Future Models Research Articles

The Price Trend of Polypropylene

Since the polypropylene (PP) is one of the most widely used plastics, the futures prices of polypropylene are of great significance for studying the Chinese plastic market and related investors. This paper analyzes the factors that influence the changes in PP futures prices during and before the pandemic period by examining the correlation between upstream raw materials, downstream product markets, and PP futures prices. The results show that the futures prices of the raw materials are positively correlated with PP futures prices. Additionally, during the COVID-19 pandemic, due to the reduction in domestic PP demand in China, blocked crude oil imports, and refinery closures caused by control measures, the impact of crude oil futures prices on PP futures prices is relatively diminished. Furthermore, the increased demand for plastic packaging during the pandemic led to abnormal fluctuations in the packaging stock index, making it less valuable for analyzing PP futures prices. Finally, a regression analysis is conducted to establish a generalized predictive model for PP futures prices during non-pandemic periods, which is 9.91469 * crude oil futures prices + (-1.22343) * packaging stock index + 3.96203 * textile stock index + (-0.26201) * medical devices stock index. Its reliability is tested using data tests, residual vs. fitted analysis, and K-fold cross-validation, among other methods.

Diverse perspectives in zebrafish seizure models: An exploration of chemical inducers

Numerous studies have investigated seizures in zebrafish, employing diverse methods to induce and observe epilepsy. Chemical inducers have become indispensable tools for precisely manipulating neural activity in controlled experiments. This research is dedicated to a meticulous comparison and analysis of various seizure-inducing chemicals in zebrafish. The selected chemical inducers include pentylenetetrazole (PTZ), kainic acid (KA), picrotoxin (PTX), pilocarpine, and other substances. The primary mechanism of these inducers involves altering neurotransmitter receptors, particularly GABA and glutamate, at inhibitory or excitatory synapses. This alteration creates an imbalance between inhibitory and excitatory signals in the brain, contributing to epileptogenesis. The resulting hyperactivity in the zebrafish brain disrupts metabolism, elevates reactive oxygen species (ROS), and causes neuronal damage. Chemical induction offers valuable insights into the neurological activity of zebrafish, enabling the observation of behaviors such as whole-body contractions, loss of posture, or freezing. Various parameters can be adjusted to examine the effects of chemical inducers on zebrafish development and to replicate various types of seizures, such as status epilepticus and temporal lobe epilepsy. Despite the array of options available, many chemical inducers remain underutilized in zebrafish studies. To ensure the successful induction of models, monitoring gene expression markers such as c-fos, which represents neuronal activity, becomes crucial. Additionally, a scoring system is employed to analyze seizure severity and identify treatment efficacy. The existing gaps in utilizing chemical inducers present great opportunities for future model development. This chemically-induced seizure model in zebrafish is pivotal for unraveling seizure mechanisms, contributing significantly to advancements in neurological research.

In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models.

In vitro models of Mycobacterium tuberculosis (Mtb) infection are a valuable tool for examining host-pathogen interactions and screening drugs. With the development of more complex in vitro models, there is a need for tools to help analyze and integrate data from these models. To this end, we introduce an agent-based model (ABM) representation of the interactions between immune cells and bacteria in an in vitro setting. This in silico model was used to simulate both traditional and spheroid cell culture models by changing the movement rules and initial spatial layout of the cells in accordance with the respective in vitro models. The traditional and spheroid simulations were calibrated to published experimental data in a paired manner, by using the same parameters in both simulations. Within the calibrated simulations, heterogeneous outputs are seen for bacterial count and T cell infiltration into the macrophage core of the spheroid. The simulations also predict that equivalent numbers of activated macrophages do not necessarily result in similar bacterial reductions; that host immune responses can control bacterial growth in both spheroid structure dependent and independent manners; that STAT1 activation is the limiting step in macrophage activation in spheroids; and that drug screening and macrophage activation studies could have different outcomes depending on the in vitro culture used. Future model iterations will be guided by the limitations of the current model, specifically which parts of the output space were harder to reach. This ABM can be used to represent more in vitro Mtb infection models due to its flexible structure, thereby accelerating in vitro discoveries.

Insect population dynamics under Wolbachia-induced cytoplasmic incompatibility: Puzzle more than buzz in Drosophila suzukii.

In theory, the introduction of individuals infected with an incompatible strain of Wolbachia pipientis into a recipient host population should result in the symbiont invasion and reproductive failures caused by cytoplasmic incompatibility (CI). Modelling studies combining Wolbachia invasion and host population dynamics show that these two processes could interact to cause a transient population decline and, in some conditions, extinction. However, these effects could be sensitive to density dependence, with the Allee effect increasing the probability of extinction, and competition reducing the demographic impact of CI. We tested these predictions with laboratory experiments in the fruit fly Drosophila suzukii and the transinfected Wolbachia strain wTei. Surprisingly, the introduction of wTei into D. suzukii populations at carrying capacity did not result in the expected wTei invasion and transient population decline. In parallel, we found no Allee effect but strong negative density dependence. From these results, we propose that competition interacts in an antagonistic way with Wolbachia-induced cytoplasmic incompatibility on insect population dynamics. If future models and data support this hypothesis, pest management strategies using Wolbachia-induced CI should target populations with negligible competition but a potential Allee effect, for instance at the beginning of the reproductive season.

Opportunities and challenges for gas coproduction from coal measure gas reservoirs with coal‐shale‐tight sandstone layers: A review

AbstractThe extraction of coal measure gas has been shifted toward thin gas reservoirs due to the depletion of medium‐thick gas reservoirs. The coproduction of coalbed gas, shale gas, and tight sandstone gas (called a multisuperposed gas system) is a key low‐cost technology for the enhancement of natural gas production from thin gas reservoirs in coal measure. As an emerging engineering exploitation technology at its early stage of development, gas coproduction confronts various engineering challenges in hydraulic fracturing, bottom‐hole pressure regulation, well network arrangement, and extraction sequence. The current understanding of the opportunities and challenges in the gas coproduction from the multisuperposed gas system is not comprehensive enough. In this case, the previous achievements in the field of gas coproduction should be urgently reviewed to provide valuable guidance and recommendations for further development. This review first discusses the regional and spatial distribution characteristics and possible reservoir combinations of gas reservoirs in coal measure. Then, the basic properties of different reservoirs, engineering challenges, and interlayer interference are comparatively analyzed and discussed. The current simulation models for gas coproduction and potential future research directions are further explored. The results indicate that the coupling effects of reservoir heterogeneity, interwell interference, and geological structure for increasing coproduction prediction accuracy should be included in future simulation models for gas coproduction. Careful investigation is required to explore the mechanisms and their further quantifications on the effects of interlayer interference in gas coproduction. The fractal dimension as a scale can play an important role in the characterization of the gas and water transport in different reservoirs. The machine learning methods have tremendous potential to provide accurate and fast predictions for gas coproduction and interlayer interference.

Ankle-Brachial Index and Risk of Sudden Cardiac Death in the Community: The ARIC Study.

Sudden cardiac death (SCD) is a significant global public health problem accounting for 15% to 20% of all deaths. A great majority of SCD is associated with coronary heart disease, which may first be detected at autopsy. The ankle-brachial index (ABI) is a simple, noninvasive measure of subclinical atherosclerosis. The purpose of this study was to examine the relationship between ABI and SCD in a middle-aged biracial general population. Participants of the ARIC (Atherosclerosis Risk in Communities) study with an ABI measurement between 1987 and 1989 were included. ABI was categorized as low (≤0.90), borderline (0.90-1.00), normal (1.00-1.40), and noncompressible (>1.40). SCD was defined as a sudden pulseless condition presumed to be caused by a ventricular tachyarrhythmia in a previously stable individual and was adjudicated by a committee of cardiac electrophysiologists, cardiologists, and internists. Cox proportional hazards models were used to evaluate the associations between baseline ABI and incident SCD. Of the 15 081 participants followed for a median of 23.5 years, 556 (3.7%) developed SCD (1.96 cases per 1000 person-years). Low and borderline ABIs were associated with an increased risk of SCD (demographically adjusted hazard ratios [HRs], 2.27 [95% CI, 1.64-3.14] and 1.52 [95% CI, 1.17-1.96], respectively) compared with normal ABI. The association between low ABI and SCD remained significant after adjustment for traditional cardiovascular risk factors (HR, 1.63 [95% CI, 1.15-2.32]). Low ABI is independently associated with an increased risk of SCD in a middle-aged biracial general population. ABI could be incorporated into future SCD risk prediction models.

MineCam: Application of Combined Remote Sensing and Machine Learning for Segmentation and Change Detection of Mining Areas Enabling Multi-Purpose Monitoring

Our study addresses the need for universal monitoring solutions given the diverse environmental impacts of surface mining operations. We present a solution combining remote sensing and machine learning techniques, utilizing a dataset of over 2000 satellite images annotated with ten distinct labels indicating mining area components. We tested various approaches to develop comprehensive yet universal machine learning models for mining area segmentation. This involved considering different types of mines, raw materials, and geographical locations. We evaluated multiple satellite data set combinations to determine optimal outcomes. The results suggest that radar and multispectral data fusion did not significantly improve the models’ performance, and the addition of further channels led to the degradation of the metrics. Despite variations in mine type or extracted material, the models’ effectiveness remained within an Intersection over Union value range of 0.65–0.75. Further, in this research, we conducted a detailed visual analysis of the models’ outcomes to identify areas requiring additional attention, contributing to the discourse on effective mining area monitoring and management methodologies. The visual examination of models’ outputs provides insights for future model enhancement and highlights unique segmentation challenges within mining areas.

Going back for the future: Incorporating Pleistocene fossil records of saiga antelope into habitat suitability models

AbstractAimMany species have suffered anthropogenic range contraction and no longer occupy all available suitable environmental conditions. This is particularly problematic for the construction of habitat suitability models (HSMs), which assume that a species' contemporary range reflects its full species–environment relationship. HSMs therefore risk underestimating suitable environment areas, and misinforming conservation decisions. Incorporating historic (centuries‐old) records partly reduces this bias, but even these records are also subject to human disturbance. We incorporated fossil records of the critically endangered saiga antelope (Saiga tatarica, L., 1776), alongside historic and current records, into current and future habitat suitability models. Saiga has experienced drastic range contraction and may have a truncated species–environment relationship. The results allowed us to test whether its current habitat provides optimal environmental conditions, or whether saiga should be considered a refugee species.LocationNorthern Hemisphere.TaxonSaiga tatarica (Bovidae, Artiodactyla).MethodsWe collated historic and fossil saiga occurrence records from published literature, museum archives and global databases. Modern occurrence records were obtained from the International Union for Conservation of Nature Red List assessment. Four bioclimatic variables were downloaded from Worldclim.org. HSMs were generated through Maxent, using the maxnet package in R. Three HSMs were developed: present only, present historic and present fossil. Each of these models was projected onto current and two future (2070) climate change scenarios.ResultsSaiga fossil records increased the predicted suitable environment area by 783% and 1416% for current and future climate projections respectively. Our results suggest the saiga is not a refugee species but occupies only a portion of its potential environmental niche. The saiga's contemporary range is predicted environmentally suitable throughout all models and projections, and therefore in situ conservation management is recommended.Main ConclusionsThis study highlights the importance of incorporating fossil records into HSMs to better understand species–environment relationships and develop more robust conservation strategies for appropriate endangered species.

Modelling foraging strategies of honey bees as agents in a dynamic landscape representation

Introduction: Both intrinsic colony mechanisms and external environmental variables affect the honey bee colony development rates and response and a key aspect of this is the use of resources within the landscape by honey bees. Although several models have been developed to explore the foraging behaviour of bees, none fully considered the spatial and temporal dynamics of landscape resources and the role of the colony in the process. Methodology: Here, we introduce a new honey bee foraging model being developed as a part of the ApisRAM honey bee colony model. Based on agent-based modelling, we used a dynamic ALMaSS landscape model enhanced with floral resource modelling to assess the impacts of weather conditions and resource availability on the possible foraging behaviour of honey bees. Several possible mechanisms (defined, based on honey bee traits) for scouting and foraging were investigated, separately for nectar and pollen collection, including prioritising foraging polygons for nectar foraging according to their distance to the colony, the quality or the energetic efficiency and, for pollen foraging, according to their distance to the colony and pollen quantity. Results: If model foraging bees prioritised the polygons, based on their distance from the colony, the number of unsuccessful flights increased compared to other tested strategies and the total amount of sugar collected showed a high variability. Contrary to expectations, the energetic efficiency strategy did not provide the colony with the highest amount of sugar. Overall, the tested strategies provide different outcomes on the collection of resources, the number of performed flights and their success rate, evidencing that the model's outcome at the colony level arises from the individual types of behaviour. Conclusions and Relevance: Variability in the mass of collected nectar and pollen was found mostly when scout bees applied the distance strategy. This higher variability in sugar collection means that model bees were not able to find the most profitable foraging sites at the landscape level, but only at the local level. Other strategies showed less dependence on the surrounding landscape (i.e. quality or random), but it comes at a cost (i.e. lower production for both nectar and pollen collection). These outputs help us evaluate which strategies could be used for future model development and confirm the models' ability to create dynamic responses. These responses at the colony level were only possible thanks to the implementation of a dynamic landscape model and dynamic spatiotemporal resource model, as well as implementing a social communication mechanism for bees to share information about the resources. Plant nectar production and quality information is essential to predict honey bee foraging distribution. In future model development, the implementation of pollen quality should also be explored to evaluate if it influences the overall pollen collection.

Structural and practical identifiability analysis in bioengineering: a beginner’s guide

Advancements in digital technology have brought modelling to the forefront in many disciplines from healthcare to architecture. Mathematical models, often represented using parametrised sets of ordinary differential equations, can be used to characterise different processes. To infer possible estimates for the unknown parameters, these models are usually calibrated using associated experimental data. Structural and practical identifiability analyses are a key component that should be assessed prior to parameter estimation. This is because identifiability analyses can provide insights as to whether or not a parameter can take on single, multiple, or even infinitely or countably many values which will ultimately have an impact on the reliability of the parameter estimates. Also, identifiability analyses can help to determine whether the data collected are sufficient or of good enough quality to truly estimate the parameters or if more data or even reparameterization of the model is necessary to proceed with the parameter estimation process. Thus, such analyses also provide an important role in terms of model design (structural identifiability analysis) and the collection of experimental data (practical identifiability analysis). Despite the popularity of using data to estimate the values of unknown parameters, structural and practical identifiability analyses of these models are often overlooked. Possible reasons for non-consideration of application of such analyses may be lack of awareness, accessibility, and usability issues, especially for more complicated models and methods of analysis. The aim of this study is to introduce and perform both structural and practical identifiability analyses in an accessible and informative manner via application to well established and commonly accepted bioengineering models. This will help to improve awareness of the importance of this stage of the modelling process and provide bioengineering researchers with an understanding of how to utilise the insights gained from such analyses in future model development.

Numerical model of pressure generated by elastic compression garments on a compressible human limb analogue.

This study aimed to formulate a numerical approach (finite element modelling (FEM)) to calculate pressure values generated by compression garments on a compressible limb analogue, and to validate the numerical approach using experimental measurements. Existing models were also compared. Experimentally measured pressure values and deformation caused by compression bands on a compressible human limb analogue were compared with values predicted using the Young-Laplace equation, a previously formulated analytical model and the FEM. The FEM provided greater accuracy in predicting the pressure generated by compression bands compared to existing models. The FEM also predicted deformation of the limb analogue with good agreement relative to experimental values. It was concluded that modelling the non-uniform manner in which the way a limb analogue is compressed should be incorporated into future modelling of the pressures generated by compression garments on a compressible limb analogue. The authors have no conflicts of interest to declare.

Framework for the Classification of Facial Emotions Using Soft Computing Techniques

Background:: Facial emotion recognition (FER) technology is enumerated as a productive interface in several operations, which has been specifically focused on as a substitute communication path among a user and an appliance for human computer interface in the previous decade. The efficiency of the facial identification model straightaway relies on the capability of classification methods. In addition, an appropriate swap between recognition efficiency and computational cost is reckoned as the most important factor for planning such models. Methods:: The objective of this paper was to classify the facial emotion electromyogram (EMG) signals by means of a neural network algorithm (NN), support vector machine (SVM) algorithm, and Naive-Bayes algorithm. This research work was directed towards the correlation among the classification accuracies by applying distinct feature extraction procedures on fEMGs. At first, eight participants (six male and two female) were recruited for data recording. Four electrodes were placed on each participant's face for capturing facial gestures (happy, angry, sad, and fear) and two electrodes were placed on the wrist for grounding purposes. Data were recorded by using BIOPAC MP150. After this, the signals were filtered using a band-pass filter and segmentation techniques for enhanced processing. After that, the time-domain and frequency-domain feature extraction procedures were carried out. Time domain and frequency domain features were applied to recorded signals. In this research, we used LabVIEW and MATLAB to produce a set of characteristics from fEMG signals for four emotional conditions, such as anger, sad, fear, and happy. After the feature extraction process, the extracted features were aligned into respective emotions by applying classifiers. The extracted features were further trained and classified by applying the SVM classifier, neural network classifier, and Naive Bayes classifier in MATLAB 2020. Results:: The SVM classifier and neural network classifier generated an accuracy of 93.80% and 96.90%, respectively, whereas the Naive Bayes classifier generated an accuracy of 90.60%. Conclusion:: Facial emotion recognition (FER) is foresighted as a progressive or futuristic model, which has attracted the attention of researchers in several areas of learning due to its higher prospects in distinct applications. Acknowledgment of the emotions through biomedical signals produced from movements of facial muscles is lately presented using an explicit and authentic route.

Biochemical and metabolic characterization of a G6PC2 inhibitor

Three glucose-6-phosphatase catalytic subunits, that hydrolyze glucose-6-phosphate (G6P) to glucose and inorganic phosphate, have been identified, designated G6PC1-3, but only G6PC1 and G6PC2 have been implicated in the regulation of fasting blood glucose (FBG). Elevated FBG has been associated with multiple adverse clinical outcomes, including increased risk for type 2 diabetes and various cancers. Therefore, G6PC1 and G6PC2 inhibitors that lower FBG may be of prophylactic value for the prevention of multiple conditions. The studies described here characterize a G6PC2 inhibitor, designated VU0945627, previously identified as Compound 3. We show that VU0945627 preferentially inhibits human G6PC2 versus human G6PC1 but activates human G6PC3. VU0945627 is a mixed G6PC2 inhibitor, increasing the Km but reducing the Vmax for G6P hydrolysis. PyRx virtual docking to an AlphaFold2-derived G6PC2 structural model suggests VU0945627 binds two sites in human G6PC2. Mutation of residues in these sites reduces the inhibitory effect of VU0945627. VU0945627 does not inhibit mouse G6PC2 despite its 84% sequence identity with human G6PC2. Mutagenesis studies suggest this lack of inhibition of mouse G6PC2 is due, in part, to a change in residue 318 from histidine in human G6PC2 to proline in mouse G6PC2. Surprisingly, VU0945627 still inhibited glucose cycling in the mouse islet-derived βTC-3 cell line. Studies using intact mouse liver microsomes and PyRx docking suggest that this observation can be explained by an ability of VU0945627 to also inhibit the G6P transporter SLC37A4. These data will inform future computational modeling studies designed to identify G6PC isoform-specific inhibitors.

The Historical Process and Deep Influence of Sino-Thai Educational Intermingling—A Case Study on Yunnan Context

Background and Aims: As China's radiation center faces South and Southeast Asia and the forefront of the "the Belt and Road" construction, Yunnan's unique geographical location and rich cultural heritage provide valuable opportunities for educational exchanges between the two countries. Therefore, to further analyze the profound impact of cultural exchange between the two countries on China. This article aims to explore the future direction and possibilities of education cooperation between China and Thailand, taking Yunnan as an example. From a more comprehensive and profound perspective, it provides feasible ways to further strengthen the relationship between the two countries and deepen cooperation. Methodology: By using the methods of historical records, policy analysis, and comparative study, this paper examines the evolution of educational exchanges between China and Thailand and its impact on the educational policies of the two countries. This paper first reviews the historical evolution of educational exchanges between China and Thailand, and analyzes the cooperation mechanism and contents of cooperation between the two countries in the field of education. Then, this paper compares the characteristics of the internationalization of higher education between China and Thailand, as well as the cooperation between the two countries in personnel training, teaching method improvement, and education quality assurance. Finally, this paper discusses the impact of Sino-Thai educational exchanges on the economic, cultural, and educational changes of the two countries, as well as the experiences and feelings of students in educational exchanges between the two countries. Results: the educational cooperation between China and Thailand has developed from the early language and cultural exchanges to a deeper level of educational cooperation, including degree education and professional training. This paper finds that the driving force of Sino-Thai educational cooperation mainly comes from the political, economic, and cultural interdependence of the two countries, as well as the interest and curiosity of the students of the two countries. This paper also finds that the effect of Sino-Thai educational cooperation is mainly reflected in the improvement of the knowledge, skills, and literacy of the students of the two countries, as well as the enhancement of cross-cultural awareness and cross-cultural communication ability of the students of the two countries. Conclusion: The educational exchanges between China and Thailand have not only enriched the learning experience of the students of the two countries but also promoted friendly cooperation and exchanges between the two countries in the field of education. This paper holds that the educational exchange between China and Thailand is not only an important way for cultural exchange between the two countries but also an important basis for the friendly relations between the two countries. This paper suggests that the future Sino-Thai educational cooperation model should pay more attention to the actual needs of the market and society, as well as the development of high-level cooperative education. This paper also suggests that the two countries should strengthen the supervision and evaluation of educational exchanges to ensure the quality and effectiveness of education.

Key Factors Determining Nightside Energetic Electron Losses Driven by Whistler‐Mode Waves

AbstractEnergetic electron losses by pitch‐angle scattering and precipitation to the atmosphere from the radiation belts are controlled, to a great extent, by resonant wave particle interactions with whistler‐mode waves. The efficacy of such precipitation is primarily modulated by wave intensity, although its relative importance, compared to other wave and plasma parameters, remains unclear. Precipitation spectra from the low‐altitude, polar‐orbiting ELFIN mission have previously been demonstrated to be consistent with energetic precipitation modeling derived from empirical models of field‐aligned wave power across a wide swath of local‐time sectors. However, such modeling could not explain the intense, relativistic electron precipitation observed on the nightside. Therefore, this study aims to additionally consider the contributions of three modifications—wave obliquity, frequency spectrum, and local plasma density—to explain this discrepancy on the nightside. By incorporating these effects into both test particle simulations and quasi‐linear diffusion modeling, we find that realistic implementations of each individual modification result in only slight changes to the electron precipitation spectrum. However, these modifications, when combined, enable more accurate modeling of ELFIN‐observed spectra. In particular, a significant reduction in plasma density enables lower frequency waves, oblique, or even quasi field‐aligned waves to resonate with near ∼1 MeV electrons closer to the equator. We demonstrate that the levels of modification required to accurately reproduce the nightside spectra of whistler‐mode wave‐driven relativistic electron precipitation match empirical expectations and should therefore be included in future radiation belt modeling.

Do they stay, or do they go? Children presenting to five emergency departments across New South Wales, Australia with acute burn injuries: a retrospective review

ObjectiveThe overall objective of the study was to describe the disposition status of children presenting with a burn injury to five emergency departments (ED) across New South Wales (NSW), Australia.DesignA...

Insights and Challenges for the Development of Mosquito Control Action Thresholds Using Historical Mosquito Surveillance and Climate Datasets.

Strategies to advance action threshold development can benefit both civilian and military vector control operations. The Anastasia Mosquito Control District (AMCD) has curated an extensive record database of surveillance programs and operational control activities in St. Johns County, Florida, since 2004. A thorough exploratory data analysis was performed on historical mosquito surveillance and county-wide climate data to identify climate predictors that could be used in constructing proactive threshold models for initiating control of Aedes, Culex, and Anopheles vector mosquitoes. Species counts pulled from Centers for Disease Control and Prevention (CDC) light trap (2004-2019) and BG trap (2014-2019) collection records and climate parameters of temperature (minimum, maximum, average), rainfall, and relative humidity were used in two iterations of generalized linear models. Climate readings were incorporated into models 1) in the form of continuous measurements, or 2) for categorization into number of "hot," "wet," or "humid" days by exceedance of selected biological index threshold values. Models were validated with tests of residual error, comparison of model effects, and predictive capability on testing data from the two recent surveillance seasons 2020 and 2021. Two iterations of negative binomial regression models were constructed for 6 species groups: container Aedes (Ae. aegypti, Ae. albopictus), standing water Culex (Cx. nigripalpus, Cx. quinquefasciatus), floodwater Aedes (Ae. atlanticus, Ae. infirmatus), salt-marsh Aedes (Ae. taeniorhyncus, Ae. sollicitans), swamp water Anopheles (An. crucians), and a combined Total Mosquitoes group. Final significant climate predictors varied substantially between species groups. Validation of models with testing data displayed limited predictive abilities of both model iterations. The most significant climate predictors for floodwater Aedes, the dominant and operationally influential species group in the county, were either total precipitation or frequency of precipitation events (number of "wet" days) at two to four weeks before trap collection week. Challenges hindering the construction of threshold models were discussed. Insights gained from these models provide initial feedback for streamlining the AMCD mosquito control program and analytical recommendations for future modelling efforts of interested mosquito control programs, in addition to generalized guidance for deployed armed forces personnel with needs of mosquito control but lacking active surveillance programs.

The relationship between niche breadth and phylogenetic characteristics of eight species of rhubarb on the Qinghai-Tibet Plateau, Asia.

The relationship between spatial distribution and phylogeny has been widely debated in recent decades. To understand biogeographic and evolutionary history relationships and to explore the interspecific similarities and phylogenetic correlations of niche characteristics, we collected and recorded all distribution points for eight species of rhubarb on the Qinghai-Tibet Plateau, used different methods to describe the ecological niche, and explored the relationship between phylogeny, ecological niche, and distribution range. The results reveal that: (1) the ranges of optimal environmental variables for species with close kinship are not exactly the same, ecologically similar species are not necessarily sister species, and the overlap of rhubarb has no significant correlation with phylogeny. Therefore, the impact of ecological dimensions on species formation is greater than that of geographical latitude for the eight species of rhubarb. (2) Among the eight species of rhubarb, the breadth of ecological niche is positively correlated with the current suitable habitat area and negatively correlated with fluctuations in future suitable habitat area. In the future, except for Rheum tanguticum and Rheum palmatum, the suitable planting areas for the other six species of rhubarb will decrease as greenhouse gas emissions concentrations and time increase. Therefore, species with smaller ecological niches are at a greater risk of habitat loss compared to species with larger ecological niches. (3) In both existing and future distribution prediction models of rhubarb, we observed that both the widely distributed Rheum spiciforme and the niche narrow Rheum nobile, all eight species of rhubarb are present in the Hengduan Mountains, based on our analysis, we propose that the Hengduan Mountains should be regarded as a priority conservation area for rhubarb, to preserve the species' biodiversity. Our study lays the groundwork for identifying evolutionary trends in ecological specialization.

Predicting Cloud Conditions in Substellar Mass Objects Using Ultracool Dwarf Companions

We present results from conducting a theoretical chemical analysis of a sample of benchmark companion brown dwarfs whose primary star is of type F, G, or K. We summarize the entire known sample of these types of companion systems, termed “compositional benchmarks,” that are present in the literature or recently published as key systems of study in order to best understand brown dwarf chemistry and condensate formation. Via mass balance and stoichiometric calculations, we predict a median brown dwarf atmospheric oxygen sink of 17.8−2.3+1.7% by utilizing published stellar abundances in the local solar neighborhood. Additionally, we predict a silicate condensation sequence such that atmospheres with bulk Mg/Si ≲0.9 will form enstatite (MgSiO3) and quartz (SiO2) clouds, and atmospheres with bulk Mg/Si ≳0.9 will form enstatite and forsterite (Mg2SiO4) clouds. The implications of these results on C/O ratio trends in substellar-mass objects and the utility of these predictions in future modeling work are discussed.

Authorized and Unauthorized Consumption of SVOD Content: Modeling Determinants of Demand and Measuring Effects of Enforcing Access Control

Consumers are attracted by the increasing number of available SVOD platforms, but it would be too expensive to pay the subscription fees for all of them. To reduce costs, consumers can combine the use of proprietary subscriptions, non-proprietary subscriptions, and illegal streaming sites. In turn, platforms could enforce access control, a decision that might produce the desired reduction in non-proprietary subscriptions but also an undesired reduction in proprietary subscriptions. The effects of this decision and the determinants of SVOD content demand remain largely unexplored. We propose a baseline model where the SVOD content demand is driven by variety seeking, household financial situation, ethical evaluation, and social norms, as well as a change model where the subscription variation is driven by users’ trait reactance and perceived fairness of the decision. We conducted a survey on the current ways SVOD content is consumed and responses to a hypothetical access control enforcement, with four randomized versions of the authentication mode. Results confirmed many of the proposed determinants and showed a noteworthy reduction in proprietary subscriptions due to the control enforcement but no effect due to the authentication modes. All these findings may help improve future models of SVOD content consumption and better address the difficult challenge of converting unauthorized users into authorized ones.