The growth and dynamics of multicellular tissues involve tightly regulated and coordinated morphogenetic cell behaviours, such as shape changes, movement, and division, which are governed by subcellular machinery and involve coupling through short- and long-range signals. A key challenge in the fields of developmental biology, tissue engineering and regeneration is to understand how relationships between scales produce emergent tissue-scale behaviours. Recent advances in molecular biology, live-imaging and ex vivo techniques have revolutionised our ability to study these processes experimentally. To fully leverage these techniques and obtain a more comprehensive understanding of the causal relationships underlying tissue dynamics, computational modelling approaches are increasingly spanning multiple spatial and temporal scales, and are coupling cell shape, growth, mechanics and signalling. Yet such models remain technically challenging: modelling at each scale requires different areas of technical skills, while integration across scales necessitates the solution to novel mathematical and computational problems. This review aims to summarise recent progress in multiscale modelling of multicellular tissues and to highlight ongoing challenges associated with the construction, implementation, interrogation and validation of such models.

Crop breeding is as ancient as the invention of cultivation. In essence, the objective of crop breeding is to improve plant fitness under human cultivation conditions, making crops more productive while maintaining consistency in life cycle and quality. The applications of predictive breeding has been gaining momentum in agricultural industry and public breeding programs for the last decade, in the aftermath of genomic selection being recognized and widely applied for accelerating genetic gain in breeding programs. The massive amounts of data that has been generated by industry and farmers year after year through several decades has finally been recognized as an asset. A wide range of analytical methods such as machine learning, deep learning and artificial intelligence that were initially developed for diverse quantitative disciplines are now being adopted to crop breeding decision making processes. New technologies are currently being developed that would enable integration of data from various domains such as geospatial variables and a multitude of phenotypic responses as well as genetic information, in order to identify, develop and improve crop faster via partial or full automation of the decisions that pertain to variety development. Here we will discuss and summarize efforts from public and private domains for predictive analytics, and its applications to crop breeding and agricultural product development, and provide suggestions for future research.

Featured Application: Bio-electronic devices take advantages of some specific duties of biological matter. The specific ability of some proteins to use sunlight is considered for the realization of photo-electronic devices . Here the focus is on the role of the pH, whose variations seem to affect the protein conductance

This study aims to develop an in-situ field repair approach, special for aircraft composite structures, to enhance the interlaminar toughness of plain woven composites (PWCs) by adding multi-walled carbon nanotubes (MWCNTs). MWCNTs are dispersed at each interface between prepreg layers by means of solvent spraying with the density is 1.58 g/m2. And then, the layers are stacked with the predefined sequence and cured at 120℃ and 1 bar pressure using the heat repairing instrument. Moreover, double cantilever beam (DCB) standard test is used to investigate the interlaminar toughening effect due to the MWCNTs. For comparison, original samples are also prepared, the results indicate that the introduction of MWCNTs can favorably enhance the interlaminar toughness of PWCs at field repair approach and the Mode I fracture energy release rate GIC increases by 102.92%. Based on finite element method (FEM) of continuum damage mechanics, the original and MWCNTs toughening specimen under DCB Mode I fracture are modeled and analyzed. The simulation and experiment are in good agreement. Finally, the toughening mechanism of MWCNTs is explored by scanning electron microscope (SEM), it is founded that a large amount of Fiber-matrix (F-M) interface debonding and matrix cracking of mountain shape are the major modes of fracture accompanied with few fiber breakage and matrix peeling off for the MWCNTs toughening specimens.

Gas sensor arrays, also known as electronic noses, leverage a diverse set of materials to identify the components of complex gas mixtures. Metal-organic frameworks (MOFs) have emerged as promising materials for electronic noses due to their high-surface areas and chemical as well as structural tunability. Using our recently reported genetic algorithm design approach, we examined a set of 50 MOFs and searched through the over 1.125x1015 unique array combinations to identify optimal arrays for the detection of CO₂ in air. We found that despite individual MOFs having lower selectivity for O2 or N2 relative to CO₂, intelligently selecting the right combinations of MOFs enabled accurate prediction of the concentrations of all components in the mixture (i.e. CO₂, O₂, N₂). We also analyzed the physical properties of the elements in the arrays to develop an intuition for improving array design. Notably, we find that diversity among the surface areas in the MOFs leads to improved sensing. Consistent with the observation, we found that, as one might expect, the best arrays consistently had more structural diversity than the worst arrays.

Numerical modelling is becoming a major tool for supporting environmental studies at different scales, thanks to the capability of up-to-date codes in reproducing the natural behaviour in a quite reliable manner. In evaluating the habitat diversity of anthropized rivers, however, many issues are rising because of the intrinsic complexity of the processes involved. Using a reach of the Po River in Italy as a case study, the present works aims to provide an estimate of the changes of the Eco-Environmental Diversity as a response to different constant flow discharges. The goals are achieved by means of two solvers of the iRIC suite, applied in sequence to firstly simulate the fluvial hydrodynamics and subsequently provide an estimate of the habitat conditions. Despite the simplifications intrinsically present in the models and the ones introduced for practical purposes, the results pointed out that the reduction of the flow discharge recently observed can threat the overall biological status of the river. Because of the modelling uncertainties, on the other side, these preliminary outcomes show the need for more research, both in terms of data acquisition and numerical schematization, for adequately evaluate the effects of transient hydrology on the river ecosystems. Moreover, additional field surveys are necessary to calibrate and validate the used model for having sufficiently reliable estimates.

To date, several different approaches are available to study sediment dynamics at reach or watershed scale, based on very different hypothesis. One of such assumptions, the so-called “morphodynamic equilibrium hypothesis” is becoming little unpopular for its embedded simplifications. The aim of this work is to demonstrate how this approach proves yet effective in modelling landscape morphodynamics at the watershed scale, for what concerns the longitudinal profile of a river and the sedimentary aspects. The application of a 1-D model based on the equilibrium hypothesis has been implemented for several large rivers worldwide. Geomorphological parameters have been analysed, which describe the evolution of longitudinal profile (concavity) and sediments characteristics (aggrading and fining), and the results show a reasonably good correspondence with qualitative estimation of the same parameters. At the scale of analysis and for the chosen systems, which show high inertia to geomorphological changes likely owing to their longitudinal extension, the model can detect where the present conditions reflect a big disturbance to the “natural equilibrium” thus allowing water managers to identify present issues to be addressed.

Agriculture and farming worldwide are responsible for numerous environmental threats, including degradation of land and water resources depletion. Underlining the dynamic interaction between bio-physical and socio-economic drivers is the key towards a more sustainable land and water management. With regard to a highly developed agricultural area in southern Italy, multi-regression models were developed to interpret the observed inter-annual variability of cropped land. Main drivers related to Common Agricultural Policy support, product market prices, crop yield and irrigation water availability were investigated. The adopted models revealed the different weighs of each driver. The findings reported the role that direct payments played in supporting the extension of irrigated crops, such as processing tomato. Likewise, the models pointed to decoupled payment scheme as the most important driver of change in the crop pattern over the last years.

In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses and possibly anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in five years by some 100 inspectors throughout Italy. The paper discusses in the detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, Machine Learning techniques have been used as a preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

Solid state sensors having timing capabilities are becoming an absolute need in particle tracking techniques of future experiments at colliders. In this sense, silicon sensors having 3D structure are becoming an interesting solution, due to their intrinsic speed and radiation resistance. A characteristics of such devices is the strict dependence of their performance on their geometric structure, which can be widely optimised by design, thus requiring suitable tools for an accurate modeling of their behaviour. This paper illustrates the development, performance and use of the TCoDe simulator, specifically dedicated to the fast simulation of carrier transportation phenomena in solid state sensors. Some examples of its effectiveness in the design and analysis of 3D sensors is also given.

We review the state of dynamical downscaling with scale-constrained regional and global models. The methodology, in particular spectral nudging, has become a routine and well-researched tool for hindcasting climatologies of sub-synoptic atmospheric disturbances in coastal regions. At present, the spectrum of applications is expanding to other phenomena, but also to ocean dynamics and to extended forecasting. Also new diagnostic challenges are appearing such as spatial characteristics of small-scale phenomena such as Low Level Jets.

Schistosomiasis is a parasitic disease acquired through contact with contaminated freshwater. The definitive hosts are terrestrial mammals, including humans, with some Schistosoma species crossing the animal-human boundary through zoonotic transmission. An estimated 12 million people live at risk of zoonotic schistosomiasis caused by Schistosoma japonicum and Schistosoma mekongi, largely in the World Health Organization’s Western Pacific Region and in Indonesia. Mathematical models have played a vital role in our understanding of the biology, transmission, and impact of intervention strategies, however, these have mostly focused on non-zoonotic Schistosoma species. Whilst these non-zoonotic-based models capture some aspects of zoonotic schistosomiasis transmission dynamics, the commonly-used frameworks are yet to adequately capture the complex epi-ecology of multi-host zoonotic transmission. However, overcoming these knowledge gaps goes beyond transmission dynamics modelling. To improve model utility and enhance zoonotic schistosomiasis control programmes, we highlight three pillars that we believe are vital to sustainable interventions at the implementation (community) and policy-level, and discuss the pillars in the context of a One-Health approach, recognising the interconnection between humans, animals and their shared environment. These pillars are: (1) human and animal epi-ecological understanding; (2) economic considerations (such as treatment costs and animal losses); and (3) sociological understanding, including inter- and intra-human and animal interactions. These pillars must be built on a strong foundation of trust, support and commitment of stakeholders and involved institutions.

Low levels of physical activity (PA) and sedentarism are associated with the onset of different pathologies and health problems. Regular physical activity has been linked with being beneficial to the health of the general population. Within this framework of analysis, the aim of the present study is to analyse the existing association between the time spent doing physical activity and the expressed motives for doing so, from which the first innovative aspect of the paper emerges: the use of the time spent doing PA as a study variable of the phenomenon. The data analysed come from the latest special Eurobarometer survey about the sport and physical activity done in Europe. Using an exploratory factorial analysis and a structural equations model, we were able to find a six-dimensional factorial model that explains the reasons for doing PA, demonstrating that there is no relationship between the reasons for and time spent doing PA. Therefore, motivation is not a variable that explains the time spent doing PA and another type of variable must be used to explain the phenomenon if PA is to be incentivised.

MPC (Magneto-Photonic Crystal) Optimisation is a feature-rich Windows software application designed to enable researchers to analyze the optical and magneto-optical spectral properties of multilayers containing gyrotropic constituents. A set of computational algorithms aimed at enabling the design optimization and optical or magneto-optical (MO) spectral analysis of 1D magnetic photonic crystals (MPC) is reported, together with its Windows software implementation. Relevant material property datasets (e.g., the optical spectra of refractive index, absorption, and gyration) of several important optical and MO materials are included, enabling easy reproduction of the previously published results from the field of MPC-based Faraday rotator development, and an effective demonstration-quality introduction of future users to the multiple features of this package. We also report on the methods and algorithms used to obtain the absorption coefficient spectral dispersion datasets for new materials, for which the film thickness, transmission spectrum, and refractive index dispersion function are known.

The study of neurodegenerative diseases using pluripotent stem cells requires new methods to assess neurodevelopment and neurodegeneration of specific neuronal subtypes. The cholinergic system, characterized by its use of the neurotransmitter acetylcholine, is one of the first to degenerate in Alzheimer’s disease and is also affected in frontotemporal dementia. We developed a differentiation protocol to generate basal forebrain cholinergic neurons (BFCNs) from induced pluripotent stem cells (iPSCs) aided by the use of small molecule inhibitors and growth factors. Ten iPSC lines were successfully differentiated into BFCNs using this protocol. The neuronal cultures were characterised through RNA and protein expression, and functional analysis of neurons was confirmed by whole-cell patch clamp. We have developed a reliable protocol using only small molecule inhibitors and growth factors, while avoiding transfection or cell sorting methods, to achieve a BFCN culture that expresses the characteristic markers of cholinergic neurons.

Sustained water management requires quantitative information and the knowledge of spatiotemporal dynamics of hydrological system within the basin. This can be achieved through the research. Several studies have investigated both surface water and groundwater in Beterou catchment. However, there are few published papers on the application of the SWAT modeling in Beterou catchment. The objective of this study was to evaluate the performance of SWAT to simulate the water balance within the watershed. The inputs data consist of digital elevation model, land use maps, soil map, climatic data and discharge records. The model was calibrated and validated using the Sequential Uncertainty Fitting (SUFI2) approach. The calibrated started from 1989 to 2006 with four years warming up period (1985-1988); and validation was from 2007 to 2020. The goodness of the model was assessed using five indices, i.e., Nash–Sutcliffe efficiency (NSE), the ratio of the root means square error to the standard deviation of measured data (RSR), percent bias (PBIAS), the coefficient of determination (R²), and Kling Gupta efficiency (KGE). Results showed that SWAT model successfully simulated river flow in Beterou catchment with NSE = 0.79, R2 = 0.80 and KGE= 0.83 for the calibration process against validation process that provides NSE = 0.78, R2 = 0.78 and KGE= 0.85 using site-based streamflow data. The relative error (PBIAS) ranges from -12.2% to 3.1%. The parameters runoff curve number (CN2), Moist Bulk Density (SOL_BD), Base Flow Alpha Factor (ALPHA_BF), and the available water capacity of the soil layer (SOL_AWC) were the most sensitive parameter. The study provides further research with uncertainty analysis and recommendations for model improvement and provision of an efficient means to improve rainfall and discharges measurement data.

Demographic processes directly affect patterns of genetic variation within contemporary populations as well as future generations, allowing for demographic inference from patterns of both present day and past genetic variation. Advances in laboratory procedures and sequencing and genotyping technologies in the last decades have resulted in massive increases in high quality genome-wide genetic data from present day populations and allowed retrieving genetic data from archaeological material, also known as ancient DNA. This has resulted in an explosion of work exploring past changes in population size, structure, continuity and movement. However, as genetic processes are highly stochastic, patterns of genetic variation only indirectly reflect demographic histories. As a result, past demographic processes need to be reconstructed using an inferential approach. This usually involves comparing observed patterns of variation with model expectations from theoretical population genetics. A large number of approaches have been developed based on different population genetic models that each come with assumptions about the data and underlying demography. In this article I review some of the key models and assumptions underlying the most commonly used approaches for past demographic inference and their consequences for our ability to link the inferred demographic processes to the archaeological and climate records.

'Huangguan' pear has significant social and economic value, and phosphorus, as one of the three main mineral elements of plants, has an irreplaceable effect on the normal growth of 'Huangguan' pear trees. The objective of this study was to predict the content of phosphorus in the pulp and peel of ‘Huangguan’ pears nondestructively and conveniently by using near-infrared spectroscopy (900–1700 nm) technology. First, twelve algorithms are used to preprocess the original spectral data, and the partial least squares regression algorithm and the gradient boosting regression tree algorithm are used to build a full-band prediction model based on the original spectral data and the processed spectral data. The characteristic wavelengths were extracted using genetic algorithms, followed by establishing a characteristic wavelength prediction model. The prediction accuracy of the models was evaluated according to the coefficient of determination R² and the relative analysis error RPD. The study found that the best prediction model for predicting phosphorus content in the pulp of 'Huangguan' pear was MSC-GA-PLSR, which had R²=0.843 and RPD=1.857 in the modelling set and R²=0.989 and RPD=7.041 in the prediction set. The best prediction model for predicting phosphorus content in the peel of ‘Huangguan’ pear was SG+SNV+FD-GA-PLSR, which had R²=0.991 and RPD=7.470 in the modelling set and R²=0.974 and RPD=4.414 in the prediction set, and the effect was good and met expectations. The results demonstrated that near-infrared spectroscopy could successfully achieve nondestructive detection of phosphorus content in the pulp and peel of 'Huangguan' pears.

Storm Ophelia made landfall over Ireland as an extra-tropical storm on the morning of the 16th of October 2017. The storm caused major power outages, lifted roofs, caused coastal flooding in Ireland, and resulted in the loss of three lives. A model’s capability to forecast extreme weather events such as Storm Ophelia is of utmost importance and now with a changing climate, it becomes more important to improve and enhance model forecasting capability. The Weather Research and Forecasting (WRF) model V3.9 has been configured for the Irish domain and this study presents a preliminary evaluation of the Model during Storm Ophelia. Simulated wind speed and direction were compared with hourly remote sensing (lidar) and in-situ (wind speed and wind direction at 10m) observations at the coastal site of Mace Head Atmospheric Research Station on the West coast of Ireland (53.33◦ N, 9.90 49 ◦ W). The model simulation has generally small biases in the simulated wind speed and wind direction during this case study. The model also realistically simulated the magnitude and geographical distribution of the wind speed and wind direction observed during Ophelia.

The growth of urban population, combined with an increase of extreme events due to climate changes call for a better understanding and representation of urban floods. Rainfall and infiltration are two important factors that affect the watershed response to a given precipitation event. In this paper, we evaluate the influence of the representation of infiltration and spatially variable rainfall on the computer simulation of the floods that affected the city of Hull, UK in June 2007. This work compares a uniform rainfall with one generated using Kriging with External Drift and a constant infiltration equal to the soil hydraulic conductivity with a neglected infiltration. The results of the four simulations are then compared with the flood extents observed by public authorities. It results that the computer model is able the reproduce the general dynamic of the flood and identify the main inundated areas. We found that neglecting the infiltration induce a better representation of this flood event. Furthermore, the use of radar rainfall results in an accuracy similar to the one obtained with a constant rainfall. This study indicates that when the spatial resolution of the rainfall data is low compared to the catchment size and the precipitation distribution is uniform, the spatial variability of the rainfall might not add significant information.

To prevent, mitigate and treat movement impairments, we need to recognize early signs of decline and understand how to best compensate for limitations. The mechanisms leading to movement impairments are complex, overlapping, and interdependent and the fields of biomechanics, motor control, and physiology must be combined to understand these mechanisms. This article introduces CaReMoOC, a framework incorporating neuromusculoskeletal capacity (accumulation of neuromusculoskeletal resources over the lifespan), reserve (task-specific difference between capacity and task demand), movement objectives (considerations made to plan a movement), and compensation (use of NMSK resources to respond to the task demand). The framework is demonstrated for healthy ageing, providing an overview of age-related capacity decline (neural, skeletal, muscular system) and shifted weighting of movement objectives (energy, pain, stability, speed) relevant for biomechanics and motor control. Two forms of compensation are Compensation for Capacity, when capacity does not meet the task demands, and Compensation for Movement Objectives, when the movement is changed due to for example a fear of falling. Understanding the interrelationships between decline in the variables within capacity and the effect on compensation strategies will provide benefit in preventing mobility impairments and will support clinicians in their rehabilitation practice.

Sub-Saharan social-ecological systems are undergoing changes in environmental conditions, including modifications in rainfall pattern and biodiversity loss. Consequences of such changes depend on complex causal chains which call for integrated management strategies whose efficiency could benefit from ecosystem dynamic modelling. However, ecosystem models often require lots of quantitative information for estimating parameters, which is often unavailable. Alternatively, qualitative modelling frameworks have proved useful for explaining ecosystem response to perturbations, while requiring fewer information and providing more general predictions. However, current qualitative methods have some shortcomings which may limit their utility for specific issues. In this paper, we propose the Ecological Discrete-Event Network (EDEN), an innovative qualitative dynamic modelling framework based on "if-then" rules which generates many alternative event sequences (trajectories). Based on expert knowledge, observations and literature, we use this framework to assess the effect of permanent changes in surface water and herbivores diversity on vegetation and socio-economic transitions in an East African savanna. Results show that water availability drives changes in vegetation and socio-economic transitions, while herbivore functional groups had highly contrasted effects depending on the group. This first use of EDEN in a savanna context is promising for bridging expert knowledge and ecosystem modelling.

The way Norway is spearheading electrification in the transport sector is of global interest, and details of its policies and emission impacts represent a reference. We used the NERVE model, a bottom-up high-resolution traffic emission model, to calculate all exhaust emissions in Norway (2009-2020). This allows us to evaluate the co-benefit of policies to target climate change mitigation and air quality. We have analyzed local municipal data with regards to traffic growth, road network influences, vehicle composition, emissions and energy consumption. Light vehicle CO2 emissions per kilometre have been reduced by 22% since 2009, mainly driven by an increaseing bio-fuel mixing and BEV share. BEVs are mostly located in and around the main cities, areas with young vehicle fleets, and strong local incentives. Beneficiaries of all BEVs incentives have been a subset of the population with strong economic indicators. The incentivized growth in the share of diesel-fuelled passenger vehicle has been turned, and together with Euro6 emission standards, light vehicle NOx emissions have been halved since peaking in 2014. BEV represent an investment in emission reductions in years to come and current sales set up for an accelerated decline in emissions despite growth in traffic.

Offshore grids can have a key role in the transition of the energy system to sustainability. Although they require extensive infrastructure investments, they open up for the exploitation of additional resources and may be important to provide for part of the increasing electricity demand driven by sector coupling. This paper quantifies the socio-economic value of offshore grids and identifies their major drivers, performing energy system optimization in a model application of the Northern-central European energy system and the North Sea offshore grid towards 2050. The increasing wake loss with the size of hub-connected wind farms is integrated in the modelling. We find that without sector coupling no offshore grid may develop, and that the higher the level of sector coupling, the higher the value of offshore grids. Therefore, it can be strongly stated that offshore grid infrastructure development should not be discussed as a separate political topic, but seen in connection to sector coupling.

Development of the central nervous system (CNS) depends on accurate spatiotemporal control of signalling pathways and transcription programs. Forkhead Box G1 (FOXG1) is one of the master regulators that plays fundamental roles in forebrain development, from the timing of neurogenesis to the patterning of the cerebral cortex. Mutations in the FOXG1 gene cause a rare neurodevelopmental disorder called FOXG1 syndrome, also known as congenital form of Rett syndrome. Patients presenting with FOXG1 syndrome manifest a spectrum of phenotypes ranging from severe cognitive dysfunction and microcephaly to social withdrawal and communication deficits with varying severities. To develop and improve therapeutic interventions, there has been considerable progress towards unravelling the multi-faceted functions of FOXG1 in neurodevelopment and pathogenesis of FOXG1 syndrome. Moreover, recent advances in genome editing and stem cell technologies, as well as increased yield of information from high throughput omics opened promising and important new avenues in FOXG1 research. In this review, we provide a summary of clinical features and emerging molecular mechanisms underlying FOXG1 syndrome, and explore disease-modelling approaches in animals and human-based systems to highlight prospects of research and possible clinical interventions.

Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g. enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from NMR spectroscopy time courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python API. Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms.

The arterial network in healthy young adults is thought to be structured to minimise wave reflection in conduit arteries, producing an ascending aortic pressure waveform with three key features: early systolic peak, negative systolic augmentation, and diastolic hump. One-dimensional computer models have provided significant insights into arterial haemodynamics, but no previous models of the young adult have exhibited these three features. Since the latter was likely to be related to unrepresentative or non-optimised impedance properties of the model arterial networks, we developed a new ‘YoungAdult’ model that incorporated 1) a novel and more accurate empirical equation for approximating wave speeds, based on area and relative distance to elastic-muscular arterial transition points, 2) optimally-matched arterial junctions, and 3) an improved arterial network geometry that eliminated ‘within-segment’ taper (which causes wave reflection in conduit arteries) whilst establishing ‘impedance-preserving’ taper. These model properties led to wave reflection occurring predominantly at distal vascular beds, rather than in conduit arteries. The model predicted all three typical characteristics of an ascending aortic pressure waveform observed in young adults. When compared with non-invasively acquired pressure and velocity measurements (obtained via tonometry and Doppler ultrasound in 7 young adults), the model was also shown to reproduce the typical waveform morphology observed in the radial, brachial, carotid, temporal, femoral, and tibial arteries. The YoungAdult model provides support for the concept that the arterial tree impedance in healthy young adults is exquisitely optimised, and it provides an important baseline model for investigating cardiovascular changes in ageing and disease states.

Many complex behaviours in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans multiple length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modelling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modelling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviours offers a means to take synthetic biology beyond single molecules or cells and towards the creation of systems with functions that can only emerge from collectives at multiple scales.

Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high seeding density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells. Using a metabolic network and flux balance analysis, we compared standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched in amino acids. We observed high glycolytic fluxes and depletion of asparagine simultaneously with the lactate shift building kinetic models around this and other observations. An ensemble of kinetic models assessed the impact of a feeding medium enriched with additional asparagine, revealing a missing lactate shift without enhancing mAb productivity in the experiments. This research provides valuable model-based insights into cellular metabolism during HSD processes, laying the foundation for refined CHO cell-based mAb production. The proposed hypothesis on the regulations guides future experimental validation and bioprocess optimization for enhanced mAb production efficiency.

One route to reducing CO2 emissions is to improve the energy efficiency of machines. For example, conventional combustion engines are being downsized (and down-speeded), are now running on lower viscosity engine lubricants (such as SAE 0W-20 or lower viscosity grades) and often also have stop-start systems fitted (to prevent engine idling when the vehicle is stopped). Some of these changes result in higher levels of mixed and boundary friction, and so accurate estimation of mixed/boundary friction losses is becoming of increased importance, both for estimating friction losses, and also for estimating wear volumes. Traditional approaches to estimating mixed/boundary friction, which employ real area of contact modelling such as the Greenwood-Tripp model [1] are widely used, but recent experimental data suggests that these approaches underestimate mixed/boundary friction losses [2-5]. A new model has been developed, based on experimental data, which estimates the proportion of mixed/boundary lubrication, X, as a function of the value (where is the ratio of the oil film thickness separating the surfaces to the combined root mean square surface roughness of the interface surfaces). The precise equation that describes the way in which X varies with takes the form of a “reverse S-curve”, which makes physical sense since S-curves arise naturally in growth processes, and in mixed/boundary lubrication, the real area of contact of rough surfaces grows as the load (and 1/) increases. Numerical estimates of mixed/boundary lubrication losses in internal combustion engines are presented here and compared with published data [2,6]. In addition, the improved method described here is used to estimate both the financial cost of mixed/boundary lubrication for today’s passenger car fleet, and the CO2 emissions associated with these friction losses.

A discharge mixing zone (DMZ) is a defined geographical area or volume of water in the receiving environment of a discharge where initial dilution of the effluent occurs and where exceedance of water quality criteria may be permitted. DMZs are essential to inform determination of discharge consent conditions and an important element of risk management frameworks to reduce any effects of the discharges on the environment and human health. In this review, we describe the principles and technical application of DMZs. We present an overview of the physical processes that govern the dispersion and dilution of wastewater discharges and the fate of contaminants in coastal environments and define key criteria for determining the size of DMZs. We summarize DMZ requirements in international legislation and guidance and exemplify their application to different types of discharges by means of case studies. The selected case studies illustrate different modelling tools for defining DMZs and different monitoring approaches to assess their effectiveness in achieving ecological and human health objectives.

This paper has three objectives, the first is to present a detailed overview in the form of a tutorial for the developments of several key quantile time series modelling approaches. The second objective is to develop a general framework to represent such quantile models in a unifying manner in order to easily develop extensions and connections between existing models that can then be developed to further extend these models in practice. In this regard, the core theme of the paper is to provide perspectives to a general audience of core components that go into construction of a quantile time series model and then to explore each of these core components in detail. The paper is not addressing the concerns of estimation of these models, as there is existing literature on these aspects in many settings, we provide references to relevant works on these aspects in several classes of model. Instead, the focus is rather to provide a unified framework to construct such models for practitioners, therefore the focus is instead on the properties of the models and links between such models from a constructive perspective. The third objective is to compare and discuss the application of the different quantile time series models on several sets of interesting demographic and mortality based time series data sets of relevance to life insurance analysis. The exploration included detailed mortality, fertility, births and morbidity data in several countries with more detailed analysis of regional data in England, Wales and Scotland.

Capsizing accidents are regarded as marine accidents with a high rate of casualties per accident. Approximately 89% of all such accidents occur in small ships (vessels with gross tonnage less than 10 tons). Stability calculations are critical for assessing the risk of capsizing incidents and evaluating a ship's seaworthiness. Despite the high frequency of capsizing accidents involving small ships, they are generally exempt from adhering to stability regulations, thus remaining systemically exposed to the risk of capsizing. Moreover, the absence of essential design documents complicates direct ship stability calculations. This study utilizes hull form feature data—obtained from the general arrangement of small ships—as input for a deep learning model. The model is structured as a multilayer neural network and aims to infer hydrostatic curves, which is required data for stability calculations.

Electrochemical Impedance Spectroscopy (EIS) is a powerful non-invasive technique used to characterize Lithium-ion cells. Its application allows to identify and track the evolutions of cell degradation processes within a short testing time. This review collects all the available works which have used EIS spectra either to characterize Li-ion cell degradation or to develop Electric Circuit Models (ECMs). The objectives of this work are: (i) to highlight the influence of different aging test conditions on the EIS spectra; (ii) to find the correlations between EIS spectra variations and the underlying degradation mechanisms and (iii) to list the available options to formulate ECMs from EIS spectra of aged cells. After an exhaustive analysis of the state-of-the-art, a critical review is presented to discuss the existing links between degradation mechanisms and the most reliable solutions to model them.

Zimbabwe has ambitious and laudable GHG mitigation targets. Compared to a coal based future emissions reductions by 33% per capita by 2030 are targeted, by implementing a set of identified nationally determined contributions (NDCs). If historical climate conditions continue, it can do this at low or negative cost. However, anticipated conditions may not continue, and of the planned emissions reductions in the NDCs, 88% would come from the expansion of hydropower, which is driven by rainfall. If climate change causes the extreme droughts witnessed in recent years to become more frequent, embarking on Zimbabwe’s NDC future (underpinned by its official system development plan) may be expensive and further cripple the economy. Note that the economy is already being strangled by constrained power supplies due to unusually dry conditions in the Zambezi river basin. If the NDC Future is pursued, but the climate becomes drier, proactive efforts might be made to overcome the power shortages. However, this may result in a rapid ramp up of greenhouse gas emissions if the country turns to coal to reinforce its system and increase its resilience against hydropower vulnerability and the costs that would otherwise ensue. If the country were to keep its NDC investments and supplement them with more aggressive deployment of clean adaptation options, strongly positive outcomes appear possible. Specifically, this would require increased deployment of renewable energy technologies, a restructured power market, and deep increases in energy efficiency investments. In so doing, the country would not only exceed its NDC targets, but also reduce costs in a manner that is climate resilient. This would not remove the country's need for hydropower and some level of coal reliance. However, it will introduce requirements to ensure flexibility in both hydropower and coal power production. For hydropower, power stations will need to provide and be recompensed for providing ‘balancing services’, that is, storing water and producing electricity when the wind is not blowing, nor sun shining. In order to ensure continuous output from mines, it may require intelligent stockpiling combined with dynamic forecasting. This would apply not only to production in Zimbabwe, but potentially for neighboring countries. Doing so would allow predictable mining activities, but allow electricity systems to absorb low cost, low carbon hydropower at high rainfall periods. To make the NDCs resilient via clean adaptation, strong institutional restructuring is required. However, internalizing those costs and moving to advanced market structures and business cases may strain the capacity of current institutions.

Building Information Modelling (BIM) has gained significant relevance in civil engineering, particularly in the infrastructure sector. It offers the potential to increase efficiencies, optimize processes, and enhance sustainability throughout infrastructure projects’ life cycles. BIM consolidates information about asset components into a single model, enabling real-time updates, cost reduction, and improved consistency. Digital twins serve as valuable extensions of BIM in infrastructure, playing a crucial role during operation. By capturing real-time data from sensors and IoT devices, digital twins enabls continuous monitoring, proactive maintenance, efficient energy management, and informed decision-making for optimized operational efficiency. Advancements in 3D point cloud technologies, such as 3D laser scanning, have expanded BIM’s application in operation and maintenance. These technologies rapidly capture accurate and detailed three-dimensional information, prompting the exploration of automated alternatives to manual point cloud modeling. This paper demonstrates the application of supervised machine learning, specifically support vector machines, for analyzing and segmenting 3D point clouds, a crucial step in 3D modeling. Various approaches for semantic segmentation are introduced, investigated, and evaluated using diverse data sets. The results highlight the effectiveness of supervised machine learning techniques in achieving accurate segmentation of 3D point clouds.

The formulation and analytic solution of a new mathematical model with constitutive curvature for analysis of tunnel ventilation shaft wall is proposed. Based on the Mindlin-Reissner theory for thick shells, this model also takes into account the shell constitutive curvature and considers an expression of the shear correction factor variable (αn) in terms of the thickness (h) and the radius of curvature (R). The main advantage of the proposed model is that it has the possibility to analyze thin, medium and thick tunnel ventilation shafts. As a result, two comparisons were made: the first one, between the new model and the Mindlin-Reissner model without constitutive curvature with the shear correction factor (α_n=6/5) as a constant, and the other, between the new model and the tridimensional numerical models (solids and shells) obtained by finite element method for different slenderness ratios (h/R). The limitation of the proposed model is that it is to be formulated for a general linear-elastic and axial-symmetrical state with continuous distribution of the mass.

Real-time estimation of the parameters characterising infectious disease transmission is important for optimization quarantine interventions during outbreaks. One of the most significant parameters is the effective reproduction number - number of secondary cases produced by a single infection. The current study presents an approach for estimating the effective reproduction number and its application to COVID-19 outbreak. The method is based on fitting SIR epidemic model to observation data in a sliding time window and allows to show real-time dynamics of reproduction number at any phase of epidemic for countries globally. Online data on COVID-19 daily cases of infections, recoveries, deaths are used.Finally, time-dependent reproduction number is explored in connection with dynamics of peoples mobility. The method allows to assess the disease transmission potential and understand the effect of interventions on epidemics spread. It also can be easily adapted to future outbreaks of different pathogens. The tool is available online as Python code from the Github repository.

Floods, recognized for their erratic behavior and devastating effects, are a significant threat as a natural disaster. In Brunei Darussalam, the impact of climate change is becoming increasingly evident through higher temperatures and an expected increase in annual rainfall (5.0mm per year from 2021 to 2050), highlighting the critical need for updated flood risk management techniques. In line with the climate resilience and adaptation objectives of WAWASAN 2035, this paper introduces an innovative approach—a cutting-edge flood risk management framework that utilizes the capabilities of big data analytics, sophisticated machine learning algorithms, and smart Internet of Things (IoT) sensors. Traditional methods, which rely on numerical and physical data modeling, often do not provide precise short-term flood forecasts. Our suggested framework uses machine learning to analyze real-time flood forecasting, sensor data and enhance warning systems, addressing the shortcomings of traditional approaches. The key advantage of this approach is its potential to drastically decrease flood-related damages, protect lives, and boost infrastructure resilience. This proposal is not only in aligns with Brunei's vision for a safe and climate-adaptable future but also marks a significant step forward in adopting innovative and sustainable flood risk management solutions amidst an evolving climate.

The Hollow fiber membrane contactor (HFMC) is an intensification process that combines membrane and absorption technologies, being a technology in expansion, so the dynamics of this process is crucial for an increase in industrial scale-up. In this manuscript a rigorous dynamic model of an HFMC is developed and simulated in Python software for the absorption of post-combustion CO2 using a monoethanolamine (MEA) solution, which was validated using available data in literature in steady state. Dynamic simulations were performed to analyze the model's capability to predict the effect of disturbances on the responses of CO2 capture ratio and loading. Four distinct experiments were conducted by step changes and pulse disturbances at the following variables: gas and liquid volumetric flow rates, CO2 composition in flue gases, and CO2 lean load. The gas phase showed a faster response compared to the liquid. The CO2 capture ratio and rich loading presented opposite responses when disturbing both liquid flows, and for the composition and lean loading variations, the capture ratio presented an inverse response characteristic, while the loading showed a delayed response. As the model proved to be capable of returning to the reference state, it can potentially be used for control studies purposes.

Significant amounts of anthropogenic radionuclides were introduced in ocean waters following nuclear atmospheric tests and development of the nuclear industry. Dispersion of artificial dissolved radionuclides has been extensively measured for decades over the European continental shelf. The radionuclide measurement and release fluxes databases provided here represent an exceptional opportunity to validate dispersion hydrodynamic models. MARS hydrodynamic model have been applied at different scales to reproduce in realistic conditions the measured dispersion. Specific methods have been developed to obtain qualitative and quantitative results and perform model/measurement comparisons. Model validation concerns short to large scales with dedicated surveys following the dispersion: it was performed within two and three dimensions framework and from minutes and hours following a release up to several years. Results are presented concerning the dispersion of radionuclides in marine systems deduced from standalone measurements, or according to model comparisons. It allows characterising dispersion over the continental shelf, pathways, transit times, budgets and source terms. This review exhibits the main features retained from the point of view of radiotracers, hydrodynamic models and model/measurement methods with perspectives of applications in other areas or oceanographic domains.

Current commitments by European governments seek to improve energy consumption as a means to reduce carbon emissions from building stock by 2050. Within such context, retrieving reliable three-dimensional contours from point clouds becomes an important step in developing facade retrofitting solutions since facade retrofitting projects often make use of as-built 3D models to help reduce inaccuracies by narrowing interpretation and measurement errors. This work aims to provide a method that uses topology-based parametric modelling for reconstructing building envelopes from point clouds. Through a semi-automated process that gives permanent visual feedback, the user adjusts parameters to custom standards of acceptability. A solution under the form of a Grasshopper definition delivers building envelope 3D contours in various file formats as a means for increasing interoperability. The main contributions of this work consist of a parametric reconstruction workflow capable of solving building topology for retrieving 3D contours, a strategy to bypass point cloud occlusion, and a strategy for converting those contours into an IFC model directly from the parametric modelling environment.

The cities of the future are places of sustainable mobility and a continuous reduction in the amount of harmful substances entering the surrounding environment. Current emission models are mainly dedicated to fully combustion vehicles and do not fully reflect the actual emissions of vehicles in the fleet, which is becoming increasingly diverse. The increasing share of hybrid and electric vehicles is generating the need to measure emissions and energy consumption from these vehicles and to develop new emission models that are accurate. This problem mainly concerns low-emission zones in cities, where mobility planning should be based on simulation models that are based on a continuously updated database. This work concerns the proposal of a two-dimensional model of emissions from hybrid vehicles using artificial neural networks for low-emission zones. The results can be used to further develop such emission models.

This paper summaries the main findings from the full-scale CFD analyses conducted at SINTEF Ocean on the case of MV REGAL, which is one of the benchmark vessels studied in the ongoing joint industry project JoRes. The numerical approach is described in detail, and comparative results are presented regarding the propeller open water characteristics, ship towing resistance, and ship self-propulsion performance. The focus of numerical investigations is on the assessment of the existing simulation best practices applied to a ship scale case in a blind simulation exercise, and performance of thereof with different turbulence modelling methods. The results are compared directly with full-scale performance predictions based on model tests conducted at SINTEF Ocean and sea trails data obtained in the JoRes project.

The research field of energy system analysis is dealing with increasingly complex energy systems and their respective challenges. Moreover, the requirement for open science has become a focal point of public interest. Both drivers have triggered the development of a broad range of (open) energy models and frameworks in recent years. However, there are hardly any approaches on how to evaluate these tools in terms of their capabilities to tackle energy system modelling challenges. This paper describes a first step towards a flexible evaluation of software to model energy systems. We propose a qualitative approach as an useful supplementary to existing model fact sheets and transparency checklists. We demonstrate the applicability by evaluating the newly developed “Open Energy Modelling Framework” with respect to existing challenges in energy system modelling. The case study results highlight that challenges related to complexity and scientific standards can be tackled to a large extent while the challenges of model utilization and interdisciplinary modelling are only tackled partially. However, the challenge of uncertainty remains for the most part unaddressed at present. Advantages of the evaluation approach lie in its simplicity, flexibility and transferability to other tools. Disadvantages mostly stem from its qualitative nature. Our analysis reveals that some challenges in the field of energy system modelling cannot be addressed by a software as they are on meta level like model result communication and interdisciplinary modelling.

The emergence of Digital Twin (DT) technology is creating unique opportunities for human so-ciety in terms of real-time data transfer from the physical environment to its digital replica. DT technology has two-way interaction capabilities that enable data exchange between physical and virtual environments, creating bidirectional data flows. Progress has been made in different in-dustry sectors, but the Architecture, Engineering, Construction, and Operation (AECO) sector still requires further development. Recent studies have shown the potential for DT-based platform implementation in the lifecycle management of buildings and infrastructure. This paper examines the development and implementation of DT technology in the construction sector, identifying key definitions, enablers and use cases, while comparing its use with other industries and evaluating benefits and future potential.

The exposure of Mediterranean forests to large wildfires requires mechanisms to prevent and mitigate their negative effects on the territory and ecosystems. Fuel models synthesize the complexity and heterogeneity of forest fuels and allow understanding and modelling of fire behavior. However, it is sometimes challenging to define the fuel type in a structurally heterogeneous forest stand due to the mixture of characteristics from different types and limitations of qualitative field observation and passive and active airborne remote sensing. This can impact the performance of classification models that rely on in situ identification of fuel type as ground-truth, which can lead to a mistaken prediction of fuel types over larger areas in fire prediction models. In this study, a handheld mobile laser scanner (HMLS) system were used to assess its capability to define Prometheus fuel types in 43 forest plots in Aragón (NE Spain). The HMLS system captured the vertical and horizontal distribution of fuel at extremely high resolution to derive high-density three-dimensional point clouds (average: 63,148 points/m2) which were discretized into voxels of 0.05 m3. The total number of voxels in each 5 cm height stratum was calculated to quantify the fuel volume in each stratum, providing the vertical distribution of fuels (m3/m2) for each plot at centimetric scale. Additionally, fuel volume was computed for each Prometheus height stratum (0.60, 2, and 4 m) for each plot. The Prometheus fuel types were satisfactory identified for each plot, which was compared with the fuel type estimated in the field. This led to the modification of the ground-truth in 10 out of the 43 plots, with estimation errors between types 2–3, 5–6, and 6–7. These results demonstrate the ability of HMLS systems to capture fuel heterogeneity at centimetric scales for the definition of fuel types in Mediterranean forests, making them powerful tools for fuel mapping and fire modelling, and ultimately for improving wildfire prevention and forest management.

Adopting Building Information Modeling (BIM) within the Architecture, Engineering, and Construction (AEC) industry presents an opportunity to address enduring challenges, including chronic productivity deficits and emerging imperatives such as cleaner production and sustainability. However, the pace of BIM implementation (BIMI) across European Union (EU) countries is uneven due to varying contexts and the inherent complexity of BIM adoption. Notably, Portugal lags in BIMI among these countries. Using the Portuguese context, the manuscript endeavours to identify the main barriers to BIMI by the late-adopting countries in the EU and, based on the dynamics of their relationships, design effective mitigation measures to lessen their impact and eventually leverage BIMI. A comprehensive list of barriers was compiled from the literature and ranked by experts through a Delphi survey, resulting in 15 critical barriers. Subsequently, an Interpretive Structural Modelling (ISM) model was constructed to elucidate the hierarchical relationships among these barriers, while a fuzzy Impact Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) analysis was employed to determine their driving and dependence powers. The resulting main barriers were the lack of evaluation and feedback mechanisms on BIMI, ignorance of BIM capabilities/benefits, scarcity of BIM-capable professionals, lack of experience and cooperation in BIMI, resistance to change, and lack of support from top management. Finally, experts formulated mitigation measures aimed at tackling these main barriers. These measures were designed to not only address each barrier individually but also to have a comprehensive and practical impact on the entire barrier system as a whole. These findings will assist researchers, policymakers, and practitioners in comprehending the barriers and hierarchical structures affecting BIMI in EU late-adopting countries.

Water is essential to all life-forms including various ecological, geological, hydrological, and climatic processes/activities. With changing climate, associated El Nino/Southern Oscillation (ENSO) events appear to stimulate highly uncertain patterns of precipitation (P) and evapotranspiration (EV) processes across the globe. Changes in P and EV patterns are highly sensitive to temperature variation and thus also affecting natural streamflow processes. This paper presents a novel suite of stochastic modelling approaches for associating streamflow sequences with climatic trends. The present work is built upon a stochastic modelling framework HMM_GP that integrates a Hidden Markov Model with a Generalised Pareto distribution for simulating synthetic flow sequences. The GP distribution within HMM_GP model is aimed to improve the model's efficiency in effectively simulating extreme events. This paper further investigated the potentials of Generalised Extreme Value Distribution (EVD) coupled with an HMM model within a regression-based scheme for associating impacts of precipitation and evapotranspiration processes on streamflow. The statistical characteristic of the pioneering modelling schematic has been thoroughly assessed for their suitability to generate/predict synthetic river flows sequences for a set of future climatic projections. The new modelling schematic can be adapted for a range of applications in the area of hydrology, agriculture and climate change.

Aim: To establish, through molecular modelling, safe and clinically acceptable putative antagonists of E571K-mutated exportin-1 among the bioactive compounds in various parts of Juglans mandshurica. Methods: The bioactive compounds were subjected to compendium of druglikeness and lead-likeness filter workflows prior to docking of the resultant compounds into E571K exportin-1 active site using PyRx AutoDock vina to establish their binding affinity and interaction profile. The evolutionary algorithm of Osiris property explorer DataWarrior software as well as lead-likeness filter were employed for generation of novel non-promiscuous analogues of the lead compound with better putative selectivity and clinical acceptability as E571K Exportin-1 antagonists.Results: The findings of this study present taxifolin as the putatively effective and lead-like E571K Exportin-1 inhibitor with high potential of qualifying for clinical evaluation but is associated with high promiscuity tendency in high throughput screening. The evolutionary derivation of novel analogues of the compound, however, results in the generation of putatively non-promiscuous, non-toxic, and lead-like E571K Exportin-1 antagonists with high synthetic accessibility and clinical developability for evaluation in the strategy for treatment of drug-resistant KRAS-mutant lung adenocarcinoma condition.

Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. Nevertheless, some works don’t ensure sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs. In this paper, we propose a novel method to accurately and automatically reconstruct tree branches from laser scans. We first extract an initial tree skeleton from the input tree point cloud, then simplify the skeleton through iteratively removing redundant components. A global-optimization approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. Experiments on various types of trees from different data sources demonstrate the effectiveness and robustness of our method. The resulted tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc.

Additive manufacturing (3D printing) and computer-aided design (CAD) still have limited up-take in biomedical and bioengineering research and education, despite the significant potential of these technologies. The utility of organ-scale 3D-printed models of living structures is widely appreciated, while the workflows for microscopy data translation into tactile-accessible replicas are not well developed yet. Here, we demonstrate an accessible and reproducible CAD-based methodology for generating 3D-printed scalable models of human cells cultured in vitro and imaged using conventional scanning confocal microscopy and fused deposition modelling (FDM) 3D printing. We termed this technology CiTo-3DP (Cells-in-Touch for 3D Printing). As a proof-of-concept, we created CiTo-3DP models of human pancreatic cancer cells and healthy dermal fibroblasts by using selectively stained nuclei and the cytoskeleton components (f-actin and α-smooth muscle actin). The production of dismountable sets of cellular components was al-so shown. The CiTo-3DP approach can be adapted to comprehensively present various cell types, subcellular structures and extracellular matrices. We envisage that the resulting CAD and 3D printed models could be used for further applications, including but not limited to in silico simulations for biology, medicine, pharmacological research, tissue engineering, morphometrical analysis, multiphysics modelling, education, rehabilitation of visually impaired people, and integration into virtual reality.

A major challenge for the dissemination, replication, and reuse of epidemiological forecasting studies during COVID-19 pandemics is the lack of clear guidelines and platforms to exchange models in a Findable, Accessible, Interoperable, and Reusable (FAIR) manner, facilitating reproducibility of research outcomes. During the beginning of pandemics, models were developed in diverse tools that were not interoperable, opaque without traceability and semantics, and scattered across various platforms - making them hard to locate, infer and reuse. In this work, we demonstrate that implementing the standards developed by the systems biology community to encode and share COVID-19 epidemiological models can serve as a roadmap to implement models as a tool in medical informatics, in general. As a proof-of-concept, we encoded and shared 24 epidemiological models using the standard format for model exchange in systems biology, annotated them with cross-references to data resources, packed up all associated files in COMBINE archives for easy sharing, and finally, disseminated the models through BioModels repository to significantly enhance their reproducibility and repurposing potential. We recommend the use of systems biology standards to encode and share models of epidemic and pandemic forecasts to improve their findability, accessibility, interoperability, reusability, and reproducibility.

The aim of this paper is the presentation of an analytical model of insulator flashover and its application for air at atmospheric pressure and pressurized SF6. After a review of the main existing models in air and compressed gases, we develop a relationship of flashover voltage on the basis of an electrical equivalent circuit and the thermal properties of the discharge. The model includes the discharge resistance, the insulator impedance, and the gas interface impedance. The application of this model to a cylindrical resin-epoxy insulators in air medium and SF6 gas with different pressures gives results close to the experimental measurements.

In recent years, the need to make the built environment more resilient and adaptable to climate change has become increasingly evident. In Europe, this aspect concerns the vast majority of existing buildings, which present several deficiencies from the energy-efficiency point of view, considering they were designed before the introduction of modern energy codes. Nowadays, it is possible to retrofit existing buildings using advanced and high-efficient technologies such as Double Skin Façades (DSFs). The research aims to evaluate the use of properly designed DSFs for the energy restoration of existing buildings. In detail, various DSF configurations are applied to a residential building located in central Italy and investigated under present and future climate conditions, estimated through regional climate models. The results underline that all the analysed Double Skin Façades confirm to be a useful option, and, in particular, the Multi-Storey typology allows drastic energy needs decrement. Moreover, the general increase in temperatures and solar radiations could affect the building energy performance, and the insertion of DSF can mitigate the climate change effects, reducing the predicted energy consumption and ensuring better behaviours than the building in its original state.

Landform Evolution Modelling (LEM) provides an avenue for simulating how a landscape may evolve over extended time periods of thousands of years. The CAESAR-Lisflood LEM that includes a hydrologic model (TOPMODEL) and a hydraulic model (Lisflood) can be used to assess the proposed final landform morphology of a mine site by simulating how the mine landform and the landscape would evolve over a 1,000-year period. The accuracy of future simulations depends on the calibration and validation of the model to the past and present events. Calibration and validation of the model involves finding a combination of parameters of the model which when applied and simulated gives model outputs similar to those observed for the real site scenario for corresponding input data. Calibrating the sediment output of the CAESAR-Lisflood model at the catchment level and using it for studying the equilibrium conditions of the landform is an area yet to be explored. Therefore, the aim of this study was to calibrate the CAESAR-Lisflood model and then validate it. To achieve this, the model was run for a rainfall event with a set of parameters, plus discharge and sediment data for the input point of the catchment, to analyze how similar the model output would behave when compared with the discharge and sediment data for the output point of the catchment. The model parameters were then adjusted until the model closely approximate the real site values of the catchment. It was then validated by running the model for a different set of events and checking that the model gave similar results to the real site values. The outcomes demonstrated that while the model can be calibrated to a greater extent for hydrology (discharge output) throughout the year, the sediment output calibration may be slightly improved by having the ability to change parameters to take into account the seasonal vegetation growth during the start and end of the wet season. This study is important for designing and testing post-mining rehabilitated landscape systems that assess hydrology and sediment movement in seasonal biomes.

Blockchain decentralized applications (ÐApps) are applications which run on Blockchains nodes. Thus, in order to interact directly with this sort of applications, users need to have a blockchain address, wallet and knowledge about how to make transactions in order to interact with ÐApps. Therefore, the knowledge required to use a ÐApp can easily make users to desist when trying to interact with them. In order to tackle this issue, we propose a software architecture that will be located in the middle of the user and the ÐApp, thus making users initially unaware that they are interacting with a ÐApp. This is achieved by analyzing the relationship between ÐApps and Apps by using UML modelling. Next, based in the previous analysis, we created a middleware for users to interact with a ÐApp in the same manner the do with a traditional web app, i.e. by using usernames, passwords and UI elements instead of addresses, private keys or transactions. Finally, in order to put the developed middleware into practice, we developed a ÐApp that makes use of it. This ÐApp registers the time control of workers from companies by using Blockchain to store the data in a secure and non-modifiable manner.

Mapping building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep learning models have been the main focus of most recent research endeavours aiming to extract pixel-based building roof plane areas from remote sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pix-el-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: Stadsveld – 't Zwering neighbourhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld – 't Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings.

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these popular distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is the upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of a random discrete variable. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

This study has developed a Matlab application for simulating statistical models project (SMp) probabilistic distributions that are similar to binomial and Poisson, which were created by mathematical procedures. The simulated distributions are graphically compared with these legendary distributions. The application allows to obtain many probabilistic distributions, and shows the trend (τ ) for n trials with success probability p, i.e. the maximum probability as τ=np. While the Poisson distribution PD(x;µ) is a unique probabilistic distribution, where PD=0 in x=+∞, the application simulates many SMp(x;µ,Xmax) distributions, where µ is the Poisson parameter and value of x with generally the maximum probability, and Xmax is upper limit of x with SMp(x;µ,Xmax) ≥ 0 and limit of the stochastic region of the random discrete variable X. It is shown that by simulation via, one can get many and better probabilistic distributions than by mathematical one.

The hygrothermal transfer is very important for the design of a building envelope for thermal comfort and economic and energy analysis of the building envelope. The applications of various materials in building envelope have been studied extensively. The study presents several models for the hygrothermal transfer for various building walls. Several energy and mass conservation equations with different boundary conditions and input considerations were presented in this paper for concrete, bricks and wooden walls. The effect of hysteresis was ignored in developing most model equations, while few considered flow pattern of fluid through the wall surfaces. Due to the flexibility of Luikov models, it formed the basis for modelling the coupled heat and mass transfer for porous material independent of hygroscopic nature with different boundary conditions defined according to the geometry and orientations. The influence of type of wall, orientation, thickness, the density of the material and climatic variations on the temperature and moisture evolutions within the building materials was more pronounced. Literature, presenting imaging models using imagery software like COMSOL multi-physics, CFD etc. were scarce considering that microscopic imagery is now deployed to measure the heat and moisture evolution in materials. Future models should include shrinkage or expansion influence on the fibrous material like wood due to their behaviour under environmental condition.

Drug discovery has been initially attributed to coincidence or trial and error where the traditional approach was complex, lengthy, and expensive. Conventional drug discovery methods require the costly random screening of synthesized compounds or natural products. Another downside for this approach is the wide dependency on the experimental use of animals for in vi-vo testing. Currently, in silico modeling has become a vital tool for drug discovery and repurposing, and molecular docking is being used to find the best matching between a ligand and a molecule. Practical application of in silico docking will predict the biomolecular interactions between the drug and the target host. Beauvericin (BEA) is an emerging mycotoxin produced by the entomopathogenic fungus Beauveria bassiana. Originally investigated for its pesticide capability, BEA is now considered as a molecule of interest for its potentially diverse biotechnological applications in the pharmacological industry and the field of medicine. In this manuscript, we will provide an overview of the repurposing of BEA into a potentially superior therapeutic molecule in a broad range of diseases. Furthermore, considerable attention has been given to the fundamental role of in silico techniques to i) further investigate the spectrum of this secondary metabolite and ii) elucidate the pathways of BEA for its promising therapeutic action

Academic failure, low attendance, financial difficulties, and mental illness are some of the major reasons most students drop out of high schools. We investigated how high school dropout risk is affected by socioeconomic, demographic, and institutional characteristics. Therefore, research design is composed of a literature review, Questionnaire surveys by interviewing each student. Besides, the analysis and statistical modelling, the Logistic Regression method was used. The results show that academic performance, job, lack of interest and low learning ability were the significant individual characteristics that affect the high school dropout. Moreover, parental characteristics, including financial condition, lack of awareness, and education, are significant factors associated with high school dropout rates. Some other related factors, such as adaptability and influence of the peer groups, abandoning school by siblings, and extra responsibility at home and a joint family system, are also some notable factors that significantly affect the dropout rate in schools Malakand District of KP province. This research study delivered the results that contribute to the literature in education to identify different factors like student's own, parental, institutional, and household characteristics, which affect a student being a dropout from high school in Malakand district Pakistan.

In this paper, we analyzed the live fish trajectory recorded from an experiment in an experimental vertical slot fishway. Combined with a numerical simulation, we demonstrated that randomness shown in fish trajectory might not merely be attributed to fish's random choices in its swimming, also could be an adaption consequence to the bulk unsteady turbulent flow structure. Simple superposing the fish trajectory on the time-averaged flow field obtained either by interpolating on discrete point measurements or numerical simulation is not an ideal method for fish movement description in fishway engineering. How to model the fish paths in transient flow and the necessity of simultaneous recording of the flow field and the fish locomotion are challenging topics. The suggested spectrum analysis of the flow field may provide a new general method to reproduce the fish trajectory in a complex turbulent flow.

Models come in different forms: visual, arithmetic, mental, physical. The most common type of model is arguably the mental model, which people use to view and interpret the world. A model can be described as a representation of a problem or a situation – a simplified representation. The process of building or developing a model is called modeling. A model once developed by the modeller, can be ‘owned’ by a manager or decision maker. The ideal is to make the model an extension of the user’s ability to think about and analyse problems or situations. When used properly – taking into consideration its limitations – an economic model for a project can provide insight for decision makers, when making the crucial decision to approve a project. An economic model for a liquefied natural gas (LNG) project is shown as an example.

Fiber aggregation in nanocomposites has an important effect on the macroscopic electrical performance. To quantitatively evaluate its effect, an index to characterize the degree of aggregation is imperative, and ideally it should have three features simultaneously, i.e., single-parametric, dimensionless and physically meaningful, applicable to different aggregation topologies, and one-to-one corresponding to material electrical properties. To this end, a new aggregation degree is proposed, which is defined as the average increased number of fibers that connect with each one when fibers aggregate from a uniformly distributed state. This index is applicable to different aggregation topologies from lump-like aggregating clusters to network-like aggregating clusters. This index is proven to only depend on the local features of aggregating clusters, and can be concisely expressed by the characteristic parameters of local structure, via geometric probability analysis and numerical validations. Further, a one-to-one linear relation between the aggregation degree and percolation threshold is established by Monte Carlo simulations, which is independent of the distribution law of the fibers. This work provides a guide to the property characterization, performance prediction and material design of nanocomposites, and also gives a physical insight into the understanding of systems with similar non-uniform distributions.

Fouling by activated sludge in membrane bioreactor (MBR) processes for wastewater treatment can be reduced using several strategies such as backflushing, relaxation, and chemical cleaning. Some proprietary systems such as Avanti’s RPU-185 Flexidisks MBR use novel circular rotating, flat sheet membranes to assist in limiting this fouling. An attempt has already been made to model this novel rotating fouling process by developing a simulation model based on first principles and traditional fouling mechanisms. In order to directly compare the potential benefits of rotational MBR system, a follow-up study was carried out using Avanti’s newly developed static (non-rotating) Flexidisks MBR system. This new process uses the same proprietary and patented membrane modular arrangement as used in the circular rotational unit, but is configured instead as a static square-shaped unit which is in-line with the more traditional and popular format used for submerged flat sheet MBR systems. During this study, the results from operating the static pilot unit were simulated and modelled using a standard fouling model coupled with a viscosity to mixed liquor relationship model. These results were then compared with those obtained from running the rotating MBR model however with rotational switching functions turned off and rotational parameters set to a static mode. This comparison was done to ascertain whether the basic premise of the developed rotational model was sound in empirical terms when compared to a standard MBR flux model. The study concluded that relatively good agreement was reached between the two types of models, thus vindicating the usage of a complex rotational MBR model. Follow on studies will now compare results from the rotating MBR system using rotational models developed by other researchers to ascertain the effectiveness rotating MBR modelling approach.

Climate change is expected to affect the agricultural sector in ways that are often unclear to predict. If in the short- medium-terms farmers may adapt to climate change by adjusting agricultural practices, in the long-term these adjustments may become insufficient, forcing farmers to change their farming systems. The extent and direction in which these farming system transitions will occur is still a subject underexplored in the literature. We propose a new framework to explore the marginal effect of climate change on the choice of the farming system, while controlling the effect of other drivers known to also influence farming system choice. Using a spatial-explicit farming system choice-model developed by a previous study in an extensive agricultural region of southern Portugal, we applied a space-for-time approach to simulate the effect of climate change on the future dynamics of the farming systems in the study area. Results suggest that climate change will force many farmers to change the farming system in a foreseeable future. The extent of the projected changes in farming systems is likely to trigger significant social, economic, or environmental impacts, which should require early attention from policy makers.

Open Inertia Modelling (OpInMod) is a modelling framework designed to create unit commitment and economic inertia dispatch optimisation problems. Present energy system modelling generators do not consider power system inertia in unit commitment and economic dispatch modelling to assess future energy system pathways. However, maintaining sufficient power system inertia in power systems is the foundation for power frequency controllability. The work at hand describes the functionality and approaches of open source tool OpInMod. The softwares universal design approach increases reuse potential. OpInMod is distributed with a set of examples to test and understand OpInMod

We developed a novel approach in the field of spatiotemporal modelling, based on the spatialisation of time: the Timescape algorithm. It is especially aimed at sparsely distributed datasets in ecological research, whose spatial and temporal variability is strongly entangled. The algorithm is based on the definition of a spatiotemporal distance that incorporates a causality constraint and that is capable of accommodating the seasonal behaviour of the modelled variable as well. The actual modelling is conducted exploiting any established spatial interpolation technique, substituting the ordinary spatial distance with our Timescape distance, thus sorting, from the same input set of observations, those causally related to each estimated value at a given site and time. The notion of causality is expressed topologically and it has to be tuned for each particular case. The Timescape algorithm originates from the field of stable isotopes spatial modelling (isoscapes), but in principle it can be used to model any real scalar random field distribution.

This research integrates systemic and participatory techniques to model the Russian Federation construction innovation system. Understanding this complex construction innovation system, and determining the best levers for enhancing it, requires the dynamic modelling of a number of factors such as flows of resources and activities, policies, uncertainty and time. To build the foundations for such a dynamic model, the employed study method utilised an integrated stakeholder-based participatory approach coupled with structural analysis (MICMAC - Matrice d'Impacts Croisés Multiplication Appliquée à un Classement Cross-Impact Matrix). This method identified the key factors of the Russian Federation construction innovation system, their causal relationship (i.e. influence/dependence map) and ultimately a causal loop diagram. The generated model reveals pathways to improving construction innovation in the Russian Federation, and underpins the future development of an operationalised systems dynamic model.

Limited accessibility in some parts of Indonesia causes difficulties in logistic distribution, especially for emergency and medical supplies. Emerging drone cargo technology is a potential solution to improve logistic distribution in those areas. However, implementing drone cargo technology involves unknown risks, both in technical and non-technical aspects. Since data on drone operations in Indonesia is limited, a new method is explored to build a Bayesian Network (BN) model for risk analysis of drone crashes in Indonesia’s outermost and underdeveloped areas. The method optimizes the modelling process, in which significant risk factors are selected based on three drone operator companies’ experiences, which include wind speed, rain intensity, and system component failure or malfunction. Real wind speed and rain probability data are then implemented in the model. The operator’s data shows that wind speed contributes to drone crashes, which can be appropriately modelled in the BN model. The model produced a probability of safe operation of 94.1%, comparable to the annual operator’s data. The result shows that most operations are safe, with a minimum case of crashing and no case of harming human life.

In this work a new hydroelectric basin modelling approach is described and applied to the Pontecosi basin, Italy. Several types of data sources were used to learn the model: a number of weather stations, satellite observations, Reanalysis dataset and basin data. With the goal of predicting the water level of the basin, the model was composed by three cascade modules. Firstly, different spatial interpolation methods, such as Kriging, Radial Basis Function and Natural Neighbours, were compared and applied to interpolate the weather stations data nearby the basin area to infer the main environmental variables (air temperature, air humidity, precipitation and wind speed) in the basin area. Then, using these variables as inputs, a neural network was trained to predict the mean soil moisture concentration over the area, also to improve the low availability due to satellite orbits. Finally, a non-linear auto regressive exogenous input (NARX) model was trained to simulate the basin level with different prediction horizons, using the data from the previous modules and past basin data (water level, discharge flow rate, turbine flow rate). Accurate predictions of the basin water level were achieved within 1 to 6 hours ahead, with mean absolute errors (MAE) between 2cm and 10cm respectively.

Digital Terrain Models (DTMs) are currently a fundamental source of information in Earth Sciences. However, DTM-based studies can contain remarkable biases if limitations and inaccuracies of these models are disregarded. In this work, four freely available datasets such as SRTM C-SAR DEM, ASTER GDEM V2 and two airborne LiDAR derived DTMs (at 5 and 1 m spatial resolution, respectively) were analysed in a comparative study in three geomorphologically contrasted catchments located in Mediterranean geoecosystems under intensive human land use influence. Vertical accuracy as well as the influence of each dataset characteristics on hydrological and geomorphological modelling applicability were assessed by using classic geometric and morphometric parameters and the more recently proposed index of sediment connectivity. Overall vertical accuracy – expressed as Root Mean Squared Error (RMSE) and Normalized Median Deviation (NMAD) – revealed the highest accuracy in the cases of the 1 m (RMSE = 1.55 m; NMAD = 0.44 m) and 5 m LiDAR DTMs (RMSE = 1.73 m; NMAD = 0.84 m). Vertical accuracy of SRTM was lower (RMSE = 6.98 m; NMAD = 5.27 m) but considerably higher than in the case of ASTER (RMSE = 16.10 m; NMAD = 11.23 m). All datasets were affected by systematic distortions. As a consequence, propagation of these errors caused negative impacts on flow routing, stream network and catchment delineation and, to a lower extent, on the distribution of slope values. These limitations should be carefully considered when applying DTMs for hydrogeomorphological modelling.

This article shortly introduces Gaussian processes as a new approach for modelling time series in the field of blazar physics. In the second part of the paper, recent results from an application of GP modelling to the multi-wavelength light curves of the blazar PKS 1502+106 are discussed.

Mining and processing tailings often contain significant amounts of valuable metals, that can represent valuable sources of secondary raw materials. Especially this is case in early-stage operations, in which the head grades were higher, and the tailings were higher grade. These tailings can also present a substantial risk to the environment. Serbia has copper deposits which have been exploited since ancient times, and these operations have generated large amounts of mineral processing tailings. The main objective of this study is to show how valuables can be recovered from chemically and mineralogically challenging tailings. After detailed chemical and mineralogical characterization, the laboratory scale flotation tests focused on evaluating the effect of particle size, different types of collectors, pH, and pulp potential. Based on the test work, copper and gold can be recovered effectively into pyrite concentrate

The aim of this study is to model hydrodynamic processes of the Istanbul Strait with its stratified flow characteristic and calibrate the most important parameters using local and global search algorithms. For that two open boundary conditions are defined, which are in the North and South part of the Strait. Observed bathymetric, hydrographic, meteorological and water level data are used to set up the Delft3D-FLOW model. First, the sensitivities of model parameters on the numerical model outputs are assessed using PEST toolbox. Then, the model is calibrated based on the objective functions focusing on the flowrates of upper and lower layers. The salinity and temperature profiles of the Strait are only used for model validation. The results show that the calibrated model outputs of Istanbul Strait are reliable and consistent with the in-situ measurements. The sensitivity analysis reveals that the Spatial Low-Pass Filter Coefficient, Horizontal Eddy Viscosity, Prandtl-Schmidt Number, Slope in log-log Spectrum and Manning Roughness Coefficient are most sensitive parameters affecting flowrate performance of the model. The agreement between observed salinity profiles and simulated model outputs is promising whereas the match between observed and simulated temperature profiles is weak showing that the model can be improved particularly for simulating the mixing layer.

The flow stress of face-centered cubic (FCC) metals exhibits a rapid increase near a strain rate of 104 s-1 under fixed strain conditions. However, many existing constitutive models either fail to capture the mechanical characteristics of this plastic deformation or use piecewise strain rate hardening models to describe this phenomenon. Unfortunately, the piecewise models may suffer from issues such as discontinuity of physical quantities and difficulties in determining segment markers, and struggling to reflect the underlying physical mechanisms that give rise to this mutation phenomenon. In light of this, this paper proposes that the abrupt change in flow stress sensitivity to strain rate in FCC metals can be attributed to microstructural evolution characteristics. To address this, a continuous semi-empirical physical constitutive model for FCC metals is established based on the microstructural size evolution proposed by Molinari-Ravichandran and the dislocation motion slip mechanism. This model effectively describes the mutation behavior of strain rate sensitivity under fixed strain, particularly evident in the annealed OFHC. The predicted results of the model across a wide range of strain rates (10-4 − 106 s-1) and temperatures (77 − 1096 K) demonstrate relative errors generally within ±10% of the experimental values. Furthermore, the model is compared to five other models, including Mechanical Threshold Stress (MTS), Nemat-Nasser-Li (NNL), Preston-Tonks-Wallace (PTW), Johnson-Cook (JC), and Molinari-Ravichandran (MR) models. A comprehensive illustration of errors reveals that the proposed model outperforms the other five models in describing the plastic deformation behavior of OFHC. The error results offer valuable insights for selecting appropriate models for engineering applications and provide significant contributions to the field.

Accurate estimation of vehicle emissions is essential and can be helpful in the decision-making process. Thanks to calculation methods, it is possible to accurately estimate the emissions that result from driving a car and to determine their impact on, for example, the health of pedestrians who are on the pavement near an arterial road. Recent years have seen an increase in the use and importance of emission models, as well as vehicle traffic simulation models. Increasingly, emission models are being combined with traffic simulation models, as it is not practical to actually measure the on-road emissions of an entire fleet of vehicles over a given stretch of road. This paper provides an overview of the selected work on the topic of emission modelling and vehicle traffic simulation models. The models are distinguished according to their respective scales of accuracy, i.e., micro, meso, and macro. Selected work combining emission and traffic simulation models is also presented, as well as development trends in the field. In particular, the paper also highlights the fact of proper calibration of vehicle traffic simulation models in order to obtain the most reliable vehicle emissions results from computational models. The review of the works carried out may be helpful for future projects concerning the use of simulation tools for transport and environmental decision-makers in the area of arterial roads.

We use COMSOL Multiphysics software to model optical processes in media and the effects of light propagation in whispering gallery mode resonators (WGMRs). Although plane wave irradiating on resonator does not create resonances, it was shown that they can be obtained when focused tangentially on the perimeter of the resonator. We demonstrate that resonances can be excited in resonators by prism coupling when the incident angle of the light is larger than the angle of total internal reflection. We determined the optimal distance between the prism and the resonator to get the strongest resonances. Bio-sensing principle of WGMRs is shown by demonstrating that the shift of resonances increases with a larger number of "bio-molecules" attached to the surface of WGMR. Surface functionalization and surface roughness were explored.

(1) Background: Type-2 Diabetes (T2DM) is a long-term medical disorder characterized by Insulin deficiency and high blood glucose levels. Among other medication to cure T2DM, the review of the literature found that various Pyrimidine derivative act as an agonist for G-protein-coupled receptor 119 (GPR119) was proposed to control blood glucose levels by enhancing the function of pancreatic beta-cells and its mechanism of action with fewer adverse effects.; (2) Methods: In the present research work, in-silico investigations were carried out to investigate the potential of Pyrimidine analogue as an agonists to the protein target GPR119 receptor. We performed exhaustive molecular modelling and protein modelling methodologies such as homology mod-elling, molecular docking along with various drug designing tools such as 3D-QSAR and pharmacophore mapping to ascertain the design of better GPR119 agonists.; (3) Results: On the basis of in-depth computa-tional studies, we designed new pyrimidine moiety and analyzed them for GPR-119 receptor agonist and fur-ther explored the ADME properties. Designed compounds found to exhibit better predicted activities as com-pared to reference compound; (4) Conclusions: The current research on pyrimidine derivatives, using molec-ular docking, 3D-QSAR and Pharmacophore mapping demonstrated that the obtained computational model has significant properties and the designed molecules and Dataset from this model, produced antidiabetic compound against the target GPR119 i.e., compound 1S, 1Z and 1D with the docking score of −11.696, -9.314 and -8.721 respectively. The pharmacokinetics and drug-likeness studies revealed that these compounds may be the future candidate for the treatment of diabetes acting via GPR119 agonist mechanism.

Groundwater (GW) flooding mechanisms differ from river flooding both spatially and temporally, and preventative methods against groundwater flooding must take this into account. Although groundwater flooding caused by river water rise occurs seldom, it can occasionally become severe and last for a long time if the river is significantly flooded. In the southwest portion of the research domain, Friedrichshafen with a few urban communities, the level of the groundwater table was discovered to be roughly 1 m below the surface. It was discovered that the urban settlement area only has one-story buildings. In the study region, it is typical for the single-story building's foundation bottom level to extend up to a depth of about 1.5 meters. Therefore, flood mitigation methods are taken into account for the southwest portion of the study region. The installation of a pumping well, drainage, and a barrier in the affected area are three different flood control strategies that are taken into consideration for the study area. From a technical and cost-benefit perspective, installing a pumping well that withdraws water and lowers the groundwater table was found to be the most effective flood control measure locally in a small region (e.g., 1km x 1km). By contrast, removing groundwater by building drainage and barriers was also shown to be ineffective to lower the groundwater table over an extended region and was significantly more expensive than the installation of wells. Additionally, when river flooding is taken into account compared to the default scenario where no intake of water from the river is included along the western border of the study area, it is discovered that the spread of pollution is significantly greater.

Dampers provide safety by control of unwanted motion, due to conversion of mechanical work into another form of energy (e.g., heat). State of the art materials are elastomers including thermoplastic-elastomers. For polymer-appropriate replacement of multi-component shock absorbers comprising mounts, rods, hydraulic fluids, pneumatic devices, or electro-magnetic devices, among others, deep insights of the dynamic thermo-mechanical characteristics of damper materials have to be gained. The ultimate objective is to reduce complexity by utilizing inherent material damping rather than structural (multi-component) damping properties. The objective of this work was to compare the damping behavior of different elastomeric materials including thermoplastic poly(urethane) (TPU), and silicone rubber blends (mixtures of different poly(dimethylsiloxane) (PDMS)). Therefore, the materials were hyper- and viscoelastic characterized, a finite element calculation of a ball-drop test was performed, and for validation the rebound resilience was measured experimentally. In an attempt, the coil-over shock absorber of a model car was replaced by a damper made of the examined and modeled materials. The results revealed that the material parameter determination methodology is reliable, and the data applied for simulation lead to realistic predictions. Interestingly, the rebound resilience of the mixture of soft and hard PDMS (50:50)w% is the highest and the lowest values were measured for TPU.

Heat and power cogeneration plants based on fuel cells are interesting systems for energy- conversion at low environmental impact. Different fuel cells have been proposed, but experimental testing rigs are expensive and the development of commercial systems is time consuming if based on fully experimental activities. Furthermore, tight control of operation of fuel cells is compulsory to avoid damage, which must be based on accurate models, able to predict the cell behaviour and prevent stresses and shut-down. Some selected examples of steady state, dynamic and fluid dynamic modelling of different types of fuel cells are proposed, mainly PEMFC and SOFC type. The general ideas behind the thermodynamic, kinetic and transport description are recalled, with some examples of models derived for single cells, stacks and integrated power co-generation units.

A novel machine learning approach is presented in this paper, based on extracting latent information and using it to assist decision making on ambulance attendance and conveyance to a hospital. The approach includes two steps: in the first, a forward model analyzes the clinical and, possibly, non-clinical factors (explanatory variables), predicting whether positive decisions (response variables) should be given to the ambulance call, or not; in the second, a backward model analyzes the latent variables extracted from the forward model to infer the decision making procedure. The forward model is implemented through a machine, or deep learning technique, whilst the backward model is implemented through unsupervised learning. An experimental study is presented, which illustrates the obtained results, by investigating emergency ambulance calls to people in nursing and residential care homes, over a one-year period, using an anonymized data set provided by East Midlands Ambulance Service in United Kingdom.

The growth of demand for metallic minerals has faced with the need for new techniques and improving technologies for all mine life cycle operations. Nowadays, the exploitation of old tailings and mine wastes facilities could represent a solution to this demand, with economic and environmental advantages. W-Sn Panasqueira Mine has been operating for more than 100 years. Its first processing plant “Rio” was located near Zêrere river being the mineral processing residues deposited on the top hillside on the margin of this river in Cabeço do Pião tailings dam. The lack of maintenance and monitoring of this enormous structure in the last twenty years represents high risks to the environment and population of the surrounding region. The re-mining of the tailings by hydrometallurgical methods was considered, in order to satisfy these two enounced conditions - metals demand and environmental risk, aiming for the sale of the metal to pay the environmental intervention. Field samples campaign allowed collecting data and results from laboratory tests driving to use regression optimization. The re-mining solution was studied, taking into account the technical, economic, social, and environmental aspects.

For the generation of representative volume elements a statistical description of the relevant parameters is necessary. These parameters usually describe the geometric structure of a single grain. Commonly, parameters like area, aspect ratio and slope of the grain relative to the rolling direction are applied. However, usually simple distribution functions like log normal or gamma distribution are used. Yet, these do not take the interdependencies between the microstructural parameters into account. To fully describe any metallic microstructure though, these interdependencies between the singular parameters need to be accounted for. To accomplish this representation, a machine learning approach was applied in this study. By implementing a Wasserstein generative adversarial network, the distribution, as well as the interdependencies could accurately be described. A validation scheme was applied to verify the excellent match between microstructure input data and synthetically generated output data.

Human behaviour was tipped as the mainstay in the control of further SARS-CoV-2 (COVID-19) spread especially after the lifting of restrictions by many countries. Countries in which restrictions were lifted soon after the first wave, had subsequent waves of the COVID-19 infections and it remains to be seen whether there will be more waves. In this paper, we formulate a deterministic model for COVID-19 incorporating dynamic non-pharmaceutical interventions, dubbed social dynamics. The model steady states are determined and their stability analysed. Numerical simulations are carried out to determine the pack of various parameters that influence the social dynamics. In South Africa, the first wave was the only wave in which the only interventions rested solely on human behavior. The model is thus fitted to COVID-19 data on the first wave in South Africa. The results presented in this paper have implications on the trajectory of the pandemic in the presence of changing social processes.

To proactively manage information security, enterprises often employ information security risk assessment techniques. Asset value - which is used to calculate the financial impact of possible threats - is one of the key parameters of information security risk. However, assets in a system are rarely independent, and their values are typically interdependent. Asset owners and IT teams may hold different views as regards these values, and there is thus the need to reduce subjectivity in a qualitative risk assessment. The research entails the development of a conceptual framework derived from the literature to minimize subjectivity, and the design of a system based on those concepts. The study uses the Unified Modeling Language as a design tool and puts forward an object-oriented model for defining asset values, in which the relationships between assets, vulnerabilities and related threats are identified. A “segregation of duties” approach is integrated into the risk management system to mitigate against subjectivity and better determine asset values. Survey responses from 16 practitioners working in the private and public sectors confirm the validity of the approach, but suggest it may be more workable in larger organisations where resources allow dedicated risk professionals to operate.

Glacial relicts, especially those with very narrow habitat requirements, are particularly affected by global warming and thus good models for studying the future biodiversity patterns of the Alps. We have used as a model taxon Papaver occidentale, a glacial relict endemic to the Western Prealps, belonging to the alpine poppy complex (P. alpinum aggr.). All known localities were visited, each population was georeferenced and the number of individuals was estimated. Species Distribution Modelling (SDM) was used to evaluate the present and future potential distribution range and habitat suitability, taking into account the specificity of its habitat (calcareous screes). According to our study, there are globally 19 natural populations of P. occidentale. The total number of individuals was estimated at 30756 (with 72% of all individuals in canton of Bern). The taxon is a highly specialized alpine plant growing preferentially between 1900 and 2100 m a.s.l. on north facing screes. Predictions for the end of the 21st century indicate that suitable area will significantly decrease, for both the entire studied area (0-30% remaining) and sites nearby current P. occidentale populations (0-17% remaining). Under the most severe scenario, the species risks complete extinction. The long-term in situ conservation of P. occidentale, and all other taxa of the P. alpinum complex, is unlikely to be achieved without slowing global climate change. More generally, our fine-scale study shows that local environmental buffering of large-scale climate change in high-mountain flora may be very limited in specialised taxa of such patchy environments as screes.

A compartmental epidemiological model with seven groups is introduced herein, to account for the dissemination of diseases similar to the Coronavirus disease 2019 (COVID-19). In its simplified version, the model contains ten parameters, four of which relate to characteristics of the virus, whereas another four are transition probabilities between the groups; the last two parameters enable the empirical modelling of the effective transmissibility, associated in this study with the cumulative number of fatalities due to the disease within each country. The application of the model to the fatality data (the main input herein) of five countries (to be specific, of those which had suffered most fatalities by April 30, 2020) enabled the extraction of an estimate for the basic reproduction number $R_0$ for the COVID-19 disease: $R_0=4.91(33)$.

False positives on multiparametric (mp)-MRI result in a large number of unnecessary biopsies in men with clinically insignificant diseases. This study investigates whether quantitative diffusion MRI can improve differentiation between false positives, true positives and normal tis-sue. Twenty-three patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffu-sion for Cytometry in Tumours (VERDICT)-MRI, followed by transperineal biopsy. The patients were categorised into two groups following biopsy: (1) significant cancer - true positive (2) atro-phy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN) - false positive. The clinical apparent diffusion coefficient (ADC) values of the lesions were obtained, and the intravoxel inco-herent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted using a deep learning approach. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular-extravascular vol-ume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were only found for the VERDICT fIC. These results demonstrate that model-based diffusion MRI could reduce the number of unnecessary biopsies due to false positive prostate lesions and shows promising sensitivity to benign diseases that mimic cancer.

Climate and land use changes are two factors affecting the hydrology of watersheds. The greatest effect of climate change is probably the change in the hydrological cycle, thus water availability for human consumption, industry, and power generation. While the change in land use can cause changes in the hydrological components of the basin, such as evapotranspiration, runoff, and groundwater recharge. From these projections, it is necessary to understand the processes involved in climate change and land use and its impact on water systems. In this context, the objective of this study was to simulate climate change scenarios combined with the land use change and to analyze the impacts on the hydrology in the Piracicaba basin. Two periods were selected based on the preliminary result of detection of the abrupt change point of the annual flow series, using Petitt's non-parameter test. The first period selected prior to the occurrence of the point of the change was from 1985 to 2000. The other period after the point of the change was 2001 to 2015. Land use maps of the years 1990 and 2010 were used to represent the two periods. The results showed a good performance of the SWAT model. The NS (Nash-Sutcliffe coefficient) was 0.88 in calibration for both periods. In the validation, the NS was 0.92 and 0.94 for the first and second periods respectively. We then simulate climate and land use changes scenarios and compare them to the current scenario (baseline). The simulation results showed that the hydrological processes in the Piracicaba basin in the period 1985-2015, had more intense responses to land use change than climate.

Anthropogenic activities such as the damming of rivers have resulted in the formation of depositional systems for sediments that are naturally transported along river channels all over the world. The steady accumulation of sediments has profound effects on how reservoirs function. As the reservoir ages, the impact of sedimentation becomes more pronounced. In an alluvial channel, the dynamics of sediment movement, scour, and deposition are exceedingly complicated. The quantitative and qualitative management of river engineering heavily relies on sediment motion. One of the key concerns affecting reservoir storage capacity is reservoir sedimentation. Understanding the form and behavior of the river or reservoir is necessary for a scientific approach to sedimentation. An overview of reservoir sedimentation processes, and models of reservoir and shallow water bodies is provided in this article. More research in the developing fields is advised for continuing studies on reservoir sedimentation due to climate change effects

Eutrophication in the Lobo watershed remains a major problem. The work carried out has focused on chemical and biological analyzes in the lake or in its immediate environment: they did not sufficiently take into account the diffuse transfer of nutrients over the entire watershed. This study aims to assess the nutrient (N and P) loads in the Lobo watershed, an agricultural area, to understand the spatio-temporal impacts of land management practices on eutrophication. The methodology uses two steps: streamflow calibration and nutrient (N and P) estimation using the Soil and Water Assessment Tool (SWAT) watershed model. Thus, the nutrient inputs were estimated based on the levels of N and P in every kilogram of NPK-type fertilizers applied by farmers. The average quantities of N and P applied to the crops were 47.24 kg N/ha and 21.25 kg P/ha. Results show a good performance on flow calibration as evidenced by evaluation criteria R2, NSE and PBIAS of 0.63, 0.62 and -8.1, respectively. The yields of inorganic N and soluble P varied from 0 to 0.049 kg N/ha and from 0 to 0.31 kg P/ha. These results show that the crops’ in-organic nitrogen requirements were higher than the demands for soluble phosphorus. Simulations relating to the organic N transfer revealed values ranging from 0.2 to 5 kg N/ha, while the transport of organic phosphorus was estimated to vary from 0.3 to 1.3 kg P/ha.

During the launch and return of a spacecraft, the intense combustion of propellants generates strong electromagnetic radiation, which interferes with the operation of electronic equipment in the spacecraft. To improve the electromagnetic compatibility of electronic equipment in spacecraft, it is necessary to study the electromagnetic radiation characteristics of rocket fuel. An electromagnetic radiation measurement system based on antennas is designed to measure the electromagnetic radiation generated by rocket fuel, and the electromagnetic radiation characteristics are obtained through data analysis. The mechanism of the electromagnetic radiation generated by rocket fuel is comprehensively analysed through the spatial, time-domain, frequency-domain, and energy-domain characteristics. A characterization model is established to provide a reliable scheme for evaluating the influence of rocket fuel electromagnetic radiation on electronic equipment in spacecraft.

Background: There is little clarity in the application of content related to improper integrals in university students, due to the absence of meaning, which prevents them from making a connection with everyday problem situations. Methods: we designed a mathematical modelling proposal where a specific situation involving the instrumentation, use and application of this type of integrals is experimented and solved with a population of engineering students, who learn to use them. Results: The importance of using mathematical modelling as a didactic-dynamic resource is highlighted because it helps students to reach an understanding of real situations involving improper integrals in different contexts. Conclusions: Despite the numerous errors detected in the students, this strategy made it possible to demonstrate the development of advanced mathematical thinking skills in young people.

The dark proteome as we define it, is the part of the proteome where 3D structure has not been observed either by homology modeling or by experimental characterization in the protein universe. From the 550.116 proteins available in Swiss-Prot (as of July 2016) 43.2% of the Eukarya universe and 49.2% of the Virus universe are part of the dark proteome. In Bacteria and Archaea, the percentage of the dark proteome presence is significantly less, with 12.6% and 13.3% respectively. In this work, we present the map of the dark proteome in Human and in other model organisms. The most significant result is that around 40%- 50% of the proteome of these organisms are still in the dark, where the higher percentages belong to higher eukaryotes (mouse and human organisms). Due to the amount of darkness present in the human organism being more than 50%, deeper studies were made, including the identification of ‘dark’ genes that are responsible for the production of the so-called dark proteins, as well as, the identification of the ‘dark’ organs where dark proteins are over represented, namely heart, cervical mucosa and natural killer cells. This is a step forward in the direction of the human dark proteome.

Based on rapeseed plantation biodiesel production system requires transportation of goods, like raw materials, machines and tools, and products between various conversion stages of agricultural as well as industrial subsystems. Each transportation step requires consumption of some energy. This consumption, decreases the net amount of energy delivered out of the biofuel production system, and consequently decreases energetic efficiency of the system. The present work deals with computer modelling of the influence of energy consumed on those transport routes on the energetic efficiency of production system. The effects caused by variation of several parameters like fuel consumption and load capacity of transportation means, size of plantation, distribution and sizes of individual fields, distances between fields, as well as plantation yield, and finally the distance between plantation and the industrial facility are studied using the numerical model developed

Energy system models have become indispensable to shape future energy systems by providing insights into different trajectories. However, sustainable systems with high shares of renewable energy are characterized by growing cross-sectoral interdependencies and decentralized structures. To capture important properties of increasingly complex energy systems, sophisticated and flexible modelling tools are needed. At the same time open science becomes increasingly important in energy system modelling. This paper presents the Open Energy Modelling Framework (oemof) as a novel approach in energy system modelling, representation and analysis. The framework forms a toolbox to construct comprehensive energy system models and has been published open source under a free license. With a collaborative development based on open processes the framework seeks for a maximum level of participation and transparency to facilitate open science principles in energy system modelling. Based on a generic graph based description of energy systems it is well suited to flexibly model complex cross-sectoral systems and incorporate various modelling approaches. This makes the framework a multi-purpose modelling environment for modelling and analyzing different systems ranging from an urban to a transnational scale.