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Pesticide exposure poses significant environmental and human health concerns, particularly given its extensive use in agricultural activities. The assessment of pesticide risks is a multifaceted and resource-intensive process, often requiring time-consuming toxicity studies. In response to this challenge, advanced computational models, remote sensing, and GIS (geographic information systems) have emerged as efficient and precise tools for evaluating pesticide exposure risks. This comprehensive review aims to provide an in-depth examination of the latest research methodologies for assessing the risks associated with pesticide exposure and their practical applications. These methodologies encompass the assessment of pesticide exposure in air, soil, and water, offering a comprehensive understanding of potential environmental pathways. The paper also delves into the effective utilization of these tools for pesticide risk assessment and examines the potential implications of their findings. The approaches outlined in this review hold promise for a thorough and insightful assessment of pesticide risks and are positioned to equip researchers and policymakers with valuable knowledge to mitigate the impacts of pesticide exposure on human health and the environment. [ABSTRACT FROM AUTHOR]

Information technology applications are crucial to the proper utilization of manufacturing equipment in the new industrial age, i.e., Industry 4.0. There are certain fundamental conditions that users must meet to adapt the manufacturing processes to Industry 4.0. For this, as in the past, there is a major need for modeling and simulation tools in this industrial age. In the creation of industry-driven predictive models for machining processes, substantial progress has recently been made. This paper includes a comprehensive review of predictive performance models for machining (particularly analytical models), as well as a list of existing models' strengths and drawbacks. It contains a review of available modeling tools, as well as their usability and/or limits in the monitoring of industrial machining operations. The goal of process models is to forecast principal variables such as stress, strain, force, and temperature. These factors, however, should be connected to performance outcomes, i.e., product quality and manufacturing efficiency, to be valuable to the industry (dimensional accuracy, surface quality, surface integrity, tool life, energy consumption, etc.). Industry adoption of cutting models depends on a model's ability to make this connection and predict the performance of process outputs. Therefore, this review article organizes and summarizes a variety of critical research themes connected to well-established analytical models for machining processes. [ABSTRACT FROM AUTHOR]

Pesticide exposure poses significant environmental and human health concerns, particularly given its extensive use in agricultural activities. The assessment of pesticide risks is a multifaceted and resource-intensive process, often requiring time-consuming toxicity studies. In response to this challenge, advanced computational models, remote sensing, and GIS (geographic information systems) have emerged as efficient and precise tools for evaluating pesticide exposure risks. This comprehensive review aims to provide an in-depth examination of the latest research methodologies for assessing the risks associated with pesticide exposure and their practical applications. These methodologies encompass the assessment of pesticide exposure in air, soil, and water, offering a comprehensive understanding of potential environmental pathways. The paper also delves into the effective utilization of these tools for pesticide risk assessment and examines the potential implications of their findings. The approaches outlined in this review hold promise for a thorough and insightful assessment of pesticide risks and are positioned to equip researchers and policymakers with valuable knowledge to mitigate the impacts of pesticide exposure on human health and the environment.

The interest in sustainability and energy efficiency is constantly increasing, and the noticeable effects of climate change and rising energy prices are fueling this development. The residential sector is one of the most energy-intensive sectors and plays an important role in shaping future energy consumption. In this context, modeling has been extensively employed to identify relative key drivers, and to evaluate the impact of different strategies to reduce energy consumption and emissions. This article presents a detailed literature review relative to modeling approaches and techniques in residential energy use, including case studies to assess and predict the energy consumption patterns of the sector. The purpose of this article is not only to review the research to date in this field, but to also identify the possible challenges and opportunities. Mobility, electrical devices, cooling and heating systems, and energy storage and energy production technologies will be the subject of the presented research. Furthermore, the energy upgrades of buildings, their energy classification, as well as the energy labels of the electric appliances will be discussed. Previous research provided valuable insights into the application of modeling techniques to address the complexities of residential energy consumption. This paper offers a thorough resource for researchers, stakeholders, and other parties interested in promoting sustainable energy practices. The information gathered can contribute to the development of effective strategies for reducing energy use, facilitating energy-efficient renovations, and helping to promote a greener and more sustainable future in the residential domain. [ABSTRACT FROM AUTHOR]

Simple Summary: Automatic milking systems (AMSs) are revolutionizing dairy farming worldwide. Not only do they control the milking process, but they also bring changes to the whole farm system management. In this review, the examination focused on the study of AMSs using various modeling approaches. Our review primarily encompassed published articles addressing cows' health, production, and behavior/management. Within this field, Machine Learning (ML) emerged as the prevailing modeling approach. Most of the studies were aimed at detecting cows' health problems, especially mastitis. However, there is still a lack of a robust methodology for utilizing ML techniques in this domain, and it was also observed that the number of positive and negative cases is often unequal, leading to populations that are not balanced when predicting health issues. Only a small number of studies focused on milk production, even though accurate forecasting of individual cow milk yields could be very useful. Additionally, the study of cow behavior and herd management using AMSs is still not very explored. Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows' health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows' health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows' behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models. [ABSTRACT FROM AUTHOR]

Studies on the efficacies of vermicomposting and composting in countering the toxic impacts of pollutant cocktails in municipal solid waste (MSW) are scarce. Moreover, further research is needed to explore earthworms' remediation preferences for various pollutants in heterogeneous vermicomposting feedstocks, such as MSW. Therefore, removal dynamics of pesticides (chlorpyrifos, cypermethrin, and carbofuran), pharmaceuticals (diclofenac and carbamazepine), and heavy metals (Pb, Zn, Cu, and Mn) in MSW-based vermicomposting (Eisenia fetida and Eudrilus eugeniae) and composting systems were evaluated through multivariate analytical techniques (principal component (PCA) and multi-factor (MFA)) on the R-platform. Both earthworms satisfactorily increased their population and augmented NPK (nitrogen, phosphorous, and potassium) availability, cation exchange, microbial biomass C&N, and their metabolic activity 2–3 folds more than composting, accompanied by a 3–4 folds reduction of organic C, pH, and bulk density. Correspondingly, heavy metals, pesticides, and pharmaceuticals decreased by 8-10-folds via earthworm's significant pollutant removal efficiencies that subsided MSW-driven ecological risks by 60–90%. PCA and MFA revealed that N, P, and K-availability, organic C, and microbial activity were the indicative attributes for heavy metal and emerging organic micropollutant (EOMP)-removal during biocomposting; however, earthworms remove pesticides faster than pharmaceuticals and heavy metals. PCA-based novel empirical models demonstrated that in MSW-only feedstock, earthworm-mediated pollutant detoxification followed the order of pesticides > pharmaceuticals > heavy metals. However, in MSW combined with cow dung (1:1 ratio) feedstock, the detoxification order shifted to pharmaceuticals > heavy metals > pesticides. Therefore, this study provides fresh insights into pollutant-focused feedstock optimization for vermicomposting through model-based approaches, advancing the eco-friendly valorization of toxic MSW. [Display omitted] • Vermitechnology has stronger positive influence on valorized MSW than composting. • Earthworms removed metals, pesticides, and pharmaceuticals from MSW by > 70%. • PCA and MFA showed that TOC, P, K & Ca reduction indicates high pollutant removal. • Novel empirical models could gauge earthworms' toxicant remediation preferences. [ABSTRACT FROM AUTHOR]

The interest in sustainability and energy efficiency is constantly increasing, and the noticeable effects of climate change and rising energy prices are fueling this development. The residential sector is one of the most energy-intensive sectors and plays an important role in shaping future energy consumption. In this context, modeling has been extensively employed to identify relative key drivers, and to evaluate the impact of different strategies to reduce energy consumption and emissions. This article presents a detailed literature review relative to modeling approaches and techniques in residential energy use, including case studies to assess and predict the energy consumption patterns of the sector. The purpose of this article is not only to review the research to date in this field, but to also identify the possible challenges and opportunities. Mobility, electrical devices, cooling and heating systems, and energy storage and energy production technologies will be the subject of the presented research. Furthermore, the energy upgrades of buildings, their energy classification, as well as the energy labels of the electric appliances will be discussed. Previous research provided valuable insights into the application of modeling techniques to address the complexities of residential energy consumption. This paper offers a thorough resource for researchers, stakeholders, and other parties interested in promoting sustainable energy practices. The information gathered can contribute to the development of effective strategies for reducing energy use, facilitating energy-efficient renovations, and helping to promote a greener and more sustainable future in the residential domain.

Research on the coupling of soil, vegetation, and hydrological processes is not only a research hotspot in disciplines such as pedology, ecohydrology and Earth system science but also important for achieving sustainable development. However, scientists from different disciplines usually study the coupling mechanism of soil-vegetation-hydrological processes at very different space and time scales, and the mechanistic connections between different scales are quite few. This article reviewed research advances in coupled soil-vegetation-hydrological processes at different spatial scales—from leaf stomata to watershed and regional scales—and summarized the spatial upscaling methods and modeling approaches of coupled soil-vegetation-hydrological processes. We identify and summarize the following coupling processes: (1) carbon-water exchange in leaf stomata and root-soil interface; (2) changes in soil aggregates and profile hydraulic properties caused by plant roots and water movement; (3) precipitation and soil moisture redistribution by plant canopy and root; (4) interactions between vegetation patches and local hydrological process; (5) links between plant community succession and soil development; and (6) links between watershed/regional water budget and vegetation phenology and production. Meanwhile, the limitations and knowledge gaps in the observations, mechanisms, scaling methods, and modeling approaches of coupled soil-vegetation-hydrological processes were analyzed. To achieve a deep integration of various coupling processes across different spatiotemporal scales, future work should strengthen multiscale, multifactor and multiprocess soil-vegetation-hydrology coupling observations and mechanism studies, develop new scaling methods, identify different feedback pathways, and take time-variable plant behavior and soil hydraulic properties into account during modeling. [ABSTRACT FROM AUTHOR]

Although many mathematical models have been developed for predicting the density, viscosity, and thermal conductivity of CO2, their accuracy and applicability have not been sufficiently established in the gas/liquid and supercritical regions and, in particular, in the region around the critical point. To understand their performances, the models are compared and analyzed, including (i) the cubic equations of state, multi‐parameter equations, and the Helmholtz free energy‐based model for density, (ii) residual theory‐ and regression‐based models for viscosity and thermal conductivity, and (iii) artificial intelligence‐based models (neural networks/machine learning) for these properties. A technical assessment is then performed. The results may provide a positive reference for determining the thermophysical properties of CO2. [ABSTRACT FROM AUTHOR]

Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows’ health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows’ health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows’ behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models.

Optimizing power consumption in grinding, the most consumed stage in the mining industry, plays an influential role in reducing operating costs. Obtaining an efficient model to predict tumbling mills' power consumption accurately took the attention of researchers, mineral processing engineers, and tumbling mill manufacturers. This article comprehensively reviews the published mill power models and the most critical studies on this topic since 1919. Furthermore, the employed approaches for modelling the tumbling mills' power draw, the incorporated parameters into the developed models, the models' performances in predicting the industrial mills' power draw, and the potential gaps in the available literature are discussed. Moreover, based on the shortages identified in this review, some recommendations have been made to enhance the modelling mill power draw. [ABSTRACT FROM AUTHOR]

The dissolved inorganic carbon (DIC) produced by carbonate weathering may be lost by degassing or be biologically fixed as it transports from inland water to the ocean. Knowledge of DIC stability under these two processes in surface water systems is crucial for evaluating the role of carbonate weathering in the global carbon cycle. Here, we evaluated the net ecosystem productivity of aquatic ecosystems (NEPs), water‒air CO 2 exchange fluxes (FCO 2), and Carbon budgets (Carbon budget = ( T O C o u t p u t + 0.5 D I C o u t p u t ) − ( T O C i n p u t + 0.5 D I C i n p u t )) in five simulated spring-pond systems controlled by carbonate weathering and different land uses. Using these parameters and the unique structure of our simulation site, the stability of the carbonate weathering-driven carbon sink (DIC) in surface water was discussed. The results showed that the NEP and FCO 2 of the study site presented significant negative relationships and were mainly controlled by aquatic ecosystem metabolisms than abiotic processes. Most aquatic ecosystems in our study site were autotrophic (NEP>0) due to the strong biological carbon pump effect (BCP) induced by carbonate weathering. Moreover, the DIC input enhanced the FCO 2 in the warm season. The intensive BCP enhanced both CO 2 release and atmospheric CO 2 invasion. Moreover, we found that the stability of DIC was positively related to DIC input, which can be influenced by BCP and human land use. After considering the net changes in Carbon budget through the groundwater-surface water systems, a high average carbon stabilization rate (CSR) of approximately 103.7% of carbonate weathering carbon sink was found in our study site. This indicated that BCP could retain most of the DIC that was driven by carbonate weathering. Therefore, due to the high stability of the carbonate weathering carbon sink under the BCP, we stress that it should be considered a significant carbon sink in the global carbon cycle. [Display omitted] • The degassing of DIC in karst water is dominated by the aquatic metabolism. • Atmospheric CO 2 can directly invade karst water with strong aquatic photosynthesis. • Adjustment of land use will increase the stability of DIC. • The carbonate weathering carbon sink is a vital part of the global carbon cycle. [ABSTRACT FROM AUTHOR]

Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The essential step in applying these methods is to determine the environmental predictors of SOC optimally. This research was carried out for modeling and digital mapping of surface SOC aided by soil properties ie., silt, clay, sand, calcium carbonate equivalent percentage, mean weight diameter (MWD) of aggregate, and pH by machine learning methods. In order to evaluate the accuracy of random forest (RF), cubist, partial least squares regression, multivariate linear regression, and ordinary kriging models for predicting surface SOC in 141 selected samples from 0-30 cm in 680 hectares of agricultural land in Khorramabad plain. The sensitivity analysis showed that silt (%), calcium carbonate equivalent, and MWD are the most important driving factors on spatial variability of SOC, respectively. Also, the comparison of different SOC prediction models, demonstrated that the RF model with a coefficient of determination (R2) and root mean square error (RMSE) of 0.75 and 0.25%, respectively, had the best performance rather than other models in the study area. Generally, nonlinear models rather than linear ones showed higher accuracy in modeling the spatial variability of SOC.

This study summarizes reviews on climate change’s impact on the water environment and hydrological regime. The results indicate a strong relationship between the climatological parameters and hydrological patterns. This relationship can be determined in two steps: (1) define the variations in climatological factors, particularly temperature and precipitation, and (2) measure the variations in runoff and inflows to streams and river systems using different statistical and global climate modeling approaches. It is evident that the increasing global temperatures have significant positive effects on runoff variations and evapotranspiration. Similarly, the increase in temperature has speeded up the melting of glaciers and ice on hilly terrains. This is causing frequent flash floods and a gradual rise in the sea level. These factors have altered the timing of stream flow into rivers. Furthermore, the accumulation of greenhouse gases, variations in precipitation and runoff, and sea-level rise have significantly affected freshwater quality. These effects are likely to continue if timely mitigation and adaptation measures are not adopted.

Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The essential step in applying these methods is to determine the environmental predictors of SOC optimally. This research was carried out for modeling and digital mapping of surface SOC aided by soil properties ie., silt, clay, sand, calcium carbonate equivalent percentage, mean weight diameter (MWD) of aggregate, and pH by machine learning methods. In order to evaluate the accuracy of random forest (RF), cubist, partial least squares regression, multivariate linear regression, and ordinary kriging models for predicting surface SOC in 141 selected samples from 0-30 cm in 680 hectares of agricultural land in Khorramabad plain. The sensitivity analysis showed that silt (%), calcium carbonate equivalent, and MWD are the most important driving factors on spatial variability of SOC, respectively. Also, the comparison of different SOC prediction models, demonstrated that the RF model with a coefficient of determination (R2) and root mean square error (RMSE) of 0.75 and 0.25%, respectively, had the best performance rather than other models in the study area. Generally, nonlinear models rather than linear ones showed higher accuracy in modeling the spatial variability of SOC. [ABSTRACT FROM AUTHOR]

• Three innovative simplified methods of slatted floor in pig houses were developed. • Four CFD modeling approaches of slatted floor were used and compared. • The velocity and pressure under different modeling approaches were compared. • The theoretical basis for CFD simulation of different cases was provided. The slatted floor system is often used in pig housing. Computational Fluid Dynamics (CFD) is a valuable technique for determining the airflow and gas dispersion within the pit headspace through the slatted floor. However, there is a practical issue related to modeling the thousands of small slot openings in real livestock buildings for CFD simulation. It is unpractical to simulate the slatted floor with geometry details due to the large mesh number and the limited computer capacity. In this study, several methods to simplify the model of the slatted floor were explored to reduce the computational cost. The study was based on CFD analysis supported by experimental validation. The slatted floor was modeled in four ways: direct geometry method (DGM), porous-region method (POM), and profiled-porous-region method (including PPOM-cell (PPOM-C) and PPOM-floor (PPOM-F)). To assess the feasibility of the simplification, the four modeling approaches were compared with the experimental data. The results showed that it's feasible to model the slatted floor as POM or PPOM. The accuracy of POM was high, but it was greatly affected by the calculation of the resistance coefficient. PPOM-C has greatly reduced the amount of calculation, which could be used to simplify the large-scale pig house model whose flow fields and gas emissions below the slatted floor could be ignored. The simulation result of PPOM-F was the closest to DGM, however, due to the large number of meshes and complex setup, it was not suitable for practical simulation. [ABSTRACT FROM AUTHOR]

The thermal and moisture properties of building envelope materials determine their performance over many years of use. Moisture has a particularly negative impact, impairing all the technical parameters and adversely affecting the microclimatic conditions inside the building. This article presents research and analysis on the moisture behavior of partitions made of autoclaved aerated concrete. Autoclaved aerated concrete is a very popular material for building external walls because of its relatively good thermal insulation and sufficient strength, if it is not subjected to increased moisture. This study investigated how the moisture content of this material changes with the change in relative air humidity. The four most popular density classes were studied. The sorption isotherms were determined by the static gravimetric method throughout the whole hygroscopic range. Moreover, the applicability of various models to describe sorption isotherms of this material group has been extensively evaluated. The tested models (Peleg, Redlich, Chen, Oswin, Henderson, Lewicki, Caurie, and GAB) all provided a very good fit with the experimental results for the tested material group (R2 ranged from 0.9599 to 0.9978). This paper indicates that the use of two additional approximation parameters (SSE and RMSE) allows a more precise assessment of the quality of individual models.

G protein-coupled estrogen receptor, sexual dimorphism, central nervous system, GPER turnover, heart tissue, signaling cascade, endocrine-disrupting chemicals, modeling approaches Since GPER binds the female hormone estradiol, one "basic" question is: does GPER play a role in sexual dimorphism? Keywords: G protein-coupled estrogen receptor; sexual dimorphism; central nervous system; heart tissue; signaling cascade; endocrine-disrupting chemicals; GPER turnover; modeling approaches EN G protein-coupled estrogen receptor sexual dimorphism central nervous system heart tissue signaling cascade endocrine-disrupting chemicals GPER turnover modeling approaches 1 3 3 12/02/21 20211129 NES 211129 Since the pioneering work of Elwood V. Jensen (1920-2012), which led to the discovery of estrogen-binding "substances" shortly afterwards called estrophilin, the concept of estrogen receptor (ER) has evolved considerably ([1]-[3]). [Extracted from the article]

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

• A critical review on asphaltene precipitation/deposition is provided. • Mechanisms, main characteristics, theoretical models, and pros and cons of asphaltene precipitation are discussed. • Parametric sensitivity analysis highlights vital factors/impacts in asphaltene precipitation. • Focal challenges for research/engineering activities in asphaltene precipitation are listed. • Useful guidelines to manage asphaltene deposition in petroleum industry are recommended. Asphaltene is the heaviest and most polar fraction of crude oils, which is usually defined as a solubility class, soluble in light aromatics, benzene and toluene, and insoluble in paraffins. Asphaltene precipitation/deposition negatively affects a variety of oil and gas processes, including oil recovery, transportation, and petroleum processing as it exhibits some undesirable impacts such as an increase in the viscosity of crudes and stability of the emulsions, a decrease in the distillate yields, and unstable phase separation. Experimental and theoretical investigations have revealed that asphaltene precipitation/deposition is influenced by several parameters/variables, including pressure, temperature, characteristics of the involved mixtures, properties and amount of precipitants, and characteristics of porous media. Knowing the undeniable consequences of asphaltene precipitation/deposition such as reduction of well productivity and/or blockage of the surface facilities, the effective treatments or inhibition techniques have gained widespread attention in the petroleum industry so far. Hence, a comprehensive knowledge of previous findings and an adequate understanding of different aspects of asphaltene precipitation/deposition (e.g., mechanisms and characterization) seem to be pivotal. Various attempts have been made to investigate different aspects of this phenomenon including thermodynamics of precipitation, kinetics of flocculation, and deposition mechanisms. The previous studies and importance of asphaltene in the oil and gas industry inspired us to provide a comprehensive review of the asphaltene precipitation/deposition that helps to further understand the governing mechanisms, thermodynamic behaviors, and transport phenomena prospects in this area. In this review work, the theories and mechanisms of precipitation/deposition, experimental and modelling approaches, the effects of operational parameters, and treatment/inhibition techniques are reviewed. Moreover, the economic analysis of flow assurance and the most common theoretical and practical challenges in asphaltene precipitation/deposition are studied. [ABSTRACT FROM AUTHOR]

Abstract The applications of ejectors are many and encompass the refrigeration, the power generation and the chemical sectors. On one hand, ejector technology needs limited maintenance, has low operational costs and has no restrictions concerning the working fluids; on the other hand, the complex single- and multi-phase fluid dynamics make ejector design and performance prediction a real challenge. This perspective explores the main advancements in ejector technology and proposes a critical discussion with an outlook for the future research; the proposed discussion is grounded on the multi-scale relationship between the " local-scale " phenomena and the " component-scale " performances. After a look at the past, this perspective examines the ongoing research activities and achievements regarding four state-of-the-art research areas, namely refrigeration systems, power conversion plants, chemical process and technology and computational methods. For the different research areas discussed, directions and opportunities for the future research activities are appraised. Finally, this perspective defines a fundamental challenge that needs be addressed in the forthcoming long-term activities: " the multi-scale ejector challenge ". Highlights • " the multi-scale ejector challenge " should guide the future research. • Medium-term and long-term research activities are discussed. • Ongoing research activities and achievements are discussed. • Ejector technology and applications are discussed. • Ejector modeling approaches are presented and discussed. [ABSTRACT FROM AUTHOR]

Abstract The ideal picture of a near-perfect 3D microscope often presented regarding Atom Probe Tomography faces several issues. These issues degrade the metrological performance of the instrument and find their roots in the phenomena acting at the atomic to the mesoscopic level in the vicinity of the surface of a field emitter. From the field evaporation process at the atomic scale, to the macroscopic scale of the instrument, the path to model the imaging process and to develop more accurate and reliable reconstruction algorithms is not a single lane road. This paper focused on the numerical methods used to understand, treat, and potentially heal imaging issues commonly affecting the data in atom probe experiments. A lot of room for improvement exists in solving accuracy problems observed in complex materials by means of purely electrostatic models describing the image formation in a classical approach. Looking at the sample at the atomic scale, the phenomena perturbing the imaging process are subtle. An examination of atomic scale modifications of the sample surface in the presence of a high surface electric field is therefore mandatory. Atomic scale molecular dynamic models integrating the influence of the high surface electric are being developed with this aim. It is also demonstrated that the complex behavior of atoms and molecules in high fields, and consequences on collected data, can be understood through the use of accurate ab-initio models modified to include the impact of the extreme surface electric field. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of literature on integrated infrastructure systems with an emphasis on interdependency types and definitions, complex network abstractions, and on different modeling and simulation techniques. An infrastructure interdependency matrix is presented to provide a better mapping of implementation, utilization, and in some cases a lack of integration among infrastructure systems. It is expected that this review will contribute to the advancement of the growing area of integrated infrastructure systems. Assessment of the research on integrated infrastructure modeling literature showed that the focus of most modeling efforts is mainly on short term operational purposes and extreme events. Long-term efforts to examine the impacts and assessment of future policies and scenarios are needed. Although ‘integration’ is being used in many studies on civil infrastructure, the level of detail or type of integration is not consistent and in some cases almost trivial. [ABSTRACT FROM AUTHOR]

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Microplastic (MP) pollution is ubiquitous in the oceans and poses serious threats to the marine ecosystems. Nowadays numerical modeling has become one of the widely used tools for monitoring and predicting the transport and fate of MP in marine environments. Despite the growing body of research on numerical modeling of marine MP, the advantages and disadvantages of various modeling methods have not received systematic evaluation in published works. Important aspects such as parameterization schemes for MP behaviors, factors influencing MP transport, and proper configuration in beaching are essential for guiding researchers to choose proper methods in their work. For this purpose, we comprehensively reviewed the current knowledge on factors influencing MP transport, classified modeling approaches according to the governing equations, and summarized up-to-date parameterization schemes for MP behaviors. Critical factors such as vertical velocity, biofouling, degradation, fragmentation, beaching, and washing-off were reviewed in the frame of MP transport processes. [Display omitted] • Microplastic particles are widely distributed in marine environments. • Factors influencing microplastic transport processes are summarized. • Modeling approaches of microplastic transport and fate are classified. • Parameterization schemes in microplastic modeling are summarized. [ABSTRACT FROM AUTHOR]

Groundwater is a critical resource that serve as a source of drinking water to large human population and, provide long-term water for irrigation purposes. In recent years; however, this precious resource being increasingly threatened, due to natural and anthropogenic activities. A variety of contaminants of emerging concern such as pharmaceuticals and personal care products, perfluorinated compounds, endocrine disruptors, and biological agents detected in the groundwater sources of both developing and developed nations. In this review paper, various studies have been included that documented instances of groundwater pollution and vulnerability to emerging contaminants of concern, pesticides, heavy metals, and leaching potential of various organic and inorganic contaminants from poorly managed residual waste products (biosolids, landfills, latrines, and septic tanks etc.). Understanding vulnerability of groundwater to pollution is critical to maintain the integrity of groundwater. A section on managed artificial recharge studies is included to highlight the sustainable approaches to groundwater conservation, replenishment and sustainability. This review paper is the synthesis of studies published in last one year that either documented the pollution problems or evaluated the vulnerability of groundwater pollution. [ABSTRACT FROM AUTHOR]

• The characteristics of current, electric fields and modeling approaches of lightning M-component mode of charge transfer were summarized. • M-component-type ICC pulses can be distinguished from mixed-mode pulses using four different criteria. • The significance of M-components is reviewed according to practical aspects in transformer secondary, surge protective devices (SPD), grounding systems. In this paper, we present the characteristics of current, electric fields and modeling approaches of lightning M-component mode of charge transfer. We consider both the classical M-components (occurring after return strokes) and M-component-type ICC (Initial Continuous Current) pulses occurring during the initial (ICC) phase of upward flashes. M-component-type ICC pulses can be distinguished from mixed-mode pulses using different criteria: (i) the 10–90% current risetime at the channel-base with respect to an 8-µs risetime; (ii) the time lag between the onset of the current and electric fields with a respect to a threshold of 10 µs; (iii) an asymmetrical waveform coefficient (AsWc) with respect to a value of 0.8; (iv) the relative height of the junction or connection points on the grounded channel above the ground. The features of M-component electric field waveforms are summarized for close, intermediate, and far distance ranges. The observed millisecond-scale slow-part pulse shows a polarity reversal from an initial-negative waveform at close range, to a full positive-flattening late-time response at intermediate range and a bipolar wave-shape at the far distance range. One or some microsecond-scale fast pulses (junction pulse) are observed to precede the millisecond-scale slow part pulse at intermediate and far distance ranges. The microsecond-scale fast pulses are dominated by unipolar pulses along with several cases of bipolar pulses exhibiting initial polarities of both signs. The main advantage of the guided wave model and its variations is their simplicity and straightforward implementation. The guided wave model is also able to reproduce reasonably well the observed slow electric fields. The nonlinear models are more physics-based compared to the guided wave models. They are based on an important number of adjustable parameters, many of which cannot be directly inferred from experimental observations. The significance of M-components is reviewed according to practical aspects in transformer secondary, surge protective devices (SPD), grounding systems. [ABSTRACT FROM AUTHOR]

The design and implementation of management policies for plug-in electric vehicles (PEVs) need to be supported by a holistic understanding of the functional processes, their complex interactions, and their response to various changes. Models developed to represent different functional processes and systems are seen as useful tools to support the related studies for different stakeholders in a tangible way. This paper presents an overview of modeling approaches applied to support aggregation-based management and integration of PEVs from the perspective of fleet operators and grid operators, respectively. We start by explaining a structured modeling approach, i.e., a flexible combination of process models and system models, applied to different management and integration studies. A state-of-the-art overview of modeling approaches applied to represent several key processes, such as charging management, and key systems, such as the PEV fleet, is then presented, along with a detailed description of different approaches. Finally, we discuss several considerations that need to be well understood during the modeling process in order to assist modelers and model users in the appropriate decisions of using existing, or developing their own, solutions for further applications. [ABSTRACT FROM AUTHOR]

The modeling of flotation has been reviewed with the aim of identifying their usefulness, significance as well as limitations. From literature it is clear that, various flotation models have been developed on the basis of the processes and sub-processes occurring in flotation. An overview of the literature indicates that various approaches have been adopted in quantifying the process. Flotation models based on kinetics have prevailed in almost all flotation conditions regardless of the ore type and ore characteristics as well as flotation cell configurations. It may be concluded that the classical first-order kinetic model is comparatively a better model and can be utilized to optimize the flotation process as it is applicable to both batch and continuous flotation processes with high confidence level. Finally, it is suggested that future work should focus on improving the models which can more accurately predict the prevailing conditions and enable better optimization of the flotation process. [ABSTRACT FROM AUTHOR]

A chemical-transport model (CTM), Polyphemus/Polair3D, is used to investigate the contributions of various anthropogenic and biogenic sources to total organic carbon (OC) in PM in Beirut, Lebanon, during the summer of 2011. Those results are compared to a tracer-based source apportionment of OC at an ambient site in Beirut where a measurement campaign was conducted in July 2011. The results obtained from the CTM in the base simulation S1 suggest contributions to total simulated OC mass (3.24 μg/m) of 66 % (2.14 ± 1.07 μg/m) from fossil fuel burning (FFB) and 8 % (0.27 ± 0.135 μg/m) from biogenic secondary organic carbon (BSOC). The tracer-based approach leads to contribution estimates to total measured OC mass (5.6 μg/m) of 16 % (0.9 μg/m ± 0.22) from FFB, 53 % (2.9 ± 1.7 μg/m) from BSOC, and 32 % (1.8 ± 0.88 μg/m) from cooking activities. In a second CTM simulation S2, emissions related to cooking activities were added to the emission inventory, monoterpene and sesquiterpene secondary organic aerosol (SOA) surrogate species were added to the boundary conditions, and a lower ratio of semi-volatile organic compounds to primary organic aerosols (SVOC/POA) was used. The S2 results obtained showed contributions to total simulated OC mass (3.01 μg/m) of 33 % (0.98 ± 0.49 μg/m) from FFB, 18 % (0.53 ± 0.27 μg/m) from BSOC, and 39 % (1.2 ± 0.6 μg/m) from cooking activities. The differences between these two methods are discussed in terms of their uncertainties and biases. The comparison of both approaches showed that the model underestimates the secondary fraction of OC, which may be due to underestimations of some biogenic volatile organic compound (VOC) emissions and/or boundary concentrations as well as the use of SOA yields that may not be representative of the eastern Mediterranean region. Concerning the tracer-based approach, the use of tracer/OC ratios that are not specific to Lebanon because of a lack of data could represent a limitation of this methodology. Nevertheless, this comparative analysis suggests that on-road transportation and diesel generators used for electricity production are major sources of atmospheric PM and should be targeted for emission reduction. Finally, cooking activities, which were identified as a significant source of PM with the tracer-based approach, should be studied further. [ABSTRACT FROM AUTHOR]