Search Results (155)

This study assessed the spatial distribution and composition of airborne particulate matter within a 10 km long urban green corridor in Zacatecas, Mexico, using magnetic biomonitoring with Tillandsia recurvata and SEM-EDS particle characterization. A total of 44 samples were collected from distinct urban park contexts (e.g., commercial zones, malls, bus stops), revealing mass-specific magnetic susceptibility χ values ranging from −6.71 to 61.1 × 10⁻⁸ m³ kg⁻¹. Three compositional groups were identified based on a PCA performed using elemental concentrations from SEM-EDS and magnetic data, which are associated with traffic emissions and industrial inputs. SEM-EDS images confirmed abundant magnetite-like particles (1–8 μm) with hazardous metals including Pb (up to 5.6 wt.%), Ba (up to 67.6 wt.%), and Cr (up to 31.5 wt.%). Wind direction data indicated predominant SSW–NNE transport, correlating with hotspots in central and northeastern park areas. Overall, vegetated zones exhibited markedly lower magnetic loads (mean χ = 8.84 × 10⁻⁸ m³ kg⁻¹) than traffic-exposed sites (mean χ = 17.27 × 10⁻⁸ m³ kg⁻¹), representing an approximate 50% reduction in magnetic particle accumulation, which highlights the effective role of continuous vegetation cover as a functional green barrier that attenuates the lateral transport and deposition of airborne particulate matter within the park. This research highlights the applicability of combined magnetic and microscopic techniques for evaluating the dynamics of airborne pollution in urban parks and supports their use for identifying both pollution hotspots and mitigation zones, reinforcing the role of urban green spaces as biofunctional filters in cities facing vehicular air pollution. Full article

This study examines how climatic factors influence the predictive power of LEED credits in determining certification outcomes for hotel buildings across the United States. Using data from 259 LEED-NC v2009 certified hotels, project-level information was integrated with 30-year climate normals from the PRISM database and Building America climate zones. A three-step hierarchical linear regression was conducted to identify the LEED credits that most strongly predict total certification points while controlling for project size, certification year, and baseline climatic conditions, and to test whether climatic factors moderate these relationships. Regularized Linear Regression (LASSO) was then applied to address multicollinearity and assess model stability, followed by Support Vector Regression (SVR) to capture potential nonlinear relationships. This integrated methodological framework, combining hierarchical regression for interpretability, LASSO for coefficient stability, and Support Vector Regression for nonlinear verification, provides a novel, multi-dimensional assessment of climate-sensitive credit behavior at the individual credit level. Results show that energy- and site-related credits, particularly Optimize Energy Performance (EA1), On-Site Renewable Energy (EA2), Green Power (EA6), and Alternative Transportation (SS4), consistently dominate LEED performance across all climate zones. In contrast, indoor environmental quality credits exhibit modest but significant climate sensitivity: higher mean temperatures reduce the contribution of Increased Ventilation (EQ2) while slightly enhancing Outdoor Air Delivery Monitoring (EQ1). Cross-model consistency confirms the robustness of these findings. The findings highlight the need for climate-responsive benchmarking of indoor environmental quality credits to improve regional equity and advance the next generation of climate-adaptive LEED standards. Full article

Background: Lipedema is a progressive adipofascial disorder marked by painful nodular fat deposition that is often mistaken for obesity. While tumescent liposuction reduces limb volume with relative lymphatic safety, persistent large, painful lobules frequently remain, and excisional strategies risk iatrogenic lymphatic injury. We evaluated the application of intraoperative indocyanine green (ICG) lymphography to identify and preserve lymphatic channels during debulking surgery for symptomatic lipedema. Methods: We conducted a single-center case series (University of Pittsburgh Medical Center, July 2023–December 2024) of adults with lipedema refractory to conservative therapy who underwent a selective dermato-lipectomy (lobule/skin excision) with or without tumescent liposuction. Patients with clinical lymphedema or dermal backflow in ICG were excluded. Near-infrared ICG (SPY-PHI) was used for pre-incision mapping and real-time intraoperative guidance; lymphatic trajectories were marked and spared during lobule excision. Primary measures included dermal backflow patterns and lymph node transit time; secondary outcomes were complications and symptom burden (Lymphedema Life Impact Scale, LLIS) through ≥24 months. Results: Eight patients (five female/three male; mean age 49.5 ± 14.4 years; median BMI 52.65 kg/m²) underwent ICG-guided surgery. Preoperatively, linear lymphatic patterns were visualized up to the knee in all patients, but dermal backflow patterns could not be visualized in 83% from the level of the knee to the groin. Still, 67% demonstrated inguinal nodal uptake (mean transit 24 min), suggesting preserved lymphatic transport. All cases achieved intraoperative confirmation of intact lymphatic flow after debulking. The mean liposuction aspirate was 925 ± 250 mL per lower extremity; the mean excision mass was 2209 ± 757 g per lower extremity. Complications included two superficial cellulitis events (25%) and one wound dehiscence (12.5%); no hematomas or skin necrosis occurred. No patient developed clinical or imaging evidence of iatrogenic lymphedema during follow-up. Conclusions: Intraoperative ICG lymphography is a practical adjunct for lymphatic-sparing debulking of symptomatic lipedema, enabling real-time identification and preservation of superficial collectors while addressing focal lobules. This hybrid approach—targeted tumescent liposuction followed by ICG-guided superficial dermato-lipectomy—was associated with meaningful symptom improvement and a low morbidity in this early series. Full article

High-resolution air quality data are critical for exposure assessment, regulatory compliance, and urban planning. In this study, we present modelled annual mean concentrations of NO₂, PM_2.5, PM₁₀, Black Carbon (BC), and particle number concentration (PNC) for all ~2.5 million Danish addresses in 2019 using the Air Quality at Your Street 2.0 system. The modelling framework combines coupled chemistry–transport models (DEHM/UBM/OSPM) with input from the Green Mobility Model and GPS-based vehicle speed data. Model outputs were evaluated against observations from the Danish Air Quality Monitoring Programme, showing strong agreement for NO₂, PM_2.5, PM₁₀, and BC, but notable overestimation of PNC background levels and underestimation of street contributions. Indicative exceedances of NO₂ EU limit values decreased markedly from 2012 to 2019, while exceedances of updated EU and WHO guidelines persist, especially for particulate matter. This work identifies key sources of model uncertainty and supports high-resolution national-scale assessment and citizen access via an interactive map. Full article

Rising carbon emissions from international road freight transport in the EU—increasing from 29.4% in 2023 to 31.4% in 2025 under the With Existing Measures (WEM) Road Transport scenario—necessitate the implementation of additional measures within the framework of the EU Carbon Border Adjustment Mechanism (CBAM). For Ukraine, operating under martial law and pursuing a post-war green recovery of its transport and trade sectors, the adoption of EU experience in distributed generation (DG) from renewable energy sources (RESs) is particularly critical. This study evaluates the synergy between energy-efficient and low-carbon management in logistics chains for road freight transportation in Ukraine, drawing on EU evidence of DG based on RESs. To this end, a decoupling analysis was conducted to identify the factors influencing low-carbon and energy-efficient management of logistics chains in Ukraine’s freight transport sector. Under wartime conditions, the EU practice of utilising electric vehicles (EVs) as an auxiliary source of renewable energy for distributed electricity generation within microgrids—through Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies—was modelled. The results confirm the relevance of RES-based DG and the integration of EVs as a means of enhancing energy resilience in resource-constrained and conflict-affected regions. The scientific novelty of this research lies in identifying the conditions for achieving energy-efficient and low-carbon effects in the design of logistics chains through RES-based distributed generation, grounded in circular and inclusive economic development. The practical significance of the findings lies in formulating a replicable model for diversifying low-carbon fuel sources via the development of distributed generation of electricity based on renewable resources, providing a scalable paradigm for energy-limited and conflict-affected areas. Future research should focus on developing innovative logistics chain models that integrate DG and renewable energy use into Ukraine’s transport system. Full article

Accurate quantification of campus vegetation carbon stocks is essential for advancing carbon neutrality goals and refining urban carbon management strategies. This study pioneers the integration of drone and backpack LiDAR data to overcome limitations in conventional carbon estimation approaches. The Comparative Shortest-Path (CSP) algorithm was originally developed to segment tree crowns from point cloud data, with its design informed by metabolic ecology theory—specifically, that vascular plants tend to minimize the transport distance to their roots. In this study, we deployed the Comparative Shortest-Path (CSP) algorithm for individual tree recognition across 897 campus trees, achieving 88.52% recall, 72.45% precision, and 79.68% F-score—with 100% accuracy for eight dominant species. Diameter at breast height (DBH) was extracted via least-squares circle fitting, attaining >95% accuracy for key species such as Magnolia grandiflora and Triadica sebifera. Carbon storage was calculated through species-specific allometric models integrated with field inventory data, revealing a total stock of 163,601 kg (mean 182.4 kg/tree). Four dominant species—Cinnamomum camphora, Liriodendron chinense, Salix babylonica, and Metasequoia glyptostroboides—collectively contributed 84.3% of total storage. As the first integrated application of multi-platform LiDAR for campus-scale carbon mapping, this work establishes a replicable framework for precision urban carbon sink assessment, supporting data-driven campus greening strategies and climate action planning. Full article

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

Electronic transport through T-shaped double quantum dots (TDQDs) connected to normal metallic leads is studied theoretically by using a nonequilibrium Green’s function method. It is assumed that the Coulomb interaction exists only in the central QD (QD-1) sandwiched between the leads, and it is absent in the other reference QD (QD-2) side-coupled to QD-1. We also consider the impacts of Majorana bound states (MBSs), which are prepared at the opposite ends of a topological superconductor nanowire (hereafter called a Majorana nanowire) connected to QD-2, on the electrical current and differential conductance. Our results show that by the combined effects of the Coulomb interaction in QD-1 and the MBSs, a negative differential conductance (NDC) effect emerges near the zero-bias point, where MBSs play significant roles. Now, the electrical current decreases despite the increasing bias voltage. The NDC is prone to occur under conditions of low temperature, and both of the two QDs’ energy levels are resonant to the leads’ zero Fermi energy. Its magnitude, which is characterized by a peak-to-valley ratio, can be enhanced up to 3 by increasing the interdot coupling strength, and it depends on the dot-MBS hybridization strength nonlinearly. This prominent NDC combined with the previously found zero-bias anomaly (ZBA) of the differential conductance is useful in designing novel quantum electric devices, and it may also serve as an effective detection means for the existence of MBSs, which is still a challenge in solid-state physics. Full article

This study proposes a novel vehicle speed prediction model for plateau transportation—CLS-DW Stacking (Constrained Least Squares Dynamic Weighting Model Stacking)—which holds significant implications for the sustainable development of transportation systems in high-altitude regions. Research on sharp-curved roads on mountainous plateaus remains scarce. Compared with plain areas, data acquisition in such regions is constrained by government confidentiality policies, while complex environmental and topographical conditions lead to substantial variations in road alignment and elevation. To address these challenges, this study presents a sustainable data acquisition and construction method: unmanned aerial vehicle (UAV) video data are processed through road image segmentation, trajectory tracking, and three-dimensional modeling to generate multi-source heterogeneous datasets for both single-curve and continuous-curve scenarios. Building upon these datasets, the proposed framework integrates constrained least squares with multiple deep learning methods to achieve accurate traffic flow prediction. Bi-LSTM (Bidirectional Long Short-Term Memory), Informer, and GRU (Gated Recurrent Unit) are employed as base learners, and the loss function is redefined with non-negativity and normalization constraints on the weights. This ensures optimal weight coefficients for each base learner, with the final prediction obtained via weighted summation. The experimental results show that, compared with single deep learning models such as Informer, the proposed model reduces the mean squared error (MSE) by 1.9% on the single curve dataset and by 7.7% on the continuous curve dataset. Furthermore, by combining vehicle speed predictions across different altitude gradients with decision tree-based interpretable analysis, this research provides scientific support for developing altitude-specific and precision-oriented speed limit policies. The outcomes contribute to accident risk reduction, traffic congestion mitigation, and carbon emission reduction, thereby improving road resource utilization efficiency. This work not only fills the research gap in traffic prediction for sharp-curved plateau roads but also supports the construction of green transportation systems and the broader objectives of sustainable development in high-altitude regions. Full article

Magnetic tunnel junctions (MTJs) are pivotal for spintronic applications such as magneto resistive memory and sensors. Two-dimensional van der Waals heterostructures offer a promising platform for miniaturizing MTJs while enabling the twist-angle engineering of their properties. Here, we investigate the impact of twisting the insulating barrier layer on the performance of a van der Waals MTJ with the structure graphene/1T-VSe₂/h-BN/1T-VSe₂/graphene, where 1T-VSe₂ serves as the ferromagnetic electrodes and the monolayer h-BN acts as the tunnel barrier. Using first-principles calculations based on density functional theory (DFT) combined with the non-equilibrium Green’s function (NEGF) formalism, we systematically calculate the spin-dependent transport properties for 18 distinct rotational alignments of the h-BN layer (0° to 172.4°). Our results reveal that the tunneling magnetoresistance (TMR) ratio exhibits dramatic, rotation-dependent variations, ranging from 2328% to 24,608%. The maximum TMR occurs near 52.4°. An analysis shows that the twist angle modifies the d-orbital electronic states of interfacial V atoms in the 1T-VSe₂ layers and alters the spin polarization at the Fermi level, thereby governing the spin-dependent transmission through the barrier. This demonstrates that rotational manipulation of the h-BN layer provides an effective means to engineer the TMR and performance of van der Waals MTJs. Full article

This study investigates urban heat vulnerabilities in Seville, Spain, using a multidimensional framework that integrates remote sensing, Space Syntax, and social vulnerability metrics. This research identifies Heat Boundaries (HBs), which are critical urban entities with elevated Land Surface Temperatures (LSTs) that act as barriers to adjacent vulnerable neighbourhoods, disrupting both physical and social continuity and environmental equity, and examines their relationship with the urban syntax and social vulnerability. The analysis spans two temporal scenarios: a Category 3 heatwave on 26 June 2023 and a normal summer day on 14 July 2024, incorporating both daytime and nighttime satellite-derived LST data (Landsat 9 and ECOSTRESS). The results reveal pronounced spatial disparities in thermal exposure. During the heatwave, peripheral zones recorded extreme LSTs exceeding 53 °C, while river-adjacent neighbourhoods recorded up to 7.28 °C less LST averages. In the non-heatwave scenario, LSTs for advantaged neighbourhoods close to the Guadalquivir River were 2.55 °C lower than vulnerable high-density zones and 3.77 °C lower than the peripheries. Nocturnal patterns showed a reversal, with central high-density districts retaining more heat than the peripheries. Correlation analyses indicate strong associations between LST and built-up intensity (NDBI) and a significant inverse correlation with vegetation cover (NDVI). Syntactic indicators revealed that higher Mean Depth values—indicative of spatial segregation—correspond with elevated thermal stress, particularly during nighttime and heatwave scenarios. HBs occupy 17% of the city, predominantly composed of barren land (42%), industrial zones (30%), and transportation infrastructure (28%), and often border areas with high social vulnerability. This study underscores the critical role of spatial configuration in shaping heat exposure and advocates for targeted climate adaptation measures, such as HB rehabilitation, greening interventions, and Connectivity-based design. It also presents preliminary insights for future deep learning applications to automate HB detection and support predictive urban heat resilience planning. Full article

Meeting the European Green Deal’s goal of climate neutrality by 2050 calls for a 90 percent decrease in emissions from the transportation sector. Thus, there is need to accelerate the shift to more sustainable mobility for integrated and smarter multimodal and intermodal mobility. In European countries, more than 70% of the inhabitants live in metropolitan areas. Achieving low-carbon and more sustainable mobility is important to ensuring sustainable urban infrastructure. However, current mobility planning frameworks do not consider the key factors and strategies that encourage residents to choose sustainable transport modes. Hence, there is a need to identify the most efficient actions that should be employed either in the short or long term to achieve accessible, safe, cost-effective, and green transport systems specifically through the development of sustainable public transportation. Moreover, a paradigm shift is needed to explore the synergy between transportation and its relationship to the city. Accordingly, this article presents an action plan as an approach to assess key strategies needed to foster sustainable and smart mobility planning and design by deploying effective strategies and design solutions that support different green means of transportation for smart urban development. Qualitative data on sustainable mobility planning and design strategies was collected via secondary sources from the literature, and descriptive data analysis was carried out. Findings from this study identify internal and external factors required to promote sustainable multimodal and intermodal mobility based on the city’s transport policies and actions. Implications from this study provide a use case for the technological requirements required for electric mobility planning, design, and system operation for the actualization of sustainable public transportation to improve smart urban development. Full article

Urban park vitality, a key indicator of public space performance, has garnered significant research attention. However, existing studies often neglect the temporal variability in vitality patterns, thus failing to accurately reflect actual park performance and limiting their relevance for strategic urban planning and sustainable resource allocation. This study constructs a “temporal behavior–spatial attributes–park typology” framework using high-precision (50 m) mobile signaling data to capture hourly vitality fluctuations in 59 parks of Hangzhou’s Gongshu District. Using dynamic time-warping-optimized K-means clustering, we identify three vitality types—Morning-Exercise-Dominated, All-Day-Balanced, and Evening-Aggregation-Dominated—revealing distinct weekday/weekend usage rhythms linked to park typology (e.g., community vs. comprehensive parks). Geographical Detector analysis shows that vitality correlates with spatial attributes in time-specific ways; weekend morning vitality is driven by park size and surrounding POI density, while weekday evening vitality depends on interactions between facility density and residential population. These findings highlight how transportation accessibility and commercial amenities shape temporal vitality, informing time-sensitive strategies such as extended evening hours for suburban parks and targeted facility upgrades in residential areas. By bridging vitality patterns with strategic planning demands, the study advances the understanding of how sustainable park management can optimize resource efficiency and enhance public space equity, offering insights for urban green infrastructure planning in other regions. Full article

The implementation of low-cost sensitive and selective gas sensors for monitoring fruit ripening and quality strongly depends on their long-term stability. Gas sensor drift undermines the long-term reliability of low-cost sensing platforms, particularly in precision agriculture. We present a real-time drift compensation framework based on a lightweight Temporal Convolutional Neural Network (TCNN) combined with a Hadamard spectral transform. The model operates causally on incoming sensor data, achieving a mean absolute error below 1 mV on long-term recordings (equivalent to <1 particle per million (ppm) gas concentration). Through quantization, we compress the model by over 70%, without sacrificing accuracy. Demonstrated on a combustion-type gas sensor system (dubbed GMOS) for ethylene monitoring, our approach enables continuous, drift-corrected operation without the need for recalibration or dependence on cloud-based services, offering a generalizable solution for embedded environmental sensing—in food transportation containers, cold storage facilities, de-greening rooms and directly in the field. Full article

This paper analyzes the potential of retrofitting in “greening” of European inland vessels and coastal ships, which are normally not the focus of major international environmental policies aimed at waterborne transport. Therefore, greening of the examined fleets would result, for the most part, in additional emission reductions to the environmental targets put forth by the International Maritime Organization. By scoping past and ongoing pilot projects, the most prominent retrofit trends in the greening of inland and coastal ships are identified. Assuming a scenario in which the observed trends are scaled up to the fleet level, the possible emission abatement is estimated (both on the tank-to-wake and well-to-wake bases), as well as the capital and operational costs associated with the retrofit. Therefore, the paper shows what can be achieved in terms of greening if the current trends are followed. The results show that the term “greening” may take a significantly different meaning contingent on the approaches, perspectives, and targets considered. The total costs of a retrofit of a single vessel may be excessively high; however, the costs may significantly vary depending on the vessel power requirements, operational profile, and technology applied. While some trends are worth following (electrification of ferries and small inland passenger ships), others may be too cost-intensive and not satisfactorily efficient in terms of emissions reduction (retrofit of offshore supply vessels with dual-fuel methanol engines). Nevertheless, the assessment of different retrofit technologies strongly depends on the adopted criteria, including but not limited to the total cost of the retrofit of the entire fleet segment, cost of the retrofit of a single vessel, emission abatement achieved by the retrofit of a fleet segment, average emission abatement per retrofitted vessel, and cost of abatement of one ton of greenhouse gases, etc. Full article