Search Results (7,411)

This paper deals with the issue concerning the optimization of energy consumption saving in passenger railway traffic. The background is mainly related to the decision to modernize existing trains or purchase new units, which is a key dilemma for rail transport managers. Concerning the methods used for the determination of the proper results, there is a very wide range of possibilities. This issue is complex, encompassing technical, economic, environmental, and social aspects; therefore, artificial intelligence methods were used for analysis. The obtained results have shown that the choice is not clear-cut, as each option offers both benefits and limitations. The investigations are based on real measurement values obtained from a Polish regional railway. In conclusion, it can be found that the final decision should take into account the long-term goals and the specific characteristics of the given rail system. Several factors influencing the energy consumption were taken into account. So, the aim of this paper was achieved, and the main factors were determined, which have influenced energy consumption and its impact, as well as the possibility of energy consumption reduction. Full article

U.S. hospitals generate considerable food waste, contributing to environmental degradation strategies. This study evaluated the feasibility, impact, and perception of a novel composting program implemented at Rhode Island Hospital over six months beginning in December 2024. Compostable waste bins were installed in the cafeteria with educational signage. Surveys assessing composting knowledge, attitudes, and roles in waste management were distributed to staff, patients, and administrators. Collected food waste was transported to Bootstrap Compost, which provided daily weight data used to estimate greenhouse gas emissions reductions, compare composting with landfill disposal costs, and project annual outcomes. Over the study period, 490.6 kg of food waste were diverted from landfills, corresponding to a reduction of 0.35 metric tons of CO₂-equivalent emissions. While composting was more expensive than landfill disposal ($6.45/kg vs. $0.24/kg), cost neutrality could be achieved with diversion rates at or above 116 kg per day. Surveys revealed strong support for composting but limited awareness of its relevance to healthcare’s environmental footprint. Respondents suggested improvements in education, signage, and infrastructure. This program demonstrated how hospital-based composting initiatives align with healthcare institutions’ environmental stewardship goals while highlighting financial and logistical challenges relevant for pilot–scale efforts. Full article

Ensuring robust railway safety is paramount for efficient and reliable transportation systems, a challenge increasingly addressed through advancements in artificial intelligence (AI). This review paper comprehensively explores the burgeoning role of AI in enhancing the safety of railway operations, focusing on key contributions from machine learning, neural networks, and computer vision. We synthesize current research that leverages these sophisticated AI methodologies to mitigate risks associated with railroad accidents and optimize railroad tracks management. The scope of this review encompasses diverse applications, including real-time monitoring of track conditions, predictive maintenance for infrastructure components, automated defect detection, and intelligent systems for obstacle and intrusion detection. Furthermore, it delves into the use of AI in assessing human factors, improving signaling systems, and analyzing accident/incident reports for proactive risk management. By examining the integration of advanced analytical techniques into various facets of railway operations, this paper highlights how AI is transforming traditional safety paradigms, paving the way for more resilient, efficient, and secure railway networks worldwide. Full article

Background/Objectives: Severe traumatic brain injury (TBI) carries high mortality, and outcomes are particularly poor when prehospital cardiopulmonary resuscitation (CPR) is required. Because these patients are often excluded from research, epidemiological data and prognostic insights are limited. This study aimed to describe characteristics and outcomes of patients with suspected severe TBI who received prehospital CPR. Methods: We performed a sub-analysis of the prospectively collected multicenter BRAIN-PROTECT registry, including all patients with suspected severe TBI who underwent prehospital CPR and were transported to a participating trauma center. Results: A total of 256 patients with suspected severe TBI who received prehospital CPR were included. Early mortality was high, with 22.6% declared dead in the emergency department and an additional 28.9% within 24 h, resulting in 48.5% 24 h survival. Thirty-day mortality was 79.9%. Among survivors, 45.7% achieved moderate disability or good recovery at discharge. Outcomes, 30-day mortality, and neurological status at discharge did not differ between isolated and non-isolated TBI. Characteristics often seen in survivors included shockable initial rhythm, reactive pupils, and lack of anisocoria. All patients without prehospital return of spontaneous circulation died. Conclusions: Although overall 30-day mortality was high, survival among patients for whom resuscitation was attempted and who reached hospital care was not negligible, and a substantial proportion of the survivors achieved moderate to good neurological recovery. Prehospital ROSC and shockable rhythms were associated with better outcomes, suggesting that resuscitation may be valuable and warranted in selected patients with potentially reversible conditions. Further studies are needed to better define prognostic factors and guide management in this highly vulnerable population. Full article

The rapid evolution of smart transportation systems necessitates the integration of advanced sensing technologies capable of supporting the real-time, reliable, and cost-effective monitoring of road infrastructure. Fiber-optic sensor (FOS) technologies, given their high sensitivity, immunity to electromagnetic interference, and suitability for harsh environments, have emerged as promising tools for enabling intelligent transportation infrastructure. This review critically examines the current landscape of classical mechanical and electrical sensor realization in monitoring solutions. Focus is also given to fiber-optic-sensor-based solutions for smart road applications, encompassing both well-established techniques such as Fiber Bragg Grating (FBG) sensors and distributed sensing systems, as well as emerging hybrid sensor networks. The article examines the most topical physical parameters that can be measured by FOSs in road infrastructure monitoring to support traffic monitoring, structural health assessment, weigh-in-motion (WIM) system development, pavement condition evaluation, and vehicle classification. In addition, strategies for FOS integration with digital twins, machine learning, artificial intelligence, quantum sensing, and Internet of Things (IoT) platforms are analyzed to highlight their potential for data-driven infrastructure management. Limitations related to deployment, scalability, long-term reliability, and standardization are also discussed. The review concludes by identifying key technological gaps and proposing future research directions to accelerate the adoption of FOS technologies in next-generation road transportation systems. Full article

Background: Artificial intelligence (AI) in urban and multimodal transport has demonstrated strong potential; however, real-world deployment remains constrained by limited governance-ready design, fragmented data ecosystems, and single-objective optimization practices. The resulting problem is that agencies lack a reproducible, deployment-ready architecture that links data fusion, multi-objective optimization, and electrification constraints into daily multimodal operational decision making. Methods: This study presents a systematic review and synthesis of 145 peer-reviewed studies on network control, green routing, digital twins, and electric-bus scheduling, conducted in accordance with PRISMA 2020 using predefined inclusion and exclusion criteria. Based on these findings, a deployment-oriented operational AI framework is developed. Results: The proposed architecture comprises five interoperable layers—data ingestion, streaming analytics, optimization services, decision evaluation, and governance monitoring—supporting scalability, reproducibility, and transparency. Rather than producing a single optimal solution, the framework provides decision-ready trade-offs across service quality, cost efficiency, and sustainability while accounting for uncertainty, reliability, and electrification constraints. The approach is solver-agnostic, supporting evolutionary and learning-based techniques. Conclusions: A Thessaloniki-based multimodal case study demonstrates how reproducible AI workflows can connect real-time data streams, optimization, and institutional decision making for continuous multimodal transport management under operational constraints. Full article

The increasing complexity of fleet operations often forces drivers and administrators to alternate between fragmented tools for geolocation, messaging, and spreadsheet-based reporting, which slows response times and increases cognitive load. This study evaluates a comprehensive architectural framework designed to automate fleet management in personnel transport companies. The research proposes a unified methodology integrating Internet-of-Things (IoT) telemetry, cloud analytics, and Conversational AI to mitigate information fragmentation. Through a Lean UX iterative process, the proposed system was modeled and validated, with 30 participants (10 administrators and 20 drivers) who performed representative operational tasks in a simulated environment. Usability was assessed through the System Usability Scale (SUS), obtaining a score of 71.5 out of 100, classified as “Good Usability”. The results demonstrate that combining conversational interfaces with centralized operational data reduces friction, accelerates decision-making, and improves the overall user experience in fleet management contexts. Full article

Multimodal electric transport systems demand substantial active and reactive energy, making power-quality management essential for ensuring efficient and reliable operation. This paper analyses reactive-energy transport in mass-transit networks and introduces a unified current-based framework that enables a consistent interpretation of the conventional power factor under harmonic distortion, fundamental unbalance, and short-term load fluctuation, without modifying its original definition. The framework enables a consistent assessment of compensation needs, independent of billing schemes, and is aligned with the way modern compensation equipment is specified and controlled. Applied to the Metro de Medellín system, field measurements and digital simulations show that traditional reactive-energy limits fail to distinguish between harmful and beneficial operating conditions, leading to disproportionate charges under the former Colombian regulation. Beyond this case, the proposed framework is directly applicable to other electric-mobility systems—including railways, trams, trolleybuses, and electric-bus networks—providing clearer technical signals for compensation planning and offering a comprehensive basis for future regulatory approaches that integrate multiple power-quality phenomena. Full article

Water-quality degradation from nutrient pollution remains a major challenge for resource managers. Developing effective strategies requires tools to characterize nutrient sources and transport. This study used the RSPARROW framework to develop and assess new, smaller-scale models for Total Nitrogen (TN) and Total Phosphorus (TP) transport across Mississippi (MS). These state-level models were built using 15 years (2005–2020) of observation data and considered variables including multiple nutrient sources, land characteristics, and attenuation processes. The MS models demonstrated comparable accuracy to larger regional SPARROW models, validating the use of smaller-scale models for local management. Results showed agricultural sources are the major contributors to TN, dominated by fertilizer in northern MS and livestock manure in the south. Urban land cover also significantly influenced TN and was the second most significant source of TP, following geologic material (background P). Fertilizer and manure were also important TP sources. This study provides valuable, spatially explicit data on nutrient distribution in MS streams, supporting the state’s nutrient reduction planning. It concludes by highlighting the need for future model improvements via updated source data and mean annual flow estimates. Full article

Accurate traffic flow forecasting is a fundamental component of Intelligent Transportation Systems and proactive urban mobility management. However, the inherent complexity of urban traffic flow, characterized by non-stationary dynamics and multi-scale temporal dependencies, poses significant modeling challenges. Existing spatio-temporal models often struggle to reconcile the discrepancy between static physical road constraints and highly dynamic, state-dependent spatial correlations, while their reliance on fixed temporal receptive fields limits the capacity to disentangle overlapping periodicities and stochastic fluctuations. To bridge these gaps, this study proposes a novel Multi-scale Graph-Decoupling Spatial–temporal Network (MS-GSTN). MS-GSTN leverages a Hierarchical Moving Average decomposition module to recursively partition raw traffic flow signals into constituent patterns across diverse temporal resolutions, ranging from systemic daily trends to high-frequency transients. Subsequently, a Tri-graph Spatio-temporal Fusion module synergistically models scale-specific dependencies by integrating an adaptive temporal graph, a static spatial graph, and a data-driven dynamic spatial graph within a unified architecture. Extensive experiments on four large-scale real-world benchmark datasets demonstrate that MS-GSTN consistently achieves superior forecasting accuracy compared to representative state-of-the-art models. Quantitatively, the proposed framework yields an overall reduction in Mean Absolute Error of up to 6.2% and maintains enhanced stability across multiple forecasting horizons. Visualization analysis further confirms that MS-GSTN effectively identifies scale-dependent spatial couplings, revealing that long-term traffic flow trends propagate through global network connectivity while short-term variations are governed by localized interactions. Full article

Amidst the increasing aerial traffic and road traffic congestion, Urban Air Mobility (UAM) has emerged as a new mode of aerial transport offering less travel time and ease of portability. A critical factor in reducing travel time is the emerging electric Vertical Take-Off and Landing (eVTOL) vehicles, which require infrastructure such as vertiports to operate smoothly. However, the dynamics of vertiport operations, particularly the integration of battery charging facilities, remain relatively unexplored. This work aims to bridge this gap by delving into vertiport management by utilizing separate taxing and parking levels. The study also focuses on the time eVTOLs spend at the vertiport to anticipate potential delays. This factor helps optimise arrival and departure times via a scheduling strategy that accounts for hourly demand fluctuations. The simulation results, conducted with hourly demand, underscore the significant impact of battery charging on operational time while also highlighting the role of parking spots in augmenting capacity and facilitating more efficient scheduling. Full article

Gestational diabetes mellitus (GDM) is a common metabolic complication of pregnancy associated with significant short- and long-term risks for both mother and offspring. Increasing evidence indicates that genetic susceptibility plays a central role in GDM pathogenesis, particularly through variants affecting insulin secretion and pancreatic β-cell function. This narrative review integrates molecular, clinical, and epidemiological perspectives, highlighting population-specific effects and gene–environment interactions. Improved understanding of the genetic risk architecture may support earlier risk stratification and enable the future development of personalized strategies for GDM prevention and management, with particular emphasis on genetic polymorphisms in SLC30A8, CDKAL1, and HHEX genes consistently implicated in glucose homeostasis and β-cell integrity. These genes contribute to distinct but complementary molecular pathways underlying GDM, including impaired insulin biosynthesis, defective zinc transport within insulin granules, and altered paracrine regulation within pancreatic islets. While associations between these variants and GDM have been repeatedly demonstrated, their clinical relevance and mechanistic impact remain incompletely understood. Available evidence suggests that CDKAL1 represents the strongest genetic determinant, followed by SLC30A8, while HHEX appears to play a modulatory role. This review summarizes current findings on the molecular functions and clinical significance of these polymorphisms, highlighting population-specific effects and gene–environment interactions. Improved understanding of genetic risk architecture may support earlier risk stratification and enable future development of personalized strategies for GDM prevention and management. Full article

Enhanced silicate rock weathering (ESRW) has been proposed as a promising carbon dioxide removal strategy, yet its carbon sequestration pathways, durability, and ecosystem dependence remain incompletely understood. Here, we synthesize evidence from field experiments, observational studies, and modeling to compare ESRW-induced carbon dynamics across forest and cropland ecosystems using a unified SOC–SIC dual-pool framework. Across both systems, ESRW operates through shared geochemical processes, including proton consumption during silicate dissolution and base cation release, which promote atmospheric CO₂ uptake. However, carbon fate diverges markedly among ecosystems. Forest systems, characterized by high biomass production, deep rooting, and strong hydrological connectivity, primarily favor biologically mediated pathways, enhancing net primary productivity and mineral-associated organic carbon (MAOC) formation, while facilitating downstream export of dissolved inorganic carbon (DIC). In contrast, intensively managed croplands more readily accumulate measurable soil inorganic carbon (SIC) and soil DIC over short to medium timescales, particularly under evapotranspiration-dominated or calcium-rich conditions, although SOC responses are often moderate and variable. Importantly, only a subset of ESRW-driven pathways—such as MAOC formation and secondary carbonate precipitation—represent durable carbon storage on decadal to centennial timescales. By explicitly distinguishing carbon storage from carbon transport, this synthesis clarifies the conditions under which ESRW can contribute to climate change mitigation and highlights the need for ecosystem-specific deployment and monitoring strategies. Full article

Improving the accuracy of climate and long-range meteorological forecasts is an important objective for many economic sectors: agriculture, energy and utilities, transportation and logistics, construction, disaster risk management, insurance and finance, retail, tourism and leisure. Traditional physical models face limitations at ultra-long lead times, which motivates the development of empirical–statistical approaches, including those leveraging deep learning techniques. In this study, using ERA5 reanalysis data and archives of major climate indices for the period 1950–2024, we examine statistical relationships between climate indices associated with large-scale atmospheric and oceanic patterns in the Northern Hemisphere and surface air temperature anomalies in selected mid- and high-latitude regions. The aim is to assess the predictive skill of these indices for seasonal temperature anomalies within empirical forecasting frameworks. To this end, we employ cross-correlation and cross-spectral analyses, as well as regression modeling. Our findings indicate that the choice of the most informative predictors strongly depends on the target region and season. Among the major indices, AMO and EA/WR emerge as the most informative for forecasting purposes. The Niño 4 and IOD indices can be considered useful predictors for the Eastern Arctic. Notably, the strongest correlations between the AMO, EA/WR, Niño 4, and IOD indices and surface air temperature occur at one- to two-year lags. To illustrate the predictive potential of the four selected indices, several multiple regression models were developed. The results obtained from these models confirm that the chosen set of indices effectively captures the main sources of variability relevant to seasonal and interannual temperature prediction across the analyzed regions. In particular, approximately 64% of the forecasts have errors less than 0.674 times the standard deviation. Full article

The optimal branch bending angle for Pyrus sinkiangensis Yü (Korla fragrant pear) remains undefined. In this study, the optimal angle was determined by integrating the phenological, nutritional, hormonal, and fruit-quality responses across a 15-day bloom window. Four branch angles (40°, 60°, 80°, and 100°) were applied to 8-year-old trees in spring 2022, and flowering dynamics, bud carbon/nitrogen status, leaf morphology/mineral content, fruiting-shoot architecture, endogenous hormones, and fruit quality were comprehensively evaluated. The 80° angle maximized the fruit set (11.77%) and bud soluble sugar content (8.84 mg/g DW), significantly outperforming the other angles (p < 0.05). The flowering rate peaked at 100° (7.89%) but was statistically comparable to that at 60° and 80° (p > 0.05); calyx removal was greatest at 60° (73.33%), with no significant difference from that at 80° (71%, p > 0.05). These reproductive benefits aligned with enhanced leaf source capacity—80° pulling resulted in the greatest leaf area (59.51 cm²), the greatest amount of chlorophyll (3.11 mg/g DW), and elevated N/Mg/Cu concentrations. Branch architecture was optimized at 80°, with the percentage of medium fruiting spurs reaching 41.1% and the xylem:phloem dry-weight ratio peaking at 1.78, indicating the development of efficient assimilate transport pathways. Hormonally, 80° triggered a distinct cascade: a transient GA₄/GA₇ surge (50.6 and 1.34 ng/g DW) on 28 April, followed by sustained IAA elevation (2.05 ng/g DW) and zeatin stabilization (0.27–0.29 ng/g DW) during ovary development. Consequently, the fruit quality was comprehensively improved at 80°—the single-fruit weight (110.7 g), soluble sugar content (10.08 mg/g DW), and sugar/acid ratio (17.08) were greatest, whereas the stone-cell content was lowest (0.49 mg/g DW). Principal component analysis of 57 traits confirmed 80° as the system-wide optimum (D = 0.718). These results demonstrate that an 80° bending angle synchronizes carbohydrate supply, hormone signaling, and fruit quality in Korla fragrant pear, providing a low-cost, nonchemical benchmark for precision canopy management in high-density orchards. An 80° branch-bending angle optimizes carbon-hormone synergy via a transient GA₄/GA₇ surge and sustained IAA-zeatin signaling, maximizing fruit set and quality in high-density Korla fragrant pear orchards. Full article