Transactions on Environment, Energy and Earth Sciences
Transactions on Environment, Energy and Earth Sciences (TEEES) aims to publish peer-reviewed proceedings from conferences on research and applications in the field of environment, energy and earth sciences. Major disciplines include: soil sciences, hydrology, oceanography, climatology, geology, geography, energy engineering (production, distribution and storage), renewable energy, sustainable development, natural resources management, environmental health, etc.
Sources, production methods, and applications of biochar
With the rapid depletion of resources like oil and coal, the world is actively seeking sustainable, efficient, and eco-friendly energy alternatives. Biochar, a versatile material, can be produced from diverse sources including agricultural residues, forestry waste, and urban organic matter. Moreover, biochar preparation methods such as pyrolysis, hydrothermal carbonization, and microwave-assisted techniques offer flexible production options. Concurrently, physical, chemical, and microbial modifications can enhance biochar’s properties, enabling it to address energy challenges, restore the environment, and promote agricultural development effectively.
Biochar; Preparation method; Modification method; Agricultural applications; Environmental remediation.
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Dynamic Study of Farmland Ecosystem Based on Lotka-Volterra Model with Human Intervention Factors
Addressing the ecosystem dynamics during forest-to-farmland conversion and human intervention impacts, this study establishes a multi-species biomass dynamic differential equation model based on the Lotka-Volterra framework. By incorporating seasonal periodic functions and agricultural management measures (e.g., herbicides, insecticides) as disturbance factors, combined with MATLAB numerical simulations, this study simulated biomass variation curves under different intervention conditions. The simulation results revealed several key dynamics: seasonal variations and agricultural cycles were found to significantly dictate crop growth rhythms, inducing a biomass fluctuation amplitude of approximately 30%. The application of herbicides provided a temporary boost to crop biomass accumulation, increasing the growth rate by 20%. Conversely, insecticides triggered a sharp decline in insect populations, raising mortality by 40%, which in turn produced cascading effects evidenced by a 3-to-5-month lag in the decline of bird populations. Predators at higher trophic levels, such as eagles, exhibited a dampened response to these interventions, showing a 60% reduction in sensitivity compared to lower trophic levels. These findings emphasize the necessity of considering trophic level differences and time lags in ecological impact assessments of agricultural interventions, offering valuable insights for sustainable land-use planning and ecosystem management in converted landscapes.
Lotka-Volterra model; Farmland ecosystem; Biomass dynamics; Seasonal disturbance; Human intervention.
Research on Strategy Optimization Based on Linear Programming and Conditional Value at Risk
Arable land resources are limited in mountainous areas. How to develop scientific planting strategies that maximize economic benefits while coping with market uncertainties is a key issue for sustainable agricultural development. This paper constructs a comprehensive model framework that extends from deterministic optimization to risk-quantified decision-making. First, a linear programming model aimed at maximizing total profit is established to devise basic planting strategies under ideal market conditions. Second, to address the inherent uncertainties in expected sales volume, yield per mu, planting costs, and sales prices, the Conditional Value at Risk (CVaR) method is introduced to build an optimization model designed to balance expected returns and downside risks. Finally, Spearman correlation analysis is used to quantify the substitutive and complementary relationships between crops, as well as the interrelationships among key economic variables; these correlation constraints are then incorporated into the CVaR model to better reflect real market dynamics. The model is solved using a genetic algorithm. Results show that, within the deterministic model, a strategy considering discounted product sales yields a total profit of 58.44 million yuan, which is significantly higher than in an unsalable scenario. Although introducing the risk-averse CVaR model adjusts the total profit to 32.80 million yuan, it markedly enhances the robustness of the strategy.
Arable Land Optimization; Linear Programming; Conditional Value at Risk; Genetic Algorithm; Correlation Analysis.
Study on Fluorescence Recognition of CrO₄²⁻ and Cr₂O₇²⁻ Based on Zn-MOF
In contemporary society, as industrialization and urbanization advance rapidly, the problem of chromium-containing wastewater discharge has grown increasingly conspicuous. Hexavalent chromium salts (CrO₄²⁻ and Cr₂O₇²⁻), as common heavy metal ions, pose a severe threat to both the environment and human health. Therefore, the progress of efficient and sensitive detection technologies is crucial for monitoring such environmental pollutants. In this study, 2,6-naphthalenedicarboxylic acid and 4,7-bis (4-pyridyl) benzothiadiazole were employed as ligands to synthesize a zinc-based metal-organic framework (MOF-1) through a solvothermal approach. The successful preparation of the MOF-1 was verified through powder X-ray diffraction (PXRD) analysis. MOF-1 exhibits specific recognition capabilities for CrO₄²⁻ and Cr₂O₇²⁻ through the mechanism of fluorescence quenching. Interference experiments, conducted using Cr₂O₇²⁻ as a representative anion, confirmed that MOF-1 can accurately identify Cr₂O₇²⁻ even in the presence of other coexisting anions, demonstrating excellent selectivity. Titration experiments revealed that the fluorescence intensity of MOF-1 diminishes as the concentrations of CrO₄²⁻ and Cr₂O₇²⁻ increase, showing a typical concentration-dependent quenching behavior. The quenching constants (Ksv) were determined to be 1.96×10⁴ M⁻¹ and 2.68×10⁴ M⁻¹ for CrO₄²⁻ and Cr₂O₇²⁻, respectively, indicating high sensitivity. This sensing mechanism is attributed to the significant spectral overlap between the ultraviolet absorption spectra of CrO₄²⁻/Cr₂O₇²⁻ and the excitation spectrum of MOF-1. The competitive absorption of excitation energy by these ions reduces the energy available for MOF-1, thereby decreasing its fluorescence intensity. The combination of high sensitivity and selectivity highlights the significant advantages of MOF-1 as a fluorescent sensing agent for hexavalent ion detection, providing reliable technical support for environmental monitoring and analytical testing applications.
Luminescence sensor; Metal-organic frameworks; CrO₄²⁻and Cr₂O₇²⁻.
Non-coding RNAs-Mediated Regulation of Tumor Innate Immunity and Its Clinical Application Prospects
Tumors severely threaten human health, and traditional therapies often struggle with immune escape and drug resistance. Non-coding RNAs (ncRNAs), which do not encode proteins, can regulate tumor innate immune cell functions and related signaling pathways, becoming a research focus. This study explores ncRNAs’ mechanisms in tumor innate immunity and clinical prospects. It first sorts out ncRNAs’ classification and characteristics, then analyzes their roles in remodeling immune cell functions (regulating immune cell differentiation, polarization, etc., to affect tumor progression) and regulating immune signaling pathways. It also elaborates that ncRNAs interact with cells/factors via exosomes in the tumor microenvironment to modulate immune cells, cytokines, and the extracellular matrix, noting ncRNAs’ potential as therapeutic targets and biomarkers. Unlike previous studies that mostly focused on a single type of ncRNA or a single mechanism, this article constructs a comprehensive regulatory network by analyzing the synergistic and antagonistic effects among miRNAs, lncRNAs, and circRNAs, providing a more systematic perspective for related research. Despite challenges, with advances in single-cell sequencing and nano-delivery, ncRNAs are expected to advance tumor innate immunity research and clinical translation.
Non-Coding RNA; Tumor Innate Immunity; Tumor Microenvironment; Biomarker.
Research on GPCR-Ligand Binding Affinity Prediction Models Based on GA-SVR and RF
G protein-coupled receptors (GPCRs) are a class of cell surface receptors regulating signal transduction and represent important drug targets. Traditional experimental methods face bottlenecks such as being time-consuming and costly. This study aims to construct high-precision prediction models for GPCR-ligand binding affinity. Utilizing support vector machines optimized by a genetic algorithm and random forest models, based on 933 sets of GPCR-ligand binding data, molecular fingerprints and protein sequence features were extracted. After normalization, the data was partitioned into training, test, and validation sets in an 8:1:1 ratio. Model performance was evaluated using five-fold cross-validation. Experimental results demonstrated that GA-SVR outperformed RF in terms of RMSE and MAE metrics, while RF performed better in R². GA-SVR achieved RMSE, R², MAE values of 0.3388±0.0115, 0.8378±0.0126, and 0.2137±0.0054, respectively. RF achieved RMSE, R2, MAE values of 0.4360±0.0192, 0.9478±0.0042, and 0.3172±0.0110, respectively. GA-SVR showed greate <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1025" DrawAspect="Content" ObjectID="_1835251665"> </o:OLEObject> </xml><![endif]-->r stability in error control, making it suitable for fine-grained prediction, while RF, due to its powerful nonlinear fitting capability, performed better in capturing overall trends. This study effectively overcomes the efficiency limitations of traditional experimental methods in drug screening, significantly enhances lead compound screening efficacy, and provides innovative solutions for advancing drug discovery and desig.
G protein-coupled receptors; Ligand binding affinity; Support Vector Regression; Genetic Algorithm; Random Forest.
A Multi-Modal Machine Learning Framework for Type 2 Diabetes Risk Stratification and Progression Time Prediction
The growing global burden of Type 2 Diabetes (T2D) urgently requires advanced predictive tools that go beyond traditional binary risk classification. This study presents an innovative machine learning framework that integrates multimodal data, including clinical parameters, lifestyle factors, Nuclear Magnetic Resonance (NMR) metabolomics data, and 12-lead Electrocardiogram (ECG) signals, to simultaneously predict T2D risk and disease progression time. This study employs a two-stage prediction framework: an AdaBoost classifier optimized by Optuna is used for risk stratification (AUROC=0.9468, F1=0.8857), combined with an Elastic Net regression model for progression time estimation (R²=0.7649, MSE=15.1535). This framework achieves superior predictive performance compared to single-source models by synergistically integrating complementary data modalities, guides clinical intervention timing through quantitative estimation of individualized progression time, and enhances model interpretability through rigorous feature importance analysis, demonstrating key advantages. This method, which combines high accuracy with clinical utility, fills a critical gap in current T2D prediction methodologies and provides a practical tool for personalized prevention strategies and optimized healthcare resource allocation.
Warwick Evans; Publishing; These keywords will also be used by the publisher to produce a keyword index.
Mechanism of Tea Drinking in Regulating Mental Health and Obesity
Tea provides notable health benefits through bioactive components, and various properties highlight tea’s potential as a functional drink for mental and physical health. However, the mechanism by which tea drinks regulate mental state and obesity remains to be further analyzed. In order to clarify this regulatory mechanism, this research investigates tea’s effects on mental health, particularly mood regulation, and physical health, focusing on lipid metabolism, anti-inflammatory responses, and bile acid metabolism. Tea consumption enhances cognitive functions, reduces depression risk via anti-inflammatory and neuroprotective effects, and mitigates obesity by promoting fat oxidation, suppressing inflammation, and modulating gut microbiota. Long-term green tea intake improves sleep quality, testosterone levels, and gray matter volume in middle-aged and elderly men, while various teas reduce fat accumulation in obese murine models through distinct pathways, AMPK activation, and IL-6 suppression. Future research should focus on mechanistic studies isolating tea components, and validating tea-based interventions for public health. This research reveals tea’s potential and effects as a cost-effective dietary beverage for mental and physical health management.
Tea; Mood; Obesity; Effects; Mechanisms.
Can Plants Talk? Unlocking the Power of Ultrasound in Plant Communication and Precise Irrigation
Plants in adverse conditions, such as drought, emit ultrasonic waves between 20 kHz and 150 kHz. Research indicates that plant cells have many mechanically sensitive channels that can detect sound vibrations, then cause physiological changes or gene expression responses. Because plants do not have a nervous system, we hypothesize that they utilize ultrasonic signals for inter-plant communication, particularly in regulating transpiration on their leaves. The findings are revealing: exposure to ultrasonic waves led to increased leaf surface temperatures compared to control conditions, which indicates a significant reduction in transpiration. Non-contact infrared thermal imaging was employed to monitor the leaf surface temperatures of hydrated potted plants under conditions without ultrasonic exposure and with 22 kHz and 30 kHz ultrasonic waves, proving that ultrasound directly affects leaf temperature and transpiration rates. Under 22 kHz ultrasound and 30 kHz ultrasound, leaf surface temperatures rose by an average of 1.8°C and 1.9°C, respectively, while they rose by 1.0°C in the control group. The results show that ultrasound exposure lowers the effectiveness of transpiration by about 40– 50%, which causes changes in the body that can be seen from the stomata. These results not only prove a new dimension of plant communication but also create opportunities for non- invasive plant health diagnostics and smart irrigation systems, transforming modern agriculture. This provides strong evidence that plants use ultrasonic signals to regulate transpiration; these findings have significant impact on precision farming and water conservation. Understanding the auditory language of plants is not just a research project but also a discovery that might change our interaction with the environment and propel environmentally friendly living.
ultrasonic wave, mechanically sensitive channel, regulate transpiration.
Study on Factors Influencing Fetal Y Chromosome Concentration Based on Beta Regression and Rank Correlation Analysis
This study conducted an association analysis and regression modeling of factors influencing fetal Y chromosome concentration in male fetuses, aiming to quantify the complex relationship between fetal Y chromosome concentration (proportional data) and physiological indicators such as maternal gestational age and BMI. First, the attached data underwent Shapiro-Wilk tests, confirming that all variables—including fetal Y chromosome concentration—failed to meet the assumption of Gaussian distribution. Consequently, the nonparametric Spearman rank correlation coefficient was selected for association analysis. The correlation heatmap revealed a positive correlation between Y chromosome concentration and gestational age (correlation coefficient: 0.188) and a negative correlation with maternal BMI (correlation coefficient: -0.155). Considering that Y chromosome concentration is a ratio-type variable within an interval, a beta regression model was established, incorporating nonlinear terms and mixed effects to enhance model accuracy. Comparing the beta regression model with various nonlinear regression models, the beta regression model demonstrated superior parameter significance and an MAE value of 0.026146, confirming strong correlation among model indicators. In terms of research significance, the established Beta regression model outperforms traditional nonlinear models in both parameter significance and predictive accuracy (MAE: 0.026146). This model systematically reveals the influence of various factors on Y chromosome concentration, holding significant value for clinically precise determination of optimal NIPT testing timing and personalized risk assessment.
Beta regression model; Spearman rank correlation coefficient; non-invasive prenatal testing.
Research on Pathways to Achieve the "Dual Carbon" Goals Driven by New Energy Vehicle Popularization Based on a Multi-Source Data Fusion Model
Amid growing global concerns over greenhouse gas emissions, the adoption of new energy vehicles (for short: NEVs) is a key pathway for China to achieve its "Dual Carbon" goals. Existing research examines NEVs from four main dimensions: life cycle emissions, policy mechanisms, regional impacts, and multi-source data integration. While NEVs have lower life cycle emissions than fuel vehicles, their benefits depend heavily on the power mix. Policy studies highlight the importance of subsidies and infrastructure, while regional analyses reveal spatial spillover effects and scenario-dependent emission reductions. This study employs multi-source data fusion, integrating atmospheric and power system data, to build high-precision carbon monitoring models. Using the Bass diffusion model, grey relational analysis, and consumer survey data, we analyze market trends and decision factors. A multi-objective optimization model, enhanced by particle swarm and genetic algorithms, evaluates policy scenarios under constraints of emissions, cost, and efficiency. Results show NEVs significantly reduce urban emissions, with policy-market synergy crucial for long-term growth. Findings emphasize the need to coordinate innovation, incentives, and public engagement to promote green mobility and inform adaptive, efficient policymaking for China’s low-carbon transition.
Carbon Emissions; New Energy Vehicles (NEVs); Bass Diffusion Model; Grey Relational Analysis (GRA); Policy Simulation.
Research on TOBRFV Infection in Summer Tomato Seed Production in the Wafangdian Area
This study aimed to assess the infection status of Tomato brown rugose fruit virus (TOBRFV) in tomato seed breeding in Wafangdian area during early and late summer. By comparing infection rates and cycle threshold (Ct) values from early and late summer seed samples, we found a significant increase in infection rates and lower Ct values at the end of the growing season. The study revealed that seeds harvested at the end of summer are at a higher risk of carrying TOBRFV, which could impact seed quality and facilitate virus spread in subsequent plantings. The findings underscore the importance of monitoring and implementing control measures targeted at the end of the growing season to mitigate the risk of TOBRFV infection in tomato seeds. The results also highlight the need for further research into the factors contributing to the increased virus concentration and infection rates over the growing season.
Tomato brown rugose fruit virus (TOBRFV); Seed infection; Wafangdian area; Summer seed breeding.
Construction of Lignin-based Aqueous Coacervates and Their Mechanisms for Enhancing Pesticide Targeted Delivery
This review systematically explores the construction strategies and functional mechanisms of lignin-based aqueous coacervates for targeted pesticide delivery. By integrating lignin valorization with coacervate technology, we analyze the structural characteristics of different lignin types, compare various coacervate construction methods, and elucidate targeting mechanisms including environmental responsiveness and passive accumulation. Key findings demonstrate that lignin-based coacervates significantly enhance pesticide stability, bioavailability, and environmental compatibility. Despite challenges in scalability and precise control, these systems hold great promise for advancing sustainable agriculture through intelligent delivery platforms.
Lignin; Aqueous coacervates; Pesticide delivery; Targeted; Sustainable agriculture.
Green Bonds and Corporate Innovation Efficiency
The impact of green bonds on corporate innovation efficiency is a significant practical issue. This study utilizes data from listed companies between 2007 and 2024 to examine the effects of green bond issuance on corporate innovation efficiency. Findings reveal that green bond issuance exerts a significant positive influence on corporate innovation efficiency, a conclusion that remains robust after undergoing a series of stability tests. Heterogeneity analysis indicates that the effect is more pronounced among non-state-owned enterprises, heavily polluting industries, eastern regions, and large-scale enterprises. Mechanism analysis indicates that green bonds enhance corporate innovation efficiency by alleviating financing constraints, improving environmental disclosure quality, and elevating executives' green awareness. These findings align with the logic that green bonds provide stable, targeted funding for corporate innovation activities, thereby stimulating their innovative vitality. This conclusion holds significant implications for guiding green finance to empower corporate technological innovation and enhance new-quality productive forces.
Green Bonds, Innovation Efficiency; Financing Constraints; Environmental Disclosure; Green Awareness Among Executives.
From Regulatory Networks to Precision Breeding: Integrating miRNA and Gene Editing for Trait Improvement in Forest Trees
In recent years, with the development of high-throughput sequencing and molecular biology techniques, researchers have identified a large number of miRNA sequences in plants, which play crucial roles in developmental regulation, signal transduction, stress response, and maintenance of nutrient homeostasis. Compared with crops, research on miRNA function in forest trees started later due to their long life cycle and complex genome. However, they have unique regulatory patterns in adaptive evolution, morphological differentiation and stress resistance formation. This article systematically summarizes the functions of plant miRNAs in developmental and hormonal signaling pathways, and in abiotic and biotic stresses, focusing on their mechanisms of action within the forest tree gene regulatory network. Meanwhile, combining the latest advancements in gene editing and viral vector technologies, this study analyzes the application prospects of miRNA-mediated gene silencing in molecular design breeding, and proposes a strategic framework for the precise improvement of complex traits in forest trees by integrating miRNA regulatory modules with CRISPR/Cas technology, providing new theoretical and technical support for molecular breeding of forest trees.
Gene editing; miRNA; target gene regulation; abiotic stress regulation.
Advanced Nanomaterials in Organic Photovoltaics
Organic photovoltaics (OPVs) have emerged as a sustainable alternative for electrical energy generation. The integration of nanomaterials has been instrumental in advancing the performance and functionality of OPVs, marking a significant evolution in solar energy technologies. This review aims to analyze the fundamental occupation of nanomaterials in the OPV process and give clarified categories with comprehensive function, and the potential risks in future development are pointed out as well. This review also discusses the basic OPV process, the role of nanomaterials in OPV, the specific nanomaterials of organic and inorganic with their distinguished function, the fabrication of nanomaterials in OPV, and possible difficulties in the nanomaterial’s development. By comparing various types of nanomaterials, the review aims to demonstrate that all nanomaterials under consideration perform effectively in enhancing the capabilities of organic solar cells. Furthermore, it will be shown that the development of these nanomaterial composites comes with both distinct advantages and limitations. In conclusion, nanomaterials have a great capability of competing with other materials, they can better improve the power conversion efficiency in OPV, and the environmental effects are also enhanced.
Organic Photovoltaics; Nanomaterials; Nanocomposites.
The Potential and Deployment Barriers of Carbon Capture and Storage Technology
Carbon capture and storage (CCS) is a pivotal technology for combating climate change and achieving carbon neutrality, yet its large-scale deployment faces multiple challenges. A systematic analysis of the role, development status, and constraints of CCS technology in global emission reduction reveals an unevenly developed technology chain, presenting systemic bottlenecks such as high energy consumption, elevated costs, insufficient infrastructure, a lack of policy incentives, and low social acceptance. Addressing these barriers necessitates a synergistic approach that integrates technological innovation, policy incentives, and social consensus. Key measures include developing low-energy consumption capture technologies and novel storage solutions to improve economic viability, enhancing commercial feasibility through carbon pricing mechanisms and tax credits, and strengthening storage safety monitoring with transparent data disclosure and community benefit-sharing to alleviate social concerns. The analysis concludes that substantial support from CCS technology for the global carbon neutrality goal is contingent upon holistic advancement across technology, policy, and societal engagement.
Carbon capture and storage; carbon neutrality; policy incentives.
Artificial Intelligence in Carbon Capture and Storage Technological Advances and Future Challenges
The escalating climate crisis necessitates the rapid development of innovative carbon reduction technologies, among which Carbon Capture and Storage (CCS) is considered a pivotal solution. However, the widespread deployment of conventional CCS is hindered by challenges related to efficiency, cost, and risk management. This review explores the transformative role of Artificial Intelligence (AI), particularly machine learning and deep learning, in overcoming these challenges. By analyzing recent literature, we demonstrate AI's significant potential in optimizing CCS processes, from accelerating the discovery of novel capture materials to enhancing the safety and efficiency of storage site monitoring and enabling intelligent system management. Despite this promise, critical challenges remain, including data scarcity, the carbon footprint of AI model training, and the need for robust regulatory frameworks. This study concludes that interdisciplinary collaboration and a focus on developing dynamic, data-driven approaches are crucial for realizing the full potential of AI-CCS integration, paving the way for its effective contribution to global decarbonization goals.
Carbon capture and storage; artificial intelligence; machine learning.
Driving Green Port Development with Carbon Capture and Storage Applications and Prospects
As global warming intensifies and extreme weather events occur more frequently, reducing greenhouse gas emissions has become a shared challenge for the international community. Against the urgent global backdrop of climate change mitigation, Carbon Capture and Storage (CCS) technology has emerged as a critical component of port decarbonization strategies. This paper comprehensively analyzes its multi-dimensional benefits—environmental, economic, and social—while critically examining the challenges related to high costs, potential environmental risks, and public acceptance barriers. In response, targeted optimization strategies encompassing technological advancement and policy coordination are proposed. The effective implementation of these strategies is essential for unlocking the full potential of CCS in the maritime sector. The study concludes that CCS technology holds significant potential for enabling deep decarbonization in ports, notwithstanding existing hurdles. To inform decision-making by port authorities and stakeholders, future research should pursue techno-economic assessments and explore the integration of CCS with smart port systems.
Carbon capture and storage; green port; techno-economic assessment.
Preface: 5th International Conference on Green Environmental Materials and Food Engineering (GEMFE 2026)
We gratefully acknowledge the presence of all participants on the 2026 5th International Conference on Green Environmental Materials and Food Engineering (GEMFE 2026), which was successfully held during January 24-25, 2026 in Rome, Italy. GEMFE 2026 aimed to provide a platform for experts, scholars, engineers, technicians, and R&D personnel to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen academic research, and promote cooperation in the industrialization of academic results. GEMFE 2026 will bring together innovative academics and industrial experts in the fields of environmental engineering, energy technology, green materials, chemical science, agricultural technology, and food engineering to a common forum. Many participants from academic, high-education institutes, and other organizations took part in the conference. The conference model was divided into two sessions, including oral presentations and keynote speeches. In the first part, some scholars, whose submissions were selected as the excellent papers, were given 10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-40 minutes to hold their speeches. I would like to express special gratitude to members of the conference committee and organizers of the conference. I would also like to thank the reviewers for their valuable time and advice which helped in improving the quality of the papers selected for presentation at the conference and for publication in the proceedings. GEMFE 2026 Conference Organizing Chair Rome, Italy