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This review explores fundamental analytical modelling approaches using conventional composite theory and artificial intelligence (AI) to predict mechanical properties of 3D printed particle-reinforced resin composites via digital light processing (DLP). Their mechanisms, advancement, limitations, validity, drawbacks and feasibility are critically investigated. It has been found that conventional Halpin-Tsai model with a percolation threshold enables the capture of nonlinear effect of particle reinforcement to effectively predict mechanical properties of DLP-based resin composites reinforced with various particles. The paper further explores how AI techniques, such as machine learning and Bayesian neural networks (BNNs), enhance prediction accuracy by extracting patterns from extensive datasets and providing probabilistic predictions with confidence intervals. This review aims to advance a better understanding of material behaviour in additive manufacturing (AM). It demonstrates exciting potential for performance enhancement of 3D printed particle-reinforced resin composites, employing the optimisation of both material selection and processing parameters. It also demonstrates the benefit of combining empirical models with AI-driven analytics to optimise material selection and processing parameters, thereby advancing material behaviour understanding and performance enhancement in AM applications. [ABSTRACT FROM AUTHOR]

Changes in wildfire regimes are of growing concern and raise issues about how well we can model risks in a changing climate. Process-based coupled firevegetation models, used to project future wildfire regimes, capture many aspects of wildfire regimes poorly. However, there is now a wealth of information from empirical studies on the climate, vegetation, topography and human activity controls on wildfire regimes. The measures used to quantify these controls vary among studies, but certain variables consistently emerge as the most important: gross primary production as a measure of fuel availability, vegetation cover as a measure of fuel continuity, and atmospheric humidity as a measure of fuel drying. Contrary to popular perception, ignitions are generally not a limiting factor for wildfires. In this review, we describe how empirical fire models implement wildfire processes, synthesise current understanding of the controls on wildfire extent and severity, and suggest ways in which fire modelling could be improved. [ABSTRACT FROM AUTHOR]

The neglect in the cultivation, utilization, and diverse edible indigenous crops (ICs) threatens food-nutrition security (FNS) and rural development. Here, we (i) profiled the socioeconomics, ethnobotanicals and factors influencing the decision to cultivate edible ICs among smallholder farmers and (ii) factors influencing the FNS status of the ICs farmers in rural communities in South Africa using descriptive statistics and Heckman's probit-selection model to evaluate the factors influencing the decision to cultivate ICs and FNS. In the study, the household dietary diversity score (HDDS) was 7.01 and the household's food insecurity access scores (HFIAS) was 4.40. In addition, the food security indicator shows that 40.60 % of the participants were food insecure. Taken together, these metrics shows a significant existence of food and nutrition insecurity among the participants. Additionally, the decisions to cultivate ICs among the participants was driven by the household size, access to seeds, produce market, perceived nutritional benefits of the ICs and proportion of land used for cultivation significantly affects FNS status of the participants. Overall, the cultivation of these ICs is important as their cultivation significantly contributed to the FNS of the rural communities. Policy interventions, targeted at improving the present land tenure pattern, awareness on the value of ICs and incentives for production as a means for stimulating wider acceptance, cultivation, and sustainability in South Africa was recommended.

The densely populated coast of the Gulf of Guinea (GoG) is experiencing escalating erosion from climate change (CC). To assess Longshore Sediment Transport (LST) changes in the past and future, an empirical formula was used to model the GoG's LST. Nearshore wave conditions, crucial for LST estimation, were derived by projecting significant wave height, mean wave period, and mean wave direction from deep water to the breaker zone. These wave conditions were used as input for LST computation. Analysing three time slices: Past (1979–2005), mid-century (2026–2045), and end-century (2081–2100) under RCPs 4.5 and 8.5, results indicate continuous increase in LST in most locations, with local and seasonal variations. LST magnitudes range from 104 to 105 m3y−1 in the GoG, with the Cameroonian coast having the least. This aligns with the rising wave energy from CC. LST directions are predominantly eastward, though some locations, like off Lagos and Cameroon-Gabon coasts, show westward trend, correlating with coastal orientation influenced by southward wave directions. RCP 8.5 projections suggest mid-century increase, intensifying by end of century, with yearly rates of change around 103 m3y−1 and future LST changes up to 105 m3y−1. This situation implies accelerated sediment removal, heightening erosion. [ABSTRACT FROM AUTHOR]

Background: The moisture content of litter and woody debris is a key determinant of fire potential and fire behaviour. Obtaining reliable estimates of the moisture content of dead fine fuels (i.e. 1-h and 10-h fuels) is therefore a critical requirement for effective fire management. Aims: We evaluated and compared the performance of five simple models for fuel moisture content. The models belong to two separate classes: (1) exponential functions of the vapour pressure deficit; and (2) affine functions of the (weighted) difference between air temperature and relative humidity. Methods: Model performance is assessed using error and correlation statistics, calculated using cross validation, over four empirical datasets. Key results: Overall, the best performing models were the relaxed and generalised models based on the weighted difference between temperature and relative humidity. Conclusions: Simple functions of the difference between air temperature and relative humidity can perform as well as, if not better than exponential functions of vapour pressure deficit. However, it is important to note the limitations of all these models when applied to fuels with moisture contents <10%. Implications: The moisture content of fine dead fuels and woody debris can be reliably estimated using simple models that are amenable to easy application. Five models for the moisture content of fine dead fuels were evaluated using empirical datasets. Simple functions of the difference between air temperature and relative humidity were shown to perform as well as, if not better than exponential models based on vapour pressure deficit. [ABSTRACT FROM AUTHOR]

Changes in wildfire regimes are of growing concern and raise issues about how well we can model risks in a changing climate. Process-based coupled fire-vegetation models, used to project future wildfire regimes, capture many aspects of wildfire regimes poorly. However, there is now a wealth of information from empirical studies on the climate, vegetation, topography and human activity controls on wildfire regimes. The measures used to quantify these controls vary among studies, but certain variables consistently emerge as the most important: gross primary production as a measure of fuel availability, vegetation cover as a measure of fuel continuity, and atmospheric humidity as a measure of fuel drying. Contrary to popular perception, ignitions are generally not a limiting factor for wildfires. In this review, we describe how empirical fire models implement wildfire processes, synthesise current understanding of the controls on wildfire extent and severity, and suggest ways in which fire modelling could be improved.

Abstract This study presented a facile synthesis route via the precipitation method for the production of magnetic biochar from oil palm frond (OPF). The physicochemical characteristics including surface, functional, and magnetic properties of the synthesized magnetic biochar revealed that the surface morphology, porosity, and magnetic properties enhanced its adsorption capacity for the removal of crystal violet (CV) and sunset yellow (SY) from aqueous solution. The saturation magnetization of OPF biochar was found to be 8.41 emu/g, coercivity (Hc) of 83.106 G, and retentivity (Mr) of 1.475 emu/g which implies that OPF magnetic biochar can be facilely separated from aqueous solution. The result also demonstrated superparamagnetic properties which provided suitable magnetic responsive characteristics to an external magnetic field. The interactive effect of the operational conditions of pH, adsorbent dosage, initial concentration, and temperature was investigated in a batch adsorption study using the central composite design (CCD) of the response surface methodology (RSM). It was indicated that an increase in adsorbent dosage to 1.0 g/L at a lower initial concentration (50 ppm) conducted at 20 °C favoured optimum removal of CV and SY at pH 11 and 4, respectively. The Langmuir isotherm model with maximum adsorption capacity ( $$q_{\max }$$ q max ) of 149.03 and 342.47 mg/g was achieved for CV and SY dyes, respectively. The kinetic data proved to be best fitted to the pseudo-second-order kinetic model. Thermodynamic parameters revealed spontaneous and endothermic reactions. The suitability and sustainability of the magnetic biochar were enhanced by its regeneration potential for effective adsorption of CV and SY after 5 cycles which indicates its outstanding reusability. Hence, OPF magnetic biochar exhibited prospective application for the removal of dyes from wastewater.

This study presented a facile synthesis route via the precipitation method for the production of magnetic biochar from oil palm frond (OPF). The physicochemical characteristics including surface, functional, and magnetic properties of the synthesized magnetic biochar revealed that the surface morphology, porosity, and magnetic properties enhanced its adsorption capacity for the removal of crystal violet (CV) and sunset yellow (SY) from aqueous solution. The saturation magnetization of OPF biochar was found to be 8.41 emu/g, coercivity (Hc) of 83.106 G, and retentivity (Mr) of 1.475 emu/g which implies that OPF magnetic biochar can be facilely separated from aqueous solution. The result also demonstrated superparamagnetic properties which provided suitable magnetic responsive characteristics to an external magnetic field. The interactive effect of the operational conditions of pH, adsorbent dosage, initial concentration, and temperature was investigated in a batch adsorption study using the central composite design (CCD) of the response surface methodology (RSM). It was indicated that an increase in adsorbent dosage to 1.0 g/L at a lower initial concentration (50 ppm) conducted at 20 °C favoured optimum removal of CV and SY at pH 11 and 4, respectively. The Langmuir isotherm model with maximum adsorption capacity ( q max ) of 149.03 and 342.47 mg/g was achieved for CV and SY dyes, respectively. The kinetic data proved to be best fitted to the pseudo-second-order kinetic model. Thermodynamic parameters revealed spontaneous and endothermic reactions. The suitability and sustainability of the magnetic biochar were enhanced by its regeneration potential for effective adsorption of CV and SY after 5 cycles which indicates its outstanding reusability. Hence, OPF magnetic biochar exhibited prospective application for the removal of dyes from wastewater. [ABSTRACT FROM AUTHOR]

The blasting operation is one of the technologies used for breaking rock masses and reducing the rock mass into smaller sizes to improve transportation and further particle separation. The improvement of blast fragmentation supports the maximization of mining operation and productivity. Soft computing and regression model has been developed in this study to optimize small-scale dolomite blast operations in Akoko Edo, Nigeria. WipFrag software was used to analyze the results of 35 blasting rounds. As independent variables, one uncontrollable parameter and five controllable blast parameters were chosen to predict blast particle sizes using four mathematically motivated soft computing model approaches. The prediction accuracy of the developed models was tested using various model performance indices. The study revealed that rock strength influences blast fragmentation results, and based on the rock strength properties, the fragmentation block size increases with an increase in rock strength. The results of the model performance indices used for the evaluation of the proposed models showed that the modified Artificial Neural Network (ANN) called Hunter Point (HP-ANN) has the highest predictive accuracy. A new model evaluator was also developed in this study called the decision factor. Its application indicates that the HP-ANN model is the best model suitable for the prediction of blast fragment size distribution. Therefore, the developed models can be used to predict the blast result mean size (X50) and the 80% percentage passing size (X80) for mining engineering blasting practices.

We present SPAMS: Simple Parameterization for the Motion of Soils, a model to describe the motion of deformable soils in the Vadose zone, mainly peat and clay, herein called shallow soft soils. The SPAMS model estimates the reversible and irreversible vertical component of surface displacement to within sub-centimetre RMSE, using only four parameters: three scaling factors and an integration time. Requiring only meteorological data as an input, its lightweight nature and simple implementation make it a powerful tool when used as a first approximation in inverse problems like those encountered in remote sensing. It has been validated against in-situ data from five test sites in The Netherlands with different Holocene soil strata.

Lithium-ion batteries (LIBs) are leading the energy storage market. Significant efforts are being made to widely adopt LIBs due to their inherent performance benefits and reduced environmental impact for transportation electrification. However, achieving this widespread adoption still requires overcoming critical technological constraints impacting battery aging and safety. Battery aging, an inevitable consequence of battery function, might lead to premature performance losses and exacerbated safety concerns if effective thermo-electrical battery management strategies are not implemented. Battery aging effects must be better understood and mitigated, leveraging the predictive power of aging modelling methods. This review paper presents a comprehensive overview of the most recent aging modelling methods. Furthermore, a multiscale approach is adopted, reviewing these methods at the particle, cell, and battery pack scales, along with corresponding opportunities for future research in LIB aging modelling across these scales. Battery testing strategies are also reviewed to illustrate how current numerical aging models are validated, thereby providing a holistic aging modelling strategy. Finally, this paper proposes a combined multiphysics- and data-based modelling framework to achieve accurate and computationally efficient LIB aging simulations. [ABSTRACT FROM AUTHOR]

The blasting operation is one of the technologies used for breaking rock masses and reducing the rock mass into smaller sizes to improve transportation and further particle separation. The improvement of blast fragmentation supports the maximization of mining operation and productivity. Soft computing and regression model has been developed in this study to optimize small-scale dolomite blast operations in Akoko Edo, Nigeria. WipFrag software was used to analyze the results of 35 blasting rounds. As independent variables, one uncontrollable parameter and five controllable blast parameters were chosen to predict blast particle sizes using four mathematically motivated soft computing model approaches. The prediction accuracy of the developed models was tested using various model performance indices. The study revealed that rock strength influences blast fragmentation results, and based on the rock strength properties, the fragmentation block size increases with an increase in rock strength. The results of the model performance indices used for the evaluation of the proposed models showed that the modified Artificial Neural Network (ANN) called Hunter Point (HP-ANN) has the highest predictive accuracy. A new model evaluator was also developed in this study called the decision factor. Its application indicates that the HP-ANN model is the best model suitable for the prediction of blast fragment size distribution. Therefore, the developed models can be used to predict the blast result mean size (X50) and the 80% percentage passing size (X80) for mining engineering blasting practices. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: The objective was to investigate if metformin pharmacokinetics is modulated by time-of-day in humans using empirical and mechanistic pharmacokinetic modelling techniques on a large clinical dataset. This study also aimed to generate and test hypotheses on the underlying mechanisms, including evidence for chronotype-dependent interindividual differences in metformin plasma and efficacy-related tissue concentrations. Methods: A large clinical dataset consisting of individual metformin plasma and urine measurements was analysed using a newly developed empirical pharmacokinetic model. Causes of daily variation of metformin pharmacokinetics and interindividual variability were further investigated by a literature-informed mechanistic modelling analysis. Results: A significant effect of time-of-day on metformin pharmacokinetics was found. Daily rhythms of gastrointestinal, hepatic and renal processes are described in the literature, possibly affecting drug pharmacokinetics. Observed metformin plasma levels were best described by a combination of a rhythm in GFR, renal plasma flow (RPF) and organic cation transporter (OCT) 2 activity. Furthermore, the large interindividual differences in measured metformin concentrations were best explained by individual chronotypes affecting metformin clearance, with impact on plasma and tissue concentrations that may have implications for metformin efficacy. Conclusions/interpretation: Metformin's pharmacology significantly depends on time-of-day in humans, determined with the help of empirical and mechanistic pharmacokinetic modelling, and rhythmic GFR, RPF and OCT2 were found to govern intraday variation. Interindividual variation was found to be partly dependent on individual chronotype, suggesting diurnal preference as an interesting, but so-far underappreciated, topic with regard to future personalised chronomodulated therapy in people with type 2 diabetes. [ABSTRACT FROM AUTHOR]

While many reactive species are known to cause N -nitrosation, trace nitrite (NO 2 −), which may be present in several excipients, is a source of nitrosating agents in pharmaceutical formulations. In this study we have found that the salt form of NO 2 − can influence the favored nitrosation conditions and final amount of nitrosamine being formed. Using native levels of NO 2 −, most likely present as ammonium nitrite (NH 4 NO 2), in microcrystalline cellulose, we have determined the kinetics of nitrosamine formation in solid state with dimethylamine substrate present in metformin, used as model compound. It was found that the competing degradation of NH 4 NO 2 into N 2 and H 2 O limited the amount of nitrosamine formation to a great extent. Empirically modelling the kinetic data predicted reaching at maximum 1.6% conversion over a hypothetical 3-year shelf-life. These results also showed that using other sources of NO 2 − as spiking reagents, such as NaNO 2 , may lead to unrealistic worst-case situations when the main form of NO 2 − in the drug product (DP) under evaluation may be NH 4 NO 2. As well, measuring NO 2 − in freshly manufactured excipients containing NO 2 − potentially as NH 4 NO 2 may lead to biased high NO 2 − content, which is not representative of the actual amounts present at the time of DP manufacture. [Display omitted] [ABSTRACT FROM AUTHOR]

The relationship between chemical composition and working temperature of the steel are not linear with the thermal conductivity, so empirical models are proposed for its prediction. Measurements were made on 32 rolled and annealed AISI steel markings. The K-Nearest Neighbor machine learning algorithm was used; in addition, a neural network was trained using the RStudio software, specifically the caret library, to obtain an empirical model that allowed predicting, with an adequate level of uncertainty, the thermal conductivity in the temperature range from 0-800°C. The model was tested with a group of values reserved for this purpose, obtaining low levels of uncertainty. The best results are obtained by training a neural network with 25 neurons in the hidden layer and a regularization value of 0,001, obtaining an error of 5,4% and an RMSE of 0,0228. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) are leading the energy storage market. Significant efforts are being made to widely adopt LIBs due to their inherent performance benefits and reduced environmental impact for transportation electrification. However, achieving this widespread adoption still requires overcoming critical technological constraints impacting battery aging and safety. Battery aging, an inevitable consequence of battery function, might lead to premature performance losses and exacerbated safety concerns if effective thermo-electrical battery management strategies are not implemented. Battery aging effects must be better understood and mitigated, leveraging the predictive power of aging modelling methods. This review paper presents a comprehensive overview of the most recent aging modelling methods. Furthermore, a multiscale approach is adopted, reviewing these methods at the particle, cell, and battery pack scales, along with corresponding opportunities for future research in LIB aging modelling across these scales. Battery testing strategies are also reviewed to illustrate how current numerical aging models are validated, thereby providing a holistic aging modelling strategy. Finally, this paper proposes a combined multiphysics- and data-based modelling framework to achieve accurate and computationally efficient LIB aging simulations.

Heat-assisted machining (HAM) is an alternative method for the successful machining of difficult-to-cut aerospace superalloys. However, the tool wear progression in HAM has been substantially affected by the workpiece external heating temperature along with the machining parameters. It is of great importance to precisely estimate the tool wear progression and reliability of the cutting tool operated in HAM to enhance the machining efficiency and economics. Hardened AISI4340 alloy steel was machined under plasma-arc heating conditions with WC cutting tools. The experimental results reviled that for 200°C of preheating temperature, the surface roughness noted as 2.03µm and tool life as 870s. While at 600°C, the surface roughness as 1.68µm, and tool life extended to 1885s to reach the set threshold value of 0.4mm flank wear. Notch wear as dominant tool wear mechanism at 200°C and 400°C, while it is diffusion wear at 600°C. On the establishment of stochastic tool life distribution and reliability models, tool lives measured at all heating conditions followed a Weibull distribution. Empirical models for tool wear, surface roughness, and material removal rate were postulated and validated statistically as-well-as experimentally. The derived reliability and empirical models can be used to predict the machining characteristics to enhance its performance. [ABSTRACT FROM AUTHOR]

Metal additive manufacturing (AM) processes, viz laser powder bed fusion (L-PBF), are becoming an increasingly popular manufacturing tool for a range of industries. The powder material used in L-PBF is costly, and it is rare for a single batch of powder to be used in a single L-PBF build. The un-melted powder material can be sieved and recycled for further builds, significantly increasing its utilisation. Previous studies conducted by the authors have tracked the effect of both powder recycling and powder rejuvenation processes on the powder characteristics and L-PBF part properties. This paper investigates the use of multiple linear regression to build empirical models to predict the part density and surface roughness of 316L stainless steel parts manufactured using recycled and rejuvenated powder based on the powder characteristics. The developed models built on the understanding of the effect of powder characteristics on the part properties. The developed models were found to be capable of predicting the part density and surface roughness to within ±0.02% and ±0.5 Ra, respectively. The models developed enable L-PBF operators to input powder characteristics and predict the expected part density and surface roughness. [ABSTRACT FROM AUTHOR]

Machining processes contribute significantly to the energy consumption of manufacturing industries, and reducing their energy consumption is a major challenge to achieve sustainable and cleaner manufacturing. The accurate and practical energy consumption prediction models for a machine tool are the foundation for sustainable and cleaner manufacturing. The machining of a workpiece mainly involves constant-power consumption machining processes e.g. turning and variable-power consumption machining processes e.g. end facing. The cutting power characteristics of the variable-power consumption machining processes are more complex and dynamic, due to change in one of the process parameters (e.g. cutting speed during end facing) than the constant-power consumption machining processes, and have received limited attention in the literature. In the present work, an empirical model is developed to predict the cutting energy consumption of the variable-power consumption machining process i.e. end facing. The end facing experiments were performed on a Computer Numerical Control Lathe in the dry and wet environment to obtain the fitting constants of the developed model. Four validation experiments were performed to confirm the prediction capability of the developed model. The validation experiments confirm that the accuracy of the developed model is more than 96%. Further, the predicted power profiles were in good agreement with the measured power profiles, which shows that the developed model satisfactorily encompasses the influences of the process parameters on the cutting power consumption. [ABSTRACT FROM AUTHOR]

A massive shift in agricultural practices over the past decades, to support exceptionally high yields and productivities involving intensive agriculture, have led to unsustainable agriculture practices across the globe. Sustenance of such high yields and productivities demand high use of organic and industrial fertilizers. This acts as a negative pressure on the environment. Excessive use of fertilizers leads to nutrient surplus in the fields, which, as a part of catchment runoff, flows into the water bodies as diffuse pollution. These nutrients through rivers are eventually passed into seas. High nutrients ending up into water bodies cause eutrophication. The situation is worsened when such unsustainable agricultural activities are carried out on drained peatlands. As a result, the nutrients that were not part of the nutrient cycle in the landscape for years begin to leach out due to mineralization of peatlands, thereby putting an additional load of nutrients on the environment, that was already under the negative impact of nutrient surplus. In view of the above, a small lowland catchment of the Ryck river in northeast Germany was assessed for its nitrogen losses from agricultural lands through empirical modelling. Initial empirical modelling resulted in an average annual total nitrogen loss of 14.7 kg ha−1 year−1. After a comparative analysis of these results with procured data, the empirical equation was modified to suit the catchment, yielding more accurate results. The study showed that 75.6% of peatlands in the catchment are under agricultural use. Subsequently, a proposal was made for potential wetland buffer zones in the Ryck catchment. Altogether, 13 peatland sites across 8 sub-catchments were recommended for mitigation of high nutrient runoff. In the end, nutrient efficiency of proposed WBZs in one of the sub-catchments of Ryck has been discussed. The results show that (i) the modified empirical equation can act as a key tool in application-based future strategies for nitrogen reduction in the Ryck catchment, (ii) restoration of peatlands and introduction of WBZs can help in mitigating the nutrient runoff for improved water quality of Ryck, and subsequently (ii) contribute to efficient reduction of riverine loads of nutrients into the Baltic Sea. [ABSTRACT FROM AUTHOR]

The objectives of this study were to establish the impact order of the oil-palm kernel processing variables, namely: kernel moisture content (KMC), kernel heating temperature (KHT), kernel heating duration (KHD), and kernel particle size (KPS) on the palm kernel oil (PKO) yield; to develop an empirical model for the PKO yield as influenced by the KMC, KHT and KHD; to investigate the effect of the KMC, KHT and KHD on the PKO quality; and to specify levels of the kernel variables for the maximum PKO yield with minimum variability. The study was undertaken using oil-palm kernels of unidentified variety from Nsukka, Nigeria. The statistical analysis of data was performed with Design-Expert 8P and Minitab 19 Software at P = 0.05. The impact order of the studied kernel variables on the PKO yield, using an expeller (MS-100), was found to be the KPS, KMC, KHD, and KHT. For the maximum PKO yield with minimum variability in the PKO yield, a KMC of 5% (wb), a KHT of 80 °C, a KHD of 10 min, and a KPS of 11 mm and above is recommended.

This study investigated the effects of light source [LED white (LW), fluorescent white (T5) and LED blue (LB)] and photoperiod (12:12, 16:08, 24:00 light/dark) on growth of duckweed Landoltia punctata and the resulting effects on its water quality for 16 days. The average daily relative growth rate (RGR) reached about 0.519 g d−1. Both light source and photoperiod had no significant difference on the mean RGR; however, their interaction had significant effects on duckweed's growth (p ≤ 0.05). Moreover, except T5 (24:00) > [T5 (12:12) ≥ T5 (16:08) ≥ LB (24:00)], LW (12:12) > [T5 (16:08) ≥ LB (24:00)], and LB (12:12) > LB (24:00), there were no differences in RGR in all pair‐comparisons of treatment (p ≤ 0.05). Nitrate (NO3−) mostly influenced weight increment (WI), 70%. For light source and photoperiod effects on water quality, no total ammonia nitrogen (TAN) was detected in all treatments after 16 days, while NO3− increased gradually. In addition, results show that most of the total nitrogen (TN) was contributed from NO3− (R2 = 0.9999). Overall, our findings could contribute on producing duckweed in a controlled and programmed condition for maximum production and quality. Constructed models and practical application contribute in predicting nutrients sensitivity and proven useful in water management or water quality assessments. [ABSTRACT FROM AUTHOR]

The anthropogenic carbon dioxide (CO2) denseness in the earth's atmosphere is increasing day-to-day by combusting fossil fuels for power generation. And, it is the most important greenhouse gas (GHG) responsible for 64% of global warming. Solvent-based carbon capture gained more attention towards researchers because of its easiness to integrate with the coal-fired power plant without significant modifications. During CO2 absorption, the physical property of the solvent gets changed. A change in the solvent's physicochemical property affects further CO2 absorption, thereby increasing the carbon-capture energy demand. The present experimental study encompasses CO2 absorption studies using 30 wt% aqueous monoethanolamine (MEA), 2-amino-2-methyl-1-propanol (AMP) and piperazine (PZ) followed by the detailed analysis of physicochemical properties (pH, carbon loading (α), viscosity (μ), density (ρ) and surface tension (σ)) of various CO2-loaded solutions. The results revealed that these properties are exhibiting interdependent eccentrics. Furthermore, an empirical model was developed to predict the carbon loading of the tested solvents. This model includes the tested physicochemical properties, reaction mixture temperature, diffusivity and change in the mass of solvent during carbon loading. In addition, an empirical model for viscosity as a function of temperature, carbon loading and molecular weight of solvents was developed. These models appear to predict the carbon loading and the viscosity well with greater accuracy. [ABSTRACT FROM AUTHOR]

Modelling and predicting of the kinetics of microbial growth and metabolite production during the fermentation process for functional probiotics foods development play a key role in advancing and making such biotechnological processes suitable for large-scale production. Several mathematical models have been proposed to predict the bacterial growth rate, but they can replicate only the exponential phase and require an appropriate empirical data set to accurately estimate the kinetic parameters. On the other hand, computational methods as genetic algorithms can provide a valuable solution for modelling dynamic systems as the biological ones. In this context, the aim of this study is to propose a genetic algorithm able to model and predict the bacterial growth of the Lactobacillus paracasei CBA L74 strain fermented on rice flour substrate. The experimental results highlighted that the pH control does not influence the bacterial growth as much as it does with lactic acid, which is enhanced from 1987 ± 90 mg/L without pH control to 5400 ± 163 mg/L under pH control after 24 h fermentation. The Verhulst model was adopted to predict the biomass growth rate, confirming the ability of exclusively replicating the log phase. Finally, the genetic algorithm allowed the definition of an optimal empirical model able to extend the predictive capability also to the stationary and to the lag phases.

Satellite retrieval of total suspended solids (TSS) and chlorophyll- a (chl-a) was performed for the Gold Coast Broadwater, a micro-tidal estuarine lagoon draining a highly developed urban catchment area with complex and competing land uses. Due to the different water quality properties of the rivers and creeks draining into the Broadwater, sampling sites were grouped in clusters, with cluster-specific empirical/semi-empirical prediction models developed and validated with a leave-one-out cross validation approach for robustness. For unsampled locations, a weighted-average approach, based on their proximity to sampled sites, was developed. Confidence intervals were also generated, with a bootstrapping approach and visualised through maps. Models yielded varying accuracies (R 2 = 0.40–0.75). Results show that, for the most significant poor water quality event in the dataset, caused by summer rainfall events, elevated TSS concentrations originated in the northern rivers, slowly spreading southward. Conversely, high chl-a concentrations were first recorded in the southernmost regions of the Broadwater. • Chl- a and TSS predicted for large estuarine lagoon from satellite data • Empirical models for clusters of sampling sites • Distance-specific weighted average prediction for unsampled sites • Bootstrapping approach for confidence intervals generation and visualisation • TSS originating from Northern rivers, chl- a in southern region during wet periods [ABSTRACT FROM AUTHOR]

Data from 26 vacuum residue hydrocracking (VR HC) laboratory and commercial experiments were used to model VR HC selectivity curves employing empirical functions and boundary conditions and applying the non-linear least-squares methods. It was found that VR HC products: gas, naphtha, and kerosene can be very well modelled as stable primary and secondary products. The VGO can be modelled as an unstable primary product, while the atmospheric gas oil is best modelled as an unstable primary and secondary product. The diesel yield in laboratory and commercial VR HC conditions is very well modelled as a stable primary and secondary product. The proposed empirical functions and the non-linear least-squares method could be used to test whether a VR HC product is a result of primary and secondary reactions and whether this product further participates in secondary reactions with the progress of the hydrocracking process. [ABSTRACT FROM AUTHOR]

The objectives of this study were to establish the impact order of the oil-palm kernel processing variables, namely: kernel moisture content (KMC), kernel heating temperature (KHT), kernel heating duration (KHD), and kernel particle size (KPS) on the palm kernel oil (PKO) yield; to develop an empirical model for the PKO yield as influenced by the KMC, KHT and KHD; to investigate the effect of the KMC, KHT and KHD on the PKO quality; and to specify levels of the kernel variables for the maximum PKO yield with minimum variability. The study was undertaken using oil-palm kernels of unidentified variety from Nsukka, Nigeria. The statistical analysis of data was performed with Design-Expert 8P and Minitab 19 Software at P = 0.05. The impact order of the studied kernel variables on the PKO yield, using an expeller (MS-100), was found to be the KPS, KMC, KHD, and KHT. For the maximum PKO yield with minimum variability in the PKO yield, a KMC of 5% (wb), a KHT of 80 °C, a KHD of 10 min, and a KPS of 11 mm and above is recommended. [ABSTRACT FROM AUTHOR]

Hollow core concrete-panels are prone to shear failure due to collapse of the voids. In this study, the shear behaviour of hollow precast concrete-composite structures (HPCCSs) reinforced with steel bars and composite reinforcing system (CRS) were investigated. Eleven concrete-panels with different unit-widths (175 mm, 200 mm and 300 mm) and shear-spans (300 mm, 600 mm and 900 mm) were fabricated and tested under static-bending to investigate the effect of CRS spacing and span-to-depth ( a / d ) ratio, respectively on the shear behaviour of the HPCCSs. It was found that the CRS enhanced the structural performance of HPCCSs by changing the shear crack path. The narrower CRS spacing provided better interaction between the CRS and the concrete as the flanges interlock better with most of the surrounding concrete, and exhibited higher normalised shear strength than the wider spacing panels. The contribution of CRS in shear was constant but increased in bending with the increase of a / d ratio. The finite-element-analysis confirmed that the CRS contributed highly in resisting the shear of the HPCCSs. Finally, empirical model was developed to predict shear load-capacity by considering the contribution of the CRS and a / d ratio of this new type of HPCCSs. [ABSTRACT FROM AUTHOR]

The influence of installation and environmental parameters on dust particle deposition behavior on solar photovoltaic collectors were investigated using Computational Fluid Dynamics (CFD) simulation. Parameters including tilt, height of installation, dust particle size and wind speed were investigated. Modeling of wind flow on the building and the photovoltaic array was achieved using the Shear Stress Transport k-ω turbulence model. The discrete phase model was adopted for dust motion prediction and a model was developed to assess the impact of dust accumulation on the performance of the photovoltaic array. The study revealed that rooftop installations have less dust deposition unlike the ground-mounted installations. The wind flow characteristics on rooftop installations are greatly affected by the building while on ground mounted installations wind flow is only influenced by the tilt of the solar photovoltaic collector. Different tilt angles, wind speeds and particle sizes had different deposition characteristics. The lower impact velocities experienced on ground mounted Photovoltaic (PV) arrays resulted in more deposition for smaller sized (10µm) dust particles compared to the larger sized (50µm and 150µm) particles. On rooftop installations, dust particle size of 150µm had the most deposition at a velocity of 5m/s and hence it resulted in a 22.61% reduction in solar photovoltaic efficiency while the least reduction in efficiency of 1.32% was recorded at 15m/s and 10µm size dust particles. The tilt angles of 0o and 22.5o had large sized turbulent eddies compared to the tilt of 45o. The study revealed that ground mounted photovoltaic arrays had more dust deposition compared to rooftop mounted photovoltaics. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive review on controlled release hormonal contraceptive systems that include transdermal patches, intravaginal rings (IVRs), intrauterine devices (IUDs), injectables and subdermal implants. These systems represent a substantial advance from traditional oral contraceptive pills, to improve upon safety, efficiency, and compliance among women. The widespread use of controlled release systems is hindered by limitations, which are discussed in this review. Biodegradable polymers such as poly (lactic- co -glycolic acid) and polycaprolactone have been used to formulate subdermal implants and injectable microspheres to eliminate the need for implant removal and reduce provider intervention. To address low permeability in transdermal patches, permeation enhancers such as alkanols, fatty acids, prodrugs, and vesicular delivery for steroids have been investigated. Local anesthetics in the form of creams, gels and sprays have been evaluated to alleviate the pain associated with IUD insertion. Among methods for device fabrication, 3D printing has emerged as a potential approach for fabricating customizable IVRs and IUDs. Several other modified delivery systems such as transdermal microneedle patches, in situ forming injectable implants, microspheres embedded in implants and IVRs addressing multiple clinical conditions have been investigated for controlled release of contraceptive hormones. To ensure drug release at zero-order rates, empirical and theoretical modelling have been extensively employed and evaluated. The limitations of low predictive power associated with empirical modelling may be overcome through theoretical modelling and simulation that consider underlying mechanisms. Newer approaches such as Monte Carlo based simulation and deep learning models based on artificial neural networks can prove highly beneficial in developing precise contraceptive delivery system, to enhance the quality of life for women worldwide. Unlabelled Image • · Controlled release hormonal contraceptives include transdermal patches, intravaginal rings, injectables and implants. • Biodegradable polymers can be used to formulate long acting systems such as implants and injectable microparticles. • 3D printing can be used to fabricate customizable vaginal rings and intrauterine devices. • · Artificial Neural Networks can be employed to ensure zero order drug release from long-acting formulations. [ABSTRACT FROM AUTHOR]

Sutherland et al. (2020) used simulations from a physics-based numerical fire behaviour model to investigate the effect of the ignition protocol (namely length, direction and rate of ignition) on the spread rates measured in experimental fires. They concluded that the methods used by Cruz et al. (2015) were inadequate as the fires were not spreading at the pseudo-steady state when rate of spread measurements were made, thereby raising questions about the validity of several published experimental and modelling results. Fire spread measurement data from three different outdoor experimental burning studies conducted in grass fuels are used to show that, contrary to the claims of Sutherland et al. (2020) , the fire behaviour data collected in Cruz et al. (2015) were from fires spreading in the pseudo-steady-state regime and thus are compatible with data from larger experimental plots. A discussion is presented addressing why Sutherland et al. (2020) simulations were unable to replicate real-world data. Sutherland et al. (2020) used numerical modelling simulations to critique the experimental fire methods used by Cruz et al. (2015). We show through the analysis of real-world data from different experimental fire datasets that measurements conducted by Cruz et al. (2015) were in fires spreading in the pseudo-steady-state regime. [ABSTRACT FROM AUTHOR]

Understanding of the interactions between wave and aquatic vegetation is gaining importance mainly due to plant-induced wave attenuation phenomenon for developing sustainable coastal management systems. Most of the wave–vegetation interaction studies focus mainly on monotypic coastal plant meadows; however, studies on heterospecific plant meadows are limited. The present study is aimed to understand the heterospecific vegetation–wave interactions through a three-level, four-factor response surface methodology using wave flume under controlled conditions. Hetero-specific seagrass species, Halophila spinulosa and Halophila ovalis are simulated physically by means of synthetic plant mimics to develop a relationship between wave attenuation (E%) and four direct control factors, viz. water depth (h), wave period (T), plant density (N) and bed roughness factor ( f ) through an empirical model. The developed model was tested using analysis of variance technique, and analysed for main and interaction effects of the studied parameters. The results showed that all the considered parameters are significantly effective on E% individually as well as in combination. All the model-based results were compared with a new set of experimental data and tested for validation. [ABSTRACT FROM AUTHOR]

We aimed to investigate the influence of environmental factors and predict zooplankton biomass and abundance in shallow eutrophic lakes. We employed time series of zooplankton and environmental parameters that were measured monthly during 38 years in a large, shallow eutrophic lake in Estonia to build estimates of zooplankton community metrics (cladocerans, copepods, rotifers, ciliates). The analysis of historical time series revealed that air temperature was by far the most important variable for explaining zooplankton biomass and abundance, followed, in decreasing order of importance, by pH, phytoplankton biomass and nitrate concentration. Models constructed with the best predicting variables explained up to 71% of zooplankton biomass variance. Most of the predictive variables had opposing or antagonistic interactions, often mitigating the effect of temperature. In the second part of the study, three future climate scenarios were developed following different Intergovernmental Panel on Climate Change (IPCC) temperature projections and entered into an empirical model. Simulation results showed that only a scenario in which air temperature stabilizes would curb total metazooplankton biomass and abundance. In other scenarios, metazooplankton biomass and abundance would likely exceed historical ranges whereas ciliates would not expand. Within the metazooplankton community, copepods would increase in biomass and abundance, whereas cladocerans would lose in biomass but not in abundance. These changes in the zooplankton community will have important consequences for lake trophic structure and ecosystem functioning. [ABSTRACT FROM AUTHOR]

The ability to predict forest fire risk at monthly, seasonal and above‐annual time scales is critical to mitigate its impacts, including fire‐driven dynamics of ecosystem and socio‐economic services. Fire is the primary driving factor of the ecosystem dynamics in the boreal forest, directly affecting global carbon balance and atmospheric concentrations of the trace gases including carbon dioxide. Resilience of the ocean–atmosphere system provides potential for advanced detection of upcoming fire season intensity. Here, we report on the development of a probabilistic empirical prediction system for forest fire risk on monthly‐to‐seasonal timescales across the circumboreal region. Quasi‐operational ensemble forecasts are generated for monthly drought code (MDC), an established indicator for seasonal fire activity in the Boreal biome based on monthly maximum temperature and precipitation values. Historical MDC forecasts are validated against observations, with good skill found across northern Eurasia and North America. In addition, we show that the MDC forecasts are an excellent indicator for satellite‐derived observations of burned area in large parts of the Boreal region. Our discussion considers the relative value of forecast information to a range of stakeholders when disseminated before and during the fire season. We also discuss the wider role of empirical predictions in benchmarking dynamical forecast systems and in conveying forecast information in a simple and digestible manner. [ABSTRACT FROM AUTHOR]

The relationship between wildland fire spread rate and wind has been a topic of study for over a century, but few laboratory studies report measurements in controlled winds exceeding 5 m s−1. In this study, measurements of fire rate of spread, flame residence time and energy release are reported for fires burning under controlled atmospheric conditions in shallow beds of pine needles subject to winds ranging from 0 to 27 m s−1 (measured 5 m above ground level). The data suggested that under constant flow conditions when winds are less than 10 m s−1, fire rate of spread increases linearly at a rate of ~3% of the wind speed, which generally agrees with other laboratory-based models. When wind speed exceeds 10 m s−1, the fire rate of spread response to wind remains linear but with a much stronger dependence, spreading at a rate of ~13% of the wind speed. Radiative and convective heating correlated directly to wind speed, with radiant heating increasing approximately three-fold as much as convective heating over the range of winds explored. The data suggested that residence time is inversely related to wind speed and appeared to approach a lower limit of ~20 s as wind exceeded 15 m s−1. Average flame residence time over the range of wind speeds was nominally 26 s. Measurements of fire rate of spread, energy release and residence time are presented for fires burning over beds of pine needles across a broad range of winds speeds up to 27 m s−1. Rate of spread was linearly related to wind speed, with a stronger dependence when winds exceeded 10 m s−1. Radiative and convective heating were also linearly dependent on wind speed whereas residence time was inversely proportional to wind speed. [ABSTRACT FROM AUTHOR]

Mapping and monitoring of Arctic vegetation biochemical and biophysical properties is gaining importance as global climate change is disproportionately affecting this region. Previous studies using remote sensing to model Arctic vegetation biochemical and biophysical properties have generally involved empirical modelling with nadir looking broadband sensors and have typically been conducted at the field scale in one study area. Satellite hyperspectral remote sensing has not been previously investigated for retrieving leaf and canopy biochemical and biophysical properties of Arctic vegetation across multiple sites using either empirical or physically-based modelling approaches. Furthermore, multi-angle hyperspectral sensors (CHRIS/PROBA), which can provide insight into vegetation reflectance anisotropy and potentially improve vegetation parameter estimation, have also not been investigated for this purpose. In this study, three modelling approaches previously investigated with field spectroscopy data (Kennedy et al., 2020) were used with CHRIS Mode-1 imagery to predict leaf chlorophyll content, plant area index and canopy chlorophyll content across a bioclimatic gradient in the Western Canadian Arctic. Modelling approaches included: parametric linear regression based on vegetation indices (VI), non-parametric machine learning Gaussian processes regression (GPR) and inversion of the PROSAIL radiative transfer model using a look-up table approach (LUT). CHRIS imagery was acquired with −55°, −36°, 0°, +36°, +55° view zenith angles (VZA) between 2011 and 2014 over three field sites extending from the Richardson Mountains in central Yukon, Canada to the north end of Banks Island, Northwest Territories, Canada. Field measurements were acquired within several weeks of satellite acquisitions. GPR had the best model fit (mean cross-validated (cv) coefficient of determination, r2cv = 0.61 across all vegetation variables, sites and VZAs vs. 0.59 for the simple ratio, SR) and predictive performance (normalized root mean square error, NRMSEcv = 0.13 vs. 0.14 for SR). The revised optimized soil adjusted VI (ROSAVI) performance was slightly poorer (r2cv = 0.51; NRMSEcv = 0.15). The physically-based PROSAIL model performed poorer than all empirical models (r2 = 0.50; NRMSE = 0.18). This ranking of model performance is similar to that found in the previous field spectroscopy study, where empirical model fits and predictive performance were only slightly worse. With respect to view angle performance, NRMSE varied only slightly, indicating no distinct advantage for any one VZA. Overall, strong potential has been demonstrated for empirical modelling of Arctic vegetation chlorophyll and plant area index using hyperspectral data combined with band selection/optimization procedures in the Arctic. Recently launched and future hyperspectral satellites, including next generation airborne sensors, will likely provide improvements to the model performance reported here.