Search Results (1,146)

Soil degradation resulting from intensive agricultural practices, the excessive use of agrochemicals, and climate-induced stresses has significantly impaired soil fertility, disrupted microbial diversity, and reduced crop productivity. Plant growth-promoting bacteria (PGPB) represent a sustainable biological approach to restoring degraded soils by modulating plant physiology and soil function through diverse molecular mechanisms. PGPB synthesizes indole-3-acetic acid (IAA) to stimulate root development and nutrient uptake and produce ACC deaminase, which lowers ethylene accumulation under stress, mitigating growth inhibition. They also enhance nutrient availability by releasing phosphate-solubilizing enzymes and siderophores that improve iron acquisition. In parallel, PGPB activates jasmonate and salicylate pathways, priming a systemic resistance to biotic and abiotic stress. Through quorum sensing, biofilm formation, and biosynthetic gene clusters encoding antibiotics, lipopeptides, and VOCs, PGPB strengthen rhizosphere colonization and suppress pathogens. These interactions contribute to microbial community recovery, an improved soil structure, and enhanced nutrient cycling. This review synthesizes current evidence on the molecular and physiological mechanisms by which PGPB enhance soil restoration in degraded agroecosystems, highlighting their role beyond biofertilization as key agents in ecological rehabilitation. It examines advances in nutrient mobilization, stress mitigation, and signaling pathways, based on the literature retrieved from major scientific databases, focusing on studies published in the last decade. Full article

A modular strategy for the molecular design of silicone-based antifoaming agents was developed by precisely controlling the architecture of poly (methylhydrosiloxane) (PMHS). Sixteen PMHS variants were synthesized by systematically varying the siloxane chain length (L1–L4), backbone composition (D₃T₁ vs. D₃₀T₁), and terminal group chemistry (H- vs. M-type). These structural modifications resulted in a broad range of Si-H functionalities, which were quantitatively analyzed and correlated with defoaming performance. The PMHS matrices were integrated with high-viscosity PDMS, a nonionic surfactant, and covalently grafted fumed silica—which was chemically matched to each PMHS backbone—to construct formulation-specific defoaming systems with enhanced interfacial compatibility and colloidal stability. Comprehensive physicochemical characterization via FT-IR, ¹H NMR, GPC, TGA, and surface tension analysis revealed a nonmonotonic relationship between Si-H content and defoaming efficiency. Formulations containing 0.1–0.3 wt% Si-H achieved peak performance, with suppression efficiencies up to 96.6% and surface tensions as low as 18.9 mN/m. Deviations from this optimal range impaired performance due to interfacial over-reactivity or reduced mobility. Furthermore, thermal stability and molecular weight distribution were found to be governed by repeat unit architecture and terminal group selection. Compared with conventional EO/PO-modified commercial defoamers, the PMHS-based systems exhibited markedly improved suppression durability and formulation stability in high-viscosity environments. These results establish a predictive structure–property framework for tailoring antifoaming agents and highlight PMHS-based formulations as advanced foam suppressors with improved functionality. This study provides actionable design criteria for high-performance silicone materials with strong potential for application in thermally and mechanically demanding environments such as coating, bioprocessing, and polymer manufacturing. Full article

Rehabilitation devices such as actuated lower-limb exoskeletons can provide essential mobility assistance for pediatric patients with gait impairments. Enhancing their control systems under conditions of user variability and dynamic disturbances remains a significant challenge, particularly in active-assist modes. This study presents a human-in-the-loop control architecture for a pediatric lower-limb exoskeleton, combining outer-loop admittance control with robust inner-loop trajectory tracking via a non-singular terminal sliding-mode (NSTSM) controller. Designed for active-assist gait rehabilitation in children aged 8–12 years, the exoskeleton dynamically responds to user interaction forces while ensuring finite-time convergence under system uncertainties. To enhance adaptability, we augment the inner-loop control with a twin delayed deep deterministic policy gradient (TD3) reinforcement learning framework. The actor–critic RL agent tunes NSTSM gains in real-time, enabling personalized model-free adaptation to subject-specific gait dynamics and external disturbances. The numerical simulations show improved trajectory tracking, with RMSE reductions of 27.82% (hip) and 5.43% (knee), and IAE improvements of 40.85% and 10.20%, respectively, over the baseline NSTSM controller. The proposed approach also reduced the peak interaction torques across all the joints, suggesting more compliant and comfortable assistance for users. While minor degradation is observed at the ankle joint, the TD3-NSTSM controller demonstrates improved responsiveness and stability, particularly in high-load joints. This research contributes to advancing pediatric gait rehabilitation using RL-enhanced control, offering improved mobility support and adaptive rehabilitation outcomes. Full article

Antimicrobial resistance (AMR) has emerged as a planetary health emergency, driven not only by the clinical misuse of antibiotics but also by diverse environmental dissemination pathways. This review critically examines the role of environmental compartments—water, soil, and air—as dynamic reservoirs and transmission routes for antibiotic-resistant bacteria (ARB) and resistance genes (ARGs). Recent metagenomic, epidemiological, and mechanistic evidence demonstrates that anthropogenic pressures—including pharmaceutical effluents, agricultural runoff, untreated sewage, and airborne emissions—amplify resistance evolution and interspecies gene transfer via horizontal gene transfer mechanisms, biofilms, and mobile genetic elements. Importantly, it is not only highly polluted rivers such as the Ganges that contribute to the spread of AMR; even low concentrations of antibiotics and their metabolites, formed during or after treatment, can significantly promote the selection and dissemination of resistance. Environmental hotspots such as European agricultural soils and airborne particulate zones near wastewater treatment plants further illustrate the complexity and global scope of pollution-driven AMR. The synergistic roles of co-selective agents, including heavy metals, disinfectants, and microplastics, are highlighted for their impact in exacerbating resistance gene propagation across ecological and geographical boundaries. The efficacy and limitations of current mitigation strategies, including advanced wastewater treatments, thermophilic composting, biosensor-based surveillance, and emerging regulatory frameworks, are evaluated. By integrating a One Health perspective, this review underscores the imperative of including environmental considerations in global AMR containment policies and proposes a multidisciplinary roadmap to mitigate resistance spread across interconnected human, animal, and environmental domains. Full article

Fly-ash-based bubble lightweight soil is widely used due to its environmental friendliness, load reduction, ease of construction, and low costs. In this study, 41 sets of 28 d compressive strength data on lightweight soils with different water–cement ratios, blowing agent dosages, and fly ash dosages were collected through a literature search and indoor tests. Using the compressive strength index and SEM tests, the correlation between the mix ratio design and the microscopic particle components was investigated. The findings were as follows: carbonation reactions occurred in lightweight soil during the maintenance process, and the particles were spherical; increasing the dosage of blowing agent increased the soil’s porosity and pore diameter, leading to the formation of through-holes and reducing the compressive strength and mobility; increasing the fly ash dosage and water–cement ratio increased the soil’s mobility but reduced its compressive strength; and the strength decreased significantly when the fly ash dosage was more than 16% (e.g., the strength at a 20% dosage was 17.8% lower than that at a 15% dosage). Feature importance analysis showed that the water–cement ratio (57.7%), fly ash dosage (30.9%), and blowing agent dosage (11.1%) had a significant effect on strength. ExtraTrees, LightGBM, and Bayesian-optimized Random Forest models were used for 28d strength prediction with coefficients of determination (R²) of 0.695, 0.731, and 0.794, respectively. The Bayesian-optimized Random Forest model performed optimally in terms of the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), and the prediction performance was best. The accuracy of the model is expected to be further improved with expansions in the database. A 28 d compressive strength prediction platform for fly-ash-based bubble lightweight soil was ultimately developed, providing a convenient tool for researchers and engineers to predict material properties and mix ratios. Full article

This paper proposes a low-latency and scalable architecture for Edge-Enabled Digital Twin networked control systems (E-DTNCS) aimed at multi-robot collision avoidance and remote control in dynamic and latency-sensitive environments. Traditional approaches, which rely on centralized cloud processing or direct sensor-to-controller communication, are inherently limited by excessive network latency, bandwidth bottlenecks, and a lack of predictive decision-making, thus constraining their effectiveness in real-time multi-agent systems. To overcome these limitations, we propose a novel framework that seamlessly integrates edge computing with digital twin (DT) technology. By performing localized preprocessing at the edge, the system extracts semantically rich features from raw sensor data streams, reducing the transmission overhead of the original data. This shift from raw data to feature-based communication significantly alleviates network congestion and enhances system responsiveness. The DT layer leverages these extracted features to maintain high-fidelity synchronization with physical robots and to execute predictive models for proactive collision avoidance. To empirically validate the framework, a real-world testbed was developed, and extensive experiments were conducted with multiple mobile robots. The results revealed a substantial reduction in collision rates when DT was deployed, and further improvements were observed with E-DTNCS integration due to significantly reduced latency. These findings confirm the system’s enhanced responsiveness and its effectiveness in handling real-time control tasks. The proposed framework demonstrates the potential of combining edge intelligence with DT-driven control in advancing the reliability, scalability, and real-time performance of multi-robot systems for industrial automation and mission-critical cyber-physical applications. Full article

This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically. Full article

Wildfires increasingly threaten human life, ecosystems, and infrastructure, with events like the 2025 Palisades and Eaton fires in Los Angeles County underscoring the urgent need for more advanced prediction frameworks. Existing physics-based and deep-learning models struggle to capture dynamic wildfire spread across both 2D and 3D domains, especially when incorporating real-time, multimodal geospatial data. This paper explores how generative artificial intelligence (AI) models—such as GANs, VAEs, and transformers—can serve as transformative tools for wildfire prediction and simulation. These models offer superior capabilities in managing uncertainty, integrating multimodal inputs, and generating realistic, scalable wildfire scenarios. We adopt a new paradigm that leverages large language models (LLMs) for literature synthesis, classification, and knowledge extraction, conducting a systematic review of recent studies applying generative AI to fire prediction and monitoring. We highlight how generative approaches uniquely address challenges faced by traditional simulation and deep-learning methods. Finally, we outline five key future directions for generative AI in wildfire management, including unified multimodal modeling of 2D and 3D dynamics, agentic AI systems and chatbots for decision intelligence, and real-time scenario generation on mobile devices, along with a discussion of critical challenges. Our findings advocate for a paradigm shift toward multimodal generative frameworks to support proactive, data-informed wildfire response. Full article

Chromium slag sites pose severe environmental risks due to hexavalent chromium (Cr(VI)) contamination, characterized by high mobility and toxicity. This study focused on chromium-contaminated soil from a historical chromium slag site in North China, where long-term accumulation of chromate production residues has led to serious Cr(VI) pollution, with Cr(VI) accounting for 13–22% of total chromium and far exceeding national soil risk control standards. To elucidate Cr(VI) transformation mechanisms and elemental linkages, a combined approach of macro-scale condition experiments and micro-scale analysis was employed. Results showed that acidic conditions (pH < 7) significantly enhanced Cr(VI) reduction efficiency by promoting the conversion of CrO₄²⁻ to HCrO₄⁻/Cr₂O₇²⁻. Among reducing agents, FeSO₄ exhibited the strongest effect (reduction efficiency >30%), followed by citric acid and fulvic acid. Temperature variations (−20 °C to 30 °C) had minimal impact on Cr(VI) transformation in the 45-day experiment, while soil moisture (20–25%) indirectly facilitated Cr(VI) reduction by enhancing the reduction of agent diffusion and microbial activity, though its effect was weaker than chemical interventions. Soil grain-size composition influenced Cr(VI) distribution unevenly: larger particles (>0.2 mm) in BC-35 and BC-36-4 acted as main Cr(VI) reservoirs due to accumulated Fe-Mn oxides, whereas BC-36-3 showed increased Cr(VI) in smaller particles (<0.074 mm). μ-XRF and correlation analysis revealed strong positive correlations between Cr and Ca, Fe, Mn, Ni (Pearson coefficient > 0.7, p < 0.01), attributed to adsorption–reduction coupling on iron-manganese oxide surfaces. In contrast, Cr showed weak correlations with Mg, Al, Si, and K. This study clarifies the complex factors governing Cr(VI) behavior in chromium slag soils, providing a scientific basis for remediation strategies such as pH adjustment (4–6) combined with FeSO₄ addition to enhance Cr(VI) reduction efficiency. Full article

Surfactants are widely utilized in agriculture as emulsifying, dispersing, anti-foaming, and wetting agents. In these adjuvant roles, the inherent biological activity of the surfactant is secondary to the active ingredients. Here, the hydrophilic non-ionic surface-active tri-block copolymer Pluronic^® F68 is investigated for direct biological activity in wheat. F68 binds to and inserts into lipid membranes, which may benefit crops under abiotic stress. F68’s interactions with Triticum aestivum (var Juniper) seedlings and a seed-borne Bacillus spp. endophyte are presented. At concentrations below 10 g/L, F68-primed wheat seeds exhibited unchanged emergence. Root-applied fluorescein-F68 (fF68) was internalized in root epidermal cells and concentrated in highly mobile endosomes. The potential benefit of F68 in droughted wheat was examined and contrasted with wheat treated with the osmolyte, glycine betaine (GB). Photosystem II activity of droughted plants dropped significantly below non-droughted controls, and no clear benefit of F68 (or GB) during drought or rehydration was observed. However, F68-treated wheat exhibited increased transpiration values (for watered plants only) and enhanced shoot dry mass (for watered and droughted plants), not observed for GB-treated or untreated plants. The release of seed-borne bacterial endophytes into the spermosphere of germinating seeds was not affected by F68 (for F68-primed seeds as well as F68 applied to roots), and the planktonic growth of a purified Bacillus spp. seed endophyte was not reduced by F68 applied below the critical micelle concentration. These studies demonstrated that F68 entered wheat root cells, concentrated in endosomes involved in transport, significantly promoted shoot growth, and showed no adverse effects to plant-associated bacteria. Full article

The invention of antibacterial agents (antibiotics) was a significant event in the history of the human race, and this invention changed the way in which infectious diseases were cured; as a result, many lives have been saved. Recently, antibiotic resistance has developed as a result of excessive use of antibiotics, and it has become a major threat to world health. ARGs are spread across biomes and taxa of bacteria via lateral or horizontal gene transfer (HGT), especially via conjugation, transformation, and transduction. This review concerns transduction, whereby bacteriophages or phages facilitate gene transfer in bacteria. Bacteriophages are just as common and many times more numerous than their bacterial prey, and these phages are much more influential in controlling the population of bacteria. It is estimated that 25% of overall genes of Escherichia coli have been copied by other species of bacteria due to the HGT process. Transduction may take place via a generalized or specialized mechanism, with phages being ubiquitous in nature. Phage and virus-like particle (VLP) metagenomics have uncovered the emergence of ARGs and mobile genetic elements (MGEs) of bacterial origins. These genes, when transferred to bacteria through transduction, confer resistance to antibiotics. ARGs are spread through phage-based transduction between the environment and bacteria related to people or animals, and it is vital that we further understand and tackle this mechanism in order to combat antimicrobial resistance. Full article

Ubiquitous blended learning, leveraging mobile devices, has democratized education by enabling autonomous and readily accessible knowledge acquisition. However, its reliance on traditional interfaces often limits learner immersion and meaningful interaction. The emergence of the wearable metaverse offers a compelling solution, promising enhanced multisensory experiences and adaptable learning environments that transcend the constraints of conventional ubiquitous learning. This research proposes a novel framework for ubiquitous blended learning in the wearable metaverse, aiming to address critical challenges, such as multi-source data fusion, effective human–computer collaboration, and efficient rendering on resource-constrained wearable devices, through the integration of embodied interaction and multi-agent collaboration. This framework leverages a real-time multi-modal data analysis architecture, powered by the MobileNetV4 and xLSTM neural networks, to facilitate the dynamic understanding of the learner’s context and environment. Furthermore, we introduced a multi-agent interaction model, utilizing CrewAI and spatio-temporal graph neural networks, to orchestrate collaborative learning experiences and provide personalized guidance. Finally, we incorporated lightweight SLAM algorithms, augmented using visual perception techniques, to enable accurate spatial awareness and seamless navigation within the metaverse environment. This innovative framework aims to create immersive, scalable, and cost-effective learning spaces within the wearable metaverse. Full article

With the advent of Autonomous Vehicles (AV) technology, extensive research around the design of on-demand mobility systems powered by such vehicles is performed. An important part of these studies consists in the evaluation of the economic impact of such systems for involved stakeholders. In this work, a cost–benefit analysis (CBA) is applied to the introduction of AV services in Paris-Saclay, an intercommunity, south of Paris, simulated through MATSim, an agent-based model capable of capturing complex travel behaviors and dynamic traffic interactions. AVs would be implemented as a feeder service, first- and last-mile service to public transit, allowing intermodal trips for travelers. The system is designed to target the challenges of public transport accessibility in periurban areas and high private car use, which the AV feeder service is designed to mitigate. To our knowledge, this study is one of the first CBA analyses of an intermodal AV system relying on an agent-based simulation. The introduction of AV in a periurban environment would generate more pressure on the road network (0.8% to 1.7% increase in VKT for all modes, and significant congestion around train stations) but would improve traveler utilities. The utility gains from the new AV users benefiting from a more comfortable mode offsets the longer travel times from private car users. A Stop-Based routing service generates less congestion than a Door-to-Door routing service, but the access/egress time counterbalances this gain. Finally, in a periurban environment where on-demand AV feeder service would be added to reduce the access and egress cost of public transit, the social impact would be nuanced for travelers (over 99% of gains captured by the 10% of most benefiting agents), but externality would increase. This would benefit some travelers but would also involve additional congestion. In that case, a Stop-Based routing on a constrained network (e.g., existing bus network) significantly improves economic viability and reduces infrastructure costs and would be less impacting than a Door-to-Door service. Full article

Wireless sensor networks (WSNs) have found extensive applications in a variety of fields, including military surveillance, wildlife monitoring, industrial process monitoring, and more. The gradual energy depletion of sensor nodes with limited battery energy leads to the dysfunction of some of the nodes, thus creating coverage holes in the monitored area. Coverage holes can cause the network to fail to deliver high-quality data and can also affect network performance and the quality of service. Therefore, the detection and recovery of coverage holes are major issues in WSNs. In response to these issues, we propose a method for detecting and recovering coverage holes in wireless sensor networks. This method first divides the network into equally sized units, and then selects a representative node for each unit based on two conditions, called an agent. Then, the percentage of each unit covered by nodes can be accurately calculated and holes can be detected. Finally, the holes are recovered using the average of the key perceptual intersections as the initial value of the global optimal point of the particle swarm optimization algorithm. Simulation experiments show that the algorithm proposed in this paper reduces network energy consumption by 6.68%, decreases the distance traveled by mobile nodes by 8.51%, and increases the percentage of network hole recovery by 2.16%, compared with other algorithms. Full article

Offloading decisions and resource allocation problems in mobile edge computing (MEC) emerge as key challenges as they directly impact system performance and user experience in dynamic and resource-constrained Internet of Things (IoT) environments. This paper constructs a comprehensive and layered digital twin (DT) model for MEC, enabling real-time cooperation with the physical world and intelligent decision making. Within this model, a novel Flash-Attention-enhanced Multi-Agent Deep Deterministic Policy Gradient (FA-MADDPG) algorithm is proposed to effectively tackle MEC problems. It enhances the model by arming a critic network with attention to provide a high-quality decision. It also changes a matrix operation in a mathematical way to speed up the training process. Experiments are performed in our proposed DT environment, and results demonstrate that FA-MADDPG has good convergence. Compared with other algorithms, it achieves excellent performance in delay and energy consumption under various settings, with high time efficiency. Full article