Search Results (199)

Urban rail transit (URT) is becoming an important component of a modern city’s transportation infrastructure, which greatly improves the overall efficiency of urban mobility. However, it remains unclear whether URT systems stimulate economic growth through agglomeration effects or inadvertently hinder productivity through fiscal crowding-out effects. To address the question, we analyzed panel data from 26 Chinese cities from 2007 to 2020 through the theory of public service ecosystems (PSE) to interpret the effects of URT construction and operation on the economy from the dual perspectives of value creation and value destruction. We found that URT construction follows the law of diminishing marginal returns, whereas operational efficiency is positively associated with economic growth. Furthermore, URT construction usually exhibits stronger economic benefits in the central and western regions of China, whereas the optimization of operational efficiency is more effective in the eastern regions. Our findings offer phase-specific strategies for policymakers: prioritizing network expansion for emerging URT systems and formulating service innovation roadmaps for mature systems. Full article

Cold atmospheric plasma (CAP) devices produce reactive oxygen and reactive nitrogen species, which have antimicrobial and antiviral effects, while also affecting the molecular and cellular processes in eukaryotic cells. This study investigates the effects of CAP treatment on immune responses and long-term organism health in the upper respiratory tract (URT). Using a surface-microdischarge-based plasma intensive care (PIC) device from terraplasma medical GmbH, 129Sv/Ev wildtype mice were exposed to short (single 10 min session), long (five 10 min sessions), and recovery-phase treatments (five 10 min sessions; 7 days of recovery). Bronchoalveolar lavage fluid was examined by cytospin, fluorescence-activated cell sorting, and mRNA expression analysis. Lung tissue was analyzed for morphological changes (H&E), DNA damage (γH2AX), apoptosis (TUNEL), immune cell marker alterations (CD45, Ly-6G, CD68, CD3, MCC), and fibrosis (NE). Results showed that PIC treatment increased the number of apoptotic cells and activated immune markers, such as IFN-γ, IL-6, and TNF-α, in the lungs, especially after multiple treatments. These effects largely reversed after a 7-day regeneration period. Importantly, no DNA damage or morphological lung alterations were observed across groups. The findings suggest that PIC treatment in the URT induces transient immune activation without causing tissue damage, but caution is advised for patients with cytokine release syndrome or macrophage activation syndrome due to potential cytokine surges. Full article

Bovine respiratory disease complex (BRDC) is a multifactorial and globally prevalent condition involving a combination of viral and bacterial pathogens, as well as environmental stressors. Viral agents often initiate infections in the upper respiratory tract (URT), predisposing animals to secondary bacterial infections and severe clinical manifestations. Among the key viral contributors to BRDC are bovine viral diarrhea virus (BVDV) and bovine herpesvirus 1 (BHV-1). In this study, submerged liquid cultures of undifferentiated bovine nasal epithelial cells (BNECs) were employed to investigate mono- and co-infections with BVDV and BHV-1. Epithelial barrier integrity was assessed to evaluate the cytopathic effects of BHV-1, while viral replication and release were quantified. Both viruses demonstrated polarized release, and BHV-1 infection exhibited a pronounced cytopathic effect. Notably, a preceding BVDV infection did not alter the progression or outcome of BHV-1 infection in this in vitro model. These findings suggest that primary BNEC cultures represent a valuable and physiologically relevant tool for studying viral dynamics and interactions associated with BRDC. Full article

Carbon dots (CDs) are gaining significant attention as multifunctional nanomaterials due to their optical properties, aqueous dispersibility, redox activity, and overall biocompatibility. This review presents a critical overview of the recent advances concerning the application of CDs in nucleic acid-centered diagnostics, with a specific focus on oxidative DNA damage. The use of CDs for the detection of oxidative DNA damage biomarkers, such as 8-oxo-2′-deoxyguanosine (8-oxo-dG), and their potential roles as fluorescent probes in environments related to oxidative stress is discussed in detail. The relationship between surface functionalization and biological performance is examined, highlighting how physicochemical properties dictate both the beneficial and adverse biological responses to CDs. Remarkably, CDs can act as antioxidants, mitigating oxidative damage, or as pro-oxidants, inducing cytotoxic effects, an ambivalent behavior that can be strategically harnessed for cytoprotection or selective tumor cell killing. Overall, this review outlines how CDs can contribute to the development of precision tools for studying oxidative environments affecting nucleic acids, with important implications for both diagnostics and redox-based therapeutic strategies of human diseases. Full article

Urban rail transit (URT) systems are essential to ensuring efficient and sustainable urban mobility. However, the core components of operational planning, service frequency setting, train timetabling, and train allocation are often optimized separately, leading to fragmented decision-making and suboptimal system performance. This study addresses that gap by proposing an integrated optimization framework that simultaneously considers all three planning layers under time-dependent passenger demand conditions. The problem is formulated as a bi-objective Integer Nonlinear Programming (INLP) model, aiming to jointly minimize passenger waiting time and total operational cost. To solve this large-scale, combinatorial problem, a tailored Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed. The algorithm incorporates discrete variable handling, constraint-preserving mechanisms, and a customized encoding scheme that aligns with the structural characteristics of URT operations. The proposed framework is applied to real-world data from the Addis Ababa Light Rail Transit (AALRT) system. The results demonstrate that the MOPSO-based approach offers a more diverse and operationally feasible set of trade-off solutions compared to a widely used benchmark algorithm, NSGA-II. Specifically, it provides transit planners with a flexible decision-support tool capable of identifying schedules that balance service quality and cost, based on varying strategic or budgetary priorities. By integrating interdependent planning decisions into a unified model and leveraging the strengths of a customized metaheuristic algorithm, this study contributes a scalable, adaptable, and practically relevant methodology for improving the performance of urban rail systems. Full article

Urban rail transit (URT) systems are critical for sustainable urban mobility but are increasingly vulnerable to disruptions and emergencies. While extensive research has examined the built environment’s influence on transit demand under normal conditions, the nonlinear causal mechanisms shaping URT passenger flow during emergencies remain understudied. This study proposes an artificial intelligence-based causal machine learning framework integrating causal structure learning and causal effect estimation to investigate how the built environment, network structure, and incident characteristics causally affect URT station-level ridership during emergencies. Using empirical data from Shanghai’s URT network, this study uncovers dual pathways through which built environment attributes affect passenger flow: by directly shaping baseline ridership and indirectly influencing intermodal connectivity (e.g., bus connectivity) that mitigates disruptions. The findings demonstrate significant nonlinear and heterogeneous causal effects; notably, stations with high network centrality experience disproportionately severe ridership losses during disruptions, while robust bus connectivity substantially buffers such impacts. Incident type and timing also notably modulate disruption severity, with peak-hour incidents and severe disruptions (e.g., power failures) amplifying passenger flow declines. These insights highlight critical areas for policy intervention, emphasizing the necessity of targeted management strategies, enhanced intermodal integration, and adaptive emergency response protocols to bolster URT resilience under crisis scenarios. Full article

Ocean ecosystem services provisioning is driven by phytoplankton, which form the base of the ocean food chain in aquatic ecosystems and play a critical role as the Earth‘s carbon sink. Phytoplankton is highly sensitive to temperature, making it vulnerable to the effects of temperature variations. The aim of this research was to develop and test a workflow analysis to monitor the impact of sea surface temperature (SST) on phytoplankton biomass and primary production by combining field and remote sensing data of Chl-a and net primary production (NPP) (as proxies of phytoplankton biomass). The tropical zone was used as a case study to test the procedure. Firstly, machine learning algorithms were applied to the field data of SST, Chl-a and NPP, showing that the Random Forest was the most effective in capturing the dataset’s patterns. Secondly, the Random Forest algorithm was applied to MODIS SST images to build Chl-a and NPP time series. The time series analysis showed a significant increase in SST which corresponded to a significant negative trend in Chl-a concentrations and NPP variation. The recurrence plot of the time series revealed significant disruptions in Chl-a and NPP evolutions, potentially linked to El Niño–Southern Oscillation (ENSO) events. Therefore, the analysis can help to highlight the effects of temperature variation on Chl-a and NPP, such as the long-term evolution of the trend and short perturbation events. The methodology, starting from local studies, can support broader spatial–temporal-scale studies and provide insights into future scenarios. Full article

The emergence of large language models (LLMs) has revolutionized the trajectory of NLP research. Transformers, combined with attention mechanisms, have increased computational power, and massive datasets have led to the emergence of pre-trained large language models (PLLMs), which offer promising possibilities for multilingual applications in low-resource settings. However, the scarcity of annotated resources and suitably pre-trained models continues to pose a significant hurdle for the low-resource abstractive text summarization of legal texts, particularly in Urdu. This study presents a transfer learning approach using pre-trained multilingual large models (the mBART and mT5, Small, Base, and Large) to generate abstractive summaries of Urdu legal texts. A curated dataset was developed with legal experts, who produced ground-truth summaries. The models were fine-tuned on this domain-specific corpus to adapt them for low-resource legal summarization. The experimental results demonstrated that the mT5-Large, fine-tuned on Urdu legal texts, outperforms all other evaluated models across standard summarization metrics, achieving ROUGE-1 scores of 0.7889, ROUGE-2 scores of 0.5961, and ROUGE-L scores of 0.7813. This indicates its strong capacity to generate fluent, coherent, and legally accurate summaries. The mT5-Base model closely follows with ROUGE-1 = 0.7774, while the mT5-Small shows moderate performance (ROUGE-1 = 0.6406), with reduced fidelity in capturing legal structure. The mBART50 model, despite being fine-tuned on the same legal corpus, performs lower (ROUGE-1 = 0.5914), revealing its relative limitations in this domain. Notably, models trained or fine-tuned on non-legal, out-of-domain data, such as the urT5 (ROUGE-1 = 0.3912), the mT5-XLSUM (ROUGE-1 = 0.0582), and the mBART50 (XLSUM) (ROUGE-1 = 0.0545), exhibit poor generalization to legal summaries, underscoring the necessity of domain adaptation when working in low-resource legal contexts. These findings highlight the effectiveness of fine-tuning multilingual LLMs for domain-specific tasks. The gains in legal summarization demonstrate the practical value of transfer learning in low-resource settings and the broader potential of AI-driven tools for legal document processing, information retrieval, and decision support. Full article

Urban rail transit (URT) systems frequently face operational challenges arising from temporal and spatial imbalances in passenger demand, resulting in inefficiencies in train scheduling and resource utilization. To address these issues, this study proposes a multi-objective optimization model that jointly plans short-turn and full-length train services. The objectives of the model are to minimize total passenger waiting time and train mileage while improving passenger load distribution across the rail line, subject to practical constraints such as departure frequency limitations, rolling stock availability, and coverage of short-turn services. To efficiently solve this model, an improved Pelican Optimization Algorithm (POA) is developed, incorporating techniques such as Tent chaotic mapping, nonlinear weight adjustment, Cauchy mutation, and the sparrow alert mechanism, significantly enhancing convergence accuracy and computational efficiency. A real-world case study based on Nanjing Metro Line 1 demonstrates that the proposed framework substantially reduces average passenger waiting times and overall train mileage, achieving a more balanced distribution of passenger loads. In addition, the study reveals that flexible-ratio dispatching strategies, representing theoretically optimal solutions, outperform integer-ratio dispatching schemes that reflect real-world operational constraints. This finding underscores that investigating the practical feasibility and optimization potential of flexible-ratio scheduling strategies constitutes a valuable direction for future research. The outcomes of this study provide a scalable and intelligent decision-support framework for train scheduling in URT systems, effectively contributing to the sustainable and intelligent development of rail operations. Full article

The quality of artisanal bread is strongly influenced by sourdough fermentation, where aroma development and microbial stability are key factors. This study evaluates the use of an electronic nose (E-nose) to monitor cold fermentation, integrating it with microbiological analysis and gas chromatography–mass spectrometry (SPME-GC-MS) to characterize the dough’s volatile profile. A clear correlation was observed between microbial dynamics, pH reduction (from 5.8 to 3.8), and the evolution of volatile compounds, with notable increases in acetic acid (up to 12.75%), ethanol (11.95%), and fruity esters such as isoamyl acetate (33.33%). Linear discriminant analysis (LDA) explained 96.31% of the total variance in a single component, successfully separating the fermentation stages. An artificial neural network discriminant analysis (ANNDA) model achieved 95% accuracy in the validation phase. These results confirm the E-nose’s ability to track biochemical transformations in real time and identify optimal fermentation points. This approach enhances quality control and sensory standardization in sourdough-based bakery products. Full article

This study proposes an improved YOLOv11 model to address the limitations of traditional tomato recognition algorithms in complex agricultural environments, such as lighting changes, occlusion, scale variations, and complex backgrounds. These factors often hinder accurate feature extraction, leading to recognition errors and reduced computational efficiency. To overcome these challenges, the model integrates several architectural enhancements. First, the UniRepLKNet block replaces the C3k2 module in the standard network, improving computational efficiency, expanding the receptive field, and enhancing multi-scale target recognition. Second, the RFCBAMConv module in the neck integrates channel and spatial attention mechanisms, boosting small-object detection and robustness under varying lighting conditions. Finally, the TADDH module optimizes the detection head by balancing classification and regression tasks through task alignment strategies, further improving detection accuracy across different target scales. Ablation experiments confirm the contribution of each module to overall performance improvement. Our experimental results demonstrate that the proposed model exhibits enhanced stability under special conditions, such as similar backgrounds, lighting variations, and object occlusion, while significantly improving both accuracy and computational efficiency. The model achieves an accuracy of 85.4%, recall of 80.3%, and $mAP@50$ of 87.3%. Compared to the baseline YOLOv11, the improved model increases $mAP@50$ by 2.2% while reducing parameters to 2.16 M, making it well-suited for real-time applications in resource-constrained environments. This study provides an efficient and practical solution for intelligent agriculture, enhancing real-time tomato detection and laying a solid foundation for future crop monitoring systems. Full article

Metal oxide semiconductor (MOX) sensors are emerging as a groundbreaking technology due to their remarkable features: high sensitivity, rapid response time, low cost, and potential for miniaturization. Their ability to detect volatile organic compounds (VOCs) in real time makes them ideal tools for applications across various fields, including environmental monitoring, medicine, and the food industry. This paper explores the evolution and growing utilization of MOX sensors, with a particular focus on atmospheric pollution monitoring, non-invasive disease diagnostics through the analysis of volatile compounds emitted by the human body, and food quality assessment. The crucial role of MOX sensors in monitoring the freshness of food and water, detecting chemical and biological contamination, and identifying food fraud is specifically examined. The rapid advancement of this technology offers new opportunities to improve quality of life, food safety, and public health, positioning MOX sensors as a key tool to address future challenges in these vital sectors. Full article

Introduction: Respiratory syncytial virus (RSV) is the main etiological agent in pediatric lower respiratory tract infections. The limited availability of therapeutic options for severe clinical cases associated with RSV infection makes prophylactic interventions a priority for containment. The aim of the current study was to evaluate the epidemiology of RSV in the Piedmont population and the consequences of containment measures applied during the pandemic on viral circulation in the immediate and medium-term post-pandemic phase. Methods: This study considered all biological samples analyzed for RSV at the City of Health and Science of Turin collected from 1 January 2016 to 31 December 2023. Evaluation of the positivity rates of samples was performed and differences between pediatric and adult population swabs (nasopharyngeal, pharyngeal, nasal aspirates) and bronchoalveolar samples were reported. Results: This study analyzed 14,085 samples and highlighted a trend in Piedmont RSV infections characterized by a higher pediatric population involvement of 82% compared to the adult population at 17%. A higher number of URT infections (95%) compared to LRT infections (4.6%) was also identified. This study shows a peak in RSV cases from November to April between 2016 and 2020. Our data show no RSV positivity during the 2020/2021 winter season, a result most likely due to the influence of containment measures implemented during the COVID-19 pandemic. Conclusions: Our study provided an epidemiological panorama of RSV and its high prevalence in pediatrics and adults. Pediatrics had a higher prevalence, while adults presented a delayed trend of about one month compared to pediatrics. The effectiveness of infection control measures applied during the SARS-CoV-2 pandemic to limit viral infections were proved. Future studies may further investigate the impact of the SARS pandemic on RSV epidemiology considering patients at a higher risk of severe symptoms. Full article

Agrivoltaic offers a promising solution to integrate photovoltaic energy production with ongoing agricultural activities. This research investigates the impact of agrivoltaic on food security, using a transdisciplinary approach to study the responses of crop production in terms of biomass and food quality produced. Mainly chicory plants were grown in full sunlight (control plot) and shade plots generated by potential photovoltaic panels. Two water regimes (high and low water supply) were used to analyze variations in food security in both plots. The results showed that agrivoltaic systems effectively mitigate crop water stress caused by high temperatures and heat waves, improving food security by increasing biomass production and preserving food quality. While previous research has attributed the benefits of agrivoltaics primarily to improved soil moisture, this study demonstrates that the positive effects are primarily driven by differences in light intensity and air temperature between the shaded and control plots. The results have strong implications for water resource management, showing that agrivoltaics can reduce water use by approximately 50% compared to traditional agroecosystems without compromising food security. Agrivoltaics can address the challenges of water scarcity due to declining rainfall and reduce production costs associated with water use. Properly designed agrivoltaic systems offer a cleaner, more sustainable alternative to traditional agricultural practices, helping to adapt agriculture to climate change. Full article

Air pollution (AP) is a critical environmental factor influencing public health, with well-documented associations with upper respiratory tract (URT) diseases. This study investigates the relationship between ENT emergency department (ENT-ED) visits at Azienda Ospedale Università di Padova (AOPD) and daily concentrations of environmental pollutants during the first year of the COVID-19 pandemic (March 2020–March 2021), compared to pre-pandemic data from 2017. The study focuses on patients diagnosed with URT inflammatory diseases, excluding those with COVID-19 infection, who sought care at the AOPD ENT-ED. Environmental data, including meteorological variables, air pollutants, and major aeroallergen levels, were collected from regional monitoring stations. A total of 4594 patients were admitted in 2020/2021, marking a 37% reduction from 2017, with URT inflammatory admissions decreasing by 52%. A significant decline in PM10, NO₂ and Alternaria levels was observed, whereas Betullaceae and Corylaceae concentrations significantly increased. Multivariate analyses revealed strong associations between aeroallergen exposure and ENT admissions, particularly for Alternaria, which had a notable impact on total admissions (p < 0.001) and was significantly linked to cases of otitis media and tonsillitis. PM10 concentrations on specific days preceding ED visits were associated with increased incidences of pharyngitis and rhinosinusitis (p < 0.05). These findings reinforce the connection between environmental pollutants and ENT emergency visits, highlighting the adverse effects of AP and climate variables on URT diseases, even during a pandemic when enhanced airway protection measures were in place. This study underscores the necessity of stringent air quality regulations and interdisciplinary strategies to mitigate environmental health risks and inform future public health policies. Full article