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Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the operations that do not add value to the product or processes to increase the value of each performed activity. Here the main motivation is to study how quantitative modelling approaches can support LM tools even under system and environment uncertainties. The main contributions of the article are: (i) providing a systematic literature review of 99 works related to the modelling of uncertainty in LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifying LM works under uncertainty; and (iv) identify quantitative models and their solution to deal with uncertainty in LM environments by identifying the main variables involved. Hence this article provides a conceptual framework for future LM quantitative modelling under uncertainty as a guide for academics, researchers and industrial practitioners. The main findings identify that LM under uncertainty has been empirically investigated mainly in the US, India and the UK in the automotive and aerospace manufacturing sectors using analytical and simulation models to minimise time and cost. Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce waste in a context of system uncertainty. [ABSTRACT FROM AUTHOR]

Disabled people can be successfully employed in most production processes, provided that one knows how to exploit their abilities and take into account their limitations in order to give them an appropriate job. However, because the level and type of production must be constantly adapted to the needs of the market, the involvement of disabled people in the production process may also change. Additionally, people with disabilities have limitations as well as additional rights that must be considered. As a result, the organisation and planning of their work, side by side with other employees, becomes more complex. Computer simulations can be a support for organising and planning the involvement of employees with disabilities in production processes. The aim of the article is to show how simulations can facilitate the organisation of work of employees with disabilities, with the changing demand for manufactured products. The paper identifies the factors that should be considered, and then presents how the employment of disabled people can affect the operation of the production line and the commercial image of the company. The study uses a combination of System Dynamics and Discrete Event Simulations. The relevant data for the simulation were derived from a production company. [ABSTRACT FROM AUTHOR]

Introduction: People who use drugs in community settings are at risk of a fatal overdose, which can be mitigated by naloxone administered via bystanders. In this study we sought to investigate methods of estimating and tracking opioid overdose reversals by community members with take-home naloxone (THN) to coalesce possible ways of characterizing THN reach with a metric that is useful for guiding both distribution of naloxone and advocacy of its benefits.Methods: We conducted a scoping review of published literature on PubMed on August 15, 2022, using PRISMA-ScR protocol, for articles discussing methods to estimate THN reversals in the community. The following search terms were used: naloxone AND (“take home” OR kit OR “community distribution” OR “naloxone distribution”). We used backwards citation searching to potentially find additional studies. Overdose education and naloxone distribution program-based studies that analyzed only single programs were excluded.Results: The database search captured 614 studies, of which 14 studies were relevant. Backwards citation searching of 765 references did not reveal additional relevant studies. Of the 14 relevant studies, 11 were mathematical models. Ten used Markov models, and one used a system dynamics model. Of the remaining three articles, one was a meta-analysis, and two used spatial analysis. Studies ranged in year of publication from 2013–2022 with mathematical modeling increasing in use over time. Only spatial analysis was used with a focus on characterizing local naloxone use at the level of a specific city.Conclusion: Of existing methods to estimate bystander administration of THN, mathematical models are most common, particularly Markov models. System dynamics modeling, meta-analysis, and spatial analysis have also been used. All methods are heavily dependent upon overdose education and naloxone distribution program data published in the literature or available as ongoing surveillance data. Overall, there is a paucity of literature describing methods of estimation and even fewer with methods applied to a local focus that would allow for more targeted distribution of naloxone.

This paper deals with mobile-rack automated storage/retrieval systems (AS/RS) that are a particular physical configuration of unit-deep AS/RS, i.e. the class of systems where each cell is designed to contain a single load. There are two main contributions in this paper: the first one is the analytical modelling of the average single-cycle time for mobile-rack AS/RS. The approach adopted for modelling is based on a continuous approximation, which is not recent, however, the approach proposed here for the transition from the discrete to the continuous models is different from those used previously in the literature. In a certain way, the multi-aisle and unit-load configurations can be seen as special cases of the mobile-rack configuration. This allows to deduce analytical models for the average single-cycle time for these two configurations from the one developed for mobile rack. The second main contribution of this paper is to find the dimensions of the AS/RS that minimise the average single-cycle time. The optimisation has been studied formally for the multi-aisle and unit-load configurations, and numerically for the mobile-rack configuration. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to help online grocery retailers determine the optimal return and refund policies for perishable grocery items. Three refund policies and two return requirements are modelled: no-refund, full-refund and partial-refund, with or without product return. We first derive the optimal refund policy under two scenarios, where either product return is or is not required for refund. We further analyse how market conditions impact the optimal policies. Lastly, we investigate when product return should be required for refund. Our results show that a more generous refund policy should be offered if the product has a higher profit margin, if an unsatisfactory product has a large negative impact on consumer utility, and if consumers have high return costs when product return is required for refund. Our results also show that it is optimal not to require the unwanted product to be returned when consumer's return cost and product return rate are above certain threshold values. This paper fills the gap in the literature on return and refund policies in an online grocery setting. The findings of this paper provide explicit guidance to online grocery businesses on how to optimally determine the return and refund policies. [ABSTRACT FROM AUTHOR]

BackgroundIncarceration is associated with drug-related harms among people who inject drugs (PWID). We trained >1800 police officers in Tijuana, Mexico on occupational safety and HIV/HCV, harm reduction, and decriminalization reforms (Proyecto Escudo). We evaluated its effect on incarceration, population impact and cost-effectiveness on HIV and fatal overdose among PWID.MethodsWe assessed self-reported recent incarceration in a longitudinal cohort of PWID before and after Escudo. Segmented regression was used to compare linear trends in log risk of incarceration among PWID pre-Escudo (2012-2015) and post-Escudo (2016-2018). We estimated population impact using a dynamic model of HIV transmission and fatal overdose among PWID, with incarceration associated with syringe sharing and fatal overdose. The model was calibrated to HIV and incarceration patterns in Tijuana. We compared a scenario with Escudo (observed incarceration declines for 2 years post-Escudo among PWID from the segmented regression) compared to a counterfactual of no Escudo (continuation of stable pre-Escudo trends), assessing cost-effectiveness from a societal perspective. Using a 2-year intervention effect and 50-year time horizon, we determined the incremental cost-effectiveness ratio (ICER, in 2022 USD per disability-adjusted life years [DALYs] averted).FindingsCompared to stable incarceration pre-Escudo, for every three-month interval in the post-Escudo period, recent incarceration among PWID declined by 21% (adjusted relative risk = 0.79, 95% CI: 0.68-0.91). Based on these declines, we estimated 1.7% [95% interval: 0.7%-3.5%] of new HIV cases and 12.2% [4.5%-26.6%] of fatal overdoses among PWID were averted in the 2 years post-Escudo, compared to a counterfactual without Escudo. Escudo was cost-effective (ICER USD 3746/DALY averted compared to a willingness-to-pay threshold of $4842-$13,557).InterpretationEscudo is a cost-effective structural intervention that aligned policing practices and human-rights-based public health practices, which could serve as a model for other settings where policing constitutes structural HIV and overdose risk among PWID.FundingNational Institute on Drug Abuse, UC MEXUS CONACyT, and the San Diego Center for AIDS Research (SD CFAR).

Investigations of safety issues related to the design and operation of high-pressure proton exchange membrane (PEM) water electrolysers have until now mainly focused on the electrolyser stack. The present study focuses on the risk of explosive mixture formation in the water recovery system, which is part of the balance of plant in water electrolysers. After a major electrolyser company experienced an unexpected detonation in their system, we analysed their process design as a base case: a low-pressure buffer tank where anodic and cathodic water is mixed with make-up deionised water poses a safety risk because an explosive gas mixture is likely to form there, resulting in severe accidents. Several retrofit options are explored using dynamic and steady-state simulation models: these options are then evaluated based on their effects on safety, general economics and operation of the plant, in addition to ease of retrofit. The results show that preventing the formation of explosive gas mixtures in the water recovery system is achievable in several ways, such as avoiding recycling cathodic water altogether, stripping it of hydrogen, flushing buffer tanks with nitrogen or oxygen, rearranging the balance of plant appropriately, or changing the control algorithms of the system. [Display omitted] • Common electrolyser plant designs can generate explosive mixtures in the balance of plant. • Such designs have led to unwanted accidents with potential for escalation. • The explosion threshold can be reached in less than 10 min from plant startup. • There are several retrofit options to ensure safe operation. [ABSTRACT FROM AUTHOR]

Radiotherapy has played an essential role in cancer treatment for over a century, and remains one of the best-studied methods of cancer treatment. Because of its close links with the physical sciences, it has been the subject of extensive quantitative mathematical modelling, but a complete understanding of the mechanisms of radiotherapy has remained elusive. In part this is because of the complexity and range of scales involved in radiotherapy—from physical radiation interactions occurring over nanometres to evolution of patient responses over months and years. This review presents the current status and ongoing research in modelling radiotherapy responses across these scales, including basic physical mechanisms of DNA damage, the immediate biological responses this triggers, and genetic- and patient-level determinants of response. Finally, some of the major challenges in this field and potential avenues for future improvements are also discussed. [ABSTRACT FROM AUTHOR]

As a typical 3D printing process, fused filament fabrication still has disadvantages when operating on manufacturing lines due to the non-uniform textures of the oriented surfaces of the 3D-printed components. This work investigates the effects of structural parameters, i.e., orientations angle, ABS and PLA materials, three different layer thicknesses, three different perimeters, and three different infill rates utilizing a balanced modified Taguchi experimental design and 63 different parametric combinations to characterize the surface roughness parameters: average Ra, mean roughness depth Rz, root mean square Rq, skewness Rsk, and kurtosis Rku. The analysis of the experimental results, i.e., the levels mean values analysis plots and linear residual analysis of variances, showed that the layer thickness strongly influences all surface parameters and interacts considerably with all orientations. In contrast, material type, number of perimeters, and infill rate had insignificant impacts on surface roughness parameters. Finally, the additive linear modelling approach was utilized and validated for proper predictions, making it helpful for surface engineering applications. [ABSTRACT FROM AUTHOR]

Key points Along the ascending auditory pathway, there is a broad shift from temporal coding, which is common in the lower auditory brainstem, to rate coding, which predominates in auditory cortex. This temporal‐to‐rate transition is particularly prominent in the inferior colliculus (IC), the midbrain hub of the auditory system, but the mechanisms that govern how individual IC neurons integrate information across time remain largely unknown. Here, we report the widespread expression of Glun2c and Glun2d mRNA in IC neurons. GluN2C/D‐containing NMDA receptors are relatively insensitive to voltage‐dependent Mg2+ blockade, and thus can conduct current at resting membrane potential. Using in situ hybridization and pharmacology, we show that vasoactive intestinal peptide neurons in the IC express GluN2D‐containing NMDA receptors that are activatable by commissural inputs from the contralateral IC. In addition, GluN2C/D‐containing receptors have much slower kinetics than other NMDA receptors, and we found that GluN2D‐containing receptors facilitate temporal summation of synaptic inputs in vasoactive intestinal peptide neurons. In a model neuron, we show that a GluN2C/D‐like conductance interacts with the passive membrane properties of the neuron to alter temporal and rate coding of stimulus trains. Consistent with this, we show in vivo that blocking GluN2C/D‐containing receptors decreases both the spontaneous firing rate and the overall firing rate elicited by amplitude‐modulated sounds in many IC neurons. These results suggest that GluN2C/D‐containing NMDA receptors influence rate coding for auditory stimuli in the IC by facilitating the temporal integration of synaptic inputs. NMDA receptors are critical components of most glutamatergic circuits in the brain, and the diversity of NMDA receptor subtypes yields receptors with a variety of functions. We found that many neurons in the auditory midbrain express GluN2C and/or GluN2D NMDA receptor subunits, which are less sensitive to Mg2+ blockade than the more commonly expressed GluN2A/B subunits. We show that GluN2C/D‐containing receptors conducted current at resting membrane potential and enhanced temporal summation of synaptic inputs. In a model, we show that GluN2C/D‐containing receptors provide additive gain for input‐output functions driven by trains of synaptic inputs. In line with this, we found that blocking GluN2C/D‐containing NMDA receptors in vivo decreased both spontaneous firing rates and firing evoked by amplitude‐modulated sounds. [ABSTRACT FROM AUTHOR]

Saving the phenomena is shaping the phenomena: recognising that a specific phenomenon exists implies the existence of a mechanism that gives rise to the phenomenon and that the phenomenon has a shape. These two aspects are inextricably connected: determining the best shape is determining the mechanism that produces it. What is best then depends on evaluating the nature of the simplicity of the mechanism. [ABSTRACT FROM AUTHOR]

This paper presents an efficient algorithm for voxelizing the surface of triangular meshes in a single compute pass. The algorithm uses parallel equidistant lines to traverse the interior of triangles, minimizing costly memory operations and avoiding visiting the same voxels multiple times. By detecting and visiting only the voxels in each line operation, the proposed method achieves better performance results. This method incorporates a gap detection step, targeting areas where scanline‐based voxelization methods might fail. By selectively addressing these gaps, our method attains superior performance outcomes. Additionally, the algorithm is written entirely in a single compute GLSL shader, which makes it highly portable and vendor independent. Its simplicity also makes it easy to adapt and extend for various applications. The paper compares the results of this algorithm with other modern methods, comprehensibly comparing the time performance and resources used. Additionally, we introduce a novel metric, the ‘Slope Consistency Value’, which quantifies triangle orientation's impact on voxelization accuracy for scanline‐based approaches. The results show that the proposed solution outperforms existing, modern ones and obtains better results, especially in densely populated scenes with homogeneous triangle sizes and at higher resolutions. [ABSTRACT FROM AUTHOR]

Biodiversity metrics have become a ubiquitous component of conservation assessments across scales. However, whilst indices have become increasingly widely used, their ability to perform in the face of different biases has remained largely untested under realistic conditions. Citizen science data are increasingly available, but present new challenges and biases, thus understanding how to use them effectively is essential. Here, we built a virtual world incorporating BirdLife data and accounting for their biases, then explored how well commonly‐used diversity metrics could estimate known values across a suite of representative scenarios. We used predictive modelling to model bird diversity globally and overcome biases using the approaches found most accurate in prior assessments. Performance was highly variable across the different types of biases, but in many instances Simpson's index performed best, followed by Hill numbers, whereas Pielou's index was almost universally worst. From standardised tests, we then applied these metrics to eBird data using 611 520 112 samples of 10 359 species of bird (around 88% of known species), to reconstruct global diversity patterns at five and ten km resolutions. However, when we mapped out diversity using Maxent based on these indices, Simpson's index generally over‐predicted diversity, whereas Hill numbers were more conservative. Based on an average of the better projected indices, one can map out diversity across resolutions and overcome biases accurately predicting diversity patterns even for data‐poor areas, but if a single metric is used, Hill numbers are most robust to bias. Going forward, this workflow will enable standardized best practices for diversity mapping based on a clear understanding of the performance of different metrics. [ABSTRACT FROM AUTHOR]

A new payoff function is proposed for cancer treatment optimisation, the tumour elimination payoff (TEP), that incorporates the increase in lifespan if tumour elimination is achieved. The TEP is discounted by drug toxicity and by potential risks, such as metastasis and mutation. An approximation is used for the probability of tumour elimination, e−αN(T)$e^{-\alpha N(T)}$, giving a terminal payoff with an exponential dependence on the final tumour size N(T)$N(T)$. The optimal solutions for this payoff for simple tumour growth models, (logistic and Gompertz growth), are determined. Using Pontryagin's maximum principle it is proved that bang–bang optimal solutions exist with a single switch; specifically delayed treatment and treat‐and‐stop solutions at maximum tolerated dose (MTD) exist. There is also a singular arc with constant tumour size. Solutions either have a high probability, respectively, low probability of tumour elimination; these correspond to a post‐treatment high probability of cure, and a high probability of relapse, respectively. Optimising over the time horizon T$T$ results in solutions that are either MTD throughout or no treatment, that is, treatment is either beneficial or detrimental. For the logistic growth model, the treatment benefit phase diagram is derived with respect to the patient's expected increase in lifespan and tumour size. [ABSTRACT FROM AUTHOR]

Hazel dormice Muscardinus avellanarius have severely declined since 2000 leading to increased legislative protection in the UK and Europe. Artificial nestboxes are widely used for its conservation and monitoring. Previous research has focused on how to identify suitable areas for nestboxes, but where to place individual boxes to promote occupancy is less well understood. Here, we demonstrate the use of machine learning Random Forest regression to predict nestbox occupancy from a wide range of microhabitat variables using a UK woodland as a case study. Random forest models are powerful predictive tools that allow simultaneous testing of many predictors with relatively few observations. Field data included observed nestbox occupancy (2017–2021) and measurements of 76 microhabitat variables collected in the summer of 2021 from 45 occupied and unused nestboxes located in a deciduous woodland in Berkshire, UK. We applied Random Forest regression to identify important variables and predict nestbox occupancy demonstrating robust approaches to tune model hyperparameters and evaluate importance metrics. In our study area, nestboxes were more likely to be occupied in sites with more hazel Corylus avellana, greater overall tree abundance but not fully closed canopies (optimal 80–85%), more honeysuckle Lolium periclymenum and hawthorn Crataegus monogyna, and when located further from footpaths and woodland margins. Occupancy over the study period was well predicted using microhabitat variables (13.3% OOB error) but future occupancy was more uncertain (33.3% error for 2021–2023 records). Modelling approaches that allow consideration of numerous variables from few locations or observations can be help identify relevant features and predict desirable outcomes of conservation actions. Here we demonstrate this approach identifying microhabitat variables that influence artificial nestbox occupancy by hazel dormice in a UK woodland. Findings offer some recommendations for local management that could promote nestbox occupancy and improve monitoring and conservation efforts. [ABSTRACT FROM AUTHOR]

Aim: Sampling bias and gaps have a direct influence on the perceived patterns of biodiversity, hence limiting our ability to make well‐informed decisions about biodiversity conservation. Yet most methods either disregard or underestimate the effects of sampling bias and gaps in modelling biodiversity patterns. Our objective is to test the sensitivity of commonly used methods for modelling biodiversity dimensions (richness, endemism, and beta diversity) to sampling bias and collection gaps, and as a way to mitigate those effects we introduce a novel approach that employs the sampling effort to minimize the effects of collection bias and gaps in biodiversity models. Location: South America. Methods: Here, we use controlled simulations of virtual species distribution and sampling effort to test the sensitivity to sampling bias and collection gaps by commonly used methods, that is, species distribution models (SDMs), spatial interpolation (SI), and environmental prediction (EP), for estimating species richness, endemism, and beta diversity. Our research contributes to advancing biodiversity modelling by introducing a novel approach, named uniform sampling from sampling effort (USSE), that employs the sampling effort to minimize the effects of collection bias and gaps. Results and Main Conclusions: EP with USSE has proven effective in accurately predicting species richness, especially in scenarios in which the sampling effort does not coincide with the biodiversity niches. It outperformed SI and SDMs. The latter performed poorly, yielding the lowest predictive score. In estimating endemism and beta diversity, all methods yielded similar results, without statistically significant differences. For estimating beta diversity, the generalized dissimilarity model proved to be a robust method, even in face of biased sampling. Controlled simulations are key to testing biodiversity methods. These tests can isolate confounding factors inherent to real‐world data, enabling robust methodological assessments. Although fieldwork and curation of collections must remain indispensable, novel biodiversity methods could help overcome the limitations of sampling biases, helping expedite conservation actions much needed. [ABSTRACT FROM AUTHOR]

Due to the demand for carbon neutrality, concrete carbonation has been reconsidered as an interesting topic because of its potential for capturing carbon dioxide (CO2) from the atmosphere. Concrete carbonation can significantly modify the chemical and microstructure properties of concrete and thus will have important effects on chloride diffusion. This paper presents a chloride diffusion model in which the concrete cover is divided into three different zones, each with their own defined porosity and chloride binding isotherm. The first is the fully carbonated concrete near the surface, where the porosity and chloride binding isotherm can be obtained from the experimental data of fully carbonated concrete. The second is the uncarbonated concrete near the reinforcement, where the porosity and chloride binding isotherm can be obtained from the experimental data of normal concrete. The third is the transition zone between the fully carbonated and uncarbonated concretes, where the porosity and chloride binding isotherm can be assumed to vary continuously from the carbonated concrete to uncarbonated concrete. To validate the present model, a comparison of the present model with published experimental results is provided, which demonstrates the importance of considering different zones in the chloride diffusion model when the concrete has a carbonated layer near the surface. [ABSTRACT FROM AUTHOR]

Changes in leaf temperature are known to drive stomatal responses, because the leaf‐to‐air water vapour gradient (Δw) increases with temperature if ambient vapour pressure is held constant, and stomata respond to changes in Δw. However, the direct response of stomata to temperature (DRST; the response when Δw is held constant by adjusting ambient humidity) has been examined far less extensively. Though the meagre available data suggest the response is usually positive, results differ widely and defy broad generalisation. As a result, little is known about the DRST. This review discusses the current state of knowledge about the DRST, including numerous hypothesised biophysical mechanisms, potential implications of the response for plant adaptation, and possible impacts of the DRST on plant‐atmosphere carbon and water exchange in a changing climate. Summary statement: The direct response of stomata to temperature (the response when evaporative demand is held constant by adjusting ambient humidity) is very poorly known. This review summarises the current state of knowledge about the response, a range of potential biophysical mechanisms, and its possible implications for plant‐atmosphere gas exchange and evolution. [ABSTRACT FROM AUTHOR]

There is a need to revisit movement dataset(s) currently used as egress determinants to assess whether they are truly representative of the current diverse occupant base. This is particularly important with our aging population as these sets contain very limited amounts of recent, age-specific data for older adults. This study provides data on walking speeds of older adults, obtained during standardized tests of walking, and compares those to default walking speeds used in current egress models. From experimental, short-distance walking trials (n = 451), it was seen that sex, increasing age, use of walking aids, those who have previously experienced a stroke (n = 116) and walking under cognitive load all resulted in decreases in walking speed. First iteration Pathfinder simulations showed that more realistic inputs for population walking speed resulted in simulated egress times that were on average 8 s slower compared to use of the current default range of walking speeds. Results suggest that the assumption of a uniform population in egress modelling, and consequently the standard practice of using a default walking speed for older adult occupants, should be reconsidered since, in reality the older adult population is extremely heterogeneous with regards to mobility, as reflected in the variability in walking speeds in this study. [ABSTRACT FROM AUTHOR]

Gujarat is situated within a climatic spectrum ranging from arid to semi-arid, characterised by periodic and recurrent drought phenomena coupled with an enduring and consistent challenge of water scarcity. Drought events in the Gujarat region place substantial stress on its water resource infrastructure, thereby impacting not only the hydrological aspects but also exerting notable repercussions on the intricate interplay between agriculture, economics, and the broader societal domain. Despite the implementation of numerous legislations and protocols aimed at mitigating the impact of drought and ameliorating the aftermath, the state of Gujarat experiences a recurring pattern of drought, resurfacing approximately every three years and inflicting substantial disruption upon the lives of its residents. This research focuses on analysing the current drought scenarios using the standard precipitation index (SPI) and meteorological drought modelling using autoregressive integrated moving average with exogenous variable (ARIMAX) for identifying future drought conditions, which is a statistical modelling approach. Analysis of reference locations was carried out to identify the best model and the same model used for drought modelling and forecasting in the remaining locations. A meteorological drought risk map and drought frequency map were prepared using forecasting results. Model validation was done by computing RMSE and R2 of multiple locations in the entire region. Research highlights: The study focuses on the recurring drought challenges faced by Gujarat due to its arid to semi-arid climate, leading to persistent water scarcity issues The research explores the intricate effects of droughts across hydrology, agriculture, economics, and society, highlighting their far-reaching consequences Despite mitigation efforts, a consistent drought pattern emerges approximately every three years, posing ongoing challenges to water resource management and societal stability The research employs a two-pronged strategy – utilising the standardized precipitation index (SPI) for assessing current drought conditions and leveraging the ARIMAX model for future predictions The ARIMAX model is carefully chosen and fine-tuned using reference locations to ensure accurate predictions across diverse geographical areas A significant outcome of the research is the development of drought risk maps, providing spatial insights into vulnerability levels and enabling targeted mitigation strategies The research rigorously validates the accuracy of its predictions using metrics such as root mean squared error (RMSE) and coefficient of determination (R2), enhancing the credibility of the findings The study reveals nuanced patterns, indicating that the west coast experiences frequent droughts, while inland areas face less frequent but longer-lasting events Understanding regional differences guides the formulation of customised approaches to address varying degrees of drought impact Through its comprehensive analysis, predictive capabilities, and spatial insights, the research equips stakeholders with actionable strategies to effectively manage and mitigate the recurring challenges posed by drought events [ABSTRACT FROM AUTHOR]

The construction of an accurate and high‐resolution reservoir parameter model is crucial for reservoir characterization. However, due to the band‐limited characteristics of seismic data, the inversion results heavily rely on the accuracy of the initial model. Most existing techniques for constructing an initial model interpolate well logging data within the stratigraphic framework, neglecting the effect of the stratigraphic sequence, which compromises the reliability of the initial model. The stratigraphic sequence is essential for dividing stratigraphic evolution stages and defining a geological relationship between reservoirs within the stratigraphic framework. Therefore, an initial model construction method constrained by stratigraphic sequence representation is proposed for pre‐stack seismic inversion. The process begins with establishing the stratigraphic framework using horizon and fault data. Subsequently, the collaborative sparse representation algorithm is used to learn a joint dictionary that captures the relationship of structural features between seismic data and stratigraphic sequence from the well logging data. In the process of seismic data representation, the stratigraphic sequence is accurately represented in three‐dimensional space by sharing sparse coefficients in the joint dictionary. Finally, the elastic parameter model is constructed by integrating the stratigraphic framework, stratigraphic sequence and well logging data, providing a reliable initial model for pre‐stack seismic inversion. The main innovation of the proposed method is the three‐dimensional representation of the stratigraphic sequence. A synthetic example demonstrates that the proposed method produces a more accurate initial model than conventional interpolation methods. Additionally, when applied to field data, it yields satisfactory results even without complete S‐wave velocity well logging data. [ABSTRACT FROM AUTHOR]

The high accuracy and efficiency of traveltime calculation are critical in seismic tomography, migration, static corrections, source locations and anisotropic parameter estimation. The fast‐sweeping method is an efficient upwind finite‐difference approach for solving the eikonal equation. However, the fast‐sweeping method is accurate only along the axis directions. In two‐dimensional or higher dimensional cases, the accuracy is severely decreased in the diagonal directions due to the numerical errors in these directions. These similar numerical errors also arose in higher order fast‐sweeping method and anisotropic fast‐sweeping method. To improve the accuracy of traveltime calculation in two‐dimensional or higher dimensional space, a shortest‐path‐aided fast‐sweeping method is proposed. The shortest‐path‐aided solution is embedded into the sweeping process of the standard fast‐sweeping method to improve the traveltime accuracy in the diagonal directions. Shortest‐path‐aided fast‐sweeping method is very easy to implement nearly without additional computational cost and memory consumption. Furthermore, this method is easy to extend from two‐dimensional to higher dimensional, from low‐order to higher‐order and from isotropic to anisotropic cases. [ABSTRACT FROM AUTHOR]

Full waveform inversion stands at the forefront of seismic imaging technologies, pivotal in retrieving high‐resolution subsurface velocity models. Its application is especially profound when imaging complex geologies such as salt bodies, which are regions notoriously challenging, yet essential given their hydrocarbon potential. However, with the power of full waveform inversion comes the intrinsic challenge of estimating the associated uncertainties. Such uncertainties are crucial in understanding the reliability of subsurface models, particularly in terrains like subsalt regions. Addressing this, we advocate for a nuanced approach employing the Stein variational gradient descent algorithm. Through a judicious use of a limited number of velocity model particles and the integration of random field‐based perturbations, our methodology provides a local representation of the uncertainties inherent in full waveform inversion. Our evaluations, based on the Marmousi model, showcase the robustness of the proposed technique. Yet, it is our exploration into salt‐intensive terrains, leveraging data from the Sigsbee 2A synthetic model and the Gulf of Mexico, that emphasizes the method's versatility. Findings indicate pronounced uncertainties along salt boundaries and in the deeper subsalt sediments, contrasting the minimal uncertainties in non‐salt terrains. However, anomalies like salt canyons present unique challenges, potentially due to the interplay of multi‐scattering effects. Emphasizing the scalability and cost‐effectiveness of this approach, we highlight its potential for large‐scale industrial applications in full waveform inversion, while also underscoring the necessity for prudence when integrating these uncertainty insights into subsequent seismic‐driven geological and reservoir modelling. [ABSTRACT FROM AUTHOR]

Deghosting of marine seismic data is an important and challenging step in the seismic processing flow. We describe a novel approach to train a supervised convolutional neural network to perform joint source and receiver deghosting of single‐component (hydrophone) data. The training dataset is generated by demigration of stacked depth migrated images into shot gathers with and without ghosts using the actual source and receiver locations from a real survey. To create demigrated data with ghosts, we need an estimate of the depth of the sources and receivers and the reflectivity of the sea surface. In the training process, we systematically perturbed these parameters to create variability in the ghost timing and amplitude and show that this makes the convolutional neural network more robust to variability in source/receiver depth, swells and sea surface reflectivity. We tested the new method on the Marmousi synthetic data and real North Sea field data and show that, in some respects, it performs better than a standard deterministic deghosting method based on least‐squares inversion in the τ‐p domain. On the synthetic data, we also demonstrate the robustness of the new method to variations in swells and sea‐surface reflectivity. [ABSTRACT FROM AUTHOR]

Calyx cavity is a common physiological disorder in many persimmon cultivars. It is characterised by a separation between the calyx and the surrounding flesh. The presence of calyx cavity in export fruit can lead to phytosanitary risks and reduced storage potential due to more rapid softening and increased chilling injury. Manual detection of calyx cavity at packing is very time-consuming and uneconomic. This work investigated the ability of non-destructive evaluation, combined with modelling, to identify calyx cavity in 'Fuyu' persimmons. Fruit were evaluated at harvest, followed by a period of nine-week storage in modified atmosphere packaging at 0°C. The presence and severity of calyx cavity was related with higher red colouration and fresh weight before storage. Using colour index data and weight data, binary classification of calyx cavity by linear discriminant analysis (LDA) resulted 74.1% correct segregation. The model correctly identified 70.5% of calyx cavity fruit (29.5% false negatives) and 77.4% of no cavity fruit (22.6% false positives). The use of non-destructive calyx cavity classification based on the evaluation of quality parameters is a starting point in providing the ability to segregate healthy fruit before packaging and storage. Further work is necessary to improve model performance before implementation is viable. [ABSTRACT FROM AUTHOR]

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The Artificial Neural Network (ANN) is a high-precision modelling technique applied across various applications, but their use in modelling the proton exchange membrane fuel cell (PEMFC) at the stack scale remains limited. This study aimed to enhance the modelling of a 1.2 kWp PEMFC stack using ANN integrated with the LabVIEW program, and to evaluate its accuracy compared to Multiple Linear Regression (MLR) and Analytical Mathematical Model (AMM) methods based on experimental data. The ANN model was tested on the polarization curve under pressures ranging from 1 to 9 bar and currents from 1 to 40 A. Accuracy was assessed using analysis of variance (ANOVA) with a significance level of p = 0.05, focusing on key statistical performance metrics. The results indicate that the ANN model significantly enhances PEMFC stack modelling accuracy, especially for activation and concentration loss processes. The ANN demonstrates a substantial improvement, reducing error metrics by at least 28% compared to MLR and 75% compared to AMM. [ABSTRACT FROM AUTHOR]

This paper presents an experimental and numerical study of the freezing-thawing behavior of water in fully saturated sand. A relatively inexpensive and easily replicable experimental procedure was developed to simulate the freezing-thawing cycles in a medium-sized sand sample placed in a modified commercial freezer. By insulating the sides and bottom of the sample well, while allowing good thermal conductivity at the top of the sample, a nearly vertical advance of the freezing and thawing front was achieved. A series of freeze-thaw cycles were performed with higher and lower temperature gradients. A numerical multiphysics model, assuming an axially symmetric geometry based on the transient heat transfer during the phase transition, used a parametric approach to estimate the effective thermal properties of the sand-water-ice system. A good agreement between experimental and modelling results was shown, but slightly different parameter sets were obtained for each temperature gradient. The presented method could be a simple way to characterize the freeze-thaw process in natural and artificial porous materials. [ABSTRACT FROM AUTHOR]

Wood is known for its complex hygroscopic properties. Previous studies show that both the temperature-dependency and the hysteresis in sorption should be included in the physical models, if the most accurate results are desired from simulations of building assemblies with wood-based materials. Literature is scarce on the topic, where the significance of these aspects on the reliability of mould-risk assessments is inspected. Exterior walls with glass wool insulation and LDF-sheathing were studied by using two different models with previously measured material data. Over 300 hygrothermal simulations were carried out, where mould-indices were computed on the warm side of the sheathing. According to both conventional and more sophisticated model, the studied walls perform well in cold climate if no driving rain leaks in. Temperature-dependent and hysteretic model yields slightly smaller mould-indices in general, but for certain leaky cases the difference is significant, which indicates the importance of sophisticated sorption-properties in modelling. [ABSTRACT FROM AUTHOR]

Concerns related to environmental risks associated with pharmaceuticals and personal care products (PPCPs) have led researchers to seek methods for assessing and monitoring these contaminants in the aquatic environment. Identifying and validating risk assessment tools that can evaluate ecological concerns and risks associated with PPCPs is critical. Herein, the suitability of a dose-related risk and effect assessment model, which estimates predicted environmental concentrations and allowed comparisons with predicted no effect concentrations determined, in combination with in vitro analyses of the whole effluent toxicity, was verified for the characterization of a PPCP hazard. Concentrations of the most utilized PPCPs in Norway were measured in influent and effluent samples and used to parameterize the fate model. Greater than 90% removal was attained for 12 out of 22 detected PPCPs. Removal was not dependent on the class or the concentration of the specific substance and varied between 12% and 100%. The PPCPs detected in the discharged wastewater were utilized to assess individual contributions to the risk of the effluent, and no risk was identified for the targeted 30 PPCP. The simulations provided valuable information regarding the discharge plume distribution over time, which can aid planning of future environmental monitoring investigations. Bioassays (using fish liver cells, PLHC-1) were used for assessing overall effluent toxicity, through cell viability, production of reactive oxygen species, and ethoxyresorufin-O-deethylase (EROD) activities. The present study may allow regulators to use risk-based strategies over removal criteria for monitoring studies and confirms the importance to take PPCP contamination into consideration when establishing environmental regulations. [ABSTRACT FROM AUTHOR]

Background: The harvest of fruit can be significantly advanced with the thriving development of intelligent and automated robot technologies. Nevertheless, the picking success rate of tomato fruit still requires improvement as some fruits are unexpectedly damaged inside, which is imperceptible by machine vision. Herein, a modelling method based on modified Voronoi algorithm is proposed to reconstruct the cellular structure of tomato pericarp. Results: Based on the reconstructed micro‐model, the compression physical behaviour of the pericarp cells is simulated to observe internal local stress and potential damage. It is revealed that the simulation result for pericarps of tomatoes with different ripeness is highly consistent to the experimental tests, which has well validated the feasibility of this modelling and simulation method. Conclusion: A Voronoi‐based modelling method is proposed for micro‐reconstruction of tomato pericarp, and the corresponding compression simulation results agree well with the experimental tests. Such result can be utilized as reference to improve the grasping force control for harvesting robot to avoid invisible damage induced by accident overload issue. With the predicting result, superior success rate can be achieved to enhance robot performance. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusions This study describes the pharmacokinetic (PK)/target engagement (TE) relationship of tozorakimab, an anti‐interleukin (IL)‐33 antibody, by building a mechanistic population PK/TE model using phase 1 biomarker data.The analysis included tozorakimab PK and TE in serum assessed in 60 tozorakimab‐treated participants, including healthy adults and patients with mild chronic obstructive pulmonary disease. Scenarios evaluated three dose frequencies (once every 2, 4 or 6 weeks) administered subcutaneously at seven doses of tozorakimab (30, 60, 90, 120, 150, 300 or 600 mg). For each dose, simulations were performed with 5000 virtual individuals to predict systemic TE. Inhibition of IL‐33/soluble ST2 (sST2) complex levels at trough PK at steady state was assessed in each dosing scenario. The PK/TE modelling analyses were performed using a nonlinear mixed‐effect modelling approach.The final two‐compartment PK model with tozorakimab binding IL‐33 in the central compartment adequately described the systemic PK and TE of tozorakimab at population and individual levels. The mean PK parameter estimates of absorption rate, central volume of distribution and clearance were 0.48 (90% confidence interval [CI]: 0.40–0.59, 1/day), 12.64 (90% CI: 8.60–18.62, L) and 0.87 (90% CI: 0.65–1.16, L/day), respectively. Consistent with the observed value, tozorakimab bioavailability was 45%. For all three dose frequencies, predicted inhibition of systemic IL‐33/sST2 levels was more than 95% at doses greater than 90 mg.The PK/TE model reliably quantified the relationship between PK and systemic TE of tozorakimab, with potential utility for predicting clinical dose–response relationships and supporting clinical dose selection. [ABSTRACT FROM AUTHOR]

Imitating each other is a central element of human nature and everyday life. Modelling – setting an example to others – and imitation – when the modelled behaviour is copied – are just as fundamental to learning as to maintaining and transmitting culture. Yet, the moral connotations of modelling and imitation are not well understood. Building on and extending the theoretical framework of vicarious responsibility, we investigate the imitated model's responsibility and the psychological processes underlying blame attribution to the model for their imitators’ behaviour. We argue that people understand that imitating a wrongdoing renders it potentially more consequential and that people account for these additional consequences in their appraisals of the original, modelled wrongdoing. Moreover, we hypothesized that models would be blamed for their imitators’ harmful behaviour to the extent that the observers copied the model's action. Five studies (Ntotal = 945) utilizing various contexts from animal mistreatment to online bullying, three of them preregistered, provide consistent support for our hypotheses and show that models are blamed for their imitators’ behaviour, that is, for setting a bad example for others. Extending present theories of vicarious responsibility, we demonstrate that shared group membership is not always a necessary requirement for vicarious blame attributions. [ABSTRACT FROM AUTHOR]

Electrochemical reduction of CO2 poses a vast potential to contribute to a defossilized industry. Despite tremendous developments within the field, mass transport limitations, carbonate salt formation, and electrode degradation mechanisms still hamper the process performance. One promising approach to tweak CO2 electrolysis beyond today's limitations is pulsed electrolysis with potential cycling between an operating and a regeneration mode. Here, we rigorously model the boundary layer at a silver electrode in pulsed operation to get profound insights into the dynamic reorganization of the electrode microenvironment. In our simulation, pulsed electrolysis leads to a significant improvement of up to six times higher CO current density and 20 times higher cathodic energy efficiency when pulsing between −1.85 and −1.05 V vs SHE compared to constant potential operation. We found that elevated reactant availability in pulsed electrolysis originates from alternating replenishment of CO2 by diffusion and not from pH‐induced carbonate and bicarbonate conversion. Moreover, pulsed electrolysis substantially promotes carbonate removal from the electrode by up to 83 % compared to constant potential operation, thus reducing the risk of salt formation. Therefore, this model lays the groundwork for an accurate simulation of the dynamic boundary layer modulation, which can provide insights into manifold electrochemical conversions. [ABSTRACT FROM AUTHOR]

This work provides a new mathematical model of photovoltaic (PV) solar panels integrated with proton exchange membrane (PEM) water electrolyzer cells for optimal hydrogen production. The proposed mathematical model evaluates the operating temperature of the PV solar panel and PEM water electrolyzer and the effect of membrane thickness on hydrogen production performance. In the model results, when the operating temperature of the PEM water electrolyzer increased from 25 °C to 80 °C, the hydrogen flow rate of about 1.44 and 1.82 mL/min at 2 A/cm2, respectively. Moreover, the maximum flow rate of hydrogen produced in a 7-cell and 14-cell PV-assisted PEM water electrolyzer stack is around 349.98 mL/min and 699.91 mL/min, respectively. The total efficiency of the integrated PV-assisted PEM water electrolyzer stack is around 6.0–6.5%, depending on the solar irradiance values. This mathematical model significantly contributes to green hydrogen production using the integrated PV-assisted PEM water electrolyzer system. Moreover, the mathematical model of PV-assisted PEM water electrolyzer will be a pioneer in determining the hydrogen production potential of the PEM water electrolyzer systems. • Evaluation of a new mathematical model of hydrogen production with PV-assisted PEM water electrolyzer. • Mass transfer modeling for single-cell PEM water electrolyzer is presented. • The hydrogen production rate relies on solar irradiation, temperature, and membrane thickness. • The efficiencies of PEM water electrolyzer and PV module are ∼65.5% and ∼15.37%, respectively. • Total average hydrogen production with a PV-assisted PEM water electrolyzer is ∼9.970 mL/min. [ABSTRACT FROM AUTHOR]

The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids' dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region. [ABSTRACT FROM AUTHOR]

Background: Numerous studies have developed or validated prediction models to estimate the likelihood of postoperative pneumonia (POP) in esophageal cancer (EC) patients. The quality of these models and the evaluation of their applicability to clinical practice and future research remains unknown. This study systematically evaluated the risk of bias and applicability of risk prediction models for developing POP in patients undergoing esophageal cancer surgery. Methods: PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), WanFang Database and Chinese Biomedical Literature Database were searched from inception to March 12, 2024. Two investigators independently screened the literature and extracted data. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability. Result: A total of 14 studies involving 23 models were included. These studies were mainly published between 2014 and 2023. The applicability of all studies was good. However, all studies exhibited a high risk of bias, primarily attributed to inappropriate data sources, insufficient sample size, irrational treatment of variables and missing data, and lack of model validation. The incidence of POP in patients undergoing esophageal cancer surgery ranged from 14.60% to 39.26%. The most frequently used predictors were smoking, age, chronic obstructive pulmonary disease(COPD), diabetes mellitus, and methods of thoracotomy. Inter-model discrimination ranged from 0.627 to 0.850, sensitivity ranged between 60.7% and 84.0%, and specificity ranged from 59.1% to 83.9%. Conclusion: In all included studies, good discrimination was reported for risk prediction models for POP in patients undergoing esophageal cancer surgery, indicating stable model performance. However, according to the PROBAST checklist, all studies had a high risk of bias. Future studies should use the predictive model assessment tool to improve study design and develop new models with larger samples and multicenter external validation. [ABSTRACT FROM AUTHOR]

We apply a machine learning approach to automatically infer two key attributes – the location of fault or shear zone structures and the thickness of the overburden – in an 18 km2 study area within and surrounding the Archean Fenelon gold deposit in Quebec, Canada. Our approach involves the inversion of carefully curated borehole lithological and structural observations truncated at 480 m below the surface, combined with magnetic and Light Detection and Ranging survey data. We take a computationally low‐cost approach in which no underlying model for geological consistency is imposed. We investigated three contrasting approaches: (1) an inferred fault model, in which the borehole observations represent a direct evaluation of the presence of fault or shear zones; (2) an inferred overburden model, using borehole observations on the overburden‐bedrock contact; (3) a model with three classes – overburden, faulted bedrock and unfaulted bedrock, which combines aspects of (1) and (2). In every case, we applied all 32 standard machine learning algorithms. We found that Bagged Trees, fine K‐nearest neighbours and weighted K‐nearest neighbour were the most successful, producing similar accuracy, sensitivity and specificity metrics. The Bagged Trees algorithm predicted fault locations with approximately 80% accuracy, 70% sensitivity and 73% specificity. Overburden thickness was predicted with 99% accuracy, 77% sensitivity and 93% specificity. Qualitatively, fault location predictions compared well to independently construct geological interpretations. Similar methods might be applicable in other areas with good borehole coverage, providing that criteria used in borehole logging are closely followed in devising classifications for the machine learning training set and might be usefully supplemented with a variety of geophysical survey data types. [ABSTRACT FROM AUTHOR]

Simulation software like ANSYS, COMSOL, and SimScale excel at modeling heat transfer phenomena, but their extensive functionalities necessitate a deep understanding, making them less suitable and too expensive for use in educational settings below the post-secondary level in Singapore, where the current curriculum does not demand such advanced capabilities. To provide a more accessible and cost-effective solution, this work introduces a novel universal Python code designed to simplify the understanding of 2D steady-state heat transfer on irregular shapes, utilizing only Microsoft Excel and Python. The developed code employs the Gauss–Seidel iteration method within a full multigrid framework, applying the relevant nodal finite-difference equations based on the node type within a 2D irregular shape delineated by a 65 × 65 mesh in Excel. The generated contour plots from these simulations are meticulously compared with those produced by ANSYS to validate accuracy. The comparison reveals that the results from the Python code closely align with those from ANSYS, showing only minor differences. Consequently, the Python code emerges as a viable and simplified alternative for conducting 2D steady-state heat transfer simulations, making it a valuable tool for educational purposes, bridging the gap between complex simulation software and the educational needs of students in Singapore. [ABSTRACT FROM AUTHOR]

Introduction: Soil organic carbon (SOC) content can vary significantly across a given plot. Therefore, a representative sampling is a prerequisite to obtain meaningful results from analysis and of utter importance when SOC quantification is used to quantify (temporary) carbon dioxide removal (CDR). However, certain management practices aiming to increase SOC further increase the level of heterogeneity and may challenge representative sampling schemes. This includes concentrated root-zone application of biochar, which immediately increases SOC with the input of biochar-C and may promote the local enrichment of non-biochar SOC over time. Methods: Here, we used numerical modelling to quantify the number of single sampling points (soil cores) needed to achieve a representative sample of biochar-C and total SOC on a plot after application of biochar in rows, e.g., for growing vegetables, or in the circumference of trees in agroforestry systems. Results: After row application of 5 t ha-1 biochar in soil with rather low SOC content (26 t ha-1), 140 soil cores (per 0.25 ha plot) where necessary to achieve representative sampling of C-stocks (±5% error) in 90% of the repeated sampling simulation cases. Compared to realistic and cost-effective soil sampling scenarios in agronomic practice, we conclude that concentrated root zone application of biochar makes representative sampling for quantification of SOC in soils with low baseline C-stocks virtually impossible. Discussion: This finding calls into question the soil-sampling and SOC-analysisbased ("result-based") monitoring of SOC as a (temporary) CDR when biochar might have been applied. Considering the rapid scaling of biochar production and use in agriculture, this is a considerable challenge for SOC certification. Instead, action-based incentives, rewarding farmers for carrying out specific practices, could be applied to promote carbon farming practices. [ABSTRACT FROM AUTHOR]

Cryptotermes brevis (Walker) (Blattodea: Kalotermitidae) is one of the most destructive drywood termites that attack moisture‐protected timber in service. Heat treatment has been studied to control these termites, but the low thermal conductivity of wood can result in prolonged treatment times and the need for high temperatures to eliminate termite colonies. The current study investigated heat transfer through a heat transfer model and experiments within solid timber boards and a representative wall section. The aim was to optimise targeted spot heat treatment as a cost‐effective method for eradicating this pest within structural elements. Through experimental work and the development of a deterministic heat transfer model, valuable insights were gained into temperature distribution within wooden structural elements. The findings revealed that proximity to the heated surface played a crucial role, with closer distances reaching equilibrium temperatures faster. The heat transfer model, validated against experimental data, accurately predicted temperature distributions within the timber. Termite survival was significantly influenced by heating time and distance from the heated surface when a wall section was heated at 60°C. The mean survival of C. brevis pseudergates kept inside wall studs varied from 30% to 96.7% depending on the distance from the heated surface after 1.5 h of heating, where the temperature ranged from 43°C to 45°C. However, after extending the heating duration to 3 h, the temperature in wall studs was elevated to 51°C, 49°C and 47°C at 22, 40 and 60 mm from the heated stud face, respectively. All C. brevis pseudergates across all distances were killed at a 3‐h duration. This research underscores the importance of understanding temperature distribution in structural wood elements and exposure times when employing heat as a spot treatment for drywood termite control. [ABSTRACT FROM AUTHOR]

Background: Sleeping sickness (gambiense human African trypanosomiasis, gHAT) is a vector-borne disease targeted for global elimination of transmission (EoT) by 2030. There are, however, unknowns that have the potential to hinder the achievement and measurement of this goal. These include asymptomatic gHAT infections (inclusive of the potential to self-cure or harbour skin-only infections) and whether gHAT infection in animals can contribute to the transmission cycle in humans. Methods: Using modelling, we explore how cryptic (undetected) transmission impacts the monitoring of progress towards and the achievement of the EoT goal. We have developed gHAT models that include either asymptomatic or animal transmission, and compare these to a baseline gHAT model without either of these transmission routes, to explore the potential role of cryptic infections on the EoT goal. Each model was independently calibrated to five different health zones in the Democratic Republic of the Congo (DRC) using available historical human case data for 2000–2020 (obtained from the World Health Organization's HAT Atlas). We applied a novel Bayesian sequential updating approach for the asymptomatic model to enable us to combine statistical information about this type of transmission from each health zone. Results: Our results suggest that, when matched to past case data, we estimated similar numbers of new human infections between model variants, although human infections were slightly higher in the models with cryptic infections. We simulated the continuation of screen-confirm-and-treat interventions, and found that forward projections from the animal and asymptomatic transmission models produced lower probabilities of EoT than the baseline model; however, cryptic infections did not prevent EoT from being achieved eventually under this approach. Conclusions: This study is the first to simulate an (as-yet-to-be available) screen-and-treat strategy and found that removing a parasitological confirmation step was predicted to have a more noticeable benefit to transmission reduction under the asymptomatic model compared with the others. Our simulations suggest vector control could greatly impact all transmission routes in all models, although this resource-intensive intervention should be carefully prioritised. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusions To characterize pharmacokinetic and pharmacodynamic profiles of nedosiran in patients with primary hyperoxaluria type 1 (PH1), identify influential covariates and confirm therapeutic doses.A population pharmacokinetic (PK)/pharmacodynamic (PD) (POP‐PKPD) model was developed to characterize the concentration‐time course of nedosiran and the corresponding effect on 24‐h urinary oxalate (Uox). Simulations of dosing to achieve clinically meaningful reduction in Uox in children, adolescents and adults with PH1 were performed.Analyses included PK data from 143 healthy participants and PH1/PH2 patients, and PD data from 46 PH1 patients. Nedosiran PK was described by a two‐compartment model with dual n‐transit absorption and parallel linear and nonlinear elimination. The relationship between nedosiran exposure and Uox was described by an indirect response model. Body weight, estimated glomerular filtration rate (eGFR) and disease status were identified as influential covariates for the POP‐PK model. The simulation results supported a weight‐banded dosing regimen of nedosiran sodium in adolescents and adults (≥12 years) with PH1 of 170 mg (weight ≥50 kg) and 136 mg (weight <50 kg), in children (6‐11 years) with PH1 of 3.5 mg/kg, and no dose adjustments for PH1 patients with relatively preserved kidney function (eGFR ≥ 30 mL/min/1.73m2). Following the proposed dosing regimens, the simulated median fold‐changes in PK AUC0‐τ,ss were acceptable (≤1.51 fold‐change) and ~71% of PH1 patients across all age groups achieved near‐normal Uox (<0.6 mmol) by week 52.The final POP‐PKPD model characterizes observed nedosiran PK and Uox data. Simulations support nedosiran dosing regimens in PH1 patients aged ≥6 years with relatively preserved kidney function. [ABSTRACT FROM AUTHOR]

Software modelling and model-driven engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write through domain-specific modelling languages. Several recent studies challenge the idea that these aims can always be reached and indicate that human factors play a role in the success of MDE. However, there is an under-representation of research focusing on human factors in modelling. During a GI-Dagstuhl seminar, topics related to human factors in modelling were discussed by 26 expert participants from research and industry. In breakout groups, five topics were covered in depth, namely modelling human aspects, factors of modeller experience, diversity and inclusion in MDE, collaboration and MDE, and teaching human-aware MDE. We summarise our insights gained during the discussions on the five topics. We formulate research goals, questions, and propositions that support directing future initiatives towards an MDE community that is aware of and supportive of human factors and values. [ABSTRACT FROM AUTHOR]

Steel hollow structure section (HSS) chord-to-branch joints in Vierendeel trusses are concerned for their proneness to fracture due to imperfection in fabrication. In this paper, the stress intensity factors (SIFs) for HSS chord-to-branch joints are examined based on fracture mechanics and finite element modelling. Through a well-prepared sub-modelling strategy, the influences of geometric parameters related to the crack propagation behaviour are analysed and discussed. The crack front of the HSS chord-to-branch joint without the axial stress of the branch is shown to have notably high SIFs at the deepest point and the surface point. Alternatively, the concentration of highest SIFs at the deepest point of the elliptical arc of the crack becomes significant with the increase of the axial stress of the branch. The increase of the chord wall thickness is shown to be beneficial in the reduction of the SIFs for HSS chord-to-branch joints, especially for lower chord width ratio normalized by thickness. Conversely, the influence of the crack shape indicated by larger aspect ratio on the reduced SIFs becomes obvious and consistent for greater chord width ratio normalized by thickness. The SIFs for HSS chord-to-branch joints are decreased with the increase of the normalized crack depth with respect to the chord wall thickness, particular for shallow surface cracks. Such a trend becomes obvious for the joints with lower fillet weld leg length of equal leg fillet welds with respect to the chord wall thickness or with lower weld leg length along the HSS branch with respect to that along the chord. [ABSTRACT FROM AUTHOR]

The aim of this Briefing Paper is to provide guidance to potential authors (and reviewers) on the main types of paper submissions that the Ground Improvement journal accepts. Expectations in terms of content and novelty, together with future challenges are also covered. One of the bigger challenges is bridging the elusive gap between novel fundamental laboratory-based research and practical industrial application. [ABSTRACT FROM AUTHOR]

The performance of a machine tool is directly influenced by the characteristics of the floor, subsoil, and their interaction with the installed machine. Installing a machine tool in its operational environment poses a distinct challenge that bridges mechanical and civil engineering disciplines. This interdisciplinary issue is often overlooked within the individual separate disciplines. However, effectively addressing this challenge requires a comprehensive understanding of mechanical and civil engineering principles. To address this problem, the present study proposes a method for improved modeling of the dynamic properties of the machine tool by considering the foundation and the subsoil on which it is installed. The method is based on finite element modeling. Linear models of the system components and the connections between them were used. These, supplemented with damping expressed by complex stiffness, made it possible to determine the natural frequencies, mode shapes, and frequency response functions (based on which the transmissibilities were obtained). Based on the experimentally verified models of vertical and horizontal lathes, the sensitivity analysis aimed at estimating the impact of changes in system parameters on vibration transmissibility for a floor-type and a block-type foundation was conducted. Thus, it was possible to identify those machine tool-support-foundation-subsoil system parameters that had the most significant impacts on the vibration's transmissibility. After analyzing the cases discussed, it became evident that the transmissibility of vibrations is primarily influenced by two key factors. First and foremost, the properties of the structural loop of the machine tool played a significant role. Additionally, the characteristics of the subsoil on which the foundation was situated emerged as a crucial determinant in the observed vibration transmissibility. [Display omitted] • A method of modeling the dynamic properties of machine tool-support-foundation-subsoil system was presented. • The method was presented for two types of lathes: vertical and horizontal, and two types of foundation floor and block. • The modeling method was experimentally verified. • The sensitivity analysis was conducted to estimate the impact of changes in system parameters on vibration transmissibility for a floor-type and a block-type foundation for vertical and horizontal lathes. [ABSTRACT FROM AUTHOR]

Most mathematical models that assess the vectorial capacity of disease-transmitting insects typically focus on the influence of climatic factors to predict variations across different times and locations, or examine the impact of vector control interventions to forecast their potential effectiveness. We combine features of existing models to develop a novel model for vectorial capacity that considers both climate and vector control. This model considers how vector control tools affect vectors at each stage of their feeding cycle, and incorporates host availability and preference. Applying this model to arboviruses of veterinary importance in Europe, we show that African horse sickness virus (AHSV) has a higher peak predicted vectorial capacity than bluetongue virus (BTV), Schmallenberg virus (SBV), and epizootic haemorrhagic disease virus (EHDV). However, AHSV has a shorter average infectious period due to high mortality; therefore, the overall basic reproduction number of AHSV is similar to BTV. A comparable relationship exists between SBV and EHDV, with both viruses showing similar basic reproduction numbers. Focusing on AHSV transmission in the UK, insecticide-treated stable netting is shown to significantly reduce vectorial capacity of Culicoides, even at low coverage levels. However, untreated stable netting is likely to have limited impact. Overall, this model can be used to consider both climate and vector control interventions either currently utilised or for potential use in an outbreak, and could help guide policy makers seeking to mitigate the impact of climate change on disease control. [ABSTRACT FROM AUTHOR]

Integration of different kinds of data is an important issue in archaeological prospection. However, the current methodological approaches are underdeveloped and rarely use the data to their maximum potential. Common approaches to integration in the geophysical sciences are mostly just various forms of comparison. We argue that true integration should involve the mathematical manipulation of input data such that the original values of the input data are changed, or that new variables are produced. To address this important research gap, we present an innovative approach to the analysis of geochemical and geophysical datasets in prospection-focused disciplines. Our approach, which we refer to as "multiscalar integration" to differentiate it from simpler methods, involves the application of mathematical methods and tools to process the data in a unified way. To demonstrate our approach, we focus on integrating geophysical data (magnetometry) with geochemical data (elemental content). Our approach comprises three main stages: Quantification of the data deviation from random distributions, linear modelling of geophysical and geochemical data and integration based on weighting of the different elements derived in previous steps. All the steps of the workflow can be also applied separately and independently as needed or preferred. Our approach is implemented in the R environment for statistical computing. All data, functions and scripts used in the work are available from open access repositories (Zenodo.org and Github.com) so that others can test, modify and apply our proposed methods to new cases and problems. Our approach has the following advantages: (1) It allows the rapid exploration of multiple data sources in an unified way; (2) it can increase the utility of geochemical data across diverse prospection disciplines; (3) it facilitates the identification of links between geochemical and geophysical data (or generally, between point-based and raster data); (4) it innovatively integrates various datasets by weighting the information provided by each; (5) it is simple to apply following a step-by-step framework; (6) the code and workflow is fully open to allow for customization, improvements and additions. [ABSTRACT FROM AUTHOR]

Operations Research (OR) is a branch of applied mathematics that deals with optimization problems arising from different real contexts. The solving process of its problems is based on the construction and resolution of mathematical models, showing the possible connections between mathematics and the real world. Nevertheless, OR is not typically included in most curricula of higher secondary schools (i.e. Grades 9–12), but it is usually presented mainly at university level. To show how OR could be significant for these school students' education, the authors of this paper developed an educational project consisting of three teaching units. In this paper, we share the result of the teaching experiment related to the first unit, addressed to Grade 10. Qualitative and quantitative analyses show how it is appropriate to include OR and its typology of problems in regular school mathematics lectures. Second, these data also show how modelling and problem-solving skills, developed working with OR, can be fostered by implementing a collaborative way of working, also by making use of digital technologies. Last but not least, we demonstrate the positive impact such activities have on students' appreciation of OR. [ABSTRACT FROM AUTHOR]