Maximum Entropy Research Articles

Insight into the Structural and Magnetic Properties of PrZnSi and NdZnSi Compounds Featuring Large Low-Temperature Magnetocaloric Effects.

The magnetocaloric (MC) and magnetic phase transition (MPT) properties in various types of rare earth (RE)-based magnetic materials have been intensively investigated recently, which are aimed at developing suitable MC materials for low-temperature cooling applications and better elucidating their inherent physical properties. We herein provide a combined experimental and theoretical investigation into two new light RE-based magnetic materials, namely, PrZnSi and NdZnSi compounds, regarding their structural, magnetic, MPT, and low-temperature MC properties. Both of these compounds crystallize in an AlB2-type hexagonal structure with a symmetry of the crystallographic space group P6/mmm and reveal a typical second-order-type MPT with ordering temperatures (TC) at approximately 13.5 and 18.5 K for PrZnSi and NdZnSi compounds, respectively. Moreover, they all exhibit large reversible low-temperature MC effects and remarkable performances, which are identified by the parameters of maximum magnetic entropy changes, relative cooling power, and temperature-averaged entropy change (temperature lift 5 K). The deduced values of these MC parameters under a magnetic field change of 0-7 T reach 16.3 J/kgK, 294.46 J/kg, and 15.79 J/kgK for PrZnSi and 15.4 J/kgK, 284.84 J/kg, and 14.95 J/kgK for NdZnSi, respectively, which are evidently better than those of most updated light RE-based magnetic materials with remarkable low-temperature MC performances, indicating that PrZnSi and NdZnSi compounds hold potentials for practical cooling applications.

On the observed time evolution of cosmic rays in a new time domain

Since the 1990's, it has been recognized that the full explanation of cosmic rays (CR) and their spectrum may require some new physics. The debate on the origin of CR has led to the conclusion that while most CR come from supernova explosions in the Galaxy, CR with very high energies are likely of extragalactic origin. However, a response to several open questions, still unanswered, concerning CR above 1013 eV is required. We herewith study the temporal evolution of the observational CR using data collected by several stations of the ground-based network. The obtained result states that the power spectral density of the CR temporal evolution, especially with a frequency less than 0.1 Hz, exhibits the Kolmogorov-Obukhov 5/3 law that exhibits the energy spectrum of many geophysical quantities. Any small difference found from the 5/3 exponent can be attributed to intermittency corrections and the stations' characteristics. Moreover, natural time analysis applied to the CR time series showed the critical role of the quasi-biennial oscillation to the entropy maximization which occurs following the 5/3 Kolmogorov-Obukhov power law. These findings can be used to more reliably predict extreme CR events that could have an impact even at the molecular level.

Global Distribution Prediction of Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae) Insights from the Optimised MaxEnt Model.

Cyrtotrachelus buqueti Guer is a major pest affecting bamboo forests economically, causing significant damage to bamboo forests in Sichuan Province, China. To understand how C. buqueti responds to future climate conditions, an optimized Maximum Entropy Model (Maxent) was used to simulate the potential global distribution patterns of C. buqueti under current climate conditions and three different future climate scenarios and to analyze the dominant factors influencing its distribution. The results indicate that Bio18 (precipitation of the warmest quarter), Bio04 (temperature seasonality), Bio06 (minimum temperature of the coldest month), and Bio02 (mean diurnal temperature range) are the main environmental factors affecting the distribution of this species. The global area of high-suitability habitats for C. buqueti is 9.00 × 104 km2, primarily distributed in China. Under three different future climate scenarios, there are varying degrees of expansion in both the total suitable habitat and the medium-suitability areas for C. buqueti. Under the SSP5-8.5 scenario, the medium-suitability area of the species increases the most, reaching 9.83 × 104 km2. Additionally, these findings can serve as a reference for developing and implementing control strategies, assisting relevant authorities in more effectively managing and controlling this pest, and mitigating its potential threats to bamboo forest ecosystems and economies.

Evaluation of subsurface soil water content estimate methods: Maximum entropy vs. exponential filter

Profile soil water content (SWC) is a vital variable in the atmosphere-vegetation-soil system. Although remote sensing currently can provide reliable surface SWC data (∼5 cm depth), acquiring accurate subsurface SWC data from existing reanalysis products remain challenging. In this study, we evaluated two widely used methods in estimating subsurface SWC, namely the exponential filter (ExpF) and the principle of maximum entropy (POME). The evaluation is carried out in two distinct areas on the Tibetan Plateau using ground observations collected from two monitoring networks: the Maqu network area characterized by cold humid climate and grassland and the Shiquanhe network area characterized by cold arid climate and bare ground. The results indicate that POME generally performs better than ExpF in both areas, particularly in deeper soil layers. Specifically, the accuracy of estimated SWC using the ExpF method decreases with depth, while it increases with depth using the POME method. Additionally, both methods achieve commendable performance at a depth of 10 cm in both areas. The deficiency of ExpF is mainly reflected in underestimations for dry cases, which is amplified with increasing depth. Dry cases account for 51 % in the humid area and 68 % in the arid area throughout the study period. Consequently, the ExpF method yields higher root mean square differences (RMSD) by 30 % and 113 % in the humid area at depths of 20 and 40 cm, respectively, compared to the POME method. Similarly, it results in higher RMSD values by 220 % and 200 % in the arid area. As expected, the superior performance of POME in deeper soil layers is primarily attributed to the incorporation of additional bottom and profile mean SWC observations. However, it also potentially introduces uncertainties when integrated with satellite-based data, which inherently contains errors compared to ground observations. To assess the potential of these two methods in large-scale applications combined with satellite-based datasets, this study conducted further evaluation of both methods with required input data derived from the soil moisture active and passive mission (SMAP). The results demonstrate that the performance of both methods in estimating subsurface SWC is acceptable in both humid and arid areas, although some bias is transferred from the input data. They achieve average RMSD values of 0.034 and 0.055 m3/m−3(−|−) in the humid area for the ExpF and POME methods, respectively, and 0.021 and 0.014 m3/m−3(−|−) in the arid area.

Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation

The health of mechanical components can be assessed by analyzing the vibration and acoustic signals they produce. These signals contain valuable information about the component’s condition, often encoded within specific frequency bands. However, extracting this information is challenging due to noise contamination from various sources. Narrow-band amplitude demodulation presents a robust technique for isolating fault-related information within the signal. This work proposes a novel approach based on cluster-based segmentation for demodulating the signal and extracting the frequency band of interest. The segmentation process leverages the criteria of maximum L-kurtosis and minimum entropy. L-kurtosis maximizes impulsiveness in the signal, while minimum entropy signifies a low degree of randomness and high cyclo-stationarity, and both characteristics are crucial for identifying the desired frequency band. Simulations and experimental tests using vibration signals from different gears demonstrate the effectiveness of this technique. The processed envelope of the signal exhibits distinct improvements, highlighting the ability to accurately extract the fault-related information embedded within the complex noise-ridden signals. This approach offers a promising solution for accurate and efficient fault diagnosis in mechanical systems, contributing to enhanced reliability and reduced downtime.

Spatial heterogeneity in the potential distribution of Aedes mosquitoes in India under current and future climatic scenarios

Aedes is the most globally distributed mosquito genus in the 21st century and transmits various arboviral diseases. The rapid expansion of Ae. Aegypti and Ae. albopictus breeding habitats is a significant threat to global public health, driven by temperature and precipitation changes. In this study, bioclimatic variables were employed to predict the spatial distribution of Ae. aegypti and Ae. albopictus in India. The reference coordinate points of (n = 583) Aedes occurrences at a scale of ∼1 km and nineteen bioclimatic factors were retrieved to train SDM (Species Distribution Models) for both species. Maximum entropy modelling was used to predict the species’ fundamental climatic niche distributions. Future projections were made using global climate models for 2021–2040 and 2081–2100 separately. The models performed reasonably well (AUC > 0.77). Both species thrived in reduced diurnal temperature and higher annual mean temperatures, with suitability increasing alongside precipitation. Ae. aegypti’s projected present and future distribution was broader than that of Ae. Albopictus. The expansion of Aedes suitability varied under different future climatic scenarios. Suitability for Ae. aegypti could expand from between 17.6 and 41.1 % in 2100 under SSP (shared socioeconomic pathways) scenarios 1 and 3, respectively, whereas for Ae. albopictus suitability increased from between 10.2 and 25 % under SSP scenarios 1 and 3 respectively. Preparing for future epidemics and outbreaks requires robust vector distribution models to identify high-risk areas, allocate resources for surveillance and control, and implement prevention strategies.

Structural reliability analysis with parametric p-box uncertainties via a Bayesian updating BDRM

The parametric probability-box model, often abbreviated as “p-box” is frequently used to characterize epistemic uncertainties. However, structural reliability analysis with p-box uncertainties can often be computationally intensive. This paper presents an efficient method to accurately compute the bounds of failure probabilities within this context. The method’s key innovation lies in its ability to achieve high efficiency with only a single round of model evaluations. First, the Fractional Exponential Moments-based Maximum Entropy Method (FEM-MEM) with Bivariate Dimension Reduction Method (BDRM) is employed for precise reliability assessment, where a single-round of model evaluations are carried out. Subsequently, Bayesian Updating (BU) is applied to adjust the weights obtained from BDRM in response to changes in input variables, while ensuring the invariance of integration points. Following this adjustment, the FEM-MEM is once again employed to compute the failure probability after the information change, without necessitating additional model evaluations. To compute the bounds of failure probabilities, a Kriging model is employed to construct a surrogate relationship between the interval variables and failure probabilities. The accuracy and efficiency of the proposed method are demonstrated through numerical examples, with comparisons made against pertinent double-loop method.

Integrating fine-scale habitat suitability and hydrological structures to assess population connectivity for the Kaiser's mountain newt (Neurergus kaiseri) in southwest of Iran

The unique life cycle of amphibians, encompassing both aquatic and terrestrial stages, poses challenges for examining landscape connectivity within meta-populations. Their heavy reliance on water and limited mobility exacerbate these challenges. Fine-scale investigations of environmental factors contribute to understanding the distribution range and landscape connectivity of amphibian meta-populations. The present study examines the habitat suitability and connectivity among population cores of the Luristan newt (Neurergus kaiseri), a vulnerable (VU) species in southwest Iran. Using the Digital Elevation Model (DEM) and hydrological tools, the boundary of the study area was defined based on drainage basins and Maximum Entropy (MaxEnt), incorporating eight habitat variables to investigate the distribution range. Given that amphibians rely on moisture to move, DEM was used to calculate the Sediment Transport Index (STI) as a surrogate for suitable movement contexts. Using this index, the potential movement of sediment within streams across the landscape will be assessed. A high STI value can limit amphibian movement and shelter and therefore threaten the survival of larvae. Here, the STI was fuzzified based on the presence points using the s-shaped method and the symmetric membership function. The fuzzy overlay technique was employed using the habitat suitability map and OR logic, and finally, the inverse of the fuzzy map was used as landscape resistance. Sub-basins with at least two population cores in the drainage basin were identified and their linkage was established using the Linkage Mapper. Also, the depth and number of sinks along the linkage path of the population cores in each sub-basin were identified. The MaxEnt analysis highlighted the variables of elevation, distance from streams, and proximity to the forest as the most important factors influencing N. kaiseri. All Iranian populations of N. kaiseri were located within a single drainage basin. In this drainage basin, there were five sub-basins with more than two populations. Sub-basin 4 encompassed the largest population and exhibited the highest connectivity quality (29 flow rate). The maximum depth of the links between the population cores in this sub-basin reached 100 m from the surface, and a large segment of the population was located in the upper part of the sub-basin. 432 sinks were located between the population cores that helped conserve water, create microclimates, and supply moisture.The arrangement of the populations within the sub-basin, along with the presence of sinks and variations in linkage path depths created distinct conditions for population cores. These conditions significantly influenced the quality of the linkages. The results of the present study can serve as a valuable tool to identify and prioritize population cores and sub-basins for targeted conservation efforts.

Excellent magnetocaloric effect near the hydrogen liquefaction temperature of a binary Er67.5Co32.5 metallic glass

We reported the excellent magnetocaloric effect of a binary Er67.5Co32.5 metallic glass (MG) near the hydrogen liquefaction temperature. The binary MG ribbon was fabricated by a melt-spinning method. The Er67.5Co32.5 alloy exhibits a better glass formability than most of other rare earth (RE)-Co binary alloys. The maximum magnetic entropy change (−ΔSmpeak) and refrigeration capacity (RC) of the Er67.5Co32.5 amorphous ribbon under 0–5 T reach to 17.47 J/(kg × K) near its Curie temperature (Tc, ∼ 11 K) and 472 J/kg, both of which are rather high among the RE-based MGs with a Tc around 10 K and imply the potential application perspective as magnetic refrigerants for hydrogen liquefaction. The magnetization as well as magnetocaloric behaviors of the binary MG were observed, and the mechanism involved was investigated.

Memory-extraction-based DRL cooperative guidance against the maneuvering target protected by interceptors

This paper presents an open and interesting issue for missiles, i.e., achieving collaborative parameters constrained cooperative guidance, despite the interference of pursing interceptors (INTs) and the maneuvering target, by the fact that the target-missile-interceptor (TMI) engagement induces their complex and time-varying relationships. The Memory-Extraction-based Soft-Actor-Critic (ME-SAC) approach is proposed, which enhances the collaborative performance of missiles by implicitly extracting coupling motion characteristics among TMI from historical state, achieving the joint optimization of situation awareness and strategy. Firstly, the cooperative guidance task is formulated as a multi-order Markov decision process (MOMDP) to better represent the dynamic evolution of engagement, and a memory-extraction process is introduced to alleviate the curse of dimensionality. Secondly, a memory-decision-oriented maximum entropy framework combined with memory update modules is designed for enhancing strategy search ability. Then, a domain-knowledge-based pre-training is implemented to improve convergence speed. Finally, in simulation evaluation with various scenarios, the proposed ME-SAC shows more promising than the typical DRL-based and model-based algorithms in task success rate, learning efficiency, and adaptability.

Assessing the Impacts of Climate Change on Geographical Distribution of Tea (Camellia sinensis L.) in Kenya with Maximum Entropy Model

Climate change has been disturbing the present species distribution ranges, resulting in the shifting of cultivation areas and decreases in production and quality. Tea (Camellia sinensis L.), which seeks optimum climatic resources, is a key cash crop economically in Kenya. In this study, the shifting of tea suitability was projected with the MaxEnt model under the SSP (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) climate scenarios for the 2050s and 2090s relative to the 1970–2000 distribution. Analysis under the current climatic condition showed that the proportions of optimal and medium- and marginal-suitable areas were 2%, 3%, and 24% of the total area, respectively, and located in south-western (SW), central, and north-eastern (NE) Kenya and, to some extent, in the Rift Valley. It was projected that the potential suitable tea-growing areas would migrate from the western areas to the central, eastern, and north-eastern highlands in Kenya. It was detected that the precipitation of the driest period (July), precipitation of the wettest quarter (April, May, and June), and annual temperature range could be the main climatic factors determining the shift in tea distribution. Compared to the current distribution (29%), the climatically suitable areas for tea production could reach 32.58% of Kenya’s land area under the SSP1-2.6 scenarios in the 2050s and 35.08% in the 2090s under the SSP5-8.5 scenario. On the contrary, it was found that the optimal climate-suitable habitats were projected to shrink by 2% and 1% in the 2050s and 2090s under all scenarios on the west side of the Great Rift Valley compared to the current distribution. In comparison, the sizes of medium- and marginal-suitable habitats would increase by 1% and 3%, respectively. The findings indicated that unless adaptive climate actions are taken, climate change could reduce the tea planting areas in western Kenya. Meanwhile, climate suitability was projected to expand upward on the east side of the Rift Valley, enhancing the potential distribution of tea. The developed climate information could be used to design and implement adaptation interventions in the lower elevation areas. Finally, we highlight that the available scientific literature on the climate suitability of tea in Kenya should be broadened by adding non-climatic factors.

Influences on the structure, magnetic, and magnetocaloric effect of La0.8Sr0.2MnO3 ceramics by Co ion doping with sol-gel method and first-principles calculations

This paper investigated the impact of Co ions doping on the structure, magnetic properties, and magnetocaloric effect for La0.8Sr0.2Mn1-xCoxO3 ceramics (LSMCO) by sol-gel method and first-principles calculations The use of first-principles calculations was provided for subsequent experiments. The sol-gel preparation of LSMCO ceramics and sintered at 950 °C for 10 h. It was shown that the samples' cell volume and lattice constants changed slightly with increasing Co2+ doping but remained a rhombohedral structure (No.167, R-3c space group) by statistics of TEM and XRD. The average particle size for the LSMCO ceramics ranged from 130 nm to 170 nm. LSMCO's chemical composition was verified by EDS and XPS and it was demonstrated that Co2+ was successfully doped. The oxidation state of La was found to be +3, Mn is a mixture of Mn3+ and Mn4+ valence states, and Co is a mixture of Co3+ and Co2+ valence states using XPS. In the vicinity of Tc, all samples presented the second-order magnetic phase transition from ferromagnetic to paramagnetic resorting to normalization and the Banerjee criterion. The Tc dropped from 292 K to 237 K with increasing Co2+ doping. The blocking temperature and the saturation magnetization were suppressed by the Co2+ substitution while the maximum magnetic entropy change of the LSMCO ceramics was observed to reduce as Co2+ increased. When the external magnetic field is 5 T, the La0.8Sr0.2Mn0.95Co0.05O3 ceramics has a Tc of 292 K and a maximum magnetic entropy change value of 3.47 J/(kg K).

The Impact of Cardiovascular Disease Gene Polymorphism and Interaction with Homocysteine on Deep Vein Thrombosis.

Deep vein thrombosis (DVT) affects vascular health and can even threaten life; however, its pathogenesis remains unclear. Cardiovascular disease (CVD) and DVT share common risk factors, such as dyslipidemia, aging, etc. We aimed to investigate the loci of published CVD susceptibility genes and their association with environmental factors that might be related to DVT. Genotyping by Kompetitive Allele Specific PCR (KASP), collection of lifestyle information, and determination of blood biochemical markers were performed in 165 DVT cases and 164 controls. The impact of six single nucleotide polymorphisms (SNPs) and additional potential variables on DVT morbidity was evaluated using unconditional logistic regression (ULR). To explore the high-order interactions related to genetics and the body's internal environment exposure that affect DVT, ULR, crossover analysis, and multifactor dimensionality reduction/generalized multifactor dimensionality reduction (MDR/GMDR) were employed. Sensitivity analyses were performed using the EpiR package. The polymorphisms of FGB rs1800790 and PLAT rs2020918 were significantly associated with DVT. The optimum GMDR interaction model for gene-gene (G × G) consisted of THBD rs1042579, PLAT rs2020918, and PON1 rs662. The PLAT rs2020918 and MTHFR rs1801133 polymorphisms together eliminated the maximum entropy by the MDR method. The optimum GMDR interaction model for gene-environment (G × E) consisted of MTHFR rs1801133, FGB rs1800790, PLAT rs2020918, PON1 rs662, and total homocysteine (tHcy). Those with high tHcy levels and three risk genotypes significantly increased the DVT risk. In conclusion, certain CVD-related SNPs and their interactions with tHcy may contribute to DVT. These have implications for investigating DVT etiology and developing preventive treatment plans.

Global population size and conservation priority areas for the endangered Titicaca grebe

AbstractThe Titicaca grebe (Rollandia microptera) is a poorly studied endangered species, only found in the Lake Titicaca watershed of Peru and Bolivia. Population surveys undertaken in the early twenty‐first century suggested that the species had declined in number by >70%. We conducted a population survey of the grebe between March and August 2022 in Peru and Bolivia, using both maximum entropy and Bayesian occupancy models to estimate factors affecting habitat suitability for the grebe. We conducted surveys between March and August 2022 in Peru and Bolivia. Contrary to the International Union for Conservation of Nature (IUCN) population estimates of <1,000 individuals, there are likely tens of thousands of individual grebes. The population estimation was sensitive to the type of model used. Distance from shore and fishing intensity were the most significant factors influencing grebe populations. The Reserva Nacional del Titicaca, the only protected area in the lake, covers some of the most suitable Titicaca grebe habitat across its entire range. The population of the Titicaca grebe is significantly higher than previous estimates, but we do not recommend a change to its conservation status because of the lack of conservation efforts for this species, and the long‐term risks associated with global warming and fisheries bycatch.

On-Demand Meal Delivery: A Markov Model for Circulating Couriers

On-demand meal delivery has become a feature of most cities around the world as a result of platforms and apps that facilitate it as well as the pandemic, which for a period, closed restaurants. Meals are delivered by couriers, typically on bikes, e-bikes, or scooters, who circulate collecting meals from kitchens and delivering them to customers, who usually order online. A Markov model for circulating couriers with n + 1 parameters, where [Formula: see text] is the number of kitchens plus customers, is derived by entropy maximization. There is one parameter for each kitchen and customer representing the demand for a courier, and there is one parameter representing the urgency of delivery. It is shown how the mean and variance of delivery time can be calculated once the parameters are known. The Markov model is irreducible. Two procedures are presented for calibrating model parameters on a data set of orders. Both procedures match known order frequencies with fitted visit probabilities; the first inputs an urgency parameter value and outputs mean delivery time, whereas the second inputs mean delivery time and outputs the corresponding urgency parameter value. Model calibration is demonstrated on a publicly available data set of meal orders from Grubhub. Grubhub data are also used to validate the calibrated model using a likelihood ratio. By changing the location of one kitchen, it is shown how the calibrated model can estimate the resulting change in demand for its meals and the corresponding mean delivery time. The Markov model could also be used for the assignment of courier trips to a street network. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT Conference.

Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling.

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

Modeling habitat suitability to predict the potential distribution of the Sri Lankan Green Pit Viper

Snake distribution and habitat modeling serve multiple valuable purposes, one of which involves establishing a systematic approach for collecting and assessing data related to the presence and influence of diverse factors on snake distribution patterns. The main objective of this study is to explore the habitat association of the Sri Lankan Green Pit Viper (Peltopelor trigonocephalus) and develop models to assess the suitability of different habitats within the island Sri Lanka. We modeled the suitable habitat for this species using the maximum entropy algorithm, combining presence data collected over two years (from April 2021 to April 2023) with a set of seven environmental variables (annual precipitation, annual mean temperature, precipitation of coldest quarter, elevation, land use, isothermality, Euclidean distance to water). We assessed the importance of different environmental variables through jackknife tests. We evaluated the predictive ability of the models using the area under the receiver operating characteristic curve. We discovered that the primary explanatory variables influencing the distribution of the species were annual mean temperature, annual precipitation, and elevation, contributing significantly at 35%, 31.6%, and 20%, respectively. The Sri Lankan Green pit viper inhabited riparian, forest, and open habitats, with the highest number of individuals recorded in riparian areas. We presented the first habitat suitability models for the Sri Lankan Green Pit Viper, offering valuable insights for conservation biologists and land managers involved in preserving this species.

Sim-to-real transfer of adaptive control parameters for AUV stabilisation under current disturbance

Learning-based adaptive control methods hold the potential to empower autonomous agents in mitigating the impact of process variations with minimal human intervention. However, their application to autonomous underwater vehicles (AUVs) has been constrained by two main challenges: (1) the presence of unknown dynamics in the form of sea current disturbances, which cannot be modelled or measured due to limited sensor capability, particularly on smaller low-cost AUVs, and (2) the nonlinearity of AUV tasks, where the controller response at certain operating points must be excessively conservative to meet specifications at other points. Deep Reinforcement Learning (DRL) offers a solution to these challenges by training versatile neural network policies. Nevertheless, the application of DRL algorithms to AUVs has been predominantly limited to simulated environments due to their inherent high sample complexity and the distribution shift problem. This paper introduces a novel approach by combining the Maximum Entropy Deep Reinforcement Learning framework with a classic model-based control architecture to formulate an adaptive controller. In this framework, we propose a Sim-to-Real transfer strategy, incorporating a bio-inspired experience replay mechanism, an enhanced domain randomisation technique, and an evaluation protocol executed on a physical platform. Our experimental assessments demonstrate the effectiveness of this method in learning proficient policies from suboptimal simulated models of the AUV. When transferred to a real-world vehicle, the approach exhibits a control performance three times higher compared to its model-based nonadaptive but optimal counterpart.

Stress-based topology optimization using maximum entropy basis functions-based meshless method

AbstractThis paper presents volume-constrained stress minimization-based, topology optimization. The maximum entropy (maxent) basis functions-based meshless method for two-dimensional linear elastic structures is explored. This work focuses to test the effectiveness of the meshless method in handling the stress singularities during the topology optimization process. The commonly used moving least square basis functions are replaced with maximum entropy basis functions, as the latter possess weak Kronecker delta property which leads to the finite element method (FEM) like displacement boundary conditions imposition. The maxent basis functions are calculated once at the beginning of the simulation and then used in optimization at every iteration. Young’s modulus for each background cell is interpolated using the modified solid isotropic material with penalization approach. An open source pre-processor CUBIT is used. A comparison of the proposed approach with the FEM is carried out using a diverse set of problems with simple and complex geometries of structured and unstructured discretization, to establish that maxent-based meshless methods perform better in tackling the stress singularities due to its smooth stress field.

Impact of climate change on the geographical distribution of ticks of public health significance in Colombia: Amblyomma ovale (Ixodida: Ixodidae), the Amblyomma maculatum (Ixodida: Ixodidae) complex and the Amblyomma cajennense (Ixodida: Ixodidae) complex

Abstract Ticks of the Amblyomma maculatum (Ixodida Ixodidae) complex, the Amblyomma cajennense (Ixodida Ixodidae) complex and Amblyomma ovale (Ixodida Ixodidae) are known to transmit various Rickettsia species in Colombia, but their exact distribution is unknown. We built several models based on current climate and projected future climate changes using a maximum entropy approach. A total of 314 records of the A. cajennense complex (65.9%; n = 207), A. ovale (22.9%; n = 72), and the A. maculatum complex (11.1%; n = 35) were obtained. Amblyomma ovale has a current distribution in the Pacific, Caribbean and Andean regions and could be potentially found in the Amazon. Amblyomma maculatum has a current distribution in the Andean and could potentially be found in the Caribbean and Orinoco regions. Amblyomma mixtum can be found near the Caribbean Sea and in the Pacific region, and A. patinoi is likely to be found in the Andean region and the Caribbean. In 2070, it will be possible to find an expansion of A. ovale and A. maculatum and a decrease of A. mixtum and A. patinoi. The variables that best predict the distribution of these species are isothermality (small fluctuations in temperature) and annual precipitation. Amblyomma cajennense s.l and A. ovale, A. cajennese s.l and A. patinoi, as well as A. maculatum and A. patinoi, have an important environmental sympatry. Epidemiological and acarological surveillance is crucial to investigate rickettsiosis caused by R. parkeri in A. ovale regions, by R. rickettsii in A. patinoi and A. mixtum areas, and by R. parkeri s.s in A. maculatum regions.