Zainab Tahir, Muhammad Haseeb, Syed Amer Mahmood
et al.
Abstract This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies. Landsat images from various sensors (TM, OLI) were employed for supervised classification, attaining high accuracy (> 90%). Historical LULC changes from 1994 to 2024 were analyzed, revealing significant transformations in Lahore. The build-up area expanded by 359.8 km², indicating rapid urbanization, while vegetation cover decreased by 198.7 km² and barren lands by 158.5 km². Water bodies remained relatively stable during this period. Future LULC trends were projected for 2034 and 2044 using the CA-Markov hybrid model (CA-MHM), which achieved a high prediction accuracy with a kappa coefficient of 0.92. The research indicated significant urban growth at the expense of vegetation and barren land. Future forecasts suggest ongoing urbanization, underscoring the necessity for sustainable land management techniques. This research is a significant framework for urban planners, providing insights that combine development with ecological conservation. The results highlight the necessity of incorporating predictive models into urban policy to promote sustainable development and environmental preservation in quickly changing areas such as Lahore.
In recent years, the increase in people’s demand for energy has led to the development of secondary batteries. Because of its high theoretical capacity and low electrochemical potential, lithium metal has gradually become the preferred negative electrode material for high-energy-density secondary batteries and has great application prospects in the field of energy storage technology. However, the practical application of lithium metal anodes faces major challenges mainly because of the inevitable formation of lithium dendrites and dead lithium during the charge–discharge cycle. These problems considerably reduce the Coulomb efficiency and service life of lithium metal batteries and constitute a substantial obstacle to the development and wide application of lithium metal batteries. Lithium dendrites are tree-like structures formed by uneven lithium deposition during the charging of lithium metal. These dendrites can penetrate the diaphragm and reach the cathode, causing a short circuit that can lead to catastrophic battery failure. Dead lithium refers to lithium that is separated from the anode during the discharging of a lithium battery and no longer participates in subsequent electrochemical reactions. The accumulation of dead lithium reduces the inventory of active lithium, causing battery capacity and efficiency to decline over time. Addressing these challenges requires an in-depth understanding of the formation mechanisms of lithium dendrites and dead lithium and their influencing factors. This study focuses on analyzing these mechanisms and influencing factors from the perspective of the phase field, which is a powerful computational method to simulate microstructure evolution, providing insights into the complex dynamics of lithium deposition and the conditions and influencing factors for the formation of lithium dendrites and dead Lithium. The latest research progress on the inhibition of dead lithium by temperature, pressure, diaphragm, bubble, and high active electrolyte was reviewed. First, the influence of temperature and pressure on the formation of dead lithium and the effect of two coupling fields on dead lithium are discussed. Second, starting from the diaphragm and electrolyte, the results of researchers in recent years are reviewed. For example, selecting a diaphragm with the appropriate pore size can promote the uniform deposition of lithium, better prevent the penetration of dendrites, and promote the resurrection of dead lithium. The highly active electrolyte can enhance the smooth deposition of lithium and inhibit the formation of dead lithium. These factors can regulate the deposition form of lithium to a certain extent and slow down or avoid the formation of lithium dendrites and dead lithium. By optimizing these factors, researchers can better control the deposition morphology of lithium, alleviating or even avoiding the formation of lithium dendrites and dead lithium. The phase field method is used to determine how the formation of dead lithium affects the overall life of the battery. The phase field is also used to simulate the long-term behavior of lithium metal anodes to predict the battery life under various operating conditions. Finally, this paper discusses and summarizes the shortcomings of the existing phase field method in the study of the radical elimination of dead lithium and the prospects for future development.
Extraction temperature is one of the basic factors for alginate-like exopolymers (ALE) recovery from waste activated sludge (WAS). Given the rising interest in sustainable resource recovery and the promising industrial applications of ALE, this study systematically evaluated the effects of extraction temperatures (50–95 °C) on the ALE yield, profit, compositions, structural properties and sludge reduction. The increasing extraction temperature significantly enhanced ALE yield (from 148.3 mg/g VSS at 80 °C to 218.6 mg/g VSS at 95 °C) and net profit (from 0.441 to 1.046 CNY/kg SS). The elevated temperatures notably increased protein yields compared to polysaccharides. Fluorescence spectroscopy also indicated a pronounced increase in aromatic protein-like substances (C1), whereas polysaccharides showed a comparatively modest increase. Meanwhile, UV–Vis analysis demonstrated decreased E2/E3 and E2/E4 ratios at higher temperatures, suggesting increased humification and reduced molecular weight. Structural analysis showed ALE gels extracted at higher temperatures became denser with decreased mechanical strength (compressive modulus declined from 1.45 MPa at 50 °C to 0.11 MPa at 95 °C). Furthermore, sludge reduction reached 19.8% at 95 °C, significantly alleviating disposal cost of the sludge. These findings in this study provided critical insights for optimizing ALE extraction processes, promoting sludge resource recovery for practical applications.
Weston Baxter, Peter Mandeno, Robert Aunger
et al.
This paper introduces Setting-Driven Design (SDD) and supporting tool – the Behaviour Setting Canvas (BSC) – which together address a critical gap in behavioural design by shifting the focus from individual behaviour to the broader context in which behaviour occurs. Rooted in behaviour setting theory, SDD is a powerful approach to behavioural design that offers an end-to-end structure for understanding and intervening in a behavioural design challenge. The process comprises three iterative phases: scoping the behavioural challenge, understanding the setting and intervention development. The process structure revolves around the BSC, a tool for mapping key contextual elements such as roles, motives, norms and routines. While SDD is particularly effective for behaviour change interventions, its utility extends to other design challenges, including introducing new products, shifting social norms and enhancing existing systems where behaviour remains constant. The approach integrates a theory of change to guide intervention development, prototyping and evaluation, ensuring alignment with behavioural objectives and contextual realities. A case study on handwashing in low-income Tanzanian households illustrates the method’s utility, culminating in the creation of Tab Soap, a single-use, biodegradable soap designed to improve hygiene behaviours. The study demonstrates how SDD facilitates insight generation and iterative refinement and complements user-centred design. SDD advances behavioural design by combining theoretical rigour with practical application, offering a scalable and adaptable framework for addressing complex design challenges across diverse fields.
Summary: Tropical cyclone (TC) intensity change forecasting remains challenging due to the lack of understanding of the interactions between TC changes and environmental parameters, and the high uncertainties resulting from climate change. This study proposed hybrid convolutional neural networks (hybrid-CNN), which effectively combined satellite-based spatial characteristics and numerical prediction model outputs, to forecast TC intensity with lead times of 24, 48, and 72 h. The models were validated against best track data by TC category and phase and compared with the Korea Meteorological Administrator (KMA)-based TC forecasts. The hybrid-CNN-based forecasts outperformed KMA-based forecasts, exhibiting up to 22%, 110%, and 7% improvement in skill scores for the 24-, 48-, and 72-h forecasts, respectively. For rapid intensification cases, the models exhibited improvements of 62%, 87%, and 50% over KMA-based forecasts for the three lead times. Moreover, explainable deep learning demonstrated hybrid-CNN’s potential in predicting TC intensity and contributing to the TC forecasting field.
T.M. Shevchuk, M.A. Bordyuk, V.A. Mashchenko
et al.
Based on experimental values of longitudinal and transverse ultrasonic wave propagation velocities and surface Rayleigh wave velocities were calculated phonon energies and Debye limiting frequencies and temperatures were determined of metal-filled polyurethane auxetics samples. Modeling the structural formations of such systems and obtaining the value of the lattice and Grüneisen acoustic parameter made it possible to find the root-mean-square displacement of the atomic groups of the macromolecule, as well as the limits of forced elasticity, shear deformation, and deformation of the interstructural bond. The relationship between Debye frequencies (temperatures) and Poisson's ratio, Grüneisen parameter, was established. A quantum-mechanical approach to the movement of electrons, atomic groups of macromolecules, thermal and sound phonons made it possible to estimate the size of nanoformations in the composition. The theoretical values of Poisson's ratio obtained based on models of polymer auxetics and processes of propagation of ultrasonic waves in such systems are analyzed.
This paper elucidates the challenges and opportunities inherent in integrating data-driven methodologies into geotechnics, drawing inspiration from the success of materials informatics. Highlighting the intricacies of soil complexity, heterogeneity, and the lack of comprehensive data, the discussion underscores the pressing need for community-driven database initiatives and open science movements. By leveraging the transformative power of deep learning, particularly in feature extraction from high-dimensional data and the potential of transfer learning, we envision a paradigm shift towards a more collaborative and innovative geotechnics field. The paper concludes with a forward-looking stance, emphasizing the revolutionary potential brought about by advanced computational tools like large language models in reshaping geotechnics informatics.
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
Hale Pamukçu, Pelin Soyertaş Yapıcıoğlu, Mehmet İrfan Yeşilnacar
This study majorly aimed to determine the effect of optimization on transport routes on the reduction of greenhouse gas (GHG) emissions from municipal solid waste management (MSM) within the scope of European Union (EU) Green Deal. Optimization of collection and transportation routes has been regarded as an effective methodology in order to mitigate the GHG emissions of municipal waste management, recently. Optimization of routes has been obtained using ant colony algorithm (ACA) and Monte Carlo simulation, in this study. In this context, this study investigated to reduce GHG emissions from municipal waste management using optimization of transportation routes in Diyarbakir city in Turkey. Firstly, GHG emissions which are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions from waste collection and transport have been calculated using a new developed model based on Tier-I method. Then, Monte Carlo simulation has been used to figure out the GHG emissions. Finally, life cycle assessment (LCA) approach has been applied to determine the GHG emissions. According to the route optimization resulting ACA methodology, nearly 47.43% of reduction on each GHG emissions. Approximately, 58%, 38% and 51% of reduction on CO2, CH4 and N2O emissions respectively has been achieved, in the result of the route optimization using Monte Carlo simulation. The results of LCA methodology revealed that the reduction reached up 45.71% on GHG emissions in terms of Global Warming Potential (GWP). The reduction amounts have been overlapped with each other.
This review covers the current status of chemical recycling and upcycling of poly(bisphenol A carbonate), a leading engineering plastic of great economic and environmental interest.
Introduction: Presently, the global consumption of pesticides including insecticides, herbicides, and fungicides to protect crops is increasing. Pesticides' role as Endocrine-Disrupting Chemicals (EDCs) has gained great concern in the field of thyroid hormones. Therefore, this systematic review aimed to determine the link of pesticide exposure with thyroid hormone levels among male agricultural workers and pesticide applicators. Discussion: It was discovered that the majority of reviewed articles have similar results concerning the effects of pesticide exposure on the serum levels of thyroid hormones among either farmworkers or pesticide applicators. Commonly, insecticides, herbicides, and fungicides are known as one of the EDCs. The results showed the elevation of TSH and T4 serum levels mostly occurred among groups exposed to insecticide application only rather than those exposed to various pesticide types. Moreover, the hormonal change differed based on each class of pesticide. Conclusion: This review suggests that some types of pesticides extensively used in agriculture might be involved in the increase and decrease in thyroid hormone levels among exposed individuals. Further studies should assess specific types of pesticides and the adverse health effects which involve confounding factors to yield robust analysis.
Jacobo Tabla-Hernandez, Alejandro V Dellepere, Ernesto Mangas-Ramírez
This work shows the results for the first time of calibrating and validating a mathematical model, capable of predicting the amounts of O _3 and O _2 necessary to reduce pollution levels in a lake based on the chemical oxygen demand (COD), biochemical oxygen demand (BOD _5 ), total nitrogen (TN), total phosphorus (TP) and fecal coliforms (FC) concentrations. The model was designed to treat a natural or artificial lake as though it were an aerated lagoon operating as an idealized continuous flow complete-mix reactor. The O _3 yield constant for eliminating the non-biodegradable fraction of COD and for deactivating fecal coliforms were laboratory derived and calibrated with field values. Based on the field parameters, the model accurately predicted a reduction in BOD _5 , COD, TN, TP and FC of 53%, 51%, 39%, 42% and 98%, respectively. The model proved to be effective in predicting O _2 and O _3 demand and time of recovery of a polluted water body.
The vertical distribution of sulfonamides (SAs), tetracyclines (TCs), macrolides (MLs), and their related antibiotic resistance genes (ARGs) were comprehensively investigated and characterized in a representative municipal solid waste (MSW) landfill in China. The total concentrations of target antibiotics in the MSW landfill were SAs > TCs > MLs. The abundances of mexF (10.78 ± 0.65 log10copies/g) and sul genes (9.15 ± 0.54 log10copies/g) were relatively high, while the tet genes (7.19 ± 0.77 log10copies/g) were the lowest. Both the abundance of antibiotics and genes fluctuated with landfill depth, and the ARGs of the same antibiotics were consistent with depth change. Intl1 and sul genes (sul1, sul2) were tightly connected, and a close relationship also existed between tet genes (tetM, tetQ) and MLs resistance genes (ermB, mefA). High-throughput sequencing showed the dominant genera were Sporosarcina (38%) and Thiobacillus (17%) at sampling points A and C, while the microbial community varied with depth increase at point B were Brevundimonas (20%), Sporosarcina (20%), Pseudomonas (24%), Lysobacter (28%), and Thioalkalimicrobium (14%), respectively. Network analysis further visualized the relationship among antibiotics, genes, and microbial communities and the results indicated the non-random connection among them and the possible host of the target gene. Even at 12.0 m below the landfill surface, the pollution of antibiotics resistance was still serious, which posed difficulties for subsequent landfill remediation and pollution control.
Carrie V. Breton, Remy Landon, Linda G. Kahn
et al.
Carrie Breton and colleagues review the literature supporting evidence for transgenerational health effects of environmental exposures by epigenetic mechanisms. This Review summarizes current knowledge based on animal and human cohort studies, and discusses the ethical, legal, and social implications of epigenetic research in humans
Given the problem of three-sources uncertainty in aero engine degradation process and the assumption of normal distribution of measurement error in existing research, a Wiener process aero engine residual useful life time(RUL) prediction method with logistic distribution of measurement error was proposed. Firstly, the performance degradation model was established when the measurement error obeys the logistics distribution, and the accurate mathematical expression of the RUL distribution was given under the first arrival time. Secondly, in order to improve the utilization of monitoring parameters, a key parameter screening method based on Spearman coefficient was given. Lastly, in view of the lack of historical data and prior information, a parameter estimation method based on maximum entropy unscented particle filtering and conditional expectation maximization algorithm was adopted. Meanwhile after new performance degradation data was obtained, the model parameters could be adaptively updated. With MSE as the evaluation index, the experimental results show that the MSE value of this method is 13.25, and the MSE value is 16.12 lower than that of traditional method, which can effectively improve the prediction accuracy and the engine utilization and safety.
Materials of engineering and construction. Mechanics of materials, Environmental engineering
The state of charge (SOC) estimation is one of the core functions of the battery management system; it can play a significant role in the life cycle of electric vehicles. The SOC estimation method has attracted considerable research attention in recent years, particularly about improving estimation accuracy. However, most studies are limited by only focusing on known or fixed battery model parameters and not considering their temperature dependence. This indicates a need to explore how the lithium-ion battery temperature affects the model parameters, which leads to inaccurate SOC estimation. The principal objective of this study is to investigate the robust H∞ filter-based method for the problem that temperature affects battery model parameters and thus leads to inaccurate SOC estimation. First, the second-order Thevenin equivalent circuit model with two parallel resistor–capacitor pairs is taken as the basic model of the lithium-ion battery. The influence of temperature on battery model parameters is modeled as an additive variable of the nominal resistance value and the total battery capacity, and the temperature change is considered an external disturbance of the system. Afterward, the sliding linear method is used to linearize this battery model; on this basis, a robust H∞ filter for SOC estimation is designed using linear matrix inequality technology. Finally, the effectiveness of the proposed approach is verified using four different types of dynamic current load profiles (the BJDST-Beijing Dynamic Stress Test, FUDS-Federal Urban Driving Schedule, US06-US06 Highway Driving Schedule and BJDST-FUDS-US06 joint dynamic test) compared with the Kalman filter-based SOC estimation method. The simulation analysis results indicate that the proposed SOC estimation approach can realize a higher SOC estimation accuracy even if the model parameters vary with temperature, and it has good robustness to external disturbances.
Even though enormous expectations for greenhouse gas mitigation in the land use sector exist at the same time worries about potential implications for sustainable development have been raised as many Sustainable Development Goals (SDGs) are closely tied to developments in the sector. Here we assess the implications of achieving selected key SDG indicators for Zero Hunger, Clean Water and Sanitation, Responsible Consumption and Production, and Life on Land on the land-based climate change mitigation potential. We find that protecting highly biodiverse ecosystems has profound impacts on biomass potentials (−30% at >12 US dollar per gigajoule) while other SDGs mainly affect greenhouse gas abatement potentials. Achieving SDGs delivers synergies with greenhouse gas abatement and may even in the absence of additional mitigation policies allow to realize up to 25% of the expected greenhouse gas abatement from land use required to stay on track with the 1.5 °C target until 2050. Future land use mitigation policies should consider and take advantage of these synergies across SDGs.