Search Results (876)

Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues to grow exponentially and technological advancements accelerate, classical machine learning algorithms increasingly face difficulties in solving complex real-world problems. The integration of classical machine learning with quantum information processing has led to the emergence of quantum machine learning, a promising interdisciplinary field. This work provides the reader with a bottom-up view of quantum circuits starting from quantum data representation, quantum gates, the fundamental quantum algorithms, and more complex quantum processes. Thoroughly studying the mathematics behind them is a powerful tool to guide scientists entering this domain and exploring their connection to quantum machine learning. Quantum algorithms such as Shor’s algorithm, Grover’s algorithm, and the Harrow–Hassidim–Lloyd (HHL) algorithm are discussed in detail. Furthermore, real-world implementations of quantum machine learning and quantum deep learning are presented in fields such as healthcare, bioinformatics and finance. These implementations aim to enhance time efficiency and reduce algorithmic complexity through the development of more effective quantum algorithms. Therefore, a comprehensive understanding of the fundamentals of these algorithms is crucial. Full article

Monitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus lowering the use of moment and tilt sensors that are currently being used for obtaining foundation stiffness. First, a convolutional neural network model is applied to map acceleration and wind speed data within a moving window to corresponding moment and tilt values. Rotational stiffness of the foundation is then estimated by fitting a line in the moment-tilt plane. The results obtained indicate that such a mapping model can provide stiffness values that are within 7% of ground truth stiffness values on average. Second, the developed mapping model is re-trained by using synthetic acceleration and wind speed data that are generated by an autoencoder generative AI network. The results obtained indicate that although the exact amount of stiffness drop cannot be determined, the drops themselves can be detected. This mapping model can be used not only to lower the cost associated with obtaining foundation rotational stiffness but also to sound an alarm when a foundation starts deteriorating. Full article

PM_2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 µm) pollution is one of the most common problems triggered by the acceleration of urbanization. The coordinated development of cities and the environment has been a topic of significant interest in recent years. Based on the spatiotemporal relationship between the evolution of urbanization levels and PM_2.5 concentrations, and starting from multiple factors characterizing urbanization, this study constructs a coupling coordination degree model between PM_2.5 and urbanization levels to explore the interaction and degree of coordination between urbanization and PM_2.5 in Guangdong Province from 2000 to 2021. The research reveals that the conflict between the urbanization process and PM_2.5 pollution in various cities of Guangdong Province is gradually easing. The year 2011 was a turning point as the PM_2.5 pollution levels in cities that were in an uncoordinated phase began to improve. The coupling coordination degree between urbanization and PM_2.5 pollution in Guangdong Province exhibits significant spatial heterogeneity. The coupling coordination degree in most coastal cities is higher than that in inland cities. Cities in economically underdeveloped regions also face relatively lower pressure from pollution emissions. These regions are characterized by lagging urbanization, and their coupling coordination degree is slowly increasing as urbanization progresses. In economically developed regions, the coupling coordination degree between urbanization levels and PM_2.5 pollution has reached a basic level of coordination, although the specific types vary. Full article

Driven by stringent emission regulations, advanced combustion modes utilizing turbulent jet ignition technology are pivotal for enhancing the performance of marine low-speed natural gas dual-fuel engines. This review focuses on three novel combustion modes, yielding key conclusions: (1) Compared to the conventional DJCDC mode, the TJCDC mode exhibits a significantly higher swirl ratio and turbulence kinetic energy in the main chamber during initial combustion. This promotes natural gas jet development and combustion acceleration, leading to shorter ignition delay, reduced combustion duration, and a combustion center (CA50) positioned closer to the Top Dead Center (TDC), alongside higher peak cylinder pressure and a faster early heat release rate. Energetically, while TJCDC incurs higher heat transfer losses, it benefits from lower exhaust energy and irreversible exergy loss, indicating greater potential for useful work extraction, albeit with slightly higher indicated specific NOx emissions. (2) In the high-compression ratio TJCPC mode, the Liquid Pressurized Natural Gas (LPNG) injection parameters critically impact performance. Delaying the start of injection (SOI) or extending the injection duration degrades premixing uniformity and increases unburned methane (CH₄) slip, with the duration effects showing a load dependency. Optimizing both the injection timing and duration is, therefore, essential for emission control. (3) Increasing the excess air ratio delays the combustion phasing in TJCPC (longer ignition delay, extended combustion duration, and retarded CA50). However, this shift positions the heat release more optimally relative to the TDC, resulting in significantly improved indicated thermal efficiency. This work provides a theoretical foundation for optimizing high-efficiency, low-emission combustion strategies in marine dual-fuel engines. Full article

The accelerated development of Artificial Intelligence (AI) capabilities and systems is driving a paradigm shift in productivity, innovation and growth. Despite this generational opportunity, AI is fraught with significant challenges and risks. To address these challenges, responsible AI has emerged as a modus operandi that ensures protections while not stifling innovations. Responsible AI minimizes risks to people, society, and the environment. However, responsible AI principles and practice are impacted by ‘principle proliferation’ as they are diverse and distributed across the applications, stakeholders, risks, and downstream impact of AI systems. This article presents a systematic review of responsible AI principles and practice with the objectives of discovering the current state, the foundations and the need for responsible AI, followed by the principles of responsible AI, and translation of these principles into the responsible practice of AI. Starting with 22,711 relevant peer-reviewed articles from comprehensive bibliographic databases, the review filters through to 9700 at de-duplication, 5205 at abstract screening, 1230 at semantic screening and 553 at final full-text screening. The analysis of this final corpus is presented as six findings that contribute towards the increased understanding and informed implementation of responsible AI. Full article

The electromechanical composite transmission technology for tracked vehicles demonstrates excellent performance in energy efficiency, mobility, and ride comfort. However, due to frequent operation under harsh conditions, the components of the electric drive system, such as drive motors, are prone to failures. This paper proposes three fault-tolerant control methods for three typical fault scenarios of the electromechanical composite transmission system (ECTS) to ensure the normal operation of tracked vehicles. Firstly, an ECTS and the electromechanical coupling dynamics model of the tracked vehicle are established. Moreover, a double-layer anti-windup PID control for motors and an instantaneous optimal control strategy for the engine are proposed in the fault-free case. Secondly, an anti-windup PID control law for motors and an engine control strategy considering the state of charge (SOC) and driving demands are developed in the case of single-side drive motor failure. Thirdly, a B4 clutch control strategy during starting and a steering brake control strategy are proposed in the case of electric drive system failure. Finally, in the straight-driving condition of the tracked vehicle, the throttle opening is set as 0.6, and the motor failure is triggered at 15 s during the acceleration process. Numerical simulations verify the fault-tolerant control strategies’ feasibility, using the tracked vehicle’s maximum speed and acceleration at 30 s as indicators for dynamic performance evaluation. The simulation results show that under single-motor fault, its straight-line driving power drops by 33.37%; with electric drive failure, the drop reaches 43.86%. The vehicle can still maintain normal straight-line driving and steering under fault conditions. Full article

Accurate, energy-efficient climate regulation is crucial for scaling smart greenhouse production. While nonlinear model predictive control (NMPC) can co-optimize yield and resource use, its efficacy hinges on short-range weather information and real-time solver feasibility. This paper investigates the performance of advanced NMPC strategies for smart greenhouse climate control, with particular emphasis on the roles of AI-driven disturbance prediction and warm-start initialization for real-time optimization. Six controller configurations, including feedback-only, LSTM-based forecast, and ideal disturbance models, each with and without warm-start, were tested in a 40-day simulation of a lettuce smart greenhouse. Performance metrics included final biomass, constraint violations, resource costs, profit, and solver time. Results show that feedback-only controllers maximize yield and profit, incurring higher CO₂ costs but lower heating costs, alongside greater constraint violations compared to the predictive strategies. Predictive and ideal disturbance-aware controllers effectively reduce resource consumption and improve constraint compliance at the expense of lower yields. Importantly, warm-start initialization significantly accelerates computation without affecting control quality. The study also demonstrates that penalty parameters, rather than economic weight settings, predominantly determine aggregate constraint violation. The findings provide actionable insights for designing and deploying NMPC-based greenhouse controllers, highlighting the importance of warm-start techniques and the trade-offs between productivity, resource efficiency, and environmental compliance. Full article

Rubber Tree (Hevea brasiliensis) phenology critically influences tropical plantation productivity and carbon cycling, yet topography and climate impacts remain unclear. By integrating multi-sensor remote sensing (2001–2020) and Google Earth Engine, this study analyzed spatiotemporal dynamics in Hainan Island, China. Results reveal that both the start (SOS occurred between early and late March: day of year, DOY 60–81) and end (EOS occurred late January to early February: DOY 392–406, counted from the previous year) of the growing season exhibit progressive delays from the southeast to northwest, yielding a 10–11 month growing season length (LOS). Significantly, LOS extended by 4.9 days per decade (p < 0.01), despite no significant trends in SOS advancement (−1.1 days per decade) or EOS delay (+3.7 days per decade). Topographic modulation was evident: the SOS was delayed by 0.27 days per 100 m elevation rise (p < 0.01), while the EOS was delayed by 0.07 days per 1° slope increase (p < 0.01). Climatically, a 100 mm precipitation increase advanced SOS/EOS by approximately 1.0 day (p < 0.05), preseasonally, a 1 °C February temperature rise advanced the SOS and EOS by 0.49 and 0.53 days, respectively, and a 100 mm January precipitation increase accelerated EOS by 2.7 days (p < 0.01). These findings advance our mechanistic understanding of rubber phenological responses to climate and topographic gradients, providing actionable insights for sustainable plantation management and tropical forest ecosystem adaptation under changing climatic conditions. Full article

Cystic fibrosis (CF) is the most common severe autosomal recessive disease among Caucasians. Modulators of cystic fibrosis transmembrane conductance regulator (CFTR) mutated protein significantly improved the outcome of subjects with CF. In the present study, we studied epigenetic age, applying the Horvath clock model, in 52 adult subjects with CF, all treated with elexacaftor/tezacaftor/ivacaftor (ETI). At baseline (T0), we found that half of the subjects have a significantly accelerated epigenetic age and a worse lung function, evaluated by forced expiratory volume in one second (FEV1). One year of ETI therapy (T1) impacted both the parameters, indicating that therapy with modulators must be started early, particularly in CF subjects with impaired lung function. The second group of CF subjects had an epigenetic age lower than the chronological one at T0 and lung function was better maintained. In these subjects, ETI therapy further improved lung function and tended to increase the epigenetic age, possibly improving metabolic functions and the general state of well-being. This also translates into an increase in the physical activities of a group of subjects who, before the therapy, had grown up under a glass bell. The analysis of epigenetic age may represent a potential biomarker to assess the individual outcome of the therapy in subjects with CF, although long-term studies need to evaluate it. Full article

This study presents a systematic literature review on the mathematical modeling of resilient and viable supply chains, grounded in the PRISMA methodology and applied to a curated corpus of 235 peer-reviewed scientific articles published between 2011 and 2025. The search strategy was implemented across four major academic databases (Scopus and Web of Science) using Boolean operators to capture intersections among the core concepts of supply chains, resilience, viability, and advanced optimization techniques. The screening process involved a double manual assessment of titles, abstracts, and full texts, based on inclusion criteria centered on the presence of formal mathematical models, computational approaches, and thematic relevance. As a result of the selection process, six thematic categories were identified, clustering the literature according to their analytical objectives and methodological approaches: viability-oriented modeling, resilient supply chain optimization, agile and digitally enabled supply chains, logistics optimization and network configuration, uncertainty modeling, and immune system-inspired approaches. These categories were validated through a bibliometric analysis and a thematic map that visually represents the density and centrality of core research topics. Descriptive analysis revealed a significant increase in scientific output starting in 2020, driven by post-pandemic concerns and the accelerated digitalization of logistics operations. At the methodological level, a high degree of diversity in modeling techniques was observed, with an emphasis on mixed-integer linear programming (MILP), robust optimization, multi-objective modeling, and the increasing use of bio-inspired algorithms, artificial intelligence, and simulation frameworks. The results confirm a paradigm shift toward integrative frameworks that combine robustness, adaptability, and Industry 4.0 technologies, as well as a growing interest in biological metaphors applied to resilient system design. Finally, the review identifies research gaps related to the formal integration of viability under disruptive scenarios, the operationalization of immune-inspired models in logistics environments, and the need for hybrid approaches that jointly address resilience, agility, and sustainability. Full article

Prefabricated unidirectional carbon fiber reinforced polymer (CFRP) composite strips are extensively used as a means of infrastructure rehabilitation through adhesive bonding to the external surface of structural concrete elements. Most data to date are from laboratory tests ranging from a few months to 1–2 years providing an insufficient dataset for prediction of long-term durability. This investigation focuses on the assessment of the response of three different prefabricated CFRP systems exposed to water, seawater, and alkaline solutions for 5 years of immersion in deionized water conducted at three temperatures of 23, 37.8 and 60 °C, all well below the glass transition temperature levels. Overall response is characterized through tensile and short beam shear (SBS) testing at periodic intervals. It is noted that while the three systems are similar, with the dominant mechanisms of deterioration being related to matrix plasticization followed by fiber–matrix debonding with levels of matrix and interface deterioration being accelerated at elevated temperatures, their baseline characteristics and distributions are different emphasizing the need for greater standardization. While tensile modulus does not degrade appreciably over the 5-year period of exposure with final levels of deterioration being between 7.3 and 11.9%, both tensile strength and SBS strength degrade substantially with increasing levels based on temperature and time of immersion. Levels of tensile strength retention can be as low as 61.8–66.6% when immersed in deionized water at 60 °C, those for SBS strength can be 38.4–48.7% at the same immersion condition for the three FRP systems. Differences due to solution type are wider in the short-term and start approaching asymptotic levels within FRP systems at longer periods of exposure. The very high levels of deterioration in SBS strength indicate the breakdown of the materials at the fiber–matrix bond and interfacial levels. It is shown that the level of deterioration exceeds that presumed through design thresholds set by specific codes/standards and that new safety factors are warranted in addition to expanding the set of characteristics studied to include SBS or similar interface-level tests. Alkali solutions are also shown to have the highest deteriorative effects with deionized water having the least. Simple equations are developed to enable extrapolation of test data to predict long term durability and to develop design thresholds based on expectations of service life with an environmental factor of between 0.56 and 0.69 for a 50-year expected service life. Full article

The technological growth in the automation of manufacturing processes, as seen in Industry 4.0, is characterized by a constant revolution and evolution in small- and medium-sized factories. As basic and advanced technologies from the pillars of Industry 4.0 are gradually incorporated into their value chain, these factories can achieve adaptive technological transformation. This article presents a practical solution for companies seeking to evolve their production processes during the expansion phase of their manufacturing, starting from a base architecture with Industry 4.0 features which then integrate and implement specific tools that facilitate the duplication of installed capacity; this creates a situation that allows for the development of manufacturing execution systems (MESs) for each production line and a fog computing node, which is responsible for optimizing the load balance of order requests coming from the cloud and also acts as an intermediary between MESs and the cloud. On the other hand, legacy Machine Learning (ML) inference acceleration modules were integrated into the single-board computers of MESs to improve workflow across the new architecture. These improvements and integrations enabled the value chain of this expanded architecture to have lower latency, greater scalability, optimized resource utilization, and improved resistance to network service failures compared to the initial one. Full article

Technological progress in agriculture plays an essential role in enhancing productivity, sustainability, and resilience. This study conducts a bibliometric analysis of the concept of technological progress in agriculture using data extracted from the Web of Science database for the period 1979–2025. The main aim is to identify emerging trends, the structure of collaborative networks, and the influence of research on the development of smart agriculture. The methodology is based on a co-occurrence analysis of keywords, co-author networks, and institutional and international collaborations, providing a detailed insight into the dynamics of research in this field. The results show an accelerated growth of studies on the digitization of agriculture, with a particular focus on technologies such as artificial intelligence, precision agriculture, the Internet of Things, and agricultural process automation. According to the bibliometric analysis, China accounts for the largest share of publications in this field, followed by the United States and Australia. These countries also exhibit high levels of centrality in international collaboration networks, indicating their pivotal role in knowledge production and dissemination. Europe shows a fragmented but active collaborative network, while emerging countries are starting to strengthen their position through strategic partnerships. The findings suggest the need for transdisciplinary collaborations in order to mainstream technological progress in agriculture, emphasizing the importance of policies to support technology transfer and sustainable innovation. Full article

To accelerate breeding progress for egg production traits in Lindian chickens, the genetic parameters for egg production and clutch-related traits in Lindian chickens were evaluated in the present study. Data regarding the age at first egg (AFE), egg number (EN), average clutch length (ACL), and average pause length (APL) were collected from two generations of Lindian chickens based on individual egg production records at 32 weeks of age (32–wk), 43–wk, and 52–wk. The results showed that the AFE of Lindian chickens was 179.3 d of age, with a heritability of 0.35. The heritability was 0.26 for EN32, 0.28 for EN43, and 0.34 for EN52. ACL showed moderate-to-high heritability (h² = 0.3–0.54), but APL traits showed low heritability (h² = 0.09–0.14). There were high positive genetic and phenotypic correlations for EN in the three periods from the start of laying up to 32–wk, 43–wk, and 52–wk. EN had high negative genetic correlations with AFE (r_G = −0.47–−0.80) and high positive genetic correlations with ACL (r_G = 0.45–0.81). The correlation between EN and APL was positive for 32–wk and 43–wk, but negative for 52–wk. These results indicated that the egg production of Lindian chickens could be improved by the selection of AFE, early EN, and ACL. Full article

Case histories have shown that the liquefaction behavior of soils can differ depending on the pre-seismic history of sites. Assessing the shear modulus in soils subjected to seismic events is critical for advancing the fundamental understanding of soil behavior and enhancing the accuracy of soil modeling applications. This paper aims to study the effect of small and large pre-shaking sequences on the liquefaction resistance and shear modulus of sand through shaking table tests. The experimental results indicated that small shakings increase liquefaction resistance and shear modulus. Although large shakings leading to liquefaction cause significant densification, they significantly reduce the liquefaction resistance and shear modulus of sand at shallow depths due to the upward water flow during excess pore water pressure dissipation. The high upward flow of water during liquefaction changes the soil structure and increases the horizontal displacement of densified soil in the subsequent shaking. The amplification factor of acceleration was found to be primarily influenced by the excess pore water pressure generated in the soil instead of its relative density at the start of shaking. This paper presents the variations in Ru with shear strain and the relationship between a normalized shear modulus and shear strain considering the pre-shaking history of sand for shallow depths. Full article