Search Results (686)

The correct design of effective SNCR (Selective Non-Catalytic Reduction) requires solving several technological challenges. For this purpose, CFD modeling and bench tests were used. This study investigated various parameters affecting the NOx reduction rate in a WR-25 stoker boiler. The first parameter analyzed was the NSR (normalized stoichiometric ratio), with a constant urea concentration of 12.5% in the solution injected into the boiler. CFD modeling indicated that increasing the NSR significantly enhances reduction efficiency, especially between NSR 1 and 2, where the efficiency more than doubles. Bench tests confirmed this trend across all boiler power levels, showing deeper reagent penetration in the chamber at higher NSR levels. However, further doubling of NSR did not yield significant efficiency improvements, likely due to limitations in chemical mixing under reagent excess conditions. Further, it was revealed that NOx reduction efficiency decreases as boiler power increases, probably due to reduced reagent residence time at the required thermodynamic conditions. Additionally, different nozzle tip designs were tested, with multi-hole nozzles (two-hole and three-hole), showing better NOx reduction than single-hole nozzles due to improved reagent distribution. Finally, a lower urea concentration in the reagent (12%) led to better NOx reduction compared to a 32.5% concentration, likely due to improved droplet penetration and mixing with flue gases. Full article

Sustainability in cities, and its accurate and exhaustive assessment, represent a major keystone of environmental sciences and policy making in urban planning. This study aims to provide methods for a reproducible, descriptive, predictive and prescriptive analysis of urban residential choices and mobility, which are key components of an urban system’s sustainability. Using the DPSIR framework for building agent evolution rules, we design an agent-based model of the canton of Geneva, Switzerland. The model leverages real geographical data for the canton of Geneva and its public transportation network. The resulting simulations show the dynamics of the relocation choices of commuters, in terms of the function of their travel time by public transportation to their workplace. Results show that areas around the city centre are generally preferred, but high rent prices and housing availability may prevent most residents from relocating to these areas. Other preferred housing locations are distributed around major tram and train lines and where rent prices are generally lower. The model and its associated tools are capable of spatialising aggregated statistical datasets, inferring spatial correlations, and providing qualitative and quantitative analysis of relocation dynamics. Such achievements are made possible thanks to the efficient visualisation of our results. The agent-based modelling methodology represents an adequate solution for understanding complex phenomena related to sustainability in urban systems, which can be used as guidance for policy making. Full article

The influence of global climate change on temperature-related health outcomes among vulnerable populations, particularly young children, is underexplored. Using a case time series design, we analysed 647,000 hospital admissions of children aged under five years old in New Zealand, born between 2000 and 2019. We explored the relationship between daily maximum temperatures and hospital admissions across 2139 statistical areas. We used quasi-Poisson distributed lag non-linear models to account for the delayed effects of temperature over a 0–21-day window. We identified broad ICD code categories associated with heat before combining these for the main analyses. We conducted stratified analyses by ethnicity, sex, and residency, and tested for interactions with long-term temperature, socioeconomic position, and housing tenure. We found J-shaped temperature–response curves with increased risks of hospital admission above 24.1 °C, with greater sensitivity among Māori, Pacific, and Asian compared to European children. Spatial–temporal analysis from 2013–2019 showed rising attributable fractions (AFs) of admissions associated with increasing temperatures, especially in eastern coastal and densely populated areas. Interactive maps were created to allow policymakers to prioritise interventions. Findings emphasize the need for child-specific and location-specific climate change adaptation policies, particularly for socioeconomically disadvantaged groups. Full article

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of −528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions. Full article

The main goal was to investigate the ability of a non-thermal plasma reactor with gliding arc discharge to remove diesel exhaust particulates (DEPs). A conventional knife-shaped LTP GA (low-temperature plasma gliding arc) reactor was utilized. The following three cases were studied: 140 L/min, 70 L/min, and 14 L/min of air drawn through the reactor, and diesel exhaust fumes were sampled continuously. They were assayed in terms of concentration and number particle size distribution. The higher the residence times, the higher the energy input that may be utilized for DEPs’ removal. The reactor performance definitely lowered the concentration of DEPs (250–580 nm) and altered their number size distribution. There was no effect on the number concentration, nor the particle size distribution, of DEPs of 10–250 nm in size. Regarding the effectiveness of DEPs’ removal, decreasing the flow rate from 140 L/min to 70 L/min somehow altered the values. Achieving the airflow of 14 L/min led to a substantial improvement (even to a fourfold increase for 300–480 nm particles). Non-thermal plasma reactors with gliding arc discharge may be successfully adapted to the process of DEP treatment. Their performance may be optimized by adjusting the airflow at the inlet of the reactor to guarantee the longest aerosol residence times and the highest removal efficiency. Full article

Climate change has severely impacted many species, causing rapid declines or extinctions within their essential ecological niches. This deterioration is expected to worsen, particularly in remote high-altitude regions like the Himalayas, which are home to diverse flora and fauna, including many mountainous ungulates. Unfortunately, many of these species lack adaptive strategies to cope with novel climatic conditions. The Red Goral (Naemorhedus baileyi) is a cliff-dwelling species classified as “Vulnerable” by the IUCN due to its small population and restricted range extent. This species has the most restricted range of all goral species, residing in the temperate mountains of northeastern India, northern Myanmar, and China. Given its restricted range and small population, this species is highly threatened by climate change and habitat disruptions, making habitat mapping and modeling crucial for effective conservation. This study employs an ensemble approach (BRT, GLM, MARS, and MaxEnt) in species distribution modeling to assess the distribution, habitat suitability, and connectivity of this species, addressing critical gaps in its understanding. The findings reveal deeply concerning trends, as the model identified only 21,363 km² (13.01%) of the total IUCN extent as suitable habitat under current conditions. This limited extent is alarming, as it leaves the species with very little refuge to thrive. Furthermore, this situation is compounded by the fact that only around 22.29% of this identified suitable habitat falls within protected areas (PAs), further constraining the species’ ability to survive in a protected landscape. The future projections paint even degraded scenarios, with a predicted decline of over 34% and excessive fragmentation in suitable habitat extent. In addition, the present study identifies precipitation seasonality and elevation as the primary contributing predictors to the distribution of this species. Furthermore, the study identifies nine designated transboundary PAs within the IUCN extent of the Red Goral and the connectivity among them to highlight the crucial role in supporting the species’ survival over time. Moreover, the Dibang Wildlife Sanctuary (DWLS) and Hkakaborazi National Park are revealed as the PAs with the largest extent of suitable habitat in the present scenario. Furthermore, the highest mean connectivity was found between DWLS and Mehao Wildlife Sanctuary (0.0583), while the lowest connectivity was observed between Kamlang Wildlife Sanctuary and Namdapha National Park (0.0172). The study also suggests strategic management planning that is a vital foundation for future research and conservation initiatives, aiming to ensure the long-term survival of the species in its natural habitat. Full article

Reservoir routing has been a routine procedure in hydrology, hydraulics and water management. It is typically based on the mass balance (continuity equation) and a conceptual equation relating storage and outflow. If the latter is linear, then there exists an analytical solution of the resulting differential equation, which can directly be utilized to find the outflow from known inflow and to obtain macroscopic characteristics of the process, such as response and residence times, and their distribution functions. Here we refine the reservoir routing framework and extend it to find approximate solutions for nonlinear cases. The proposed framework can also be useful for climatic tasks, such as describing the mass balance of atmospheric carbon dioxide and determining characteristic residence times, which have been an issue of controversy. Application of the theoretical framework results in excellent agreement with real-world data. In this manner, we easily quantify the atmospheric carbon exchanges and obtain reliable and intuitive results, without the need to resort to complex climate models. The mean residence time of atmospheric carbon dioxide turns out to be about four years, and the response time is smaller than that, thus opposing the much longer mainstream estimates. Full article

The authors performed an ex vivo and in vivo evaluation of the ultrastructural effects on the conjunctival epithelial cells of a new multiple-action tear substitute containing cross-linked hyaluronic acid, lipids and trehalose (Trimix^®), using scanning electron microscopy (SEM) with conjunctival impression cytology. The ex vivo study highlights the persistence and distribution of the product at 5 and 60 min on a monolayer of conjunctival epithelial cells and an increase in microvilli density at the 60 min evaluation. In vivo examination was conducted on three subjects with different grades of ocular surface inflammation, treated with one drop of the product twice daily for thirty days. At the baseline (T₀) and twelve hours after the last administration of the tear drop (T₃₀), impression cytology of the upper bulbar conjunctiva for SEM evaluation of conjunctival epithelial cells was carried out. Slit lamp examination (SLE), corneal and conjunctival Fluotest, tear film break-up time (TBUT), and ocular surface disease index (OSDI) questionnaires were also performed to correlate the ultrastructural results with the clinical findings. After 30 days of treatment, a significant improvement in all clinical and symptomatic parameters and in the condition of the ocular surface was detected, with microvillar regeneration and strengthening in all the patients, and a complete restoration in 2/3 of them. The persistence and distribution of the product on the epithelial cells was also noted 12 h after the last administration. The results, therefore, suggest a marked epitheliotropic effect along with a high residence time of the tear substitute. Full article

Strontium is a toxic chemical element widely distributed in groundwater. First of all, its appearance in water is associated with the dissolution of sulfate and carbonate rocks. The aim of this study was to assess the characteristics of strontium concentration in ancient aluminosilicate deposits that were filled with sedimentogenic brines and seawater in different geological periods. Studies were conducted on 44 water samples, in which the chemical and isotopic composition was determined with the subsequent assessment of saturation indices in relation to the main rock-forming minerals and the residence time of groundwater in the aquifer. It was found that minimal strontium concentrations are characteristic of the least mineralized waters and arise mainly due to the dissolution of carbonates. After their saturation in relation to calcite, the process of carbonate dissolution was replaced by their precipitation and an increase in silicate dissolution with an increase in strontium concentration in more mineralized waters. The incongruent dissolution of aluminosilicates resulted in the appearance of new clay minerals in the aquifer, which together with iron hydroxides and newly formed calcium carbonates created opportunities for sorption and ion exchange processes. The contribution of seawater consisted of an increase in strontium concentrations by approximately 15–20%. The effect of the duration of the water–rock interaction on strontium concentrations in groundwater was expressed in the fact that over a thousand years they increased by 0.1 mg/L, which is 20–30 times less than in the waters of carbonate deposits located 100 km to the east. An assessment of the non-carcinogenic risk to human health of contact with the groundwater showed the safety of using the studied groundwater for drinking purposes. Full article

Due to overexploitation and habitat loss, the Chinese pangolin (Manis pentadactyla) is in such extreme decline that it is so rare in the wild as to be considered functionally extinct, even in Guangdong, which was historically a major distribution area for the species. This study sought to verify whether functional extinction has occurred using observation records from field surveys, infrared wildlife cameras, rescue and enforcement cases and the published literature. The results indicated that suitable habitat occurred within 63.4% of the forested land in Guangdong, but only 17.6% of this area was deemed highly suitable, and 82.3% of all suitable habitat occurred outside of protected areas. Thus, the Chinese pangolin is not yet functionally extinct in Guangdong, but urgent conservation and restoration actions must be taken to ensure its persistence. Chinese pangolins in Guangdong Province are primarily distributed in the Lianhua Mountain and Nanling Mountains, with 91.6% belonging to a single population. From 1980 to 2020, the urban area increased by 776 km², largely via conversion from agricultural land (48.6%). Suitable habitat for Chinese pangolins was reduced and became more fragmented over this time period, highlighting the urgent need for the establishment of protected areas, habitat restoration and cooperation with local residents. Full article

The spatial distribution of Leisure Urban Spaces (LUSs) is closely linked to urban sustainability and residents’ quality of life. This study uses the Central Urban Area of Nanjing as the study area. Using POI and AOI data, the locations of LUS were precisely identified and categorized, including parks, squares, waterfront spaces, and leisure blocks. GIS spatial analysis methods, the nearest neighbor index, standard deviation ellipse, and kernel density estimation were used to analyze these spaces’ form, directivity, and density. Population activity intensity (PAI) data at various time points, collected by Baidu heat map, are correlated with LUS distribution through multiple linear regression analysis. (1) Parks and squares exhibit significant clustering tendencies, whereas waterfront spaces show weaker clustering, and leisure blocks are randomly distributed; (2) The central points of all types of LUS are located in the city center, extending from southeast to northwest, with parks and squares offering a broader range of services; (3) The overall LUS layout shows a ‘core and multiple points’ structure, with varying density patterns across different spaces, indicating concentrated and dispersed leisure areas; (4) The correlation between LUS distribution and PAI strengthens throughout the day and is greater on weekends than weekdays. Leisure blocks significantly enhance activity intensity, while parks have a limited effect, and waterfront spaces often show a negative correlation due to their remote locations. These results provide insights for future urban planning in Nanjing and underscore patterns in residents’ leisure activities. Full article

This study aims to develop and evaluate an LSTM neural network for predicting household energy consumption. To conduct the experiment, a testbed was created consisting of five common appliances, namely, a TV, air conditioner, fan, computer, and lamp, each connected to individual smart meters within a Home Energy Management System (HEMS). Additionally, a meter was installed on the distribution board to measure total consumption. Real-time data were collected at 15-min intervals for 30 days in a residence that represented urban energy consumption in Sincelejo, Sucre, inhabited by four people. This setup enabled the capture of detailed and specific energy consumption data, facilitating data analysis and validating the system before large-scale implementation. Using the detailed power consumption information of these devices, an LSTM model was trained to identify temporal connections in power usage. Proper data preparation, including normalisation and feature selection, was essential for the success of the model. The results showed that the LSTM model was effective in predicting energy consumption, achieving a mean squared error (MSE) of 0.0169. This study emphasises the importance of continued research on preferred predictive models and identifies areas for future research, such as the integration of additional contextual data and the development of practical applications for residential energy management. Additionally, it demonstrates the potential of LSTM models in smart-home energy management and serves as a solid foundation for future research in this field. Full article

With the continuous development of cities, the surface urban heat island intensity (SUHII) is increasing, leading to the deterioration of the urban thermal environment, increasing energy consumption, and endangering the health of urban residents. Understanding the spatio-temporal scale difference and gradient effect of urban spatial patterns on the impact of SUHII is crucial for improving the climate resilience of cities and promoting sustainable urban development. This paper investigated the characteristics of SUHII changes at different time periods based on local climate zones (LCZs) and downscaled land surface temperature (LST) data. Meanwhile, landscape pattern indicators and the multiscale geographically weighted regression (MGWR) model were utilized to analyze the impacts of urban spatial patterns on SUHII at multiple spatial–temporal scales. The results indicated that the SUHII of each LCZ type exhibited diverse patterns in different time periods. High SUHII occurred in summer daytime and autumn nighttime. Compact and high-rise buildings (LCZ1/2/4) showed markedly higher SUHII during the daytime or nighttime, except for heavy industry. The extent of influence and the dominant factors of LCZ spatial patterns on SUHII exhibit obvious scale differences and gradient effects. At the regional scale, highly regular and compacted built-up areas tended to increase SUHII, while single and continuously distributed built-up areas had a greater impact on increasing SUHII. At the local scale, the impact of the PLAND (1/2/4/5/10) on SUHII exhibited a trend of diminishing from urban to suburban areas. In urban areas, the PLAND of LCZ 1, LCZ 2, and LCZ4 was the major factor affecting the increase in SUHII, whereas, in suburban areas, the PLAND of LCZ 2 and LCZ 10 was the major influencing factor on SUHII. The results can provide a scientific reference for mitigating urban heat island effects and constructing an ecologically ‘designed’ city. Full article

The cross-line operation (CO) of trains in urban rail transit is an effective method to efficiently satisfy transfer passenger travel demand as well as relieve the pressure of transfer stations. The primary problem of CO is designing train services to satisfy travel demand with an uneven spatial distribution of passengers. This study constructs a nonlinear integer programming model with a novel train operation scheme, i.e., virtual coupling (VC) technology, which allows the coupling/decoupling of trains on different lines at both ends of each operation zone. This scheme makes the train capacity equitably distributed in each operation zone, thereby balancing train capacity utilization over the whole CO system. Regarding the nonlinear characteristics of the proposed model, an adaptive simulated annealing genetic algorithm (ASA-GA) was designed to quickly generate high-quality solutions. Based on real-world data from the Beijing Changping Line and Line 13, the effectiveness of the proposed model and algorithm were verified. The computation results show that in comparison to a single grouping train composition scheme without CO, a VC scheme with CO would reduce operation costs by 46.8%, with 80.6% savings of train capacity equity. Furthermore, the average passenger residence time would be reduced by 25.9%. Full article

In line with several European directives, residents are strongly encouraged to invest in renewable power plants and flexible consumption systems, enabling them to share energy within their Renewable Energy Community at lower procurement costs. This, along with the ability for residents to switch between such communities on a daily basis, leads to dynamic portfolios, resulting in non-stationary and discontinuous electrical load time series. Given poor predictability as well as insufficient examination of such characteristics, and the critical importance of electrical load forecasting in energy management systems, we propose a novel forecasting framework using Federated Learning to leverage information from multiple distributed communities, enabling the learning of domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we evaluate their effectiveness by applying different tests for white noise in the forecast error signal. The findings suggest that our proposed framework is capable of effectively forecast non-stationary as well as discontinuous time series, extract domain-invariant features, and is applicable to new, unseen data through the integration of knowledge from multiple sources. Full article