focusing on applicants’ perceptions, this study contributes to the growing literature on AI use in human resource management. It highlights the importance of explainable system design in shaping early applicant reactions. The findings also provide practical implications for organizations seeking to implement AI-based recruitment tools that are transparent, credible, and attractive to potential applicants. Full article

Reconciling economic growth with environmental protection continues to represent a central global challenge. As one of the world’s largest developing economies, China has advanced an ecological civilization strategy that offers a unique opportunity to evaluate how national policy can shape sustainable development trajectories. This study assesses whether China’s ecological civilization construction enhances urban green total factor productivity (GTFP). Using panel data for 283 Chinese cities (2006–2019), this study identifies ecological civilization pilot cities through a standardized and reproducible protocol, measures urban GTFP using the Global Malmquist–Luenberger (GML) index and estimates policy effects with a multi-period difference-in-differences (DID) design that accounts for staggered implementation and overlapping policies. The results indicate that urban GTFP exhibited an overall upward but fluctuating trend during the study period, with regional growth rates ranking East > Central > West and a tendency toward convergence in recent years. The analysis further indicates that national ecological civilization construction policies exert a statistically significant and positive effect on urban GTFP, with the findings remaining robust to parallel trend tests and multiple robustness checks. The promotion effect displays marked regional heterogeneity, being strongest in western cities, followed by eastern and central regions, and remains positive across different urban contexts, including resource-based and non-resource-based cities as well as cities within and outside the Yangtze River Economic Belt. Mechanism analysis further reveals that the policy effect operates primarily through industrial upgrading and green technological innovation, whereas the industrial structure rationalization channel is not statistically significant. Overall, this study provides a transparent and reproducible framework for pilot city identification and causal evaluation, offering policy-relevant insights for differentiated and region-specific ecological governance aimed at balanced regional development, industrial upgrading, and green technological innovation. Full article

The decarbonisation of the transport sector is a cornerstone of the European Green Deal, necessitating a transition toward integrated, digital, and sustainable mobility solutions such as Mobility-as-a-Service (MaaS). While early MaaS implementations were characterised by local bottom-up experiments, recent state-level actions mark a shift toward large-scale systemic deployment. This paper investigates the evolving role of universities within this transition, using MaaS4Italy initiative as a primary case study. Through a qualitative analysis of 11 pilot projects, conducted between January and July 2025, the research examines how academic institutions have been integrated into the national governance framework, transitioning from traditional living labs for technical testing to pivotal institutional anchors and governance buffers. The findings reveal a dual role for universities: as scientific partners and as neutral mediators. However, a relevant paradox is highlighted as well: while the institutionalisation of universities de-risks public investment and fosters data-sharing trust, it may simultaneously limit their potential as high-density operational testbeds for innovative Corporate MaaS (CMaaS) solutions. Present research supports a broader understanding for policymakers, thus underscoring the importance of formalising the role of intermediary institutions to ensure the long-term sustainability and scalability of smart mobility ecosystems. These insights prove to be pivotal towards the implementation of multi-level environmental governance mechanisms and the strategic use of recovery funds to catalyse the transition toward climate neutrality. Full article

Artificial intelligence (AI) has shown substantial potential to improve patient outcomes in parenteral nutrition by enabling individualised nutritional strategies, early prediction of metabolic and infectious complications, and optimised real-time clinical decision-making. Evidence from global clinical practice demonstrates that AI integration can enhance patient safety, reduce complication rates, and improve resource utilisation. In Bulgaria, recent developments in parenteral nutrition reflect progress toward standardisation, wider availability of modern formulations, and alignment with international clinical guidelines. However, the adoption of AI-driven systems for personalised nutrition planning and continuous risk assessment remains limited. Key barriers include the availability and quality of clinical data, regulatory and ethical considerations, and the need for targeted training of healthcare professionals. This review highlights both the opportunities and challenges associated with implementing AI in parenteral nutrition in the Bulgarian context. Potential benefits include improved patient outcomes, shorter hospital stays, more efficient healthcare delivery, and alignment with international best practices. At the same time, overcoming infrastructural, regulatory, and educational barriers is essential for successful implementation. Conclusions: The integration of AI into parenteral nutrition requires a multidisciplinary approach that combines clinical expertise, technological innovation, and supportive health policy. Such an approach offers the potential to sustainably enhance patient care in Bulgaria and position national practice in line with leading global standards. Full article

Spent carbon cathode (SCC), a hazardous solid waste discharged from aluminum electrolysis, exhibits significant fluoride and cyanide leaching toxicities. Existing high-temperature disposal strategies are constrained by high investment costs for specialized equipment, low product added value, and unclear application scenarios, hindering their large-scale implementation. Consequently, substantial quantities of SCC remain underutilized, resulting in the waste of valuable carbon and fluoride components. This study focuses on the targeted conversion of valuable components in SCC through the innovative integration of simple processes, including atmospheric high-temperature roasting, deep purification, Al-based inducer addition, and pH regulation. Volatilization kinetics and solution equilibrium chemistry were used to investigate impurity removal mechanisms and to guide cryolite synthesis, respectively. The results demonstrate the successful recovery of high-purity regenerated graphite with a high carbon content, low sulfur content, and a high degree of graphitization. Simultaneously, cryolite with a high NaF/AlF₃ molecular ratio was synthesized from the roasting flue gas absorption liquor by controlling ionic composition and pH. Guided by the principles of cleaner production and resource recycling, the entire recovery process generates negligible waste gas, wastewater, or solid residue emissions. In conclusion, the proposed disposal strategy achieved the targeted conversion of SCC into high-value products while mitigating environmental pollution risks, offering both environmental and economic benefits. This innovative design provides a feasible pathway for the large-scale disposal and utilization of SCC. Full article

The digitization of Human Resource (HR) processes is currently regarded as a vital step towards enhancing efficiency, transparency, and employee satisfaction within organizations. This research paper analyzes the circumstances under which the digitization process in human resource management within innovative business entrepreneurship will affect corporate sustainable development. The present study assesses employee satisfaction with the innovative digital HR tools used and identifies the perceived benefits and barriers in assessing the impact of digitization on the functioning of HR processes. The primary objective of the research paper is to estimate the impact of the digitization of personnel management processes, from the perspective of employees, on their satisfaction with the innovative digital personnel tools used, and to determine the extent to which innovative digitization tools would affect the sustainable development of the corporation. The estimation is based on the data assessment approach regarding the questionnaire survey conducted within the framework of the VEGA project output. The primary method employed for hypothesis verification is the chi-square test, accompanied by graphical representation. The findings of this study suggest that corporations must strategically allocate resources to invest in digital tools and cultivate digital competencies within their workforce if they are to reap the full benefits of digitization and innovation processes. The ability to adapt to and leverage innovative, cutting-edge digital technologies will be a key determinant in terms of reinforcing sustainable development in business. Full article

The rapid incorporation of solar energy (PV) systems into electrical grids has increased the demand for accurate short-term forecasts to ensure stability and improve processes. Although hybrid artificial intelligence (AI) models are increasingly being suggested to address this challenge, there is a lack of systematic compilation of their structures, effectiveness, and readiness for use in real-world applications. This paper provides a detailed analysis of 58 peer-reviewed articles (2020–2025) focused on hybrid models for short-term (1–24 h) solar photovoltaic power forecasting. We propose an innovative classification that groups hybrids into four categories: AI-AI (28%), AI with optimization (21%), decomposition-based (17%), and image-based (7%). Our research indicates that weather conditions (34%) and historical photovoltaic energy records (32%) are the most frequent inputs, and that optimized hybrids and those using decomposition achieve the best balance between effectiveness and computational efficiency. From a geographical perspective, the study focuses mainly on the United States (29%) and China (22%), suggesting that more extensive climate validation is crucial. Essentially, we have identified ongoing obstacles to implementation, such as high computational costs, data quality issues, and gaps in interpretation. In addition, we present a plan for future research focusing on hybrid architectures that are lightweight, understandable, and interactive with the grid. This analysis provides a thorough assessment of the current landscape and a strategic framework to guide the creation of operational forecasting systems capable of supporting highly solar-integrated grids. Full article

We propose Adaptive Inference, a portfolio management framework extending Active Inference to non-stationary financial environments. The framework integrates inference, control, and execution under endogenous uncertainty, modeling investment decisions as coupled dynamics of belief updating, preference encoding, and action selection rather than optimization over fixed objectives. In this approach, portfolio behavior is governed by the expected free energy (EFE) minimization, showing that classical valuation models emerge as limiting cases when epistemic components vanish. Using train–test evaluation on the ARKK Innovation ETF (2015–2025), we identify a Passivity Paradox: frozen belief transfer outperforms naive adaptive learning. A Professional Agent achieves a Sharpe ratio of 0.39 while its adaptive counterpart degrades to $-0.28$, reflecting belief contamination when learning from policy-dependent signals. Crucially, the architecture is not designed to generate alpha but to perform endogenous risk management that mitigates overtrading under regime ambiguity and distributional shift. Adaptive Inference Agents maintain long exposure most of the time while tactically reducing positions during high-entropy periods, implementing uncertainty-aware passive investing. All agents reduce realized volatility relative to ARKK Buy-and-Hold (43.0% annualized). Cross-asset validation on the S&P 500 ETF (SPY) shows that inference-guided risk shaping achieves a positive Entropic Sharpe Ratio (ESR), defined as excess return per unit of informational work, thereby quantifying the economic value of information under thermodynamic constraints on inference. Full article

As industrial internet platforms increasingly play a central role in the digital transformation of manufacturing, they have become crucial areas for manufacturing enterprises to pursue digital innovation. Current academic research has paid relatively little attention to the digital innovation of participating enterprises within industrial internet platforms, failing to fully reveal the driving mechanisms of such innovation in this context. Based on the TOE framework and adopting a platform participant perspective, this study employs fuzzy set qualitative comparative analysis (fsQCA). By surveying 169 manufacturing enterprises participating in industrial internet platforms, it integrates seven key antecedents—technology availability, technology fit, digital leadership, organizational structural flexibility, resource orchestration, policy support, and competitive pressure—to systematically explore the complex influence pathways of multi-factor concurrent interactions on digital innovation. The research results show that the high digital innovation of manufacturing enterprises on the industrial internet platform includes precise implementation type, exploration-oriented type and co-evolution type, while the non-high digital innovation paths include technology blocking type, dual-core absence type and system disorder type. These conclusions expand the theoretical framework for digital innovation in manufacturing enterprises within industrial internet platforms and offer practical recommendations for their digital innovation practices. Full article

Higher education institutions are expected to cultivate graduates capable of addressing sustainability challenges through innovation, collaboration, and digital competence. However, many business programs struggle to integrate experiential authenticity, intelligent technologies, and collaborative learning into coherent instructional models, limiting students’ intrinsic motivation and sustainability-oriented competence development. This study aims to examine how experiential learning, artificial intelligence-assisted collaborative learning, and team-based learning operate within the Conceive–Design–Implement–Operate instructional framework to influence learning interest and learning satisfaction in a sustainability-oriented business course. Survey data from 217 undergraduate students were analyzed using confirmatory factor analysis, structural equation modeling, and moderated regression analysis. The results indicate that both experiential and AI-supported collaborative learning positively enhance students’ learning interest, which partially mediates their effects on learning satisfaction. Team-based learning strengthens the experiential pathway but does not significantly moderate the AI-assisted pathway. These findings clarify differentiated motivational mechanisms within structured instructional systems and provide theoretical support for designing digitally enhanced sustainability education. Full article

Economic goals and non-economic goals often conflict in practice. Taking China as an example, this paper examines the impact of Administrative Approval Center (AAC) establishment on urban green innovation. A multi-period difference-in-difference (DID) method is applied to city-level panel data (2000–2022) in China to estimate the effect of AAC establishment on urban green innovation. The research results indicate that the establishment of AAC reduces urban green innovation due to the increased fiscal burden on local governments. This negative impact is particularly evident in the eastern and central regions, cities with stronger environmental regulations, or low-administrative-level cities. The conclusions of this paper have reference significance for other developing countries. To achieve sustainable development, central governments should enhance fiscal support and safeguards, local governments should implement differentiated environmental regulations, and digital technologies should be leveraged to reduce physical infrastructure costs and alleviate fiscal pressures. Full article

This work demonstrates the potential of fiber optic sensors for measuring thermal effects in lithium-ion batteries, using a fiber optic measurement method of Optical Frequency Domain Reflectometry (OFDR). The innovative application of fiber sensors allows for spatially resolved temperature measurement, particularly emphasizing the importance of monitoring not just the exterior but also the internal conditions within battery cells. Utilizing inert glass fibers as sensors, which exhibit minimal sensitivity to electric fields, opens up new pathways for their implementation in a wide range of applications, such as battery monitoring. The sensors used in this work provide real-time information along the entire length of the fiber. It is shown that using the herein presented novel sensors in a temperature range of 0–$80°\mathrm{C}$ reveals a linear, high-sensitivity thermal measurement characteristic with a local resolution of a few centimeters. Furthermore, this study presents preliminary findings on the potential application of fiber optic sensors in lithium-ion battery (LIB) cells, demonstrating that the steps required for battery integration do not impose any restrictive effects on thermal measurements. Full article

Hydroelectric power plants are renewable electricity generation assets that require high availability and reliability in their operation and maintenance. To justify improvement actions (modernization and investments), it is necessary to analyze the operation of the plant, the maintenance plan being implemented, and, naturally, the incidents and breakdowns that affect this asset. This paper presents research on hydroelectric power plant maintenance based on the development of a database of incidents and failures of such plants, considering the methodology of failure modes, effects and criticality analysis (FMECA) as well as the reliability-centered maintenance (RCM) methodology of the initial maintenance plan of a standard hydroelectric power plant. Different maintenance standards and analysis standards (IATF criticality of failure modes, UNE 13306, ISO 14224, etc.) were considered. The results reveal different improvement and optimization actions based on the current technological development, which can be applied to hydroelectric generation (Innovation 4.0), as well as actions to optimize the initial maintenance plan based on Maintenance 4.0. The technical justification for such improvements in hydropower generation highlights a key area of development in the expansion of renewable energies worldwide. Hydropower generation assets have contributed renewable energy to the system for many years; however, they now require redesign in their operation and maintenance. Full article

Long-acting injectable HIV pre-exposure prophylaxis (LAI-PrEP) demonstrates superior efficacy and persistence compared to daily oral PrEP. However, real-world implementation reveals that only 52.9% of prescribed individuals initiate treatment with their first injection. This implementation barrier stems from a fundamental mismatch between the traditional PrEP cascade—designed for oral formulations allowing same-day initiation—and LAI-PrEP’s unique requirements involving a 2–8 week “bridge period” between prescription and first injection to establish HIV-negative status. We synthesize data from major clinical trials (HPTN 083, HPTN 084, PURPOSE-1/2; >15,000 participants) with real-world implementation studies to characterize bridge period navigation as the critical implementation barrier. This review proposes a reconceptualized PrEP cascade explicitly recognizing the bridge period as a distinct, measurable step requiring dedicated management strategies. We examine pharmacological bases for conservative initiation protocols, quantify population-specific barriers to bridge period completion, and synthesize evidence on strategies to improve initiation success. This paradigm shift from individual behavioral adherence to structural factors within the healthcare system requires parallel innovations in cascade conceptualization, measurement frameworks, and implementation approaches. Addressing this structural barrier is essential to translate LAI-PrEP’s extraordinary clinical efficacy (>96%) into meaningful public health impact, particularly for populations experiencing the highest HIV burden. Full article