Simple Model Research Articles

Multi-tracing the primordial Universe with future surveys

The fluctuations generated by Inflation are nearly Gaussian in the simplest models, but may be non-Gaussian in more complex models, potentially leading to signatures in the late Universe. In particular, local-type primordial non-Gaussianity induces scale-dependent bias in tracers of the matter distribution. This non-Gaussian imprint in the tracer power spectrum survives at late times on ultra-large scales where nonlinearity is negligible. In order to combat the problem of growing cosmic variance on these scales, we use a multi-tracer analysis that combines different tracers to maximise any primordial signal. Previous work has investigated the combination of a spectroscopic galaxy survey with a 21 cm intensity mapping survey in single-dish mode. We extend this work by considering instead the case where the 21 cm intensity mapping survey is optimised for interferometer mode. As examples, we use two multi-tracer pairs of surveys: one at high redshift (1≤z≤2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\\le z\\le 2$$\\end{document}) and one at very high redshift (2≤z≤5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2\\le z\\le 5$$\\end{document}). The 21 cm surveys are idealised surveys based on HIRAX and PUMA. We implement foreground-avoidance filters and use detailed models of the interferometer thermal noise. The galaxy surveys are idealised surveys based on Euclid and MegaMapper. Via a simple Fisher forecast, we illustrate the potential of the multi-tracer. Our results show a ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document} 20–30% improvement in precision on local primordial non-Gaussianity from the multi-tracer. Furthermore, we investigate the effects on constraints of varying the parameter of non-Gaussian galaxy assembly bias and of varying the parameters of the intensity mapping foreground filters. We find that the non-Gaussian galaxy assembly bias parameter causes a greater change in the constraints on local primordial non-Gaussianity than the foreground filter parameters.

Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble

AbstractBase metals are key materials for various industrial sectors such as electronics, construction, manufacturing, etc. Their selling price is important both for the profitability of the mining and metallurgical companies that produce and trade them, as well as for the countries whose economies rely on their exports or tax revenues as a means for national income. Prices are also critical for companies that use base metals as inputs to fabricate end products. The prediction of prices’ future movements can serve as a tool for risk mitigation and better budget planning. In this study, the logarithmic returns of base metals are forecasted using an autoregressive Light Gradient Boosting Machine (LightGBM) as well as an ensemble comprising the aforementioned algorithm and a classical time series forecasting model (i.e., ARIMA). The two models are then compared to three simpler benchmark models, namely a global mean model, an exponential smoothing model and an ARIMA model. When comparing using RMSE, the autoregressive LightGBM model outperformed the three univariate benchmark models (and the ensemble) for forecasting 6 months ahead for aluminum and nickel returns, while copper and zinc returns were forecasted better by the ensemble. Neither of the proposed models performed better than an ARIMA model when it comes to forecasting lead and tin returns.

Mechanistic account of distinct change in organic anion transporting polypeptide 1B (OATP1B) substrate pharmacokinetics during OATP1B-mediated drug-drug interactions using physiologically-based pharmacokinetic modeling.

The role of transporters in drug clearance is widely acknowledged, directly and indirectly by facilitating tissue/enzyme exposure. Through the latter, transporters also affect volume of distribution. Drug-drug interactions (DDIs) involving organic anion transporting polypeptides (OATPs) 1B1/1B3 and SLCO1B1 pharmacogenetics lead to altered pharmacokinetics of OATP1B substrates; however, several factors may confound direct interpretation of pharmacokinetic parameters from these clinical studies using noncompartmental analysis (NCA). A review of clinical data herein indicates a single dose of OATP1B inhibitor rifampin almost never leads to increased substrate half-life but often a decrease, and that most clinical OATP1B substrates are CYP3A4 substrates and/or undergo enterohepatic cycling (EHC). Using hypothetically simple OATP1B substrate physiologically-based pharmacokinetic (PBPK) models, simulated effect of rifampin differed from specific OATP1B inhibition, due to short rifampin half-life causing dissipation of OATP1B inhibition over time combined with CYP3A4 induction. Calculated using simulated tissue data, volume of distribution indeed decreased with OATP1B inhibition and was expectedly limited to the contribution of liver volume. However, an apparent and counterintuitive effect of rifampin on volume greater than that on clearance resulted for CYP3A4 substrates, using NCA. Effect of OATP1B inhibition and rifampin on OATP1B substrate models incorporating EHC +/- renal clearance was distinct compared to simpler models. Using PBPK models incorporating reversible lactone metabolism for clinical OATP1B substrates atorvastatin and pitavastatin, DDIs reporting decreased half-life with rifampin were reproduced. These simulations provide explanation for the distinct change in OATP1B substrate pharmacokinetics observed in clinical studies, including changes in volume of distribution and additional mechanisms. Significance Statement Transporters are involved in both drug clearance and volume of distribution and distinct changes in OATP1B substrate pharmacokinetics are observed with OATP1B inhibitor rifampin. Using hypothetical and validated PBPK models and simulations we address the limitations of single-dose rifampin and complicated clinical OATP1B substrate disposition in evaluating the pharmacokinetic parameters of OATP1B substrates during rifampin DDIs. These models account for the change in volume of distribution and identify additional mechanisms underlying apparent pharmacokinetic changes in OATP1B DDIs.

Simplified Volumetric Models as an Effective Strategy for Segmenting Actin Networks in Cryo-Electron Tomograms

Simplified Volumetric Models as an Effective Strategy for Segmenting Actin Networks in Cryo-Electron Tomograms

Mechanism of Evolution of Shock Wave of Muzzle Jet under Initial Interference and Its Simplified Model

Large-caliber and long-barrel weapons are important experimental devices for exploring the impact resistance and reliability of warheads. The force of impact of the muzzle jet has a significant influence on the overload resistance of the warhead and surrounding devices. The mechanism of motion of the body inside the tube cannot be ignored owing to the high kinetic energy at the muzzle. In this study, we designed the relevant experiment and a simulation model to analyze the structural characteristics and mechanism of evolution of the shock wave and the vortex structure in a muzzle jet. The aim was to examine the evolution of the shock wave with initial jet-induced interference. And we established three other simulation models to compare the similarities and differences between the results of the models. The results showed that, in the original complex model, the initial jet restricted the free expansion of the muzzle jet, and this led to many shock–shock collisions that retarded the development of the shock waves. Multiple reflected shock waves were thus formed under a high local pressure that distorted the shock structure, while the structure of the shock wave in the simplified models was clear and simple. The parameters of motion of the body changed by a little when the initial jet-induced interference was ignored. The difference in values of the strongest impact force measured at monitoring points far from the muzzle was small, with an error of about 2%, such that the simplified model without the initial jet could be used in place of the original complex model. The other simplified models yielded significant differences. Our results provide an important theoretical basis for further research on the muzzle jet and its applications in engineering.

Building Information Modelling Facility Management (BIM-FM)

Defined digital Facilities’ Management (FM) systems will contribute to the realisation of the United Nations’ Sustainable Development Goal (SDG) 11. Of the available digital FM systems, Building Information Modelling (BIM) for FM, herein referred to as BIM-FM, is the least developed. Where BIM-FM varies from existing digital FM tools is its advanced 3D visualisation capabilities. A semi-structured literature review is undertaken to assess the current implementation of BIM-FM and identify opportunities to engender its increased adoption. This paper is part of an ongoing piece of research aimed at defining a standard methodology for the application of BIM to historically significant structures, otherwise known as Historic BIM (HBIM). Two existing approaches to BIM-FM, current and developing, are outlined. The potential value BIM-FM can provide according to the literature is discussed but there exists minimal practical evidence to justify these claims. Barriers to its adoption are discussed, with a key underlying barrier found to be a lack of defined user requirements. Consequently, functional, modelling and information requirements established within the literature are identified, and existing attempts at realising the requirements are discussed. Six information categories and two functional requirements are identified. It is theorised that the tendency to utilise simplified geometric models for FM is primarily due to software and practical limitations as opposed to actual end user needs, and it is suggested that this should be investigated further in future work. Attempts at realising BIM-FM user requirements using other advanced technologies, primarily Digital Twins, are investigated and found to be an area of increasing commonality. A new conception of BIM-FM is proposed.

Optimal design of steel exoskeleton for the retrofitting of RC buildings via genetic algorithm

In recent decades, steel exoskeletons have gathered significant attention as a seismic retrofitting technique for existing structures. The design methods proposed so far are focused on the identification of the system's overall parameters through simplified models. Although these methodologies provide helpful guidance at the preliminary design stage, they do not consider aspects such as the distribution of the exoskeletons and sizing of their components. To overcome these limitations, an optimization process based on the Genetic Algorithm is proposed in this paper to identify the optimal exoskeleton number and spatial arrangement, and to determine the optimal size of their constituent elements. The algorithm aims to minimize the weight of the retrofit solution while keeping the whole existing structure in the elastic field and ensuring the structural verification of the exoskeleton's elements. The analyses have been conducted using a finite-element code with an Open Application Programming Interface, which allows the models to be handled through automatic routines. The proposed optimization tool has been applied to several case studies, considering two different layouts for the exoskeletons. Finally, the effectiveness of the retrofit method has been demonstrated, and the proposed optimization tool has been able to significantly reduce the weight and cost of the intervention.

Distributed acoustic sensing microseismic reflection imaging for hydraulic fracture and fault lineament characterization

This study presents a novel workflow designed for migrating reflected S waves generated by microseismic events, as recorded by downhole distributed acoustic sensing (DAS), to characterize hydraulic fractures in three dimensions. In contrast to existing fracture imaging techniques, which have encountered challenges in accurately representing fracture networks and often rely on simplified models, our imaging technique does not assume that fractures are planar or in a prespecified orientation. DAS seismic measurements benefit from the large aperture and dense spatial sampling enabled by the kilometers-long fiber and, therefore, are able to capture a large number of strong reflections compared with traditional borehole geophones or accelerometers. We treat microseismic events as high-frequency sources and apply prestack Kirchhoff migration to each individual source after wavefield separation. Fracture imaging results for multiple selected events are then stacked to generate a 3D reflectivity volume, revealing the subsurface fracture and fault networks in intricate detail. The high-resolution fracture images generated by the developed reflection migrating process illuminate the heart of the stimulated volume of the reservoir, a zone that is often challenging to access using conventional surface arrays or active sources. To validate the effectiveness of our workflow, our study uses a data set acquired during a multiwell project in the Eagle Ford Shale and Austin Chalk in South Texas. To assess the accuracy and reliability of the results, the reflection imaging output is integrated with the microseismicity distribution and strain measurements from low-frequency DAS for interpretation. The results of reflection imaging improve our understanding of fracture geometry, including distal fractures that are away from the monitoring well, allow the direct estimation of fracture height and length, and potentially signify the presence of preexisting fluid-filled fault lineaments.

Flow Structure Of A Heterogeneous Seagrass Canopy

Seagrass canopies are widely recognized for their ecosystem services (Barbier et al., 2011) including flow and wave attenuation (Ondiviela et al., 2014), sediment stabilization (De Boer, 2007), carbon sequestration (Duarte & Krause-Jensen, 2017) and nutrient deposition (Gacia et al., 2002). Over the past decades, various studies have delved into the modifications imparted by a seagrass canopy under both unidirectional and oscillatory flow at various scales. However, until recently, the majority of studies have either reported on or modeled seagrass canopies without considering the heterogeneity of species within a single canopy. While simplified prototype models offer valuable insights into system dynamics, they can introduce biases, potentially leading to the reduction or amplification of relevant physical processes (Tinoco et al., 2020). For instance, the study by Weitzman et al. (2015) emphasized the significance of considering vertical heterogeneity in a canopy. They noted that the presence of an understory can modulate near-bed flow processes differently, with flow attenuation significantly increased due to the understory's presence. However, embedded physical mechanisms and quantitative limitations in ecosystem functioning and services are only little understood. Such a knowledge gap is pointed out in the recent review by Risandi et al. (2023) regarding field studies of seagrass meadows in Indonesia. Only few studies explore the interaction between such a heterogeneous canopy (Short et al., 2011) and hydrodynamics despite the abundance of seagrass species in the region (McKenzie et al., 2020). Through physical modeling of four different species of seagrass this study aims to improve the understanding of flow structure modulated by a heterogeneous canopy under unidirectional currents at full scale.

The influence of viscous slab rheology on numerical models of subduction

Abstract. Numerical models of subduction commonly use diffusion and dislocation creep laws from laboratory deformation experiments to determine the rheology of the lithosphere. The specific implementation of these laws varies from study to study, and the impacts of this variation on model behavior have not been thoroughly explored. We run simplified 2D numerical models of free subduction in SULEC, with viscoplastic slabs following (1) a diffusion creep law, (2) a dislocation creep law, and (3) both simultaneously, as well as several variations of model 3 with reduced resistance to bending. We compare the results of these models to a model with a constant-viscosity slab to determine the impact of the implementation of different lithospheric flow laws on subduction dynamics. In creep-governed models, higher subduction velocity causes a longer effective slab length, increasing slab pull and asthenospheric drag, which, in turn, affect subduction velocity. Numerical and analogue models implementing constant-viscosity slabs lack this feedback but still capture morphological patterns observed in more complex models. Dislocation creep is the primary deformation mechanism throughout the subducting lithosphere in our models. However, both diffusion creep and dislocation creep predict very high viscosities in the cold core of the slab. At the trench, the effective viscosity is lowered by plastic failure, rendering effective slab thickness the primary control on bending resistance and subduction velocity. However, at depth, plastic failure is not active, and the viscosity cap is reached in significant portions of the slab. The resulting high slab stiffness causes the subducting plate to curl under itself at the mantle transition zone, affecting patterns in subduction velocity, slab dip, and trench migration over time. Peierls creep and localized grain size reduction likely limit the stress and viscosity in the cores of real slabs. Numerical models implementing only power-law creep and neglecting Peierls creep are likely to overestimate the stiffness of subducting lithosphere, which may impact model results in a variety of respects.

Using multi‐scale spatial models of dendritic ecosystems to infer abundance of a stream salmonid

Abstract Understanding patterns of species abundance is essential for planning landscape‐level conservation. The complex hierarchies of dendritic ecosystems result in different levels of heterogeneity at distinct geographic scales. Species responses to dynamic environmental drivers may also vary spatially depending on their interactions with landscape features. Monitoring abundance by explicitly quantifying their spatial and temporal variation is important for strategic management. We analysed brook trout (Salvelinus fontinalis) count data collected from 173 sites in western North Carolina between 1989 and 2015. We developed a Bayesian hierarchical model that used single‐ and multi‐pass electro‐fishing data and characterized their respective capture probabilities. We quantified spatial variation using a multi‐scale process model representative of the nested stream habitats, and we investigated differences in population temporal trends and responses to seasonal weather patterns by space and life stage. Trout abundance was lower on the Atlantic slope of the Eastern Continental Divide than in the interior, on average, and the Atlantic slope juveniles were more adversely affected by high winter flows. However, Atlantic slope populations of both lifestages demonstrated positive temporal trends, whereas Interior juveniles demonstrated a negative trend. We found higher spatial variation than temporal variation in abundance when conditioned on the covariates, where the primary source of spatial heterogeneity was revealed at the segment level, compared to watershed or network levels. Our multi‐scale spatial model outperformed simpler models in abundance estimation and out‐of‐sample prediction. The inferred per‐pass capture probabilities indicated that single‐pass surveys were as efficient as multi‐pass surveys. Synthesis and applications. Our study suggested conservation priority should involve multiple criteria, including present‐day abundance, temporal trend and sensitivity to environmental drivers. Based on the inferred scale‐specific variations in trout abundance, we recommend that future surveys strategically combine single‐pass surveys with multi‐pass surveys to optimize abundance estimation. Our approach is widely applicable to other species and ecosystems occupying dendritic habitats.

Forecasting personal heat strain under extremely hot environments: Utilizing feature importance in machine learning

Since the frequency and intensity of heatwaves are expected to increase due to global warming, it is crucial to establish a simplified approach to preventing personal heat-related illnesses as occupational hazard under extremely hot environments, by taking into account individual differences in heat strain. In light of this, this study proposed a simplified forecast model for personal heat strain under extremely hot environments by utilizing feature importance in machine learning, focused on enhancing its field applicability. Using the 1417 records gathered from the experiments under extremely hot conditions, the simplified model was developed by utilizing four types of machine learning algorithms (i.e., random forest, extreme gradient boosting, support vector regression, and multi-layer perceptron). As a result, the proposed simplified models with four principal features (i.e., dry-bulb temperature, radiant temperature, relative humidity, and percentage body fat) were found to be the most reliable, resulting in a small difference of 0.047 °C compared to the reference model. Since the principal biometric features can be easily measured by a simple and low-cost body composition test (but not with the physical measurement of personal core body temperature) before they are put into the field, the proposed approach will aid in providing more systematic and efficient management system for proactively preventing personal heat-related illnesses. Furthermore, it is expected that the field applicability of the proposed approach can be continuously enhanced by utilizing the accumulated big data gathered from the field-based living-lab projects over a lengthy period.

Oil Quality Prediction in Olive Oil by Near-Infrared Spectroscopy: Applications in Olive Breeding

The oxidative stability index (OSI) and fatty acid (FA) composition of extra virgin olive oils (EVOOs) are key parameters in the characterization of new varieties in breeding programs. Their determination through traditional methods (Rancimat and gas chromatography, respectively) is expensive and time-consuming. Therefore, there is a need to develop rapid and cost-effective analytical procedures. This study aimed to evaluate the potential use of near-infrared spectroscopy (NIRS) for analyzing OSI and FA composition in EVOOs. A total of 318 samples sourced from different origins were evaluated using both FT-NIR MPA and MicroNIR instruments in transmittance mode, with wavelengths ranging from 1100 to 2500 nm and 908 to 1676 nm, respectively. Different accuracies were obtained in the models developed for the different evaluated traits, with simpler models (using a lower number of latent variables) for the MPA analyzer in all cases. Additionally, consistent results between instruments for the partitioning of the variance and heritability estimation, and the reliable ranking of genotypes were obtained from one of the sample sets tested. In summary, models derived from PLS regression using spectroscopic data of both instruments demonstrated promising results in determining these EVOO traits, facilitating their evaluation and selection of genotypes, particularly in breeding programs.

Accounting for spatio-temporal and sampling dependence in survey and CPUE biomass indices: simulation and Bayesian modeling framework

Abstract Estimating changes in the biomass of a fish stock is crucial for successful management. However, fishery assessment may be affected by the quality of the inputs used in stock assessment models. Survey biomass indices derived from fishery-independent and catch per unit effort (CPUE) biomass indices derived from fishery-dependent data are key inputs for model calibration. These indices have biases that could compromise the accuracy of the stock assessment models results. Therefore, there are plenty proposed methods to standardize survey or CPUE biomass data. From simpler models like generalized linear models (GLMs) to more complex models that take into account spatio-temporal correlation, like geostatistical models, and sampling dependence, like marked point processes. But many of them do not consider the underlying spatio-temporal or sampling dependence of these data. Hence, the goal of the study is to present a spatio-temporal simulation and Bayesian modeling framework to assess the impact of applying models that do not consider spatio-temporal and sampling dependence. Results indicate that geostatistical models and marked point processes achieve the lowest measures of error. Hence, to capture the underlying spatio-temporal process of the survey and CPUE biomass indices and data sampling preferentiality, it is essential to apply models that consider the spatio-temporal and sampling dependence.

Application of the fast 3D simplified simulation method for the large CAP1400 nuclear island evaporator based on the coupled source term method

As for the evaporator of the large CAP1400 nuclear power plant, it has a large volume with numerous tubes along with the complex flow heat transfer phenomenon. The traditional direct simulation method usually takes a lot of time and computing resources to obtain the flow heat transfer data. To solve the problem, a fast simulation method established on the porous medium source term was herein proposed. In this simplified simulation method, the complex evaporator model is replaced with the porous medium model, where the derived heat and momentum source terms are added into the governing equation to control the heat flows. Five kinds of simplified evaporator models with different numbers of pipes were set to be the research objects, the internal flows inside the evaporator were studied with the traditional direct simulation method and the newly proposed simplified simulation method. Via comparative analysis, it can be found that: 1) The newly proposed simplified simulation method accurately captures the distribution characteristics of pressure, temperature, and entropy production in the evaporator, serving as a viable alternative to traditional direct simulation methods. Specifically, the average simulation errors for inlet-outlet pressure difference and temperature difference are both less than 5 % and 7 %, respectively. 2) The newly proposed simplified simulation method can capture the mutual interference effects between the evaporator and the nuclear main pump. When the evaporator is connected in series with the main nuclear pump, the average head of the main pump increases by 0.34 m, while the efficiency decreases by an average of 0.34 %. The average simulation errors of the simplified simulation method are 0.06 m and 0.13 %, respectively. 3) The application of the simplified simulation method leads to a significant 85 % decrease in the average computational cost for evaporator calculations and a notable 36 % decrease for combined calculations of the evaporator and main pump. The study here is expected to provide technique support for the simulation and evaluation of the large nuclear island.

Numerical characterization of piezoelectric energy harvesting from an L-shaped frame

An L-shaped structure, intended for piezoelectric energy harvesting, is being characterized numerically to obtain extended insight into the effect of various parameters and metrics that are hard to track from experimental setups and simplified analytical models and to show the underlying mechanism of efficient piezoelectric harvesting. A numerical model was developed to analyze the structure under harmonic base excitation to obtain voltage frequency response and strain distribution data. The model takes into effect the load resistance applied between the piezoelectric patch electrodes and the damping ratio of each mode for accurate resonance amplitude evaluation. The validity of the model was confirmed by comparisons with previous experiments and showed strong agreement. The approach demonstrated that piezoelectric energy harvesting modeling, in general, is sensitive to inaccurate estimations, especially in the damping parameters, which emphasizes that rough assumptions made in analytical models will not yield results that can be discussed in correlation with experimental outcomes. This work states the guidelines for accurate piezoelectric modeling using numerical software, with the aim of enabling the investigation of complex harvesters, providing more insights to direct researchers to more proof and optimized designs that tackle broadband and energy density problems found in simple harvesters.

Sexual dimorphism in the environmental sensitivity of weaning weight in beef cattle

This study investigates the environmental sensitivity and sexual dimorphism in the genetic parameters of weaning weight (WW) in Nelore beef cattle. Five reaction norms models were employed to analyze the 148,131 WW records, considering different definitions of the environmental descriptor (ED). (Co)variance components and genetic parameters were separately estimated for males and females. Results reveal substantial sexual dimorphism in various parameters. Significant divergence across sexes was observed for environmental sensitivity, with males showing an increased response to favorable environments while females genetically responded better to more challenging environments. Including sex in defining the ED can inflate the estimated breeding values of sires with a higher number of male progeny records, highlighting the importance of choosing a reliable ED in analyses of traits influenced by sexual dimorphism and genotype-by-environment interaction (G × E). The observed sexual dimorphism and environmental sensitivity underscore the complexity of genetic evaluation of WW in beef cattle populations, with implications for selection strategies. The findings suggest that a sex-separated trait analysis considering G × E effects is most appropriate for genetic evaluations of WW. However, a simpler single-trait model excluding sex from the ED definition yielded a higher coincidence of selected sires with the sex-separated analysis. In conclusion, understanding sexual dimorphism and environmental sensitivity is crucial for genetic evaluations and selection decisions in beef cattle breeding programs, emphasizing the need for careful model selection and ED definition.

GIS-Based Regional Seismic Risk Assessment for Dubai, UAE, Using NHERI SimCenter R2D Application

GIS-Based Regional Seismic Risk Assessment for Dubai, UAE, Using NHERI SimCenter R2D Application

The role of glial and neuronal Eph/ephrin signaling in Drosophila mushroom body development and sleep and circadian behavior

The Eph receptor, a prototypically large receptor protein tyrosine kinase, interacts with ephrin ligands, forming a bidirectional signaling system that impacts diverse brain functions. Eph receptors and ephrins mediate forward and reverse signaling, affecting neurogenesis, axon guidance, and synaptic signaling. While mammalian studies have emphasized their roles in neurogenesis and synaptic plasticity, the Drosophila counterparts are less studied, especially in glial cells, despite structural similarities. Using RNAi to modulate Eph/ephrin expression in Drosophila neurons and glia, we studied their roles in brain development and sleep and circadian behavior. Knockdown of neuronal ephrin disrupted mushroom body development, while glial knockdown had minimal impact. Surprisingly, disrupting ephrin in neurons or glial cells altered sleep and circadian rhythms, indicating a direct involvement in these behaviors independent from developmental effects. Further analysis revealed distinct sleep phenotypes between neuronal and glial knockdowns, underscoring the intricate interplay within the neural circuits that govern behavior. Glia-specific knockdowns showed altered sleep patterns and reduced circadian rhythmicity, suggesting an intricate role of glia in sleep regulation. Our findings challenge simplistic models of Eph/ephrin signaling limited to neuron-glia communication and emphasize the complexity of the regulatory networks modulating behavior. Future investigations targeting specific glial subtypes will enhance our understanding of Eph/ephrin signaling’s role in sleep regulation across species.

SERT: A transfomer based model for multivariate temporal sensor data with missing values for environmental monitoring

Environmental monitoring is crucial to our understanding of climate change, biodiversity loss and pollution. The availability of large-scale spatio-temporal data from sources such as sensors and satellites allows us to develop sophisticated models for forecasting and understanding key drivers. However, the data collected from sensors often contain missing values due to faulty equipment or maintenance issues. The missing values rarely occur simultaneously leading to data that are multivariate misaligned sparse time series. We propose two models that are capable of performing multivariate spatio-temporal forecasting while handling missing data naturally without the need for imputation. The first model is a transformer-based model, which we name SERT (Spatio-temporal Encoder Representations from Transformers). The second is a simpler model named SST-ANN (Sparse Spatio-Temporal Artificial Neural Network) which is capable of providing interpretable results. We conduct extensive experiments on two different datasets for multivariate spatio-temporal forecasting and show that our models have competitive or superior performance to those at the state-of-the-art.