Search Results (1,061)

Heavy metal pollution, particularly copper contamination, threatens the ecological environment and human survival. In response to this pressing environmental issue, the development of innovative remediation strategies has become imperative. Bioremediation technology is characterized by remarkable advantages, including its ecological friendliness, cost-effectiveness, and operational efficiency. In our previous research, Rossellomorea sp. ZC255 demonstrated substantial potential for environmental bioremediation applications. This study investigated the removal characteristics and underlying mechanism of strain ZC255 and revealed that the maximum removal capacity was 253.4 mg/g biomass under the optimal conditions (pH 7.0, 28 °C, and 2% inoculum). The assessment of the biosorption process followed pseudo-second-order kinetics, while the adsorption isotherm may fit well with both the Langmuir and Freundlich models. Cell surface alterations on the Cu(II)-treated biomass were observed through scanning electron microscopy (SEM). Cu(II) binding functional groups were determined via Fourier transform infrared spectroscopy (FTIR) analysis. Simultaneously, the genomic analysis of strain ZC255 identified multiple genes potentially involved in heavy metal resistance, transport, and metabolic processes. These studies highlight the significance of strain ZC255 in the context of environmental heavy metal bioremediation research and provide a basis for using strain ZC255 as a copper removal biosorbent. Full article

A new generation of compact radon detectors with high sensitivity and fine spatial resolution (1–2 cm scale) was used to investigate indoor radon distribution and identify potential entry pathways. Solid-state nuclear track detectors (Kodak-Pathe LR-115 type II, Dosirad, France), combined with activated carbon fabric (ACC-5092-10), enabled sensitive, spatially resolved radon measurements. Two case studies were conducted: Case 1 involves a room with elevated radon levels suspected to originate from the floor. Case 2 involves a house with persistently high indoor radon concentrations despite active basement ventilation. In the first case, radon emission from the floor was found to be highly inhomogeneous, with concentrations varying by more than a factor of four. In the second, unexpectedly high radon levels were detected at electrical switches and outlets on walls in the living space, suggesting radon transport through wall voids and entry via non-hermetic electrical fittings. These novel detectors facilitate fine-scale mapping of indoor radon concentrations, revealing ingress routes that were previously undetectable. Their use can significantly enhance radon diagnostics and support the development of more effective mitigation strategies. Full article

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting, version 2.1.4) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article

Urban transport is a key driver of greenhouse gas emissions in Europe, making its decarbonization essential to achieving EU climate neutrality targets. This study examines how European strategies, such as the Green Deal, the Sustainable and Smart Mobility Strategy, and the Fit for 55 package, are reflected in Romania’s transport policies, with a focus on implementation challenges and urban outcomes in Bucharest. By combining policy analysis, stakeholder mapping, and comparative mobility indicators, the paper critically assesses Bucharest’s current reliance on private vehicles, underperforming public transport satisfaction, and limited progress on active mobility. The study develops a context-sensitive reform framework for the Romanian capital, grounded in transferable lessons from Western and Central European cities. It emphasizes coordinated metropolitan governance, public trust-building, phased car-restraint measures, and investment alignment as key levers. Rather than merely cataloguing policy intentions, the paper offers practical recommendations informed by systemic governance barriers and public attitudes. The findings will contribute to academic debates on urban mobility transitions in post-socialist cities and provide actionable insights for policymakers seeking to operationalize EU decarbonization goals at the metropolitan scale. Full article

The mental and physical health of college students, especially in urban environments like Shanghai, is crucial given the high academic and urban stressors, which were intensified by the COVID-19 lockdown. Prior research has shown gender differences in health impacts during public health crises, with females often more vulnerable to mental health issues. Objective: This study aimed to comprehensively assess the physical and psychological health of Chinese college students post-lockdown, focusing on the relationship between stress, anxiety, depression, sleep patterns, and physical health, with a particular emphasis on gender differences. Methods: We conducted a cross-sectional study involving 116 students in Shanghai, utilizing psychological scales (HAMA, IPAQ, PSQI, SDS, FS 14, PSS, SF-36) and physical fitness tests (resting heart rate, blood pressure, hand grip, forced vital capacity, standing long jump, sit-and-reach, one-minute sit-up test and the one-minute squat test, single-leg stand test with eyes closed), to analyze health and behavior during the pandemic lockdown. All students have undergone the same life habits during the pandemic. Results: The HAMA scores indicated no significant levels of physical or mental anxiety. The PSS results (42.45 ± 8.93) reflected a high overall stress level. Furthermore, the PSQI scores (5.4 ± 2.91) suggested that the participants experienced mild insomnia. The IPAQ scores indicated higher levels of job-related activity (1261.49 ± 2144.58), transportation activity (1253.65 ± 987.57), walking intensity (1580.78 ± 1412.20), and moderate-intensity activity (1353.03 ± 1675.27) among college students following the lockdown. Hand grip strength (right) (p = 0.001), sit-and-reach test (p = 0.001), standing long jump (p = 0.001), and HAMA total score (p = 0.033) showed significant differences between males and females. Three principal components were identified in males: HAMA, FS14, and PSQI, explaining a total variance of 70.473%. Similarly, three principal components were extracted in females: HAMA, PSQI, and FS14, explaining a total variance of 69.100%. Conclusions: Our study underscores the complex interplay between physical activity (PA), mental health, and quality of life, emphasizing the need for gender-specific interventions. The persistent high stress, poor sleep quality, and reduced PA levels call for a reorganized teaching schedule to enhance student well-being without increasing academic pressure. Full article

The maturity of Korla fragrant pears directly influences their harvesting, packaging, transportation, and storage. Investigating the mechanical properties of fragrant pears at various maturity stages can help minimize damage during postharvest handling. This study employs micro-CT technology combined with reverse model scanning to develop a numerical model for force damage across different maturity stages, supported by experimental validation. The results demonstrate that both rupture force and rupture strain progressively decrease as the maturity of Korla fragrant pears increases, exhibiting a sudden transition. Simultaneously, the fruit’s microstructure shifts from distinct cellular organization to an irregular, collapsed state. The proposed numerical model, which accounts for this abrupt change, provides a better fit than models based on a single physical parameter, with the R² value improving from 0.7922 to 0.9665. Furthermore, this model accurately quantifies the mechanical properties of fragrant pears at all stages of maturity. These findings offer technical support for reducing postharvest losses and serve as a reference for developing damage prediction models for other fruits and vegetables. Full article

The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors. Full article

Establishing a high-precision passenger flow prediction model is a critical and complex task for the optimization of urban rail transit systems. With the development of artificial intelligence technology, the data-driven technology has been widely studied in the intelligent transportation system. In this study, a neural network model based on the data-driven technology is established for the prediction of passenger flow in multiple urban rail transit stations to enable smart perception for optimizing urban railway transportation. The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. Experiments based on the actual passenger flow data of Beijing Metro Line 13 are conducted to compare the prediction performance of the proposed data-driven model with the other baseline models. The experimental results demonstrate that the proposed prediction model achieves lower MAE and RMSE in passenger flow prediction, and its fitted curve more closely aligns with the actual passenger flow data. This demonstrates the model’s practical potential to enhance intelligent transportation system management through more accurate passenger flow forecasting. Full article

As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article

A wheel flat is the most common fault of a railway freight car, a type of complex transport equipment. A wheel flat will cause continuous regular impact on the rail, damage the rail and the railway structure, affecting the safety and stability of rail transport. This article studied the relationship between wheel flats and wheel–rail impacts using multi-body dynamics simulation through SIMPACK and, through a field test, validates the detection of a flat wheel. The results show that using the simulation method can obtain similar data to the measured wheel–rail force in the wayside detection device. The simulation data show that the data collected by 14 shear vertical force acquisition channels can completely cover the wheel surface of the heavy-duty railway 840 mm diameter wheel. According to the flat length-speed-impact diagram, the mapping relationship can be fitted using polynomial regression. Based on the measured wheel–rail impact forces, the size of wheel flats can then be deduced from this established mapping relationship. Through a field test, the detection method has been validated. Full article

This work uses the MATLAB Reservoir Simulation Toolbox (MRST) to reduce the 3D reservoir model into a 2D version in order to investigate CO₂ storage in the Aurora model using the vertical equilibrium (VE) model. For this purpose, we used an open-source reservoir simulator, MATLAB Reservoir Simulation Toolbox (MRST). MRST is an open-source reservoir simulator, with supplementary modules added to enhance its versatility in addition to a core set of procedures. A fully implicit discretization is used in the numerical formulation of MRST-co2lab enabling the integration of simulators with vertical equilibrium (VE) models to create hybrid models. This model is then compared with the Eclipse model in terms of properties and simulation results. The relative permeability of water and gas can be compared to verify that the model fits the original Eclipse model. Comparing the fluid viscosities used in MRST and Eclipse also reveals comparable tendencies. However, reservoir heterogeneity is the reason for variations in CO₂ plume morphologies. The upper layers of the Eclipse model have lower permeability than the averaged MRST model, which has a substantial impact on CO₂ transport. According to the study, after 530 years, about 17 MT of CO₂ might be stored, whereas 28 MT might escape the reservoir, since after 530 years CO₂ plume reaches completely the open northern boundary. Additionally, a sensitivity analysis study has been conducted on permeability, porosity, residual gas saturation, rock compressibility, and relative permeability curves which are the five uncertain factors in this model. Although plume migration is highly sensitive to permeability, porosity, and rock compressibility variation, it shows a slight change with residual gas saturation and relative permeability curve in this study. Full article

Background/Objectives: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide. Even though the screening programs have decreased the incidence rates, the prognosis for CRC varies depending on the stage at diagnosis. Thus, early diagnosis is still a big challenge due to screening methods, and subsequent diagnosis is not very sensitive. Methods: In this work, LC-MS-based metabolomics, a powerful and sensitive tool to study complex dynamic changes, was used to analyze 211 human fecal samples from control individuals (CTRL), adenoma (AA), and CRC patients. Results: Multivariate and univariate statistical analysis highlighted cholesteryl esters (CEs) and fecal haemoglobin, quantified by fecal immunochemical test (FIT), as relevant biomarkers that clearly differentiate CRC from AA and CTRL. Predictive models based on random forest and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) demonstrate that CEs, together with FIT measurement, improved the CRC and CTRL classification, but not AA. This study revealed that the AA group is a transitional stage with high heterogeneity. The increased tendency observed in CEs from CTRL to CRC might be related to the imbalance of cholesterol homeostasis due to cancer cells requiring a high cholesterol level for cell development and proliferation. The free cholesterol is probably obtained from CEs, as it is the most cost/effective way to obtain the needed cholesterol. Conclusions: The accumulation of CEs is produced by two possible approaches: (1) dysfunction of cholesterol absorption in the small intestine and/or (2) transported inside exosomes from cell to cell to promote proliferation. Full article

The high water demand of rice cultivation is mainly due to flood irrigation, which requires large volumes not only to meet evapotranspiration needs, but also due to losses from percolation, lateral seepage, and surface runoff. In addition to lowering water use efficiency, surface runoff may transport nutrients. This study aimed to calibrate and validate a daily water balance model to estimate surface runoff losses across three rice-growing seasons. During the first two seasons, different model components were calibrated by comparing simulated and observed water depths. In the final season, the calibrated model was validated using direct runoff measurements obtained from weirs and flowmeters. Results showed strong agreement between model estimates and direct measurements of water depth and surface runoff. Linear regression models showed good fit, with coefficients of determination (R²) above 0.80 for water depth and 0.79 for runoff. A validated daily water balance model, combined with periodic monitoring of water depth, proved to be a reliable tool for estimating surface runoff during the rice-growing season. Total runoff—from irrigation, rainfall, and final drainage—represented between 7.5% and 18% of the total water input. This approach offers a practical tool for improving irrigation water management and understanding runoff-driven nutrient transport. Full article

Time-domain (TD) diffuse reflectance can be modeled using diffusion theory (DT) to non-invasively estimate optical transport coefficients of biological media, which serve as markers of tissue physiology. We employ an optimized N-layer DT solver in cylindrical geometry to reconstruct optical coefficients of bilayered media from TD reflectance generated via Monte Carlo (MC) simulations. Optical properties for 384 bilayered tissue models representing human head or limb tissues were obtained from the literature at three near-infrared wavelengths. MC data were fit using the layered DT model to simultaneously recover transport coefficients in both layers. Bottom-layer absorption was recovered with errors under 0.02 cm⁻¹, and top-layer scattering was retrieved within 3 cm⁻¹ of input values. In contrast, recovered bottom-layer scattering had mean errors exceeding 50%. Total hemoglobin concentration and oxygen saturation were reconstructed for the bottom layer to within 10 μM and 5%, respectively. Extracted transport coefficients were significantly more accurate when obtained using layered DT compared to the conventional, semi-infinite DT model. Our results suggest using improved theoretical modeling to analyze TD reflectance analysis significantly improves recovery of deep-layer absorption. Full article