Article
Psychology, Multidisciplinary
Shuang Zheng, Xiaomei Hu
Summary: The study collected tuberculosis data from a city from 2017 to 2019 and constructed a prediction model. Through comparative analysis, it was found that the early warning method based on ANN in deep learning performs better in improving the early warning capabilities of public health emergencies.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Kieu Lan Phuong Nguyen, Yen Hsun Chuang, Ruey-Fang Yu, Ho-Wen Chen
Summary: The study proposed using an artificial neural network to build an early-warning model, utilizing records of past episodes with high concentrations of airborne particulate matter for modeling. Among the developed models, the one considering both environmental media and pollution sources was most accurate in predicting the severity of estuarian dust events, providing useful information for preemptive disaster mitigation measures to be implemented.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Weida Yin
Summary: Artificial intelligence technology is a new idea and method to solve the problem of public health security, providing high efficiency, precision, and automation. A monitoring and early warning method based on an artificial intelligence system is proposed, using feature extraction and selection, machine learning and deep learning technologies to analyze various data and identify potential risks for public health. This method can help detect and respond to public health emergencies in a timely and effective manner, preventing the spread of diseases and reducing the impact on people's lives and health.
Article
Multidisciplinary Sciences
Kiana Peiro Ahmady Langeroudy, Parsa Kharazi Esfahani, Mohammad Reza Khorsand Movaghar
Summary: Oil viscosity is crucial in petroleum engineering, and experimental methods and compositional methods can accurately estimate it. However, experimental data is difficult to obtain, so there is a need for convenient and fast methods to predict viscosity. This study uses machine learning methods (XGBoost, CatBoost, and GradientBoosting) based on gradient boosting decision tree to reduce the prediction error of viscosity by considering dissolved gas content, temperature, pressure, and API gravity. XGBoost outperforms other methods with higher precision and lower error, showing the effectiveness of the approach.
SCIENTIFIC REPORTS
(2023)
Article
Chemistry, Multidisciplinary
Fabio Tarricone, Antonio Brunetti, Domenico Buongiorno, Nicola Altini, Vitoantonio Bevilacqua, Antonio Del Vecchio, Flavia Petrillo
Summary: Neonatal sepsis is a critical pathology that particularly affects premature and low birth weight infants, with an incidence varying between 1% and 40% depending on the onset of the disease. Prompt diagnostic and therapeutic interventions can reduce mortality rates, and a new method is proposed to improve diagnostic accuracy and reduce false positives.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Umar Farooq, Muhammad Wasif Shabir, Muhammad Awais Javed, Muhammad Imran
Summary: This paper presents two energy prediction techniques for fog nodes, based on Recursive Least Square and Artificial Neural Network, to enable intelligent energy-aware task offloading. Simulation results show that the ANN-based technique has up to 20% less root mean square error compared to the RLS-based technique.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Junjie Miao, Bingyu Li, Xuhao Du, Haobin Wang
Summary: A multi-information fusion fire prediction model based on back propagation neural network (BPNN) and fuzzy set theory is proposed in this study. The BPNN model is trained using existing data, and the artificial fish swarm algorithm (AFSA) is used for optimization, which improves the prediction accuracy. The fuzzy set theory is applied to fuse the predicted fire probability for optimal fire prevention and control decision-making.
Article
Chemistry, Multidisciplinary
Wen-Zhen Fang, Tongzhao Xiong, On Shun Pak, Lailai Zhu
Summary: Automated manipulation of small particles using external fields is an emerging technique widely used in different areas. Existing reduced-order physical models for particle manipulation are limited to highly idealized settings and do not account for complex nonlinear processes. In this study, the authors propose a data-driven architecture based on hydrodynamic manipulation and artificial neural networks to achieve precise and flexible control of particles in microfluidic systems. They successfully demonstrate various advanced manipulations, revolutionizing automated particle manipulation and showcasing the potential of data-driven approaches in improving microfluidic technologies.
Article
Computer Science, Information Systems
Tarun Ganesh Palla, Shahab Tayeb
Summary: The research proposed a novel approach using machine learning algorithm to detect Mirai malware, which achieved satisfactory performance in the experiment and conducted comparative analysis with random forest model.
Article
Geosciences, Multidisciplinary
Maryam Sadi, Abbas Shahrabadi
Summary: In this study, experimental measurements and modeling investigations were conducted to predict crude oil viscosity under various conditions. Three advanced intelligent models were developed to estimate saturated and under-saturated oil viscosity using input parameters such as crude oil API, solution gas oil ratio, bubble point pressure, molecular weight, specific gravity of C12+ fraction, mole percent of C?11components, temperature, and pressure. The results showed that the Gaussian process regression model had the best performance in viscosity prediction, with average absolute relative errors of 0.18% and 0.07% for saturated and under-saturated oil, respectively. The findings of the Leverage technique and sensitivity analysis further supported the reliability and importance of the study.
NATURAL RESOURCES RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Xiaolong Wu, Honggui Han, Junfei Qiao
Summary: This study proposed a data-driven intelligent warning method for predicting membrane fouling events in MBR. The method utilized an RFNN model, multistep prediction strategy, and SCE method to improve the accuracy of membrane permeability prediction and evaluate pollution levels in MBR for warning purposes. Experimental results confirmed the effectiveness and efficiency of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Environmental Sciences
Daoye Zhu, Yi Yang, Fuhu Ren, Shunji Murai, Chengqi Cheng, Min Huang
Summary: The study proposes a novel intelligent spatiotemporal grid model for integration analysis of multi-type geospatial data, including a seismic grid sample model and a spatiotemporal grid model based on group convolution neural network. The models show better compatibility and effectiveness in integrating multiple types of geospatial information for deep learning analysis.
Article
Green & Sustainable Science & Technology
Sujarwo, Aditya Nugraha Putra, Raden Arief Setyawan, Heitor Mancini Teixeira, Uma Khumairoh
Summary: The increasing population in Indonesia poses a challenge to rice production, as rice fields are being converted for other land uses. This study uses data analysis and simulation to predict future rice status, revealing a decrease in rice field area due to conversion to settlements and buildings. The predicted increase in rice demand and decrease in production may lead to a reduction in rice surplus and potential decrease in rice supply to other areas.
Article
Environmental Sciences
Yufei Song, Wen Fan, Ningyu Yu, Yanbo Cao, Chengcheng Jiang, Xiaoqing Chai, Yalin Nan
Summary: This study proposes a new method for calculating the spatiotemporal probability of rainfall-induced landslides based on a Bayesian approach and develops a probabilistic-based early warning model at the regional scale. The results show that the proposed model has higher warning accuracy and economic benefits compared to the conventional model.
Article
Computer Science, Artificial Intelligence
Fan Bailin, Zhang Yi, Chen Ye, Meng Lingbei
Summary: Based on the dynamic model of the intelligent firefighting vehicle, a linear 2-DOF lateral dynamic model and a preview error model are established. A Radial Basis Function neural network sliding mode controller is designed to solve the problems of non-linearity, time-varying parameters, output chattering, and poor robustness. Simulation results show that the controller has high accuracy in tracking the desired path and has good robustness to speed changes of the vehicle.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Hao Zhang, Qinhuai Tan, Qunxing Huang, Kaiyi Wang, Xin Tu, Xiaotong Zhao, Chunfei Wu, Jianhua Yan, Xiaodong Li
Summary: In this study, the conversion of CO2 into O-2-free CO using plasma and biochar was investigated. The presence of both plasma and biochar significantly enhanced the CO2 conversion. The effects of biochar source, pyrolysis temperature, and gas-solid reaction patterns were evaluated. The study revealed that the plasmatron CO2 + C process achieved a high CO2 conversion rate.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Bryony Ashford, Chee-Kok Poh, Kostya (Ken) Ostrikov, Luwei Chen, Xin Tu
Summary: Anthropogenic greenhouse gas emissions have caused changes to the Earth's climate, leading to more frequent and intense weather events. Developing carbon-neutral CO2 conversion processes using plasma-catalysis powered by renewable energy is an effective approach towards achieving a circular economy. This study focuses on the conversion of CO2 into ethane (C2H6) using a non-thermal plasma-catalytic process in a dielectric barrier discharge reactor. The results indicate that the Ru catalyst shows the highest selectivity and energy efficiency for C2H6 production, making it a promising method for carbon-neutral gas conversion and utilization of CO2.
JOURNAL OF CO2 UTILIZATION
(2022)
Article
Nanoscience & Nanotechnology
Yanling Yang, Li Zhang, Hongquan Guo, Zhenfa Ding, Weitao Wang, Jianhui Li, Liujiang Zhou, Xin Tu, Yongfu Qiu, Gui Chen, Yifei Sun
Summary: This study presents a convenient method of redispersing catalytically inert PdO nanoparticles into reactive PdOx nanoclusters anchored on a Ce-doped LaFeO3 parent. It is found that the gas atmosphere plays a crucial role in the redispersion of PdO nanoparticles, and the presence of Ce ions helps stabilize the PdOx species and extend the catalyst's lifetime. This study provides valuable insights for the design of highly performing supported catalysts for methane oxidation.
ACS APPLIED MATERIALS & INTERFACES
(2022)
Article
Chemistry, Physical
Yuxuan Zeng, Guoxing Chen, Qianyun Bai, Li Wang, Renbing Wu, Xin Tu
Summary: This study investigates the plasma-enhanced catalytic biogas reforming process for hydrogen-rich syngas production using a Ni-K/Al2O3 catalyst in a tabular dielectric barrier discharge non-thermal plasma reactor. Different reaction modes, including plasma catalysis, plasma alone, and catalysis alone, are compared to understand the synergy at elevated temperatures. The combination of Ni-K/Al2O3 and plasma shows temperature-dependent and varied synergistic effects. The results demonstrate that the plasma catalysis achieves the maximum conversions of CH4 and CO2 at 160 degrees C, while increasing the temperature enhances the H2/CO ratio.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Chemistry, Multidisciplinary
Yaolin Wang, Wenjie Yang, Shanshan Xu, Shufang Zhao, Guoxing Chen, Anke Weidenkaff, Christopher Hardacre, Xiaolei Fan, Jun Huang, Xin Tu
Summary: Plasma catalysis holds promise for decentralized NH3 synthesis using renewable energy, with a bespoke design of supported Ni catalysts showing potential for efficient NH3 production. By depositing Ni active sites on the surface of mesoporous MCM-41, it enhances plasma-catalyst interactions and shields NH3 from decomposition, driving the reaction forward effectively. This demonstrates the importance of rational catalyst design for improving plasma-catalytic processes.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
(2022)
Article
Chemistry, Multidisciplinary
Danhua Mei, Shiyun Liu, Jale Yanik, Gartzen Lopez, Martin Olazar, Zhi Fang, Xin Tu
Summary: This study proposes a hybrid plasma-catalytic system for steam reforming of tar compounds over honeycomb-based catalysts in a gliding arc discharge (GAD) reactor. The presence of Ni/γ-Al2O3 gives the best performance with high conversions of toluene and naphthalene, and yields of H2 and CO while inhibiting the formation of byproducts. Characterization of the used catalyst shows strong carbon resistance and excellent stability of the honeycomb material coated with Ni/γ-Al2O3.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2022)
Article
Engineering, Environmental
Yaolin Wang, Yanzhen Chen, Jonathan Harding, Hongyuan He, Annemie Bogaerts, Xin Tu
Summary: A promising plasma process for the single-step conversion of methane and carbon dioxide into liquid fuels and chemicals at ambient pressure and room temperature is reported. The distribution of liquid products can be tailored by tuning the reaction conditions.
CHEMICAL ENGINEERING JOURNAL
(2022)
Article
Engineering, Environmental
Li Wang, Yuezhao Wang, Linhui Fan, Hongli Xu, Bowen Liu, Jiaren Zhang, Yimin Zhu, Xin Tu
Summary: In this study, the plasma-catalytic conversion of CH4 and CO2 into high-value alcohols, with methanol as the main product, was achieved using Cu-based catalysts. By controlling the support material, calcination temperature, and copper loading, the selectivity of alcohols was significantly improved. The results provide valuable insights for designing efficient catalysts to tune the production of alcohols through the single-step plasma-catalytic conversion of CH4 and CO2.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Engineering, Environmental
Bin Xu, Jianjun Xie, Nantao Wang, Yanqin Huang, Huacai Liu, Xiuli Yin, Chuangzhi Wu, Xin Tu
Summary: Steam reforming of toluene was conducted in a DBD plasma reactor with Ni/γ-Al2O3 catalysts. The study investigated the effects of reaction temperature, catalyst calcination temperature, and permittivity of packing materials on the reaction performance and synergistic effect of plasma catalysis. The results showed that toluene conversion initially decreased and then increased with temperature, achieving a high conversion rate of 87.1% at 450°C. Catalysts prepared at lower calcination temperatures or with higher permittivity packing materials exhibited better performance due to larger Ni surface area and higher surface discharge. The study highlighted the potential of this approach for sustainable hydrogen production.
CHEMICAL ENGINEERING JOURNAL
(2023)
Review
Chemistry, Multidisciplinary
Xin Ling, Zhenghui Chen, Bo Zhao, Hua Pan, Jun Chen, Zeyu Wang, Xuming Zhang, Zhiping Ye
Summary: The reduction ability of NO by γ-MnO2 catalysts and the oxidation performance of 1,2-dichloroethane were investigated. The results showed that γ-MnO2-3 exhibited the highest catalytic activity with 63.5% conversion of NOx at 250°C and 80% conversion of DCE at 338°C. The active phase of γ-MnO2-3 was tetragonal γ-MnO2 with the selectively exposed plane of (211), and it had more low valence Mn active sites, higher surface adsorbed oxygen (Oads) /lattice oxygen (Olatt) ratio, and lower reduction temperature in H2-TPR profiles compared to other catalysts.
ARABIAN JOURNAL OF CHEMISTRY
(2023)
Article
Chemistry, Multidisciplinary
Guangyi Zhang, Gui Chen, Haomin Huang, Yexia Qin, Mingli Fu, Xin Tu, Daiqi Ye, Junliang Wu
Summary: In this study, a coaxial DBD reactor packed with gamma-MnO2 and CeO2 was used for methanol oxidation. CeO2 showed better performance with higher methanol conversion and CO2 selectivity compared to gamma-MnO2. Catalyst characterization revealed that CeO2 had more active oxygen species and adsorbed more methanol, resulting in higher catalytic activity. In addition, CeO2 produced more reactive oxygen species from ozone decomposition and accumulated less intermediate formate during methanol oxidation. Overall, CeO2 was found to be a more effective catalyst than gamma-MnO2 in the plasma catalysis system for methanol oxidation.
Article
Chemistry, Multidisciplinary
Lina Liu, Jing Dai, Sonali Das, Yaolin Wang, Han Yu, Shibo Xi, Zhikun Zhang, Xin Tu
Summary: A hybrid DBD plasma-catalytic system was developed for the low-temperature CO2 reforming of toluene, where the Ni4Fe1-R catalyst exhibited the highest activity and stability. The plasma-catalytic system showed promising results in promoting the CRT reaction by generating synergy between DBD plasma and the catalyst. In situ FTIR spectroscopy and comprehensive catalyst characterization were used to elucidate the reaction mechanism and plasma-catalyst interfacial effect.
Article
Engineering, Environmental
Xiaomai Chen, Xuefeng Shi, Peirong Chen, Bowen Liu, Meiyin Liu, Longwen Chen, Daiqi Ye, Xin Tu, Wei Fan, Junliang Wu
Summary: In this study, Pd nanoparticles confined within silicalite-1 zeolites (Pd@S-1) were demonstrated to be highly active and stable catalysts for methane oxidation. The introduction of Ce further improved the activity by promoting the formation of oxygen vacancies and inhibiting the transformation of the active PdO phase. The bimetallic PdCe0.4@S-1 catalyst showed exceptional outstanding activity and durability in complete methane oxidation, even in the presence of water vapor.
ACS ENVIRONMENTAL AU
(2023)
Review
Engineering, Environmental
Guoxing Chen, Xiao Yu, Kostya (Ken) Ostrikov, Bowen Liu, Jonathan Harding, Gert Homm, Heng Guo, Stephan Andreas Schunk, Ying Zhou, Xin Tu, Anke Weidenkaff
Summary: This article critically examines recent advances in methane pyrolysis, highlighting efforts to bridge the gap between laboratory research and industrial applications, and discusses opportunities and challenges for translation research towards commercial production of clean hydrogen.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Engineering, Chemical
Yuxuan Zeng, Guoxing Chen, Bowen Liu, Hao Zhang, Xin Tu
Summary: In this study, the hydrogenation of CO2 over M/SiO2 and M/Al2O3 catalysts was investigated at different temperatures. The results showed that the coupling of catalysts with plasma demonstrated better reaction efficiency than thermal catalysis and plasma alone modes.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Environmental Sciences
Toshimi Nakajima, Mao Kuragano, Makoto Yamada, Ryo Sugimoto
Summary: This study compared the contribution of submarine groundwater discharge (SGD) to river nutrient budgets at nearshore and embayment scales, and found that SGD-derived nutrients become more important at larger spatial scales.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Fan Liu, Lei Zhang, Chongyang Zhang, Ziguang Chen, Jingguang Li
Summary: NO2 emissions from wall-mounted gas stoves used for household heating have become a significant source of indoor pollution in Chinese urban areas. The high indoor concentration of NO2 poses potential health risks to residents. It is urgently necessary to establish relevant regulations and implement emission reduction technologies to reduce NO2 emissions from wall-mounted gas stoves.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Letter
Environmental Sciences
Hans Peter H. Arp, Raoul Wolf, Sarah E. Hale, Sivani Baskaran, Juliane Gluege, Martin Scheringer, Xenia Trier, Ian T. Cousins, Harrie Timmer, Roberta Hofman-Caris, Anna Lennquist, Andre D. Bannink, Gerard J. Stroomberg, Rosa M. A. Sjerps, Rosa Montes, Rosario Rodil, Jose Benito Quintana, Daniel Zahn, Herve Gallard, Tobias Mohr, Ivo Schliebner, Michael Neumann
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Philomina Onyedikachi Peter, Binessi Edouard Ifon, Francois Nkinahamira, Kayode Hassan Lasisi, Jiangwei Li, Anyi Hu, Chang-Ping Yu
Summary: This study investigates the relationship between dissolved organic matter (DOM) and Rare Earth Elements (REEs) in sediments from Yundang Lagoon, China. The results show four distinct fluorescent components, with protein-like substances being the most prevalent. Additionally, the total fluorescence intensity and LREE concentrations exhibit a synchronized increase from Outer to Inner to Songbai Lake core sediments. The findings demonstrate a strong correlation between DOM content and pollution levels.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Surya Gupta, Pasquale Borrelli, Panos Panagos, Christine Alewell
Summary: The objective of this study is to incorporate soil hydraulic properties into the erodibility factor (K) of USLE-type models. By modifying and improving the existing equations for soil texture and permeability, the study successfully included information on saturated hydraulic conductivity (Ksat) into the calculation of K factor. Using the Random Forest machine learning algorithm, two independent K factor maps with different spatial resolutions were generated. The results show that the decrease in K factor values has a positive impact on the modeling of soil erosion rates.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Jesmin Akter, Wendy J. M. Smith, Yawen Liu, Ilho Kim, Stuart L. Simpson, Phong Thai, Asja Korajkic, Warish Ahmed
Summary: The choice of workflow in wastewater surveillance has a significant impact on SARS-CoV-2 concentrations, while having minimal effects on HF183 and no effect on HAdV 40/41 concentrations. Certain components in the workflow can be interchangeable, but factors such as buffer type, chloroform, and homogenization speed can affect the recovery of viruses and bacteria.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Yu Luo, Xueting Yang, Diwei Wang, Hongmei Xu, Hongai Zhang, Shasha Huang, Qiyuan Wang, Ningning Zhang, Junji Cao, Zhenxing Shen
Summary: Atmospheric PM2.5, which can generate reactive oxygen species (ROS), is associated with cardiorespiratory morbidity and mortality. The study found that both the mass concentration of PM2.5 and the DTT activity were higher during the heating season than during the nonheating season. Combustion sources were the primary contributors to DTT activity during the heating season, while secondary formation dominated during the nonheating season. The study also revealed that biomass burning had the highest inherent oxidation potential among all sources investigated.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Erin L. Murphy, Leah R. Gerber, Chelsea M. Rochman, Beth Polidoro
Summary: Plastic pollution has devastating consequences for marine organisms. This study uses a trait-based framework to develop a vulnerability index for marine mammals, seabirds, and sea turtles in Hawai'i. The index ranks 63 study species based on their vulnerability to macroplastic pollution, providing valuable information for species monitoring and management priorities.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Kenji Maurice, Amelia Bourceret, Sami Youssef, Stephane Boivin, Liam Laurent-Webb, Coraline Damasio, Hassan Boukcim, Marc-Andre Selosse, Marc Ducousso
Summary: Growing pressure from climate change and agricultural land use is destabilizing soil microbial community interactions. Little is known about microbial community resistance and adaptation to disturbances, hindering our understanding of recovery latency and implications for ecosystem functioning. This study found that anthropic disturbance and natural disturbance have different effects on the topology and stability of soil microbial networks.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Yunhao Li, Yali Feng, Haoran Li, Yisong Yao, Chenglong Xu, Jinrong Ju, Ruiyu Ma, Haoyu Wang, Shiwei Jiang
Summary: Deep-sea mining poses a serious threat to marine ecosystems and human health by disturbing sediment and transmitting metal ions through the food chain. This study developed a new regenerative adsorption material, OMN@SA, which effectively removes metal ions. The adsorption mechanism and performance of the material for metal ion fixation were investigated.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Antonio Medici, Margherita Lavorgna, Marina Isidori, Chiara Russo, Elena Orlo, Giovanni Luongo, Giovanni Di Fabio, Armando Zarrelli
Summary: Valsartan, a widely used antihypertensive drug, has been detected in high concentrations in surface waters due to its unchanged excretion and incomplete degradation in wastewater treatment plants. This study investigated the degradation of valsartan and identified 14 degradation byproducts. The acute and chronic toxicity of these byproducts were evaluated in key organisms in the freshwater trophic chain.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Jiang Lin, Lianbao Chi, Qing Yuan, Busu Li, Mingbao Feng
Summary: This study investigated the photodegradation behavior and product formation of two representative pharmaceuticals in simulated estuary water. The study found that the formed transformation products of these pharmaceuticals have potential toxicity on marine organisms, including oxidative stress and damage to cellular components.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Hua Fang, Dongdong Jiang, Ye He, Siyi Wu, Yuehong Li, Ziqi Zhang, Haoting Chen, Zixin Zheng, Yan Sun, Wenxiang Wang
Summary: This study revealed that exposure to lower levels of air pollutants led to decreased pregnancy rates, with PM10, NO2, SO2, and CO emerging as the four most prominent pollutants. Individuals aged 35 and above exhibited heightened susceptibility to pollutants.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Ali Shaan Manzoor Ghumman, Rashid Shamsuddin, Amin Abbasi, Mohaira Ahmad, Yoshiaki Yoshida, Abdul Sami, Hamad Almohamadi
Summary: In this study, inverse vulcanized polysulfides (IVP) were synthesized by reacting molten sulfur with 4-vinyl benzyl chloride, and then functionalized using N-methyl D-glucamine (NMDG). The functionalized IVP showed a high mercury adsorption capacity and a machine learning model was developed to predict the amount of mercury removed. Furthermore, the functionalized IVP can be regenerated and reused, providing a sustainable and cost-effective adsorbent.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
Article
Environmental Sciences
Rita Bonfiglio, Renata Sisto, Stefano Casciardi, Valeria Palumbo, Maria Paola Scioli, Erica Giacobbi, Francesca Servadei, Gerry Melino, Alessandro Mauriello, Manuel Scimeca
Summary: This study investigated the presence of aluminum in human colon cancer samples and its potential association with biological processes involved in cancer progression. Aluminum was found in tumor areas of 24% of patients and was associated with epithelial to mesenchymal transition (EMT) and cell death. Additional analyses revealed higher tumor mutational burden and mutations in genes related to EMT and apoptosis in aluminum-positive colon cancers. Understanding the molecular mechanisms of aluminum toxicity may improve strategies for the management of colon cancer patients.
SCIENCE OF THE TOTAL ENVIRONMENT
(2024)
