Environmental Processes Research Articles

The influence of syn‐depositional compaction on clastic sediment distribution in river‐dominated deltas: A modelling study

AbstractSyn‐sedimentary compaction or consolidation is an important process in deltaic environments because it affects both the local morphodynamics and hydrodynamics as well as the delta‐scale accommodation space. However, the impact of syn‐depositional compaction on the sediment distribution and the interdependency between different delta areas related to the sediment budget are not fully understood. This paper simulates syn‐depositional compaction using improved 1D grain‐size compaction formulations, integrated into hydrodynamic and morphodynamic modelling software Delft3D. The updated code is used to model sedimentation in mud‐rich deltas under various compaction rate scenarios, which represents the maximum compaction rate potential of sediment that experiences the highest overburden stress in the delta. The simulated deltas are analysed by first classifying their plan‐view area development into depositional elements: distributary channel, underfilled channel, delta plain, mouth bar, delta front and pro delta depositional elements. Then, sedimentation by mass, accommodation space and depositional segment metrics are calculated using the interpreted depositional elements. The results for zero compaction rate scenarios (0 mm year−1) show that limited space‐varying and temporal‐varying accommodation is available to deposit sediment in the delta plain depositional element. Therefore, the sedimentation mainly occurs in the mouth bar depositional element. For low‐mid compaction rate scenarios (0.01–1 mm year−1), the additional syn‐depositional accommodation space in the delta plain depositional element increases sedimentation in this area, limiting sedimentation in the mouth bar depositional element. For high compaction rate scenarios (>1 mm year−1), a further increase in the accommodation space in the delta plain depositional element leads to lateral sedimentation attributed to channel relocation, where the sedimentation mainly occurs in the mouth bar depositional element. This study shows that, although considered a gradual process, syn‐sedimentary compaction does impact long‐term delta evolution by influencing the distribution of sedimentation in the delta.

AI-driven business analytics and decision making

The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time insights that help businesses respond quickly to changing market dynamics. This automation significantly reduces manual intervention, increases efficiency, and enhances the accuracy of predictions. The paper further discusses the integration of AI with Business Intelligence (BI) tools to deliver deeper insights from complex datasets in real time. These insights empower companies to optimize enterprise resources, improve supply chain management, and drive operational excellence. Case studies from AI-driven analytics within Systems, Applications, and Products in Data Processing (SAP) environments highlight the practical applications of AI in real-world business contexts, demonstrating its impact on decision-making and overall performance. The paper concludes with best practices for implementing AI in business analytics, focusing on data quality, system integration, and workforce readiness to embrace AI-enabled decision-making frameworks. The findings underscore the potential of AI as a game-changer in modern business landscapes, fostering smarter, faster, and more effective decision-making processes.

AI-driven monitoring systems for bioremediation: real-time data analysis and predictive modelling

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of bioremediation by enabling real-time monitoring and optimization of environmental cleanup processes. This paper explores the integration of AI-driven monitoring systems with bioremediation techniques, focusing on how real-time data analysis and predictive modelling can enhance the effectiveness of pollutant degradation. These AI systems continuously collect data from contaminated sites, such as soil and water, and analyse variables like microbial activity, pollutant concentration, and environmental conditions. By processing this data, AI models can optimize the bioremediation environment, adjusting factors such as pH, temperature, and nutrient levels to maximize microbial efficiency. Predictive modelling plays a crucial role in forecasting remediation outcomes, allowing for proactive adjustments to improve the speed and success of the process. The study also highlights the potential of AI in reducing operational costs by automating data collection and analysis, minimizing the need for manual intervention. Furthermore, it discusses challenges related to data quality, system integration, and the scalability of AI applications in real-world bioremediation projects. By leveraging AI's capability to provide real-time insights and predictive analytics, this research demonstrates its potential to significantly enhance the precision and sustainability of bioremediation efforts, paving the way for smarter environmental management.

Racial Socialization in the United States.

Our review, situated within the context of the United States, explores how societal forces shape youths' racial socialization processes. Specifically, we explore how youths learn beliefs about race through interactions with their environment, how these processes affect youths' engagement with race in multiple contexts, and how they contribute to the perpetuation and dismantling of racial inequality. First, we discuss key psychological theories that inform our understanding of racial socialization. Second, we discuss how families, peers, media, and environmental cues shape racial socialization processes. Finally, we discuss interventions to enhance racial socialization and offer directions for future psychological research to advance our understanding of both racial and broader socialization processes in the United States and internationally.

When the world gets worse: Form and feeling in Australian climate fiction

In a political context of climate inaction, Australian fiction writers have increasingly focused on the emergency; drawing attention to how the political and environmental processes underway might play out and exploring attendant emotions. The novel form – with its foregrounding of private feelings and individual agency, and its historical alignment with the development of the extractive capitalism it may wish to critique – occupies an ambivalent position as a tool to bring about change. If the task is to refigure the social imaginary towards interconnection with all forms of life, is the novel an inadequate means of social engagement? I hope to make the case – by referencing debates in climate change literature and through discussion of Scary Monsters by Michelle de Kretser (2021) and This Devastating Fever by Sophie Cunningham (2022) – that writers are using the distinctive capacities of the novel to think through contemporary challenges. These novels, distinct in tone and narrative content, both treat climate change as inextricable from other forces – such as colonialism and misogyny – and emphasise interconnectedness with other beings. Through a commitment to play, via formal experimentation and humour, they help readers to engage with the intellectual and emotional challenges of a changing world.

Closed-form solutions for Bernoulli and compound Poisson branching processes in random environments*

Abstract For branching processes, the generating functions for the limit distributions of the so-called ratios of probabilities of rare events satisfy Schröder-type integral–functional equations. With the exception of a few special cases, the corresponding equations cannot be solved analytically. I found a large class of Poisson-type offspring distributions for which the Schröder-type functional equations can be solved analytically. Moreover, for the asymptotics of limit distributions, the power and constant factors can be written explicitly. As a bonus, Bernoulli branching processes in random environments are treated. The beauty of this example is that the explicit formula for the generating function is unknown. Nevertheless, the closed-form expressions for the power and constant factors in the asymptotic can be written with the help of some effective techniques. The asymptotic expansion also contains oscillatory terms absent in the Poisson case. It is shown that at power three in the Bernoulli binomial kernels, short-phase discrete oscillations turn into long-phase continuous ones.

Characterizing southeast Greenland fjord surface ice and freshwater flux to support biological applications

Abstract. Southeast Greenland (SEG) is characterized by complex morphology and environmental processes that create dynamic habitats for top marine predators. Active glaciers producing solid-ice discharge, freshwater flux, offshore sea ice transport, and seasonal landfast-ice formation all contribute to a variable, transient environment within SEG fjord systems. Here, we investigate a selection of physical processes in SEG to provide a regional characterization that reveals physical system processes and supports biological research. SEG fjords exhibit high fjord-to-fjord variability regarding bathymetry, size, shape, and glacial setting, influencing some processes more than others. For example, during fall, the timing of offshore sea ice formation near SEG fjords progresses temporally when moving southward across latitudes, while the timing of offshore sea ice disappearance is less dependent on latitude. The rates of annual freshwater flux into fjords, however, are highly variable across SEG, with annual average input values ranging from ∼ 1 × 108 to ∼ 1.25 × 1010 m3 (∼ 0.1–12.5 Gt) for individual fjords. Similarly, the rates of solid-ice discharge in SEG fjords vary widely – partly due to the irregular distribution of active glaciers across the study area (60–70° N). Landfast sea ice, assessed for eight focus fjords, is seasonal and has a spatial distribution highly dependent on individual fjord topography. Conversely, glacial ice is deposited into fjord systems year-round, with the spatial distribution of glacier-derived ice depending on the location of glacier termini. As climate change continues to affect SEG, the evolution of these metrics will vary individually in their response, and next steps should include moving from characterization to system projection. Due to the projected regional ice sheet persistence that will continue to feed glacial ice into fjords, it is possible that SEG could remain a long-term refugium for polar bears and other ice-dependent species on a centennial to millennial scale, demonstrating a need for continued research into the SEG physical environment.

Faedo-Galerkin method for the time-fractional reaction-diffusion model

This paper investigates a time-fractional reaction-diffusion equation characterized by a Caputo derivative of fractional order , incorporates non-local and memory effects that are essential for accurately modeling complex diffusion processes in such environments. These effects arise from the fractal nature of the media, which leads to anomalous diffusion behavior that cannot be adequately described by classical diffusion models. We start by giving a definition of a weak solution for this problem, ensuring that the solution satisfies the equation in a generalized sense. Subsequently, the Faedo-Galerkin method combined with compactness arguments is employed to establish the existence and uniqueness of the weak solution. This approach involves approximating the solution using a sequence of finite-dimensional functions and then passing to the limit to obtain the desired solution . Moreover, we establish the stability of these solutions, which is fundamental for understanding their behavior under varying conditions and ensuring the reliability of the model . Our findings contribute to a deeper understanding of anomalous diffusion phenomena in fractal media and provide a solid foundation for further research in this area.

Consequence modeling and analysis of ethanol leakage from storage tank using the ALOHA program

Chemical spills pose a significant threat to the safety of people living nearby, as well as to air quality and occupational safety. Therefore, preventing chemical spills has become a key issue in environmental protection and process safety. This study aims to evaluate the effects of ethanol released from a tank at a chemical plant in Istanbul, Türkiye. The Areal Location of Hazardous Atmospheres model (ALOHA) version 5.4.7 estimates the storage tank's leakage radius and spread risk under three scenarios. ALOHA can assess the area affected by chemical hazards. The model shows that if the spill occurs 10 centimeters from the base of the tank, the toxic concentration of ethanol exceeds the Immediately Dangerous to Life or Health (IDLH) threshold (i.e., 3300 parts per million) at a distance of 31 meters from the place of release. In addition, when the ethanol concentration exceeds 60% of the Lower Explosive Limit (LEL) (i.e., 19800 parts per million), the flammable vapor cloud extends up to 31 meters from the place of release. For a Boiling Liquid Expanding Vapor Explosion (BLEVE), the mass of the fireball is assumed to be 100%, resulting in the worst possible scenario. During BLEVE, it is estimated that the fireball's diameter and time duration, determined by the amount of Ethanol present, would be 141 meters and 10 seconds, respectively. The results show that the thermal radiation effects caused by Ethanol BLEVE are extremely dangerous.

Improvement of aerosol spray scavenging efficiency by pre-injecting electrically charged water mist

Efficient aerosol scavenging is crucial for gas purification to reduce environmental pollution in various chemical industries. In nuclear reactor decommissioning, especially for damaged reactors after severe accidents like Fukushima Daiichi, controlling radioactive aerosol generation and dispersion is essential. Radioactive aerosols sizing from 0.1 to 1 μm are expected during containment cleaning and fuel debris retrieval processes. To ensure safe decommissioning and mitigate environmental radioactivity, a containment water spray system is used for aerosol scavenging. However, conventional water spray has low efficiency for these submicron aerosols. Our recent experiments at UTARTS demonstrated that pre-injection of water mist enhances scavenging efficacy by increasing aerosol size and hydrophilicity through aerosol-mist agglomeration prior to spraying. On this basis, in this study, we introduced an innovative method using electrically charged water mist, which significantly facilitates aerosol-mist agglomeration through additional electrostatic coagulation, further improving scavenging efficacy (maximum 35 % in current configurations). The present experimental results attained under various mist conditions and charging configurations provided insights into optimizing the mist charging system. Our developed technology is expected to be useful in radioactive aerosol control during damaged reactor decommissioning and has potential applications in other environmental chemical engineering processes.

Parametric Analytical Modulation Transfer Function Model in Turbid Atmosphere with Application to Image Restoration

To address the issues of image blurring and color distortion in hazy conditions, an image restoration method based on a parametric analytical modulation transfer function model is proposed under turbid atmospheric conditions. A source database is established using a numerical radiative transfer method based on discrete ordinate. Through multivariate nonlinear fitting and linear interpolation, the quantitative relationships among critical spatial frequency, turbid atmospheric MTF, and key atmospheric optical parameters—such as optical thickness, single scattering albedo, and asymmetry factor—are examined. A fast and efficient parametric analytical MTF model for turbid atmospheres is developed and applied to restore images affected by fog. The results demonstrate that, within the applicable range of the model, the model’s maximum mean relative error and the root mean square error are 7.16% and 0.0454, respectively. The computational speed is nearly a thousand times faster than that of the numerical radiative transfer method, achieving high accuracy and ease of application. Images restored using this model exhibit enhanced clarity and quality, effectively compensating for the degradation in image quality caused by turbid atmospheres. This approach represents a novel solution to the challenges of image processing in complex atmospheric environments.

Sensory Processing Measure Tools for Adolescents Aged 12 to 21 Years

Background and Objectives: Impairment in the way of sensory information by the central nervous system can cause functional problems in all areas of an adolescent’s activities, including education, self-care, social participation, and leisure. Therefore, in the presence of problems in such activities, it is necessary to evaluate and screen for sensory processing. In the present study, the sensory processing assessment tools that cover the adolescent age group were investigated. Methods: In this review, PubMed, Science Direct, Google, OTseeker, and Google Scholar databases were searched, and articles published between 2000 and 2022 on sensory processing assessment tools in adolescents and their psychometrics were selected. Keywords used included sensory integration dysfunction (SID), sensory processing disorder (SPD), adolescence, adolescent, assessment, measurement, reliability, validity, psychometry, environment, scale, and instrument. Results: The assessment tools identified to evaluate sensory processing in the adolescent age group include the “adolescent/adult sensory profile” (AASP), “Glasgow sensory questionnaire” (GSQ), “sensory processing measure (SPM)”, “sensory response in autism spectrum” (SR-AS) questionnaire, “sensory processing scale” (SPS), and “SPM-second edition” (SPM-2). Conclusion: The environment was considered as a factor that needs to be evaluated only in the second version of the SPM among the selected tools. The results of this review also showed that the only tool available in Persian in this field is the AASP, which does not seem to be a suitable tool for use in adolescence because the population studied in the psychometric evaluation of the Persian version was elderly people with dementia. Therefore, it seems necessary to prepare the Persian version and psychometric evaluation of an appropriate assessment tool for evaluating the sensory processing of Iranian adolescents.

Exploring the potential of biosurfactants produced by fungi found in soil contaminated with petrochemical wastes

Biosurfactants are a diverse group of compounds derived from microorganisms, possessing various structures and applications. The current study was seeking to isolate and identify a new biosurfactant-producing fungus from soil contaminated with petrochemical waste. The bioprocess conditions were optimized to maximize biosurfactant production for Aspergillus carneus OQ152507 using a glucose peptone culture medium with a pH of 7.0 and a temperature of 35 °C. The carbon source was glucose (3%), and ammonium sulfate (0.25%) was utilized as the nitrogen source. For Aspergillus niger OQ195934, the optimized conditions involved a starch nitrate culture medium with a pH of 7.0 and a temperature of 30 °C. The carbon source used was sucrose (3.5%), and ammonium sulfate (0.25%) served as the nitrogen source. The phenol-H2SO4 and phosphate tests showed that the biosurfactants that were extracted did contain glycolipid and/or phospholipid molecules. They showed considerable antimicrobial activity against certain microbes. The obtained biosurfactants increased the solubility of tested polyaromatic hydrocarbons, including fluoranthene, pyrene, anthracene, and fluorine, and successfully removed the lubricating oil from contaminated soil and aqueous media surface tension reduction. Based on the obtained results, A. carneus and A. niger biosurfactants could be potential candidates for environmental oil remediation processes.

General Industrial Process Optimization Method to Leverage Machine Learning Applied to Injection Moulding

ABSTRACTThe development of machine learning (ML) technologies is driving changes across many sectors. In industrial settings, this is called the fourth industrial revolution and encompasses several technologies pushing the boundaries of industrial automation. In this study, a general industrial process optimization (GIPO) methodology is formulated in the context of Industry 4.0 and tested on an industrial injection moulding machine (IMM). GIPO aims to encourage the practical inclusion of industrial artificial intelligence at all levels of the manufacturing process, while enabling industrial equipment to adapt to a changing processing environment. Special attention is given to the generality of the methodology so that it can be extended to other applications. In the example case study presented here, GIPO combines K‐nearest neighbours classification and nearest neighbours optimization methods to optimize an injection moulding process effectively. Practical implementation conducted on the IMM demonstrates a novel methodology to leverage data mining and ML methods in a real‐world setting to improve production quality, production time and energy cost.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Geoenvironmental and geophysical methods applied to identify natural attenuation process on a complex hydrogeological environment contaminated by hydrocarbons

Environmental contamination found in urban areas generally has a chemical composition that favors the generation of a variety of physical, chemical and biological processes that differ from natural soil parameters. Thus, understanding natural attenuation processes in tropical environments is still a challenge, as is understanding how biogeochemical processes can influence and modify natural soil characteristics. The study aimed to apply methods that are not commonly used in investigations of contaminated areas, in order to assess the applicability of non-invasive methodology for detailed stratigraphic characterization and biogeochemical changes resulting from contamination by hydrocarbons in a complex hydrogeological environment site. To date, few studies have presented results obtained using non-invasive techniques to understand biochemical processes. The research was based on the interpretation of results obtained by high-resolution physical environment characterization techniques (such as gamma geophysical profiling and electrical conductivity surveys) and chemical analysis of groundwater. The data was interpreted to obtain a detailed characterization of the study area and evidence of the occurrence of natural attenuation of creosote contamination. The results indicated that the combination of geoenvironmental and geophysical methods is an interesting alternative for obtaining data and understanding the mineralogical and stratigraphic variation of the contaminated area. The physical parameters variations obtained by the methods suggested the presence of local geochemical alterations resulting from natural processes of attenuation of contamination by aliphatic and aromatic hydrocarbons in a tropical environment. The natural attenuation processes were confirmed through groundwater samples, biological analysis and metagenomic classification.

WATERFALL MODEL OVER PCD.UCT MODEL REVIEW

Abstract. The software development life cycle (SDLC) has seen the evolution of numerous models, each tailored to meet specific project needs. Among these, the Waterfall Model has been a longstanding, traditional approach, characterized by its linear and sequential phases. In contrast, the PcD.UcT Model, a relatively newer framework, advocates for a more iterative and user-centered approach. This paper presents a comprehensive review of the Waterfall Model, evaluating its strengths and limitations in modern software development. It contrasts these findings with the PcD.UcT Model, analyzing how its principles better align with contemporary project demands, particularly in terms of flexibility, user engagement, and iterative refinement. Through this comparative analysis, the paper aims to provide insights into the applicability and effectiveness of each model, offering guidance for software development teams in choosing the appropriate methodology for their projects. Since the waterfall development methodology, the key requirements in system development strategies have shifted from processes to users. Because the process as well as the customers are both equally important, employing either a predictive or an adaptive methodology is extremely difficult. The primary goal of this work is mainly combining and then evaluating all the relevant methodologies in order to create a HydroGIS that accurately automates the complexities of the hydrology process in a HydroGIS environment while meeting all user requirements.

A knowledge-guided process planning approach with reinforcement learning

With the widespread application of computer-aided technologies such as CAD and CAM in the manufacturing industry, a growing number of process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, there is a need for more effective methods to mine a large amount of multi-source information and to exploit the explicit and implicit relationships between the knowledge contained in process knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning decision-making phase as a sequential recommendation of process knowledge. Then, this paper designs in detail the state representation method that combines process decision sequences and potential relationships between processes. This paper also creates the composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure has more accurate recommendation results than other methods.

Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud.

A longitudinal study of health-impairment and motivational processes in the psychosocial work environment and the subsequent influence on teachers’ intention to remain at their workplace

ABSTRACT This study examines the concurrent influence of health-impairment and motivational processes proposed by the Job Demands-Resource model on teachers’ intention to remain at their workplace at 12 and 24 months among 308 certified teachers in Sweden. Teachers’ perceptions of job demands (quantitative demands, emotional demands, work pace) and job resources (possibilities for development, social support from supervisor, social support from colleagues, recognition) were measured at baseline whereas exhaustion, work engagement, and retention intention were measured at 12 months and 24 months. Data was analysed using structural equation modelling and subsidiary analyses explored the moderating effects of job resources on the health-impairment process at 12 and 24 months. The findings indicate that the health-impairment process is a stronger predictor of teachers’ retention intention at 12 and 24 months than the motivational process and that the primary focus of teacher retention policies should be optimising teachers job demands rather than enhancing teachers’ job resources.