Search Results (6,103)

Gross Primary Productivity (GPP) plays a vital role in the terrestrial carbon cycle and ecosystem functioning. Understanding its spatio-temporal dynamics and driving mechanisms is critical for predicting ecosystem responses to climate change. China’s GPP has experienced complex responses due to heterogeneous climate, environment, and human activities, yet their impacts and interactions across ecosystems remain unquantified. This study used the Mann–Kendall test and SHapley Additive exPlanations to quantify the contributions and interactions of climate, vegetation, topography, and human factors using GPP data (2001–2020). Nationally, GPP showed a significant upward trend, particularly in deciduous broadleaf forests, croplands, grasslands, and savannas. Leaf area index (LAI) is identified as the primary contributor to GPP variations, while climate factors exhibit nonlinear interactive effects on the modeled GPP. Ecosystem-specific sensitivities were evident: forest GPP is predominantly associated with climate–vegetation coupling. Additionally, in coniferous forests, the interaction between anthropogenic factors and topography shows a notable association with productivity patterns. Grassland GPP is primarily linked to topography, while cropland GPP is mainly related to management practices and environmental conditions. In contrast, the GPP of savannas and shrublands is less influenced by factor interactions. These findings high-light the necessity of ecosystem-specific management and restoration strategies and provide a basis for improving carbon cycle modeling and climate change adaptation planning. Full article

Nitrogen dioxide (NO₂) remains one of the most relevant traffic-related air pollutants in urban environments, despite decades of regulatory efforts and advances in vehicle emission control technologies. This review synthesizes current knowledge on ambient NO₂ concentrations associated with road transport, identifies key determinants of spatial and temporal variability, and evaluates the effectiveness of mitigation approaches under increasingly stringent air quality standards. The study is based on a comprehensive review of peer-reviewed literature reporting NO₂ measurements in urban, traffic, and background environments worldwide, complemented by an assessment of regulatory frameworks and mitigation strategies. The evidence confirms that road transport is the dominant contributor to elevated NO₂ concentrations, particularly at traffic sites, with traffic intensity, fleet composition, driving behavior, cold-start emissions, and street geometry emerging as primary controlling factors. Meteorological conditions influence dispersion but generally play a secondary role compared with emission-related drivers. Urban infrastructure, especially street canyons and tunnels, amplifies near-road NO₂ levels and population exposure. Mitigation measures such as Low Emission Zones, vehicle fleet modernization, and infrastructural interventions can reduce NO₂ concentrations, but their effectiveness is moderate and highly context-dependent. Sustained compliance with EU limit values and World Health Organization guideline levels requires integrated, multi-scale mitigation strategies. Full article

Intelligent vehicle networks powered by machine learning, AI and blockchain are transforming various sectors beyond transportation. In this context, being able to remote drive a vehicle is key for enhancing autonomous driving systems. After deploying end-to-end teleoperated driving systems under 5G networks, the need to address complex challenges in other critical areas arises. These challenges belong to different technologies that need to be integrated in this particular system: video transmission and visualization technologies, artificial intelligence techniques, and network optimization features, incorporating haptic devices and critical data security. This article explores how these technologies can enhance the teleoperated driving activity experiences already executed in real-life environments by analyzing the quality of the video which is transmitted over the network, exploring its correlation with the current state-of-the-art AI object detection algorithms, analyzing the extended reality and digital twin paradigms, obtaining the maximum possible performance of forthcoming 6G networks and proposing decentralized security schema for ensuring the privacy and safety of the end-users of teleoperated driving infrastructures. An integrated set of conclusions and recommendations will be given to outline the future teleoperated driving systems design in the forthcoming years. Full article

As a novel traffic security facility to improve the environment of tunnels, the influence of tunnel sidewall decoration on drivers has been highly controversial. To analyze the impact of the multi-factor coupling of sidewall decoration effects on driving safety, eight combination schemes with different pattern elements and pattern spacings were designed to create a driving simulation environment. Twenty-seven drivers were recruited to obtain fine-grained driving behavior indicators via driving simulation experiments. The velocity following ratio, steering wheel angle, maximum deceleration, and accelerator power were selected to construct an index system. The visual information load of drivers was quantified by the landscape color quantified theory. Based on the analysis of the influence of the singular factor of the pattern element or pattern spacing on driving behavior, a coupling coordination degree model is introduced to quantify the relationship between the complexity of the pattern elements, the pattern spacing, and the coupling coordination degree, and a reasonable combination of their complexities is selected. The results show that the element complexity and pattern spacing of tunnel sidewall decoration have significant effects on driving behavior. Among the schemes considered in this study, the coupling effect of an element complexity of 562.1 and a pattern spacing of 5.5 m was found to be the optimal combination. The coupling coordination degree should be more than 0.8 as the threshold, and the model analysis results indicated that when the pattern spacing was fixed at about 10 m, the ideal element complexity was between 135.6–564.7. This study offers both theoretical and technical support for enhancing traffic safety through tunnel sidewall decoration. By defining optimal thresholds for information density and pattern spacing, it lays a solid foundation for the development of a standardized guideline on decoration content. Full article

This study examines how digital transformational leadership (DTL) drives superior and enduring organizational performance through the mediating roles of organizational agility (OA) and digital transformation (DT) while assessing the contingent moderating role of digital culture (DC). Anchored in the Resource-Based View (RBV), the study conceptualizes DTL as a strategic intangible capability that enables the orchestration of digital and agile resources into sustained performance outcomes in digitally turbulent environments. Data were collected from 284 senior and middle managers across 13 Palestinian commercial banks—a highly regulated sector undergoing intensive digital pressure in an emerging-economy context—using an online survey. The proposed relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The results reveal that DTL significantly enhances both OA and DT, which in turn contribute positively to organizational performance. OA and DT operate as both independent and sequential mediators, uncovering a multistage capability-building pathway through which leadership fosters long-term adaptability and resilience. The findings further indicate that digital culture conditions the effectiveness of leadership-driven transformation, shaping how digital initiatives consolidate into enduring organizational routines rather than short-term efficiency gains. By reframing sustainable transformation as the continuity of organizational performance through agility, digital renewal, and cultural alignment—rather than as an ESG outcome alone—this study refines RBV boundary conditions in digital contexts. The study contributes theoretically by clarifying how leadership-enabled capabilities generate sustainable competitive advantage and offers actionable managerial insights for cultivating agility, embedding digital transformation, and strengthening cultural readiness to support long-term organizational resilience. Full article

Background/Objectives: Bovine mastitis, a significant global concern in dairy farming, results in substantial economic losses and poses considerable risks to both animal and human health. With the increasing prevalence of antimicrobial resistance (AMR) in mastitis pathogens, the potential for resistant infections to spread from livestock to humans and the environment is becoming a critical public health issue. This narrative review summarizes the current evidence on antimicrobial resistance in pathogens causing bovine mastitis and examines it from a One Health perspective, encompassing animal, human, and environmental interfaces. Results: By examining the complex interplay among animal, human, and environmental health, we highlight key factors that drive resistance, including the overuse of antimicrobials, poor farm management, and environmental contamination. We also discuss innovative strategies, such as enhanced surveillance, pathogen-specific diagnostics, alternatives to antimicrobials, and sustainable farm practices, that can mitigate the emergence of resistance. Key knowledge gaps include a limited understanding of antimicrobial residues, resistant pathogens, and gene transmission pathways and inconsistent implementation of antimicrobial stewardship practices. Conclusions: This review emphasizes the need for a coordinated, multidisciplinary effort to reduce the burden of AMR in bovine mastitis pathogens, ensuring the continued efficacy of antimicrobials and safeguarding public health through responsible management and policy interventions. Full article

Changes in preservation conditions act as an important environmental filter driving shifts in microbial communities. However, the precise identities, functional traits, and ecological mechanisms of the dominant agents driving stage-specific deterioration remain insufficiently characterized. This study investigated microbial communities and dominant fungal degraders in waterlogged versus dried bamboo slips using amplicon sequencing, multivariate statistics, and microbial isolation. Results revealed compositionally distinct communities, with dried slips sharing only a small proportion of operational taxonomic units (OTUs) with waterlogged slips, while indicating the persistence of a subset of taxa across preservation states. A key discovery was the dominance of Fonsecaea minima (92% relative abundance) at the water-solid-air interface of partially submerged slips. Scanning electron microscopy (SEM) and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) indicate that this fungus forms melanin-rich, biofilm-like surface structures, suggesting enhanced surface colonization and stress resistance. In contrast, the fungal community isolated from dried slips was characterized by Apiospora saccharicola associated with detectable xylanase activity. Meanwhile, the xerophilic species Xerogeomyces pulvereus dominated (99% relative abundance) the storage box environment. Together, these results demonstrate that preservation niches select for fungi with distinct functional traits, highlighting the importance of stage-specific preservation strategies that consider functional traits rather than taxonomic identity alone. Full article

The rising levels of Co(II) in aquatic environments present considerable risks, thereby necessitating the development of effective remediation strategies. This study introduces an innovative pre-hydration method for synthesizing carbonated calcined clay dolomite (CCCD) to efficiently remove Co(II) from contaminated water. This pre-hydration treatment successfully reduced the complete carbonation temperature of the material from 500 °C to 400 °C, significantly enhancing energy efficiency. The Co(II) removal performance was systematically investigated by varying key parameters such as contact time, initial Co(II) concentration, pH, and solid/liquid ratio. Optimal removal was achieved at 318 K with pH of 4 and a solid/liquid ratio of 0.5 g·L⁻¹. Continuous flow column experiments confirmed the excellent long-term stability of CCCD, maintaining a consistent Co(II) removal efficiency of 99.0% and a stable effluent pH of 8.5 over one month. Isotherm and kinetic models were used to empirically describe concentration-dependent and time-dependent uptake behavior. The equilibrium data were best described by the Langmuir model, while kinetics followed a pseudo-second-order model. An apparent maximum removal capacity of 621.1 mg g⁻¹ was obtained from Langmuir fitting of equilibrium uptake data. Mechanistic insights from Visual MINTEQ calculations and solid phase characterizations (XRD, XPS, and TEM) indicate that Co(II) removal is dominated by mineral water interface precipitation. The gradual hydration of periclase (MgO) forms Mg(OH)₂, which creates localized alkaline microenvironments at particle surfaces and drives Co(OH)₂ formation. Carbonate availability further favors CoCO₃ formation and retention on CCCD. Importantly, this localized precipitation pathway maintains a stable, mildly alkaline effluent pH (around 8.5), reducing downstream pH adjustment demand and improving operational compatibility. Overall, CCCD combines high Co(II) immobilization efficiency, strong long-term stability, and an energy-efficient preparation route, supporting its potential for scalable remediation of Co(II) contaminated water. Full article

The field of electrochemical devices, encompassing energy storage, fuel cells, electrolysis, and sensing, is fundamentally reliant on the electrode materials that govern their performance, efficiency, and sustainability. Traditional materials, while foundational, often face limitations such as restricted reaction kinetics, structural deterioration, and dependence on costly or scarce elements, driving the need for continuous innovation. Emerging electrode materials are designed to overcome these challenges by delivering enhanced reaction activity, superior mechanical robustness, accelerated ion diffusion kinetics, and improved economic feasibility. In energy storage, for example, the shift from conventional graphite in lithium-ion batteries has led to the exploration of silicon-based anodes, offering a theoretical capacity more than tenfold higher despite the challenge of massive volume expansion, which is being mitigated through nanostructuring and carbon composites. Simultaneously, the rise of sodium-ion batteries, appealing due to sodium’s abundance, necessitates materials like hard carbon for the anode, as sodium’s larger ionic radius prevents efficient intercalation into graphite. In electrocatalysis, the high cost of platinum in fuel cells is being addressed by developing Platinum-Group-Metal-free (PGM-free) catalysts like metal–nitrogen–carbon (M-N-C) materials for the oxygen reduction reaction (ORR). Similarly, for the oxygen evolution reaction (OER) in water electrolysis, cost-effective alternatives such as nickel–iron hydroxides are replacing iridium and ruthenium oxides in alkaline environments. Furthermore, advancements in materials architecture, such as MXenes—two-dimensional transition metal carbides with metallic conductivity and high volumetric capacitance—and Single-Atom Catalysts (SACs)—which maximize metal utilization—are paving the way for significantly improved supercapacitor and catalytic performance. While significant progress has been made, challenges related to fundamental understanding, long-term stability, and the scalability of lab-based synthesis methods remain paramount for widespread commercial deployment. The future trajectory involves rational design leveraging advanced characterization, computational modeling, and machine learning to achieve holistic, system-level optimization for sustainable, next-generation electrochemical devices. Full article

The integration of flexible electronics and machine learning (ML) algorithms has become a revolutionary force driving the field of intelligent sensing, giving rise to a new generation of intelligent devices and systems. This article provides a systematic review of core technologies and practical applications of ML in flexible electronics. It focuses on analyzing the theoretical frameworks of algorithms such as the Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Reinforcement Learning (RL) in the intelligent processing of sensor signals (IPSS), multimodal feature extraction (MFE), process defect and anomaly detection (PDAD), and data compression and edge computing (DCEC). This study explores the performance advantages of these technologies in optimizing signal analysis accuracy, compensating for interference in high-noise environments, optimizing manufacturing process parameters, etc., and empirically analyzes their potential applications in wearable health monitoring systems, intelligent control of soft robots, performance optimization of self-powered devices, and intelligent perception of epidermal electronic systems. Full article

To ensure the safety of vehicles and occupants under failures or functional limitations of ego vehicles, a minimum risk maneuver (MRM) has been proposed as a key automated driving system (ADS) function. However, executing an MRM may pose certain potential risks when sensor failures occur. This study proposed an MRM strategy designed to enhance highway-driving safety during MRM execution under multiple sensor-failure conditions. A hazard and operability study analysis, based on an ADS behavior model, is conducted to systematically identify hazards, determine potential hazardous events, and categorize the associated safety risks arising from sensor failures. Within the proposed strategy, virtual objects are generated to account for potential hazards and support risk assessments. Adaptive MRM behavior is determined in real time by analyzing surrounding objects and evaluating time-to-collision and time headway. The strategy is verified by using a MATLAB–CARLA co-simulation environment across three representative highway scenarios with combined sensor failures. The result demonstrates that the proposed MRM strategy can mitigate collision risk in hazardous scenarios while effectively leveraging the remaining functional sensors to guide the ego vehicle toward an appropriate minimum risk condition during MRM execution. Full article

Accurate trajectory tracking remains a central challenge in differential-drive mobile robots (DDMRs), particularly when operating under real-world conditions. Model Predictive Control (MPC) provides a powerful framework for this task, but its performance degrades when the robot deviates significantly from the nominal path. To address this limitation, robust recovery mechanisms are required to ensure stable and precise tracking. This work presents an experimental validation of an MPC controller applied to a four-wheel DDMR, whose odometry is corrected by a SLAM algorithm running in ROS 2. The MPC is formulated as a quadratic program with state and input constraints on linear (v) and angular ($\omega$) velocities, using a prediction horizon of $N_p=15$ future states, adjusted to the computational resources of the onboard computer. A novel dynamic look-ahead re-entry strategy is proposed, which activates when the robot exits a predefined lateral error band ($\delta =0.05$ m) and interpolates a smooth reconnection trajectory based on a forward look-ahead point, ensuring gradual convergence and avoiding abrupt re-entry actions. Accuracy was evaluated through lateral and heading errors measured via geometric projection onto the nominal path, ensuring fair comparison. From these errors, RMSE, MAE, $P_{95}$, and in-band percentage were computed as quantitative metrics. The framework was tested on real hardware at 50 Hz through 5 nominal experiments and 3 perturbed experiments. Perturbations consisted of externally imposed velocity commands at specific points along the path, while configuration parameters were systematically varied across trials, including the weight R, smoothing distance $L_{\mathrm{smooth}}$, and activation of the re-entry strategy. In nominal conditions, the best configuration (ID 2) achieved a lateral RMSE of 0.05 m, a heading RMSE of 0.06 rad, and maintained 68.8% of the trajectory within the validation band. Under perturbations, the proposed strategy substantially improved robustness. For instance, in experiment ID 6 the robot sustained a lateral RMSE of 0.12 m and preserved 51.4% in-band, outperforming MPC without re-entry, which suffered from larger deviations and slower recoveries. The results confirm that integrating MPC with the proposed re-entry strategy enhances both accuracy and robustness in DDMR trajectory tracking. By combining predictive control with a spatially grounded recovery mechanism, the approach ensures consistent performance in challenging scenarios, underscoring its relevance for reliable mobile robot navigation in uncertain environments. Full article

Neurons have exceptionally high energy demands, sustained by thousands to millions of mitochondria per cell. Each mitochondrion depends on the integrity of its mitochondrial DNA (mtDNA), which encodes essential electron transport chain (ETC) subunits required for oxidative phosphorylation (OXPHOS). The continuous, high-level ATP production by OXPHOS generates reactive oxygen species (ROS) that pose a significant threat to the nearby mtDNA. To counter these insults, neurons rely on base excision repair (BER), the principal mechanism for removing oxidative and other small, non-bulky base lesions in nuclear and mtDNA. BER involves a coordinated enzymatic pathway that excises damaged bases and restores DNA integrity, helping maintain mitochondrial genome stability, which is vital for neuronal bioenergetics and survival. When mitochondrial BER is impaired, mtDNA becomes unstable, leading to ETC dysfunction and a self-perpetuating cycle of bioenergetic failure, elevated ROS levels, and continued mtDNA damage. Damaged mtDNA fragments can escape into the cytosol or extracellular space, where they act as damage-associated molecular patterns (DAMPs) that activate innate immune pathways and inflammasome complexes. Chronic activation of these pathways drives sustained neuroinflammation, exacerbating mitochondrial dysfunction and neuronal loss, and functionally links genome instability to innate immune signaling in neurodegenerative diseases. This review summarizes recent advancements in understanding how BER preserves mitochondrial genome stability, affects neuronal health when dysfunctional, and contributes to damage-driven neuroinflammation and neurodegenerative disease progression. We also explore emerging therapeutic strategies to enhance mtDNA repair, optimize its mitochondrial environment, and modulate neuroimmune pathways to counteract neurodegeneration. Full article

This paper tackles the challenge of disassembly sequence planning (DSP) in energy-efficient remanufacturing by introducing an innovative hybrid optimization framework. The proposed model integrates a Dynamic Time-Varying CRITIC–Entropy (DTVCE) decision-making framework with an Improved Honey Badger Algorithm (IHBA) to optimize disassembly sequences under key operational criteria, including idle rate, line smoothness, and energy consumption. The DTVCE framework constructs a dynamic composite score by normalizing evaluation criteria across time slices and incorporating temporal discounting to capture the evolving importance of each factor. Meanwhile, by establishing a symmetric disassembly constraint matrix to restrict the disassembly sequence and integrating exploration and exploitation mechanisms to enhance the IHBA, the solution process is empowered to efficiently generate feasible disassembly sequences and fulfill task allocation across workstations while satisfying takt time constraints. Experimental validation demonstrates that the proposed framework significantly outperforms traditional disassembly optimization approaches in both energy efficiency and line balance performance. In a case study involving an automotive drive axle, the method achieved a near-optimal configuration using only eight workstations, leading to a marked reduction in both energy consumption and idle times. Sensitivity analysis further verifies the model’s robustness, showing stable convergence and consistent performance under varying takt times and energy parameters. Overall, this study contributes to the advancement of green remanufacturing by offering a scalable, data-driven, and adaptive solution to disassembly optimization—paving the way toward sustainable and energy-aware production environments. Full article

Research on Cooperative Driving Automation (CDA) has advanced to overcome the limited perception range of onboard sensors and the difficulty of inferring surrounding vehicles’ intentions by leveraging vehicle-to-everything (V2X) communications. This paper models how an autonomous vehicle receives cooperative sensing and cooperative maneuvering information generated at an edge roadside unit (edge RSU) that integrates roadside units (RSUs) with multi-access edge computing (MEC), and how the vehicle fuses this information with its onboard situational awareness and path-planning modules. We then analyze the performance gains of edge RSU-enabled services across diverse traffic environments. In a highway-merging scenario, simulations show that employing the edge RSU’s sensor sharing service (SSS) reduces collision risk relative to onboard-only baselines. For unsignalized intersections and roundabouts, we further propose a guidance-driven Hybrid Pairing Optimization (HPO) scheme in which the edge RSU aggregates CAV intents/trajectories, resolves spatiotemporal conflicts via lightweight pairing and time window allocation, and broadcasts maneuver guidance through MSCM. Unlike a first-come, first-served (FCFS) policy that serializes passage, HPO injects edge guidance as soft constraints while preserving arrival order fairness, enabling safe concurrent passage opportunities when feasible. Across intersections and roundabouts, HPO improves average speed by up to 192% and traffic throughput by up to 209% compared with FCFS under identical demand in our simulations. Full article