Search Results (5,762)

Mercury (Hg) is a ubiquitous element in the environment that may pose a threat to human health due to its toxicity, high mobility through the food chain, and long-lasting persistence. Organic Hg compounds, particularly methylmercury, are more toxic than inorganic mercury due to their easy absorption and persistent retention within the organism. Although natural attenuation can occur in soil through various processes, excessive levels of Hg cause pollution that can adversely affect agricultural soil, making remediation necessary to either remove or stabilize Hg within the soil. This review primarily aims to summarize key remediation strategies—chemical, biological, and physical—developed in recent years for agricultural soil remediation. It discusses the influencing factors, advantages, limitations, mechanisms, and practical applications of these soil remediation technologies. The published literature focuses on identifying plant species and microorganisms capable of remediating Hg-contaminated soils. Emerging amendments, such as biochar and nanomaterials, have been tested for treating mercury (Hg)-polluted soils primarily by immobilizing mercury and reducing its bioavailability and methylation. Ex situ remediation technologies are effective for Hg-contaminated soils but are often costly, labor-intensive, detrimental to soil quality, and generate hazardous secondary waste. In contrast, in situ technologies treat Hg directly within the soil, preserving the soil matrix and its biota. According to the literature, remediation of Hg-contaminated agricultural soils can be compatible with food crop production only if the bioavailable Hg fraction is sufficiently reduced and crop uptake remains below food safety limits. The gap between laboratory trials and actual field applications in Hg-contaminated soil remediation mainly arises from differences in scale, complexity, and the uncertainty of real-world conditions, which often reduce the efficiency and predictability of treatments. This review aims to provide a practical reference for improving the effective remediation of Hg-contaminated soils in the future. Full article

Labor shortages and the need for increased productivity have accelerated the development of robotic harvesting systems for greenhouse crops; however, reliable operation under fruit occlusion and clustered arrangements remains a major challenge, particularly due to the limited integration between perception and motion planning modules. The paper presents the design and experimental validation of an autonomous robotic system for greenhouse tomato harvesting. The proposed platform integrates a rail-guided mobile base, a six-degrees-of-freedom robotic manipulator, and an adaptive end effector with a hybrid vision framework that combines convolutional neural networks and watershed-based segmentation to enable robust fruit detection and localization under occluded conditions. The proposed approach enables improved separation of overlapping fruits and provides accurate spatial localization through stereo vision combined with IMU-assisted camera-to-robot coordinate transformation. An occlusion-aware trajectory planning strategy was developed to generate collision-free manipulation paths in the presence of leaves and stems, enhancing harvesting safety and reliability. The system was trained and evaluated using a dataset of real greenhouse images supplemented with synthetic data augmentation. Experimental trials conducted under practical greenhouse conditions demonstrated a fruit detection precision of 96.9%, recall of 93.5%, and mean Intersection-over-Union of 79.2%. The robotic platform achieved an overall harvesting success rate of 78.5%, reaching 85% for unobstructed fruits, with an average cycle time of 15 s per fruit in direct harvesting scenarios. The rail-guided mobility significantly improved positioning stability and repeatability during manipulation compared with fully mobile platforms. The results confirm that integrating hybrid perception with occlusion-aware motion planning can substantially improve the functionality of robotic harvesting systems in protected cultivation environments. The proposed solution contributes to the advancement of automation technologies for greenhouse vegetable production and supports the transition toward more sustainable and labor-efficient agricultural practices. Full article

Motor neuron diseases (MNDs) encompass a clinically heterogeneous group of neurodegenerative conditions with varying impact on dexterity, mobility, decision making, respiratory and bulbar dysfunction. While consensus best-practice recommendations exist for genetic screening, diagnostic work-up, pharmacological and respiratory management, disease-specific facets of driving safety, assessment approaches and intervention strategies to support patients for safe driving have not been comprehensively reviewed. MNDs have unique, phenotype-specific clinical features, which are distinct form other neuromuscular conditions which necessitate a careful and systematic approach to evaluate driving safety. While MNDs are primarily associated with progressive motor impairment, extrapyramidal, cerebellar, cognitive, behavioural, and respiratory manifestations of the disease also affect driving safety and necessitate comprehensive driving assessments and individualised strategies to enable patients to continue to drive. The majority of existing papers focus on amyotrophic lateral sclerosis, and low-incidence MND phenotypes, such as PLS, SBMA, PPS, are glaringly understudied from a driving safety perspective despite the relatively slower progression of these conditions. Beyond the review of specific aspects of driving in MNDs, the main objective of this review paper is to raise awareness of non-motor aspects of MNDs with regard to driving safety and to explore viable strategies to support patients to maintain their independence. Despite the considerable differences in driving regulations around the globe, there are core, disease-specific aspects of MND which are universal. The careful consideration of these clinical factors, comprehensive domain-by-domain assessments, and the implementation of practical, individualised adaptations may enable patients to continue driving safely, maintain their independence and enhance their quality of life. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

Bio-based, biodegradable, and renewable polymers offer a promising alternative to traditional synthetic polymers derived from petroleum or other non-renewable resources. However, their use is limited by suboptimal properties and high costs. Incorporating sustainable reinforcements into the polymer matrix significantly improves biopolymer performance while preserving key properties, sustainability, and cost-effectiveness. Bio-based polymeric composites have emerged as a crucial category of biopolymers, playing a key role in advancing a sustainable, circular economy. This review provides an updated overview of bio-based polymer composites and nanocomposites, focusing on reinforcement strategies using natural nanofillers and engineered nanoparticles. We summarize key synthesis and processing methods, discuss structure–property relationships, and highlight recent advances in applications such as food packaging, biomedical devices, energy systems, environmental remediation, 3D printing, and supercapacitors. Polymer nanocomposites are versatile, with their performance depending on the type, size, and interactions between the fillers and the polymer matrix. Progress in metallic, ceramic, carbon-based, natural, and hybrid fillers has improved their properties. Using bio-based polymers and renewable fillers supports sustainability. Natural nanofillers derived from renewable sources and industrial byproducts offer a sustainable approach to developing high-performance, biodegradable nanocomposites. Smart nanocomposites can react to external stimuli by integrating specialized fillers that enhance their mechanical and mobility properties. Shape memory nanocomposites can be remotely activated—using heat, electricity, magnets, or light—enabling advanced applications. Finally, we address major challenges and outline future directions for scalable, circular-material solutions, drawing on perspectives from the circular economy and life cycle assessment (LCA). Full article

Extreme natural disasters may simultaneously disrupt power distribution infrastructures and their supporting communication systems, significantly degrading post-disaster recovery performance. To enhance coordinated restoration under such coupled failure conditions, this study proposes a unified optimization framework for pre-disaster deployment and post-disaster repair and service restoration in interdependent distribution–communication networks. First, an interdependency model is developed to characterize the physical and operational couplings between the distribution and communication networks. The impacts of communication outages on remotely controlled switches and repair crew dispatching are quantitatively analyzed, revealing how communication failures influence the restoration process. Based on this interdependency representation, a coordinated optimization model is established to jointly determine repair crew routing, mobile power allocation, and critical load restoration sequencing. The objective is to minimize cumulative outage losses over the recovery horizon, thereby achieving coordinated allocation and routing of multiple types of emergency repair resources. Furthermore, by jointly considering pre-disaster deployment planning and post-disaster restoration strategies, a two-stage emergency recovery framework is designed to integrate pre-event preparedness with post-event response for distribution networks. Case studies on a modified IEEE 33-bus cyber–physical distribution system demonstrate that the proposed coordinated restoration strategy restores approximately 50% of critical loads within the first 3 h, which is of direct significance for maintaining essential services such as hospitals and emergency shelters during the acute phase of a disaster. The proposed approach reduces the total load loss by 49.5% and shortens the restoration time by 120 min. In terms of pre-disaster deployment, the proposed strategy reduces average load shedding by 33.4% and 46.5% relative to the heuristic and random deployment strategies, respectively, demonstrating the effectiveness of proposed method for grid resilience enhancement. Full article

Background/Objectives: Portable and wearable ECG technologies are increasingly used in adult cardiac monitoring. However, evidence supporting their feasibility and diagnostic performance in pediatric populations remains limited. This systematic review evaluates the diagnostic accuracy, usability, artifact susceptibility, and user acceptance of mobile ECG technologies in pediatric cardiology. Methods: A systematic literature search was performed in the Embase, PubMed, Scopus, and Web of Science databases. The review was conducted in accordance with the PRISMA 2020 guidelines and was registered in the PROSPERO database. Results: A total of 30 publications were included in the final analysis. Portable ECG devices demonstrated good feasibility diagnostic utility in children. Handheld systems provided high-quality tracings with strong agreement with standard 12-lead ECGs and higher adherence, as well as user satisfaction compared with conventional event recorders. However, automated rhythm classification frequently misidentified pediatric arrhythmias. Smartwatch-based ECG recordings showed high diagnostic accuracy when manually interpreted, but automated algorithms were unreliable, particularly for tachyarrhythmias and conduction abnormalities. Alternative electrode placement strategies improved smartwatch performance, and patient acceptance was consistently high. ECG patch monitoring, particularly with extended-wear devices, achieved the highest diagnostic yield, detecting arrhythmias often missed by short-duration Holter monitoring while maintaining comparable signal quality. Conclusions: Mobile ECG technologies represent a promising adjunct for pediatric rhythm surveillance, offering diagnostic performance comparable to standard modalities when interpreted by clinicians and improved usability and patient acceptance. Persistent limitations include the poor reliability of adult-oriented automated algorithms and the underrepresentation of younger children and the predominantly off-label use of these devices in pediatric populations, underscoring the need for pediatric-specific algorithm development and age-adapted device design. Full article

Electrospinning is a versatile technique for producing polymer nanofibers with high ratios of surface area to volume and tunable porosity. Conventional approach to the optimization of processing parameters such as voltage and flow rate frequently encounters limitations in reproducibility and scalability. This review proposes a comprehensive framework that integrates macromolecular design principles with established electrohydrodynamic theories. We analyze how intrinsic molecular traits, specifically chain entanglement density, molecular weight distribution (MWD), topological architecture, and polymer–solvent thermodynamic interactions, define the boundaries of jet stability and solidification. Key findings highlight that while molecular weight establishes a baseline for spinnability, the MWD dictates the dynamic response under extreme deformation. Notably, high-molecular-weight fractions act as elastic load-bearers that suppress capillary breakup. Furthermore, we discuss here how molecular architecture and solvent-mediated segmental mobility determine whether molecular orientation is kinetically trapped or relaxed during the nanosecond timescales of jet flight. By establishing a hierarchical design logic prioritizing molecular and formulation variables over processing parameters, this framework provides a robust strategy to overcome challenges in scalability and reproducibility, positioning electrospinning as a sensitive probe for macromolecular dynamics under extreme elongation. Full article

Bike sharing is an established component of urban mobility infrastructure, offering a low-emission alternative to motorized transport for short trips in cities worldwide. Accurate demand forecasting is essential for efficient system operation: it enables better bike redistribution, reduces user wait times, and lowers the operational costs associated with rebalancing. This study evaluated multiple ensemble strategies for hourly bike-sharing demand prediction, comparing bagging methods (Random Forest, Extra Trees), boosting methods (AdaBoost, Gradient Boosting Regressor, Histogram-based Gradient Boosting Regressor), and a Voting ensemble, while systematically investigating the impact of hyperparameter optimization. A repeated hold-out protocol was used, in which the dataset was randomly divided into 80% training and 20% test subsets across 10 random splits; 5-fold cross-validation was applied within each training fold exclusively for hyperparameter tuning, ensuring the test set remained unseen during model selection. Random Search and Bayesian Optimization were compared under identical budgets of 60 configurations per model. Results show that optimization substantially improves all models, with the most pronounced gains for AdaBoost (58% RMSE reduction) and Gradient Boosting Regressor (45% RMSE reduction). A Voting ensemble combining a Random Search-tuned Gradient Boosting Regressor and a Bayesian-optimized Histogram-based Gradient Boosting Regressor achieves the best overall performance (RMSE of 38.48, R² of 0.955) with the lowest variance among all repeated splits. Feature importance analysis confirms that hour of day and temperature are the dominant demand drivers, consistent with the operational patterns of urban bike-sharing systems. The performance difference between Random Search and Bayesian Optimization is negligible for most models, suggesting that well-designed search spaces allow simpler strategies to achieve competitive results. A controlled comparison conducted under identical experimental conditions shows that the Voting ensemble is statistically equivalent to XGBoost and nominally better than LightGBM, while CatBoost achieves a statistically significant advantage, highlighting it as a strong individual alternative. Full article

Advanced Air Mobility (AAM) includes innovative aviation technologies and services that could alter how people and goods are transported. However, future AAM growth and potential regional integration are uncertain and influenced by a range of factors. In this paper, we report findings from expert interviews (n = 35) and a scenario planning workshop (n = 32 stakeholders), conducted between August 2024 and July 2025, to explore potential alternative futures for AAM at the San Francisco International Airport (SFO) and the greater San Francisco Bay Area. We applied a two-axis framework: regulatory environment (supportive vs. restrictive) and economic conditions (vibrant vs. stagnant). Building on this, we developed four plausible scenarios for the 2025 to 2030 and post-2030 time horizons. We apply the SPELT (social, political, economic, legal, technological) framework to assess cross-cutting drivers, tensions, and indicators across the four scenarios based on two timeframes, i.e., 2025 to 2030 and post-2030. Our analysis of the scenarios reveals that regulatory clarity and macroeconomic conditions are key influencers that define the pace and scale of AAM growth, while community impacts (e.g., noise), public acceptance, and infrastructure availability are constraints. These factors largely determine whether technical readiness can translate into scaled deployment. Cross-cutting themes across all of the scenarios consistently shape the outcomes: (1) equity and community acceptance strongly influence political feasibility; (2) SFO and other airports can serve dual roles as conveners and practical enablers but face risks of stranded assets; and (3) flexible, modular infrastructure and incremental investment strategies reduce uncertainty for SFO and other Bay Area airports and public agencies. Together, the findings suggest that while the future of AAM is uncertain, policy and planning responses can assist airports, local governments, and other public agencies in preparing for potential developments. Full article

Flooding is an increasingly frequent climate hazard with the potential to mobilize environmental contaminants and elevate human health risks. In this study, we assessed heavy metals and metalloids across five sites arranged along a flood-risk gradient from low to high. Six replicate samples per site (n = 30 per contaminant) were collected in a single sampling event. Contaminants were evaluated using the US Environmental Protection Agency (EPA) risk assessment framework to calculate chronic daily intake (CDI), hazard quotients (HQs), and lifetime cancer risk. Arsenic, chromium, and nickel emerged as the most concerning cancer drivers, with nickel cancer risk consistently exceeding 1 × 10⁻³ (equivalent to one additional cancer case per 1000 exposed individuals) and arsenic at 4.4 × 10⁻⁴ (about 1 in 2250). Lead posed non-cancer risks (HQ = 0.912, near the threshold of concern), while cobalt demonstrated a significant decreasing gradient with increasing flood-risk (p = 0.018). Arsenic and thallium more than doubled in concentration at high-flood sites relative to low-flood sites, while cadmium, cobalt, and nickel decreased. These findings suggest flooding may mobilize arsenic, lead, and thallium, while diluting or displacing other metals such as cadmium, cobalt, and nickel. Organs of concern include the liver and kidneys for arsenic, cadmium, nickel, and cobalt, the brain and bones for lead, and the lungs and liver for chromium. Although statistical significance was limited by the small sample size, effect sizes and fold-changes indicate meaningful flood-related differences. This study highlights the importance of considering flood-risk in contaminant hazard assessments and the need for flood-adaptive risk management strategies in vulnerable communities. Full article

Streptococcus parauberis is a major pathogen responsible for streptococcosis in both marine and freshwater fish species, causing substantial economic losses in aquaculture. The increasing prevalence of multidrug resistance has highlighted the urgent need for alternative disease control strategies. Interference with bacterial quorum sensing (QS) systems represents a promising approach. This study aimed to identify and biochemically characterize an Rgg-family transcriptional regulator and evaluate its potential as a target for quorum sensing-related regulatory interference in vitro. We hypothesized that this Rgg regulator may function as a quorum sensing-associated transcription factor capable of promoter binding and modulation by small molecules. Bioinformatic analyses were used to identify the rgg gene encoding an Rgg-family transcriptional regulator and predict its structural features. The gene was cloned, heterologously expressed, and purified. Promoter binding activity was examined using electrophoretic mobility shift assay (EMSA), and key amino acid residues were identified through site-directed mutagenesis. The inhibitory effect of the cyclic peptide cyclosporin A (CsA) on Rgg-promoter binding was further assessed. The rgg gene (864 bp) encoding a 287-amino-acid protein (34.1 kDa) was successfully identified and expressed. Purified Rgg specifically bound to its own promoter region in a concentration-dependent manner. Mutations at conserved arginine residues R12 and R15 within the helix-turn-helix DNA-binding domain abolished promoter binding activity. Furthermore, CsA disturbed Rgg-promoter binding in a dose-dependent manner. This study provides the first in vitro characterization of an Rgg-family transcriptional regulator in fish-derived S. parauberis. The findings expand current understanding of Rgg-family regulators potentially associated with quorum sensing in aquatic streptococci and provide a preliminary basis for further investigation of quorum sensing-related regulatory interference strategies for controlling streptococcal diseases in aquaculture. Full article

The mobile industry has experienced long-run changes in its knowledge structure, including identifiable transition points observable through embedding-based semantic analysis. Using abstracts from 86,674 mobile industry publications published between 2005 and 2024, we embed documents with SPECTER2, build year-specific embedding distributions, and derive knowledge regimes by combining change-point detection with inter-year distribution distances. We then extract regime-specific topics via clustering and reconstruct topic lineages by aligning topic similarities to classify inheritance, differentiation, convergence, and disappearance. The analysis delineates three regimes spanning 2005 to 2012, 2013 to 2019, and 2020 to 2024, with pronounced transitions around 2012 to 2013 and 2019 to 2020. Regime 1 centers on foundational technologies such as wireless communication, power, sensors, and reliability. Regime 2 expands toward platforms, apps, and data analytics alongside cross-domain convergence. Regime 3 is characterized by strengthened 5G operations and data-driven services, together with the independent rise in policy, governance, and regulation topics. Transitions reflect recombination built on inherited knowledge rather than abrupt replacement, and post-transition topics display distinct growth typologies by network position and growth pattern. By integrating embedding-based changepoint detection with topic lineage reconstruction, we provide a reproducible account of regime transitions and quantitative evidence to inform the timing of corporate R&D, standard and platform strategies, and policy and regulatory design. Full article

Background/Objectives: Progressive collapsing foot deformity (PCFD) is driven by multiplanar peritalar instability. This study evaluated the clinical and radiographic outcomes of a standardized four-component reconstruction protocol designed to facilitate immediate postoperative weight-bearing in Stage 1a/1b PCFD. Methods: This single-center retrospective study included 20 patients treated between 2015 and 2023 with medializing calcaneal osteotomy, spring ligament repair, flexor digitorum longus (FDL) tendon transfer with internal brace augmentation, and subtalar arthroereisis. Clinical (VAS, AOFAS) and radiographic parameters (anteroposterior and lateral Meary angles, calcaneal pitch, and talonavicular coverage angle) were assessed longitudinally, with subgroup analysis comparing implant removal versus retention. Results: The protocol yielded significant overall improvements. Mean VAS decreased by 4.37 points (p < 0.001), and final AOFAS reached 84.7 ± 7.6 at the final follow-up. Although subtalar arthroereisis was removed in 45% of patients due to symptomatic irritation, subgroup analysis revealed no significant loss of radiographic correction (p > 0.05). Notably, a significant interaction effect was observed for VAS scores (p = 0.002) and AOFAS scores (p = 0.041), with the removal group demonstrating a pronounced functional recovery trajectory following explantation. No major complications occurred. Conclusions: A standardized four-in-one reconstruction provides reliable multiplanar correction in Stage 1a/1b PCFD. The maintenance of structural alignment despite a high implant removal rate supports the role of arthroereisis as a temporary but valuable adjunct for early mobilization. This strategy offers a reproducible framework for joint-preserving PCFD management. Full article

To address the strong nonlinearity, coupling, and time-delay characteristics in greenhouse environmental regulation, as well as the large overshoot and limited robustness of conventional proportional–integral–derivative (PID) control, while considering the practical constraint that complex intelligent control methods are difficult to deploy directly on low-cost industrial controllers, this study proposes a predictive fuzzy PID control method for greenhouse environments under programmable logic controller (PLC)-based edge deployment. An integrated remote monitoring and control system with a “PLC–human–machine interface (HMI)–cloud–mobile” architecture was also developed. Based on the intelligent greenhouse experimental platform of Yunnan Agricultural University, the proposed method was validated for greenhouse temperature and air humidity regulation through MATLAB simulations, PLC deployment, and on-site operation tests. The results showed that all four control strategies were able to effectively track the setpoints of greenhouse temperature and humidity, while predictive PID and predictive fuzzy PID achieved better overall performance than conventional PID and fuzzy PID. Predictive fuzzy PID performed best in the humidity channel, whereas its performance in the temperature channel was close to that of predictive PID but with more stable disturbance recovery and better overall balance. On-site operation results further showed that, under typical operating conditions, the tracking error of the actual greenhouse temperature relative to the target temperature could be maintained within approximately ±1 °C, while the error of the actual air humidity relative to the target humidity remained within approximately −2% to 3% RH. These results verify the engineering feasibility of the proposed method on resource-constrained industrial PLC platforms. The proposed method can provide a useful reference for the lightweight and intelligent upgrading of small- and medium-sized greenhouse environmental control systems. Full article