Search Results (710)

Background/Objectives: Historically, echocardiographic imaging of the right heart has been challenging because its abnormal geometry is not conducive to reproducible anatomical and functional assessment. With the development of advanced echocardiographic techniques, it is now possible to complete an integrated assessment of the right heart that has fewer assumptions, resulting in increased accuracy and precision. Echocardiography continues to be the first-line imaging modality for diagnostic analysis and the management of acute and chronic right heart failure because of its portability, versatility, and affordability compared to cardiac computed tomography, magnetic resonance imaging, nuclear scintigraphy, and positron emission tomography. Virtually all echocardiographic parameters have been well-validated and have demonstrated prognostic significance. The goal of this narrative review of the echocardiographic parameters of the right heart chambers and hemodynamic alterations associated with right ventricular dysfunction is to present information that must be acquired during each examination to deliver a comprehensive assessment of the right heart and to discuss their clinical significance in right heart failure. Methods: Using a literature search in the PubMed database from 1985 to 2025 and the Cochrane database, which included but was not limited to terminology that are descriptive of right heart anatomy and function, disease states involving acute and chronic right heart failure and pulmonary hypertension, and the application of conventional and advanced echocardiographic modalities that strive to elucidate the pathophysiology of right heart failure, we reviewed randomized control trials, observational retrospective and prospective cohort studies, societal guidelines, and systematic review articles. Conclusions: In addition to the conventional 2-dimensional echocardiography and color, spectral, and tissue Doppler measurements, a contemporary echocardiographic assessment of a patient with suspected or proven right heart failure must include 3-dimensional echocardiographic-derived measurements, speckle-tracking echocardiography strain analysis, and hemodynamics parameters to not only characterize the right heart anatomy but to also determine the underlying pathophysiology of right heart failure. Complete and point-of-care echocardiography is available in virtually all clinical settings for routine care, but this imaging tool is particularly indispensable in the emergency department, intensive care units, and operating room, where it can provide an immediate assessment of right ventricular function and associated hemodynamic changes to assist with real-time management decisions. Full article

Boda boda motorbike taxis are a primary mode of transport in Nairobi, Kenya, and a major source of urban air pollution. This study investigates the environmental and electrical grid impacts of electrifying Nairobi’s boda boda fleet. Using real-world tracking data from 118 motorbikes, we simulated the effects of a full-scale transition from internal combustion engine (ICE) vehicles to electric motorbikes. We analysed various scenarios, including different battery charging strategies (swapping and home charging), motor efficiencies, battery capacities, charging rates, and the potential for solar power offsetting. The results indicate that electrification could reduce daily ${\mathrm{CO}}_2$ emissions by approximately 85% and eliminate tailpipe particulate matter emissions. However, transitioning the entire country’s fleet would increase the national daily energy demand by up to 6.85 GWh and could introduce peak grid loads as high as 2.40 GW, depending on the charging approach and vehicle efficiency. Battery swapping was found to distribute the grid load more evenly and better complement solar power integration compared to home charging, which concentrates demand in the evening. This research provides a scalable, data-driven framework for policymakers to assess the impacts of transport electrification in similar urban contexts, highlighting the critical trade-offs between environmental benefits and grid infrastructure requirements. Full article

The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article

Stope dilution is a major hidden cost driver for the underground operation, especially in terms of reducing ore quality, increasing the amount of processing feed, and effects on operational cost. Accurate calculation and consideration of planned and unplanned dilution and mining loss amounts are essential during mine planning. The user interface named D–Loss has been developed with MATLAB R2023b, which provides a multiparadigm numerical computing environment for faster and more practical calculation of these dilution amounts to address these challenges by quantifying dilution and linking them directly to economic and CO₂ emissions indicators. By determination and analysis of the stope overall dilution amounts, it helps us understand greenhouse gas emissions and ensures the efficient use of underground equipment. Calculation of stope dilution in a practical and rapid manner allows for stope design and operational improvements, which can help reduce dilution in underground operations. This progress is tracked through the D–Loss interface within the short- and long-term production planning. Moreover, by quantifying dilution impacts on comminution and haulage costs, D–Loss becomes a critical software for tracking economic losses and optimizing financial outcomes in the mining industry. D–Loss helps users iteratively assess the efficiency of updates and provides support in mine design, scheduling, and environmental impact control by comparing planning and operational improvements before and after. Full article

Against the backdrop of China’s “dual-carbon” initiative, this study innovatively applies a process-based life cycle assessment (PLCA) methodology, meticulously tracking energy and carbon flows across material production, transportation, and maintenance processes. By comparing six asphalt pavement maintenance technologies in Xinjiang, the research reveals that milling and resurfacing (MR) exhibits the highest energy consumption 250,809 MJ/10³ m²) and carbon emissions (15,095.67 kg CO₂/10³ m²), while preventive techniques like hot asphalt grouting reduce emissions by up to 87%. The PLCA approach uncovers a critical insight: 40–60% of total emissions originate from the raw material production phase, with cement and asphalt identified as primary contributors. This granular analysis, unique in regional road maintenance research, challenges traditional assumptions and emphasizes the necessity of upstream intervention. By contrasting reactive and preventive strategies, the study validates that early-stage maintenance aligns seamlessly with circular economy principles. Tailored to a local arid climate and vast transportation network, the study concludes that prioritizing preventive maintenance, adopting low-carbon materials, and optimizing logistics can significantly decarbonize road infrastructure. These region-specific strategies, underpinned by the novel application of PLCA, not only provide actionable guidance for local policymakers but also offer a replicable framework for sustainable road development worldwide, bridging the gap between scientific research and practical decarbonization efforts. Full article

Inefficient inland repositioning of empty containers between depots remains a persistent challenge in container logistics, contributing significantly to unnecessary truck movements, elevated operational costs, and increased CO₂ emissions. Acknowledging the importance of this problem, a large amount of relevant literature has appeared. The objective of this paper is to track the empty container flow between ports, empty depots, inland terminals, and customer premises. Additionally, it aims to simulate and assess CO₂ emissions, capturing the dynamic interactions between different agents. In this study, agent-based modeling (ABM) was proposed to simulate the empty container movements with an emphasis on inland transportation. ABM is an emerging approach that is increasingly used to simulate complex economic systems and artificial market behaviours. NetLogo was used to incorporate real-world geographic data and quantify CO₂ emissions based on truckload status and to evaluate the other operational aspects. Behavior Space was also utilized to systematically conduct multiple simulation experiments, varying parameters to analyze different scenarios. The results of the study show that customer demand frequency plays a crucial role in system efficiency, affecting container availability and logistical tension. Full article

The presence of toxic gas emissions from conventional vehicles is worrisome globally. Over the past few years, there has been a broad adoption of electric vehicles (EVs) to reduce energy usage and mitigate environmental emissions. The EVs are characterized by limited range, cost, and short range. This prompts the need for hybrid electric vehicles (HEVs). This study describes the conversion of a 2022 Volkswagen Crafter (VW) 35 TDI 340 delivery van from a conventional diesel powertrain into a hybrid electric vehicle (HEV) augmented with synchronous electrical machines (motor and generator) and a BMW i3 60 Ah battery pack. A downsized 1.5 L diesel engine and an electric motor–generator unit are integrated via a planetary power split device supported by a high-voltage lithium-ion battery. A MATLAB (R2024b) Simulink model of the hybrid system is developed, and its speed tracking PID controller is optimized using genetic algorithm (GA) and particle swarm optimization (PSO) methods. The simulation results show significant efficiency gains: for example, average fuel consumption falls from 9.952 to 7.014 L/100 km (a 29.5% saving) and CO₂ emissions drop from 260.8 to 186.0 g/km (a 74.8 g reduction), while the vehicle range on a 75 L tank grows by ~40.7% (from 785.7 to 1105.5 km). The optimized series–parallel powertrain design significantly improves urban driving economy and reduces emissions without compromising performance. Full article

Background: The increasing complexity of global supply chains and environmental expectations has highlighted the strategic importance of digital transformation in the transport, forwarding, and logistics (TFL) sector. Despite a growing portfolio of available tools, adoption rates—particularly among small and medium-sized enterprises (SMEs) in Central and Eastern Europe—remain low. This study investigates the barriers and motivations related to the implementation of digital planning tools supporting intermodal transport planning. Methods: A structured online survey was conducted among 80 Polish TFL enterprises, targeting decision-makers responsible for operational and digital strategies. The questionnaire included 17 closed and semi-open questions grouped into three thematic sections: tool usage, implementation barriers, and digital readiness. Results: The findings indicate that only 20% of respondents use dedicated route planning tools, and merely 10% report satisfaction with their performance. Key barriers include lack of awareness, organizational inertia, and the prioritization of other initiatives, with financial cost cited less frequently. While environmental sustainability is declared as a priority by most enterprises, digital support for emission tracking is limited. The results highlight the need for targeted education, integration support, and differentiated platform functionalities for SMEs and larger firms. Conclusions: This study offers evidence-based recommendations for developers, policymakers, and logistics managers aiming to accelerate digital adoption in the intermodal logistics landscape. Full article

Amidst the global strategic transition towards low-carbon energy systems, electric vehicles (EVs) are pivotal for achieving deep decarbonization within the transportation sector. Consequently, enhancing the scientific rigor and precision of their life-cycle carbon footprint assessments is of paramount importance. Addressing the limitations of existing research, notably ambiguous assessment boundaries and the omission of dynamic coupling characteristics, this study develops a dynamic regional-level life-cycle carbon footprint assessment model for EVs that incorporates time-variant carbon emission factors. The methodology first delineates system boundaries based on established life-cycle assessment (LCA) principles, establishing a comprehensive analytical framework encompassing power battery production, vehicle manufacturing, operational use, and end-of-life recycling. Subsequently, inventory analysis is employed to model carbon emissions during the production and recycling phases. Crucially, for the operational phase, we introduce a novel source–load synergistic optimization approach integrating dynamic carbon intensity tracking. This is achieved by formulating a low-carbon dispatch model that accounts for power grid security constraints and the spatiotemporal distribution of EVs, thereby enabling the calculation of dynamic nodal carbon intensities and consequential EV emissions. Finally, data from these distinct stages are integrated to construct a holistic life-cycle carbon accounting system. Our results, based on a typical regional grid scenario, reveal that indirect carbon emissions during the operational phase contribute 75.1% of the total life-cycle emissions, substantially outweighing contributions from production (23.4%) and recycling (1.5%). This underscores the significant carbon mitigation leverage of the use phase and validates the efficacy of our dynamic carbon intensity model in improving the accuracy of regional-level EV carbon accounting. Full article

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

We combine corrected Gaia DR3 astrometry with non-conservative MESA modelling to retrace the evolution of the FS-CMa binary MWC 728. The revised parallax sets the distance at $d=1.2\pm 0.1$ kpc, leading—after Monte-Carlo error propagation—to luminosities of $log{(L/L_{\odot })}_{\mathrm{acc}}=2.6\pm 0.1$ and $log{(L/L_{\odot })}_{\mathrm{don}}=1.5\pm 0.1$, corresponding to the accretor and donor, respectively. A fiducial binary track that starts with $M_{\mathrm{don}}=3.6\pm 0.1M_{\odot }$, $M_{\mathrm{acc}}=1.8\pm 0.1M_{\odot }$, and $P_0=21.0\pm 0.2$ d reproduces the observations provided the Roche-lobe overflow, which is moderately non-conservative: only $39\%$ of the transferred mass is retained by the accretor, while the remainder leaves the system via (i) a fast isotropic wind from the donor ($\alpha =0.01$), (ii) isotropic re-emission near the accretor ($\beta =0.45$), and (iii) outflow into a circumbinary torus ($\delta =0.15$, lever arm $\gamma =1.3$). These channels remove sufficient angular momentum to expand the orbit to the observed $P_{\mathrm{obs}}=27.5\pm 0.1$ d while sustaining the dusty circumbinary outflow. At $t\simeq 223$ Myr, the model matches every current observable: $M_{\mathrm{don}}=1.30\pm 0.05M_{\odot }$, $M_{\mathrm{acc}}=2.67\pm 0.05M_{\odot }$, mass ratio $q=2.0\pm 0.1$, and an ongoing transfer rate of $\dot{M}\simeq (1\pm 0.3)\times {10}^{-6}{M}_{\odot }{\mathrm{yr}}^{-1}$. MWC 728 thus serves as a benchmark intermediate-mass binary for testing how non-conservative outflows regulate angular-momentum loss and orbital growth. Full article

In this work, hybrid membranes were fabricated by depositing polyvinyl chloride (PVC) fibers onto PET track-etched membranes (TeMs) using the electrospinning technique. The resulting structures exhibited enhanced hydrophobicity, with contact angles reaching 155°, making them suitable for applications in both water–oil mixture separation and membrane distillation processes involving low-level liquid radioactive waste (LLLRW), saline solutions, and natural water sources. The use of hybrids of TeMs and nanofiber membranes has significantly increased productivity compared to TeMs only, while maintaining a high degree of purification. Permeate obtained after MD of LLLRW and river water was analyzed by conductometry and the atomic emission spectroscopy (for Sr, Cs, Al, Mo, Co, Sb, Ca, Fe, Mg, K, and Na). The activity of radioisotopes (for ¹²⁴Sb, ⁶⁵Zn, ⁶⁰Co, ⁵⁷Co, ¹³⁷Cs, and ¹³⁴Cs) was evaluated by gamma-ray spectroscopy. In most cases, the degree of rejection was between 95 and 100% with a water flux of up to 17.3 kg/m²·h. These membranes were also tested in the separation of cetane–water emulsion with productivity up to 47.3 L/m²·min at vacuum pressure of 700 mbar and 15.2 L/m²·min at vacuum pressure of 900 mbar. Full article

Exposure to fine particulate matter (PM2.5) has become an increasing public health concern, particularly in urban areas facing severe air pollution. In response, individuals are increasingly turning to real-time tracking systems and self-monitoring tools. This study aimed to examine the association between PM2.5 risk perception, self-monitoring behaviors, and anxiety levels in the general population of Thailand. A cross-sectional survey was conducted during the dry season using an online questionnaire, which included the 7-item Generalized Anxiety Disorder (GAD-7) scale. A total of 921 participants residing in Bangkok and Chiang Mai were included. Binary logistic regression analysis, adjusted for sex, age, marital status, monthly income, and years of residence, revealed a significant association between anxiety and perceived health risks of PM2.5 exposure (OR = 1.09; 95% CI: 1.06–1.13). Daily self-monitoring of air quality over the past two weeks was also significantly linked to higher anxiety levels compared to non-monitoring individuals: OR = 1.92 (95% CI: 1.11–3.33) for websites, OR = 1.65 (95% CI: 1.01–2.72) for mobile apps, OR = 1.72 (95% CI: 1.12–2.64) for air purifiers, and OR = 3.34 (95% CI: 1.77–6.31) for air quality detectors. Monitoring 4–6 days per week using apps and air detectors was similarly associated with increased anxiety (OR = 1.64 and 2.30, respectively). Heightened perception of PM2.5 health risks and frequent self-monitoring behaviors are associated with increased anxiety among urban residents in Thailand. Public health interventions should consider implementing targeted alert systems during high-pollution periods and prioritize strategies to reduce PM2.5 emissions to alleviate public anxiety. Full article

In the context of global environmental pollution and energy shortages, the use of lightweight designs of railway vehicles has become a key technological approach to improve energy efficiency and reduce carbon emissions. The use of lightweight and high-strength materials such as carbon-fiber-reinforced composites to replace traditional metal vehicle structures holds great application potential. In this study, random track loads and ballast impact loads that may occur during service were considered, and a finite-element model of the electric locomotive coupling protective cover was established. The impact resistance of CFRP, GFRP, and their interlayer hybrid configurations (C/G/C and G/C/G) against structural and ballast impacts were investigated. The calculation results showed that the CFRP protective cover exhibited the best structural impact resistance (with the lowest Tsai–Wu strength failure values), but it also had the largest maximum deformation displacement (2.36 mm) under ballast impact conditions. In contrast, the GFRP protective cover had a higher Tsai–Wu strength failure value, indicating that it had worse structural impact resistance, but it had a lower maximum deformation displacement (2.20 mm) under ballast impact conditions, demonstrating superior ballast impact resistance. The impact resistances of the hybrid-layered protective covers fell between those of the CFRP and GFRP in terms of the structural impact, while their ballast-impact resistance surpassed those of single-fiber configurations. Full article

In addition to the use of reference instruments, low-cost sensors (LCSs) are becoming increasingly popular for air quality monitoring both indoors and outdoors. These sensors provide real-time measurements of pollutants and facilitate better spatial and temporal coverage. However, these simpler devices are typically characterised by lower accuracy and precision and can be more sensitive to the environmental conditions than the reference instruments. It is therefore crucial to characterise the applicability and limitations of these instruments, for which a possible solution is their comparison with reference measurements in real-world conditions. To this end, a measurement campaign has been carried out to evaluate the PM_2.5 readings of several low- and medium-cost air quality instruments of different types and categories (IQAir AirVisual Pro, TSI DustTrak™ II Aerosol Monitor 8532, Xiaomi Mijia Air Detector, and Xiaomi Smartmi PM_2.5 Air Detector). A GRIMM EDM180 instrument was used as the reference. This campaign took place in Budapest, Hungary, from 12 November to 15 December 2020, during typically humid and foggy weather conditions, when the air pollution level was high due to the increased anthropogenic emissions, including wood burning for heating purposes. The results indicate that the individual sensors tracked the dynamics of PM_2.5 concentration changes well (in a linear fashion), but the readings deviated from the reference measurements to varying degrees. Even though the AirVisual sensors performed generally well (0.85 < R² < 0.93), the accuracy of the units showed inconsistency (13–93%) with typical overestimation, and their readings were significantly affected by elevated relative humidity levels and by temperature. Despite the overall overestimation of PM_2.5 by the Xiaomi sensors, they also exhibited strong correlation coefficients with the reference, with R² values of 0.88 and 0.94. TSI sensors exhibited slight underestimations with high explained variance (R² = 0.93–0.94) and good accuracy. The results indicated that despite the inherent bias, the low-cost sensors are capable of capturing the temporal variability of PM_2.5, thus providing relevant information. After simple and multiple linear regression-based correction, the low-cost sensors provided acceptable results. The results indicate that sensor data correction is a necessary prerequisite for the usability of the instruments. The ensemble method is a reasonable alternative for more accurate estimations of PM_2.5. Full article