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Despite technological advances in accounting systems and audit techniques, sampling remains a commonly used audit tool. For critical estimation applications involving low error rate populations, stratified mean-per-unit sampling (SMPU) has the unique advantage of producing trustworthy confidence intervals. However, SMPU is less efficient than other classical sampling techniques because it requires a larger sample size to achieve comparable precision. To address this weakness, we investigated how SMPU efficiency can be improved via three key design choices: (a) stratum boundary selection method, (b) number of sampling strata, and (c) minimum stratum sample size. Our tests disclosed that SMPU efficiency varies significantly with stratum boundary selection method. An iterative search-based method yielded the best efficiency, followed by the Dalenius–Hodges and Equal-Value-Per-Stratum methods. We also found that variations in Dalenius–Hodges implementation procedures yielded meaningful differences in efficiency. Regardless of boundary selection method, increasing the number of sampling strata beyond levels recommended in the professional literature yielded significant improvements in SMPU efficiency. Although a minor factor, smaller values of minimum stratum sample size were found to yield better SMPU efficiency. Based on these findings, suggestions for improving SMPU efficiency are provided. We also present the first known equations for planning the number of sampling strata given various application-specific parameters. [ABSTRACT FROM AUTHOR]

Simple Summary: Studies regarding insect biodiversity often require large samples, which are not always easy to obtain since preparing and cleaning the debris from samples takes much time. Insects are usually acquired using sweep netting methods, but these yield a great amount of additional material to sort. With the purpose of lightening this task, we designed a low-cost photocollector device powered by an LED light source for attracting insects. Timed trials were conducted in the grasslands of the Canadian prairies to determine its efficiency in sorting live insects from debris. For this purpose, two group of insects were considered: bees and flies. We noticed that various species of bees and flies moved at different speeds. This would mean our photocollector can serve as an effective tool to accelerate insect collection based on their speed, thereby contributing to the study of insect diversity. Arthropod biodiversity research usually requires large sample collections. The efficient handling of these samples has always been a critical bottleneck. Sweep netting along transects is an effective and commonly used approach to sample diverse insects. However, sweep netting requires the time-consuming task of sorting insects from the large amounts of debris and foliage that end up in the sweep net along with the insects. To address this, we introduce a robust, portable, and inexpensive photocollector device with an LED light source to extract insects from sweep net samples in a standardized way. Timed field trials tested the photocollector's efficiency in extracting live insect samples from debris, focusing on Hymenoptera and Diptera. We found that 73% (±13%) of undamaged specimens moved toward the collection bottle within the first hour and 79% (±13%) after four hours. Of the insects failing to move after four hours, most (81%) were damaged and likely unable to move. Accounting only for undamaged specimens, 83% (±11%) moved after 1 h and 90% (±11%) moved after 4 h. We found significant differences in when families of Hymenoptera and Diptera moved. We suggest that the photocollector can be a useful tool in standardized biodiversity assessments. [ABSTRACT FROM AUTHOR]

Background: Visual perceptual learning plays a crucial role in shaping our understanding of how the human brain integrates visual cues to construct coherent perceptual experiences. The visual system is continually challenged to integrate a multitude of visual cues, including form and motion, to create a unified representation of the surrounding visual scene. This process involves both the processing of local signals and their integration into a coherent global percept. Over the past several decades, researchers have explored the mechanisms underlying this integration, focusing on concepts such as internal noise and sampling efficiency, which pertain to local and global processing, respectively. Objectives and Methods: In this study, we investigated the influence of visual perceptual learning on non-directional motion processing using dynamic Glass patterns (GPs) and modified Random-Dot Kinematograms (mRDKs). We also explored the mechanisms of learning transfer to different stimuli and tasks. Specifically, we aimed to assess whether visual perceptual learning based on illusory directional motion, triggered by form and motion cues (dynamic GPs), transfers to stimuli that elicit comparable illusory motion, such as mRDKs. Additionally, we examined whether training on form and motion coherence thresholds improves internal noise filtering and sampling efficiency. Results: Our results revealed significant learning effects on the trained task, enhancing the perception of dynamic GPs. Furthermore, there was a substantial learning transfer to the non-trained stimulus (mRDKs) and partial transfer to a different task. The data also showed differences in coherence thresholds between dynamic GPs and mRDKs, with GPs showing lower coherence thresholds than mRDKs. Finally, an interaction between visual stimulus type and session for sampling efficiency revealed that the effect of training session on participants' performance varied depending on the type of visual stimulus, with dynamic GPs being influenced differently than mRDKs. Conclusion: These findings highlight the complexity of perceptual learning and suggest that the transfer of learning effects may be influenced by the specific characteristics of both the training stimuli and tasks, providing valuable insights for future research in visual processing. [ABSTRACT FROM AUTHOR]

This paper introduces a cyclone separation sand sampler with diversion flow to enhance soil wind erosion monitoring accuracy. Using CFD simulations and orthogonal testing, the optimal structural parameters for improved sampling efficiency were determined. A response model showed strong predictive capability with a P-value less than 0.0001 and R² of 0.9482. Wind tunnel tests demonstrated that the new sampler achieved a 91.44% efficiency, surpassing the 90.81% of the traditional design. The research provides valuable data for more accurate wind-sand flow monitoring, aiding in desertification prevention and improving wind erosion assessments in grasslands. [ABSTRACT FROM AUTHOR]

Insect trap networks targeting agricultural pests are commonplace but seldom optimized to improve precision or efficiency. Trap site selection is often driven by user convenience or predetermined trap densities relative to sensitive host crop abundance in the landscape. Monitoring for invasive pests often requires expedient decisions based on dispersal potential and ecology to inform trap placement. Optimization of trap networks using contemporary analytical approaches can help users determine the distribution of traps as information accumulates and priorities change. In this study, a Bayesian optimization (BO) algorithm was used to learn more about the optimal distribution of a fine-scale trap network targeting Helicoverpa zea (Boddie), a significant agricultural pest across North America. Four years of pheromone trap monitoring was conducted at the same 21 locations distributed across ~7,000 square kilometers in a five-county area in North Carolina, USA. Three years of data were used to train a BO model with a fourth year designated for testing. For any quantity of trap locations, the approach identified those that provide the most information, allowing optimization of trapping efficiency given either a constraint on the number of locations, or a set precision required for pest density estimation. Results suggest that BO is a powerful approach to enable optimized trap placement decisions by practitioners given finite resources and time.

Artificial Intelligence represents a rapidly expanding domain, with several industrial applications demonstrating its superiority over traditional techniques. Despite numerous advancements within the subfield of Machine Learning, it encounters persistent challenges, highlighting the importance of ongoing research efforts. Among its primary branches, this study delves into two categories, being Supervised and Reinforcement Learning, particularly addressing the common issue of data selection for training. The inherent variability in informational content among data points is apparent, wherein certain samples offer more valuable information to the neural network than others. However, evaluating the significance of various data points remains a non-trivial task, generating the need for a robust method to effectively prioritize samples. Drawing inspiration from Reinforcement Learning principles, this paper introduces a novel sample prioritization approach, applied to Supervised Learning scenarios, aimed at enhancing classification accuracy through strategic data navigation, while exploring the boundary between Reinforcement and Supervised Learning techniques. We provide a comprehensive description of our methodology while revealing the identification of an optimal prioritization balance and demonstrating its beneficial impact on model performance. Although classification accuracy serves as the primary validation metric, the concept of information density-based prioritization encompasses wider applicability. Additionally, the paper investigates parallels and distinctions between Reinforcement and Supervised Learning methods, declaring that the foundational principle is equally relevant, hence completely adaptable to Supervised Learning with appropriate adjustments due to different learning frameworks. The project page and source code are available at: . [ABSTRACT FROM AUTHOR]

Aerosol microbiome studies have received increased attention as technological advancements have made it possible to dive deeper into the microbial diversity. To enhance biomass collection for metagenomic sequencing, long-term sampling is a common strategy. While the impact of prolonged sampling times on microorganisms' culturability and viability is well-established, its effect on nucleic acid stability remains less understood but is essential to ensure representative sample collection. This study evaluated four air samplers (SKC BioSampler, SASS3100, Coriolis μ, BioSpot-VIVAS 300-P) against a reference sampler (isopore membrane filters) to identify nucleic acid stability during long-term sampling. Physical sampling efficiencies determined with a fluorescent tracer for three particle sizes (0.8, 1, and 3 μm), revealed high efficiencies (> 80% relative to reference) for BioSampler, SASS3100, and BioSpot-VIVAS for all particle sizes, and for Coriolis with 3 μm particles. Coriolis exhibited lower efficiency for 0.8 μm (7%) and 1 μm (50%) particles. During 2-h sampling with MS2 and Pantoea agglomerans, liquid-based collection with Coriolis and BioSampler showed a decrease in nucleic acid yields for all test conditions. BioSpot-VIVAS displayed reduced sampling efficiency for P. agglomerans compared to MS2 and the other air samplers, while filter-based collection with SASS3100 and isopore membrane filters, showed indications of DNA degradation for 1 μm particles of P. agglomerans after long-term sampling. These findings show that long-term air sampling affects nucleic acid stability in both liquid- and filter-based collection methods. These results highlight bias produced by bioaerosol collection and should be considered when selecting an air sampler and interpreting aerosol microbiome data. [ABSTRACT FROM AUTHOR]

Accurate and efficient sampling of fog droplets is a prerequisite for online measurement. The performance of such a sampler relies on the internal flow field, temperature, and humidity. Based on aerodynamics and turbulence theory, we designed a fog droplet sampler with multi-segment structure and specific temperature control. First, we used COMSOL to investigate the effects of key system parameters. The sampling efficiency first increased and then decreased with sampling flow. The reason was that the impact force under reasonable flows maintained particles in suspension, whereas high flows with high turbulence intensity increased the likelihood of particles colliding with the walls. Based on the simulations, we determined the optimal sampling flow (1000 L/min), segment structure (a cylindrical inlet), and segment dimension (25 mm for the optical measurement segment) of the sampler, with a sampling efficiency of 0.87. Subsequently, we investigated the effects of temperature, relative humidity, and sampling flow on the particle size. Size change increased with temperature but decreased with relative humidity and sampling flow. Additionally, temperature control contributed to condensation prevention, and size change was < 1% when the temperature was 15 °C. Finally, we conducted an experiment for verification purposes. The sampling efficiency of the fabricated system was 0.77, i.e., 11.49% lower than for the simulation. Size change was < 1 μm in both the simulations and the experiments, while variation was 6.30% in the experiment, i.e., a difference of 3.97% from the simulation. Hence, the designed fog droplet sampler achieved accurate and efficient sampling of fog droplets. [ABSTRACT FROM AUTHOR]

We investigated the richness and composition of social Hymenoptera collected with a Malaise trap in a rainforest in the Amazon. In addition, we studied the possibility of reducing the number of traps and the time the traps remain in the field to minimize the sampling effort. In reducing sampling effort, we seek to maintain taxonomic standards (similarity in composition) and reduce both the time the Malaise trap remains in the field and the number of traps per plot. We collected 63 species of social Hymenoptera, represented by 11 species of Apidae, 31 of Formicidae, and 21 of Vespidae, in 30 plots of 250 m distributed in 25 km² of rainforest in the Amazon. It was possible to reduce the permanence time of the Malaise trap in the field from 72 to 48 hours without significant loss of the maximum recorded diversity since there is high similarity (80%) to the maximum effort of 72 hours of sampling. The reduction from two to one trap directly impacted the sampled diversity. We estimate savings of 30.53% by reducing sampling effort and trap dwell time, allowing the resources saved to be invested more efficiently in biodiversity monitoring studies in the Amazon basin.

This paper introduces a method for trajectory selection using large-scale pre-trained language models, aiming to improve sample and training efficiency in reinforcement learning. By using a carefully designed prompt, the large language model can fully utilize its prior knowledge, effectively understanding and assessing the quality of trajectories produced through agent-environment interactions in reinforcement learning. This approach allows selecting more informative trajectories for the current agent’s learning. Unlike other works that indirectly improve reinforcement learning training efficiency by generating actions or decisions through large language models, our method employs these models to choose high-quality trajectories, thereby enhancing sample efficiency in reinforcement learning more directly. The approach was evaluated across multiple benchmark tasks in OpenAI’s Gym and RLcard. The results indicate a significant reduction in the number of environment interactions, and a 37% increase in the average reward compared to the original method.

Owing that ensuring true representative samples from a population is dependent on sampling via sample estimators, in this paper, we established the sample maximum likelihood (ML) estimators of the location (μ), scale (σ2) and shape (β) parameters of Log-Exponential Power Distribution (LEPD), under simple random sampling (SRS), ranked set sampling (RSS) and another introduced sampling called general RSS (GRSS). The study then obtained the Fisher information (FI) of the obtained estimators under different cases: when one parameter, two parameters and when all the parameters are unknown, for each of the sampling procedure and compute its asymptotic relative efficiency (RE). The FI estimate, when GRSS samples are obtained from ith or (ith,jth) ordered samples, were established along with its RE. Application from simulated and real life data with imperfect units ranking was used to demonstrate the relevance of the study, in terms of sampling efficiency within the shortest length of interval estimate. Application of GRSS to the Food and Agricultural Organization (FAO) projected data on cattle meat boom by 2050 was considered, the implication of the sudden increase in cattle population in Nigeria and the recommendation to prevent subsequent crises due to competition for land among farmers, herders and other populace was analytically established. Finally, the procedure was used to establish the cause of increased suicide rate in Nigeria has recently published by World Health Organization (WHO).

Introduction: Aedes albopictus, like Aedes aegypti, is a virulent vector of arboviruses especially the well-documented spread of yellow fever around the world. Although yellow fever is prevalent in Nigeria, there is a paucity of information in the Niger Delta region on the distribution of Aedes mosquito vectors and molecular detection of the virus in infected mosquitoes. This study sampled Aedes mosquitoes around houses associated with farms from four communities (Otolokpo, Ute-Okpu, Umunede, and Ute Alohen) in Ika North-East Local Government Area of Delta State, Nigeria. Methods: various sampling methods were used in Aedes mosquito collection to test their efficacy in the survey. Mosquitoes in holding cages were killed by freezing and morphologically identified. A pool of 15 mosquitoes per Eppendorf tube was preserved in RNAi later for yellow fever virus screening. Two samples were molecularly screened for each location. Results: seven hundred and twenty-five (725) mosquitoes were obtained from the various traps. The mean abundance of the mosquitoes was highest in m-HLC (42.9) compared to the mosquitoes sampled using other techniques (p<0.0001). The mean abundance of mosquitoes was lowest in Center for Disease Control (CDC) light traps without attractant (0.29). No yellow fever virus strain was detected in all the mosquitoes sampled at the four locations. Conclusion: this study suggests that Aedes albopictus are the mosquitoes commonly biting around houses associated with farms. More so, yellow fever virus was not detected in the mosquitoes probably due to the mass vaccination exercise that was carried out the previous year in the study area. More studies are required using the m-HLC to determine the infection rate in this endemic area. [ABSTRACT FROM AUTHOR]

Effective sampling for severe acute respiratory syndrome 2 (SARS-CoV-2) is a common approach for monitoring disinfection efficacy and effective environmental surveillance. This study evaluated sampling efficiency and limits of detection (LODs) of macrofoam swab and sponge stick sampling methods for recovering infectious SARS-CoV-2 and viral RNA (vRNA) from surfaces. Macrofoam swab and sponge stick methods were evaluated for collection of SARS-CoV-2 suspended in a soil load from 6-in2 coupons composed of four materials: stainless steel (SS), acrylonitrile butadiene styrene (ABS) plastic, bus seat fabric, and Formica. Recovery of infectious SARS-CoV-2 was more efficient than vRNA recovery on all materials except Formica (macrofoam swab sampling) and ABS (sponge stick sampling). Macrofoam swab sampling recovered significantly more vRNA from Formica than ABS and SS, and sponge stick sampling recovered significantly more vRNA from ABS than Formica and SS, suggesting that material and sampling method choice can affect surveillance results. Time since initial contamination significantly affected infectious virus recovery from all materials, with vRNA recovery showing limited to no difference, suggesting that SARS-CoV-2 vRNA can remain detectable after viral infectivity has dissipated. This study showed that a complex relationship exists between sampling method, material, time from contamination to sampling, and recovery of SARS-CoV-2. In conclusion, data show that careful consideration be used when selecting surface types for sampling and interpreting SARS-CoV-2 vRNA recovery with respect to presence of infectious virus. [ABSTRACT FROM AUTHOR]

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Initially, three termite sampling methods were compared in a coastal area of North Kerala to check the efficiency of the widely followed standardized belt transect protocol (Jones and Eggleton, 2000) with a simplified belt transect protocol and random search method. Of the total 11 species that belong to 4 genera collected during the study, the standardized belt transect protocol recorded only two genera and 5 species in 20 hours of sampling effort, while the simplified protocol with half the effort (10 hours) recorded two genera and 4 species. Random search method with least effort (6h) recorded 4 genera and 11 species that included all the species collected in the earlier methods. There was a marked difference in sampling efficiency; the random sampling method yielded 1.87 species per hour while standardized belt transect protocol and simplified belt transect protocol yielded only 0.25 and 0.4 species per hour respectively. The result of the study was further verified in three more habitats viz. natural forest, coffee plantation and tea plantation which gave similar results. The study indicates that the random search method which covers more area in less time yield more representative termite fauna in all the four habitats tested, than the standardized belt transect protocol which spends more time covering less area. [ABSTRACT FROM AUTHOR]

Bioaerosol generation, sampling, and cultivation-independent quantification of pathogenic bacteria play a crucial role in studying dose-response effects of Legionella pneumophila. Here, the Next Generation Impactor (NGI), initially created for pharmaceutical inhaling studies, was assessed for its potential to sample airborne bioaerosols and to separate size-dependent wet droplets by incrementally increasing the airflow speed. This stainless-steel sampler was shown in this study to be suitable for sampling prior to cultivation-independent analysis of pathogen-containing bioaerosols using washable cups. The applicability was studied by quantifying the total and intact cell count of L. pneumophila by flow cytometry after being dispersed into a droplet aerosol. Our results demonstrate a high total sampling efficiency of 95.5% ± 11.8% despite a lower biological sampling efficiency of 59.7% ± 16.5% for dry aerosols. However, by elevating the relative humidity (RH) to 100% in a liquid aerosolization unit, the biological sampling efficiency increased to over 90% for L. pneumophila. More than 50% of the cells were found in stage 1 using the liquid aerosolization unit. In comparison, 80% of the cells were sampled in stages 4–6 at 30% RH. Specifically, while at 100% RH, the droplet size mattered, at 30% RH, the size distribution of dry particles, in this case L. pneumophila , was relevant due to evaporation processes, which explains the size differences. These findings indicate the potential of the NGI for further exploration and application in studying other aerosol-borne pathogens, especially concerning the size distribution of wet droplets, viability, or effect-based bioanalysis. • Next-generation-impactor (NGI) applied for the first time with L. pneumophila in liquid aerosols. • AGI-30 and Coriolis® μ were used as already established sampling systems for comparison. • Total and biological sampling efficiencies were determined for each sampler. • With the NGI a droplet size distribution was possible. • The increased relative humidity improved cell survival with the NGI. [ABSTRACT FROM AUTHOR]

A Bayesian hierarchical model that integrated information about state and observation processes was used to estimate the number of adult Delta Smelt entrained into the southern Sacramento−San Joaquin Delta during water export operations by the California State Water Project and the Central Valley Project. The model hierarchy accounted for dynamic processes of transport, survival, sampling efficiency, and observation. Water export, mark−recapture, and fish facility count data informed each process. Model diagnostics and simulation testing indicated a good fit of the model, and that parameters were jointly estimable in the Bayesian hierarchical model framework. The model was limited, however, by sparse data to estimate survival and State Water Project sampling efficiency. Total December to March entrainment of adult Delta Smelt ranged from an estimated 142,488 fish in 2000 to 53 fish in 2014, and the efficiency of louvers used to divert entrained fish to fish facilities appeared to decline at high and low primary intake channel velocities. Though applied to Delta Smelt, the hierarchical modeling framework was sufficiently flexible to estimate the entrainment of other pelagic species.

Based on statistical theory, the Bayesian inversion method adopts the posterior probability distribution to evaluate the model parameters under the constraints of prior information and observation data. Compared to deterministic inversion theory, Bayesian inference is beneficial to quantitative evaluation of inversion uncertainty by the model parameter marginal probability distribution, maximum a posterior estimation (MAP), mean model estimation and correlation coefficient, which accurately reflects the constraint ability of observation data and prior information on model parameters. We systematically summarized the application of the Bayesian inference in geophysical inversion and proposed the basic flowchart to realize Bayesian model evaluation. Firstly, the Bayesian theory is simply introduced. The prior information probability distribution, the likelihood function formula, and the construction of the posterior probability equation are explained in detail. Secondly, the implementation process of Bayesian inversion is discussed in detail. As for model parameter updates, the concepts of fixed and trans-dimensional inversion with the hyperparameter optimization are discussed. In terms of inversion methods, the Markov Chain Monte Carlo (MCMC) sampling methods of fixed and trans-dimensional inversion are highlighted. In consideration of model parameter evaluation, the calculation of Bayesian statistical parameters under different conditions is introduced. Then the specific measures to improve the sampling efficiency of Bayesian inversion are discussed. Finally, the application of Bayesian inference in geophysical inversion is summarized.

Maumee Bay of western Lake Erie is at high risk for invasion by aquatic invasive species due to large urban and suburban populations, commercial shipping traffic, recreational boating, and aquaculture ponds. The U.S. Fish and Wildlife Service's Early Detection and Monitoring (EDM) program has been monitoring for new invasive species since 2013 and is continually looking to adapt sampling methods to improve efficiency to increase the chance of detecting new aquatic invasive species at low abundances. From 2013–2016, the program used a random sampling design in Maumee Bay with three gear types: boat electrofishing, paired fyke nets, and bottom trawling. Capture data from the initial three years was used to spatially explore fish species richness with the hot spot analysis (Getis-Ord Gi*) in ArcGIS. In 2017, targeted sites in areas with high species richness (hot spots) were added to the randomly sampled sites to determine if the addition of targeted sampling would increase fish species detection rates and detection of rare species. Results suggest that this hybrid sampling design improved sampling efficiency as species not detected or were rare in previous survey years were captured and species were detected at a faster rate (i.e., in less sampling effort), particularly for shallow-water gear types. Through exploring past data and experimenting with targeted sampling, the EDM program will continue to refine and adapt sampling efforts to improve efficiency and provide valuable knowledge for the early detection of aquatic invasive species. The use of geospatial techniques such as hot spot analysis is one approach fisheries researchers and managers can use to incorporate targeted sampling in a non-subjective way to improve species detection. [ABSTRACT FROM AUTHOR]

Static and dynamic cues within certain spatiotemporal proximity are used to evoke respective global percepts of form and motion. The limiting factors in this process are, first, internal noise, which indexes local orientation/direction detection, and, second, sampling efficiency, which relates to the processing and the representation of global orientation/direction. These parameters are quantified using the equivalent noise (EN) paradigm. EN has been implemented with just two levels: high and low noise. However, when using this simplified version, one must assume the shape of the overall noise dependence, as the intermediate points are missing. Here, we investigated whether two distinct EN methods, the 8-point and the simplified 2-point version, reveal comparable parameter estimates. This was performed for three different types of stimuli: random dot kinematograms, and static and dynamic translational Glass patterns, to investigate how constant internal noise estimates are, and how sampling efficiency might vary over tasks. The results indicated substantial compatibility between estimates over a wide range of external noise levels sampled with eight data points, and a simplified version producing two highly informative data points. Our findings support the use of a simplified procedure to estimate essential form-motion integration parameters, paving the way for rapid and critical applications to populations that cannot tolerate protracted measurements. [ABSTRACT FROM AUTHOR]

In Greece, the implementation of the Water Framework Directive for rivers is based mainly on benthic macroinvertebrates and uses a semi-quantitative method with a D-frame net, which is certified by the International Organization for Standardization. Before the official adoption of this method, a quantitative shovel sampler ("Cretan shovel") was used in southern Greece (Crete), which has been implemented for almost three decades due to the specific river habitats found in Crete (e.g., seasonal flow, narrow riverbeds, and coarse substrates). In this study, we compared community metrics, diversity indices, feeding groups, locomotion types, and ecological quality derived from data collected using timed semi-quantitative kick samples and quantitative shovel samples collected from the same sites simultaneously. In total, 20 samples from the north and south of Greece were collected. The majority of community metrics, diversity indices, and traits were comparable between samplers. However, there were statistically significant differences in the relative abundance of Ephemeroptera, Plecoptera, and Trichoptera, passive filterers' and the (semi) sessil groups, and Pielou's index. Most differences in the ecological quality between the kick and shovel samples were observed in 50% of the sites in northern Greece because the shovel is less effective at capturing motile zoobenthos. The ecological quality assessment in Crete by the Cretan shovel is comparable with the D-frame net in 75% of the samples. Thus, the Cretan shovel could efficiently sample the Cretan streams, which are characterized by coarse, narrow, turbulent, and hydrologically fluctuating river habitats. Such comparisons could improve sampling effectiveness and make additional data available to assess ecological quality. [ABSTRACT FROM AUTHOR]

Insect pollinators, including bees and wasps, are facing a marked decline in their native populations, caused mainly by human activities, such as forest fragmentation, urbanization, and the use of agrochemicals. To help mitigate the rapid decline of pollinators, new efforts towards understanding basic and applied aspects of these organisms are necessary. Among these efforts, there is a focus on increasing the sampling efficiency, including a broader range of targeted groups and collection methods. Although each method has its advantages and disadvantages, the pollinators' crisis calls for alternative methods to analyze bee and wasp diversity and population dynamics. Here, we assess the potential role of incidental captures of bees and wasps by a method widely used to collect ants but not targeted for bees: arboreal pitfall traps. We compared the sampling efficiency of human urine‐baited arboreal pitfall traps and two traditional methods for bee sampling: pan traps and scent traps. Arboreal pitfalls collected a high diversity of bees and sphecoid wasps, and when compared with pan traps and scent traps, they had the highest species richness and the second‐highest abundance. Although the three trapping methods shared most species, each method collected particular groups of species, and there were indicator species for each trapping method. When used in pairs with pan traps, arboreal pitfalls collected a higher species diversity than pan traps paired with scent traps. In addition, each trapping method responded differently to seasonal variation, and although arboreal pitfalls had lower diversity during the rainy season, scent traps detected differences only in species abundance, and pan traps detected no differences at all. Our study reinforces the importance of complementary methods in sampling bees and wasps and the use of non‐traditional methods to increase the sampling coverage of these insects. [ABSTRACT FROM AUTHOR]

Diffusion probability models (DPMs) have achieved excellent results in image generation; however, their inference process is slow and tends to produce more mixed images. The autoencoder optimal transport (OT) model addresses the mode collapse/mixture problem from the OT perspective but produces low-quality images. Therefore, to generate high-quality images and mitigate mode mixture, we propose an innovative OT-guided diffusion framework. The key is to find the optimal truncation step M to ensure that the class boundaries of the original data do not intersect during the forward process, ensuring that the generated image belongs to the same class as the initial point in the reverse process. The value of M is determined by evaluating the Peak Signal-to-Noise Ratio, enabling us to mitigate the generation of mixed images. Specifically, our approach first involves embedding the images' manifold into the latent space through an encoder. The images are subsequently decoded using latent codes, which are generated through an OT map from the Gaussian distribution to the empirical latent distribution. Finally, the trained M -step DPM is utilized to refine the image generated by the decoder. Experimental results demonstrate that our method not only improves image quality but also alleviates mode mixture in diffusion models. Additionally, it enhances sampling efficiency and reduces training cost compared to classical diffusion models. [ABSTRACT FROM AUTHOR]

Abstract Background Sampling a small number of participants from an entire country is not straightforward. In this case, researchers reluctantly sample from a single setting or few settings, which limits the generalizability of findings. Therefore, there is a need to design efficient sampling method for small sample size surveys that can produce generalizable results at the country level. Methods Data comprised of twenty proxy variables to measure health services demands, structures, and outcomes of 413 districts of Iran. We used two data mining methods (hierarchical clustering method (HCM) and model-based clustering method (MCM)) to create homogenous groups of districts, i.e., strata based on these variables. We compared the internal and stability validity of the methods by statistical indices. An expert group checked the face validity of the methods, particularly regarding the total number of strata and the combination of districts in each stratum. The efficiency of selected method, which is measured by the inverse of variance, was compared with a simple random sampling (SRS) through simulation. The sampling design was tested in a national study in Iran, which aimed to evaluate the quality and costs of medical care for eight selected diseases by only recruiting 300 participants per disease at the country level. Results MCM and HCM divided the districts into eight and two clusters, respectively. The measures of internal and stability validity showed that clusters created by MCM were more separated, compact, and stable, thus forming our optimum strata. The probability of death from stroke, chronic obstructive pulmonary disease, and in-hospital mortality rate were the most important indicators that distinguished the eight strata. Based on the simulation results, MCM increased the efficiency of the sampling design up to 1.7 times compared to SRS. Conclusions The use of data mining improved the efficiency of sampling up to 1.7 times greater than SRS and markedly reduced the number of strata to eight in the entire country. The proposed sampling design also identified key variables that could be used to classify districts in Iran for sampling from these target populations in the future studies.

Reinforcement learning (RL) is effective for autonomous navigation tasks without prior knowledge of the environment. However, traditional mobile robot navigation algorithms, based on off-policy RL, often face challenges such as low sample efficiency during training and lack of adequate safety mechanisms. In this paper, we present an off-policy RL navigation model named Soft Actor-Critic with Curriculum Prioritization and Fuzzy Logic (SCF). The model uses energy as a prioritized evaluation metric for experience replay. And through task-level curriculum, the agent's learning sequence is formulated, thereby enhancing sampling efficiency and safety. We propose a Curriculum-based Energy Prioritization (CEP) approach. It selects a replay trajectory that matches the current agent's capability based on trajectory energy. Our results show that robots using off-policy RL often have limitations in dynamic obstacle avoidance. To rectify this, our model uses a fuzzy logic controller to enhance real-time obstacle avoidance. The SCF approach enables mobile robots to navigate adeptly in unpredictable and dynamic environments, ensuring optimal planning control while being safe and robust. Experiments in Gazebo simulation environment and real world confirm the effectiveness of our proposed method. The comparison results show the superior performance of this method, especially in unknown and dynamic environments. • An end-to-end navigation framework based on off-policy RL is proposed. • Improving agent learning using a task-level curriculum learning framework. • Designing a self-paced function to adaptively compute sample prioritization. • Introducing an energy-based prioritization evaluation metric. • Designing a fuzzy logic controller to help agents avoid obstacles. [ABSTRACT FROM AUTHOR]

The reliability of power grids has always received high level concerns, since a high quality of power supply is generally required in both daily life and industrial applications, thus an appropriate reliability assessment is instructive for the design and troubleshooting of power grids. In this work, to improve the sampling efficiency of assessing processes, a pseudo-analytical mix sampling strategy is proposed for reliability assessment of power grids, which is mainly based on the ideas of average-and-scattered sampling and total probability formula method. The proposed scheme is evaluated in both IEEE reliability test system (RTS) and modified IEEE reliability test system (MRTS). The superiority of the proposed scheme is confirmed by comparison, since the results of which indicate that the sampling times can be reduced by more than 3/4 than those obtained by traditional ways.

Hughes and Madden's binary power law was used to analyse sample data of three life stages of gypsy moth (Lymantria dispar) and adult emerald ash borer (Agrilus planipennis), both important invasive forest pests in North America.Tally thresholds were used to convert quantitative gypsy moth data to binomial, presence–absence data for binary power law analysis.Presence–absence data from two emerald ash borer surveys were analysed, an initial highly intensive and expensive survey expanding out from the point of introduction in Michigan and a second extensive survey designed to detect and track the leading edge of the beetle's expanding range.Emerald ash borer was found to be moderately aggregated in the initial survey data, but very near to random in the leading edge survey. The latter result is consistent with other findings that spatial distribution is less aggregated at range edges than near the centre of the range.Estimates of the proportion of positive observations in the initial intensive survey were found to be more precise than those obtained in the leading edge survey.Comparisons of binomial power law estimates and Taylor's power law estimates of the gypsy moth data suggest the possibility of a fundamental connection between the two power laws. A possible connection, the effect of tally thresholds and their relationship with sampling efficiency are discussed. [ABSTRACT FROM AUTHOR]

Abstract Background The local pivotal method (LPM) utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories (NFIs). Its performance compared to simple random sampling (SRS) and LPM with geographical coordinates has produced promising results in simulation studies. In this simulation study we compared all these sampling methods to systematic sampling. The LPM samples were selected solely using the coordinates (LPMxy) or, in addition to that, auxiliary remote sensing-based forest variables (RS variables). We utilized field measurement data (NFI-field) and Multi-Source NFI (MS-NFI) maps as target data, and independent MS-NFI maps as auxiliary data. The designs were compared using relative efficiency (RE); a ratio of mean squared errors of the reference sampling design against the studied design. Applying a method in NFI also requires a proven estimator for the variance. Therefore, three different variance estimators were evaluated against the empirical variance of replications: 1) an estimator corresponding to SRS; 2) a Grafström-Schelin estimator repurposed for LPM; and 3) a Matérn estimator applied in the Finnish NFI for systematic sampling design. Results The LPMxy was nearly comparable with the systematic design for the most target variables. The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18, according to the studied target variable. The SRS estimator for variance was expectedly the most biased and conservative estimator. Similarly, the Grafström-Schelin estimator gave overestimates in the case of LPMxy. When the RS variables were utilized as auxiliary data, the Grafström-Schelin estimates tended to underestimate the empirical variance. In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally. Conclusions LPM optimized for a specific variable tended to be more efficient than systematic sampling, but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables. The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling. Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

Introduction: The Loxicha Region of Oaxaca, Mexico, has been historically important for the study of Nymphalidae, second in the Papilionoidea for species richness. Describing the diversity patterns of this butterfly clade in Loxicha can improve our understanding of the evolutionary history of the Sierra Madre del Sur, the Mexican Pacific slope, and Mexico in general. Objective: To describe the temporal and spatial patterns of Nymphalidae diversity along an elevational gradient (80-2 600 m), and to compare Loxicha’s fauna with other regions in Mexico. Methods: We obtained 28 756 records from 21 sites in the Loxicha Region, representing seven years of sampling. We estimate and analyze the diversity, endemism, and distributional patterns for three elevational levels and five vegetation types. We estimated species composition and similarity with other regions of the Pacific and Atlantic slopes. Results: We identified 189 taxa, including species and subspecies, from 85 genera and ten subfamilies of Nymphalidae. Loxicha contains 46 % of the species in the family recognized for Mexico, including ten endemic species and 56 endemic subspecies. Cloud forest and low elevations were the most diverse habitats for this family. There is a clear divergence between the Atlantic and Pacific faunas, and the Sierra Madre del Sur has two faunal components. High-elevation sites in Oaxaca, and in the neighboring state of Guerrero, have a distinctive fauna, apparently isolated from low-elevation sites, revealing an archipelagic distribution for cloud forest Nymphalidae. Conclusions: The Loxicha Region is one of the richest areas for Nymphalidae in Mexico. Distribution on the Pacific slope is determined by geographical history and ecological conditions, including elevation. Nymphalidae can be used to test hypotheses of biogeographic regionalization in Mexico. [ABSTRACT FROM AUTHOR]

Context: Determining the most efficient detection method for a target species is key for successful wildlife monitoring and management. Driven transects and sign surveys are commonly used to monitor populations of the endangered numbat (Myrmecobius fasciatus). Camera trapping is being explored as a new method. These methods were unevaluated for efficacy and cost for numbat detection. Aims: To compare efficacy and costing of driven transects, sign surveys and camera trapping for detecting numbats in the Upper Warren region, Western Australia. Methods: Seven repeat sign surveys and driven transects, as well as 4 months of camera trapping, were conducted concurrently at 50 sites along three transects. Numbat detection rates and costing of the three techniques were compared, and detection probabilities were compared between sign surveys and camera trapping. Key results: Numbat signs were detected during 88 surveys at 39 sites, exceeding camera trapping (26 detections at 13 sites) and driven transects (seven detections near five sites). The estimated probability for detecting a numbat or a sign thereof (at a site where numbats were present) ranged from 0.21 to 0.35 for a sign survey, and 0.02 to 0.06 for 7 days of camera trapping. Total survey costs were lowest for driven transects, followed by camera trapping and sign surveys. When expressed as cost per numbat detection, sign surveys were cheapest. Conclusions: Comparative studies of survey methods are essential for optimal, cost-effective wildlife monitoring. Sign surveys were more successful and cost effective than camera trapping or driven transects for detecting numbats in the Upper Warren region. Together with occupancy modelling, sign surveys are appropriate to investigate changes in occupancy rates over time, which could serve as a metric for long-term numbat monitoring. Implications: There is no 'best' method for wildlife surveys. Case-specific comparison of animal detection methods is recommended to ensure optimal methods. For the numbat population in the Upper Warren region, further studies are needed to improve numbat detection rates from camera trapping, and to test sign surveys in autumn (March to May), when surviving juvenile numbats have established their own territory and assumptions regarding population closure are less likely to be violated. Determining the most efficient detection method for a target species is key for successful wildlife monitoring and management. This study compared driven transects, sign surveys and camera trapping for detecting numbats (Myrmecobius fasciatus) in the Upper Warren region, Western Australia. Sign surveys were more successful and had the lowest cost per detection. We recommend sign surveys with occupancy modelling for long-term monitoring of numbats in the Upper Warren. Photograph by Anke Seidlitz. [ABSTRACT FROM AUTHOR]

Bioaerosol sampling is a critical step toward characterizing airborne pathogen exposure risks for many respiratory infectious diseases in human-to-human or human-to-animal outbreaks. The integrated performance-parameter design and sampling efficiency of aerosol-to-hydrosol samplers (cyclones) has been rarely published or studied. This review focuses on the determination of cyclone performance-parameter design and impact factors of the sampling efficiency, discusses their mechanisms of collection and describes their advantages and drawbacks. The crucial design parameters of cyclones in different applications are then discussed in detail. We also discuss how the aerosol-to-hydrosol sampling efficiency is intrinsically related to the performance parameters for bioaerosol cyclone design. In the final section of this review, scientific questions and challenges for future work are presented to encourage high collection efficiency design and high-quality cyclones.

Background: Sampling a small number of participants from an entire country is not straightforward. In this case, researchers reluctantly sample from a single setting or few settings, which limits the generalizability of findings. Therefore, there is a need to design efficient sampling method for small sample size surveys that can produce generalizable results at the country level.Methods: Data comprised of twenty proxy variables to measure health services demands, structures, and outcomes of 413 districts of Iran. We used two data mining methods (hierarchical clustering method (HCM) and model-based clustering method (MCM)) to create homogenous groups of districts, i.e., strata based on these variables. We compared the internal and stability validity of the methods by statistical indices. An expert group checked the face validity of the methods, particularly regarding the total number of strata and the combination of districts in each stratum. The efficiency of selected method, which is measured by the inverse of variance, was compared with a simple random sampling (SRS) through simulation. The sampling design was tested in a national study in Iran, which aimed to evaluate the quality and costs of medical care for eight selected diseases by only recruiting 300 participants per disease at the country level.Results: MCM and HCM divided the districts into eight and two clusters, respectively. The measures of internal and stability validity showed that clusters created by MCM were more separated, compact, and stable, thus forming our optimum strata. The probability of death from stroke, chronic obstructive pulmonary disease, and in-hospital mortality rate were the most important indicators that distinguished the eight strata. Based on the simulation results, MCM increased the efficiency of the sampling design up to 1.7 times compared to SRS.Conclusions: The use of data mining improved the efficiency of sampling up to 1.7 times greater than SRS and markedly reduced the number of strata to eight in the entire country. The proposed sampling design also identified key variables that could be used to classify districts in Iran for sampling from these target populations in the future studies. [ABSTRACT FROM AUTHOR]

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Abstract Capture–recapture methods are a common tool in ecological statistics, which have been extended to spatial capture–recapture models for data accompanied by location information. However, standard formulations of these models can be unwieldy and computationally intractable for large spatial scales, many individuals, and/or activity center movement. We provide a cumulative series of methods that yield dramatic improvements in Markov chain Monte Carlo (MCMC) estimation for two examples. These include removing unnecessary computations, integrating out latent states, vectorizing declarations, and restricting calculations to the locality of individuals. Our approaches leverage the flexibility provided by the nimble R package. In our first example, we demonstrate an improvement in MCMC efficiency (the rate of generating effectively independent posterior samples) by a factor of 100. In our second example, we reduce the computing time required to generate 10,000 posterior samples from 4.5 h down to five minutes, and realize an increase in MCMC efficiency by a factor of 25. These approaches can also be applied generally to other spatially indexed hierarchical models. We provide R code for all examples, an executable web‐appendix, and generalized versions of these techniques are made available in the nimbleSCR R package.

Entomological studies often aim to estimate species distribution, community composition, or species‐richness patterns. False absences can, however, bias these estimates and should consequently not be overlooked in insect studies. Multi‐species occupancy models (MSOMs) afford a flexible solution to cover the main topics in ecological entomology while dealing with detectability issues.We sampled Orthoptera communities at 81 mountain grasslands sites in France, using three sampling techniques: sighting, listening, and sweep netting. Five plots were sampled per site. This sampling design allowed MSOMs to be used to estimate richness, occupancy, and detection probabilities while accounting for the effect of covariates. We also used MSOMs to evaluate the efficiency of the survey design and to assess the effects of sampling optimisation.The estimates obtained for altitudinal distribution were reliable, with known species distributions confirming the relevance of MSOMs to model the effects of covariates on Orthoptera communities. The species‐specific detection probability was often less than one and varied with the detection technique used and the grass height, confirming the need to deal with detection issues in orthopteran studies.We estimated an inventory completeness superior to 0.80 for 93% of the sites, and an overall detection probability superior to 0.95 for 52% of the species, suggesting the sampling design was suitable for studying occupancy in Orthoptera communities. We also found that the sweep netting step may be omitted or the number of plots reduced without affecting species detectability or inventory completeness. Those recommendations may help to optimise future sampling strategies. [ABSTRACT FROM AUTHOR]

Occupancy methods propelled the quantitative study of species distributions forward by separating the observation process, or the imperfect detectability of species, from the ecological processes of interest governing species distributions. Occupancy studies come at a cost, however: the collection of additional data to account for nondetections at sites where the species is present. The most common occupancy designs (repeated‐measures designs) require repeat visits to sites or the use of multiple observers or detection methods. Time‐to‐detection methods have been identified as a potentially efficient alternative, requiring only one visit to each site by a single observer. A comparison of time‐to‐detection methods to repeated‐measures designs for visual encounter surveys would allow researchers to evaluate whether time‐to‐detection methods might be appropriate for their study system and can inform optimal survey design. We collected time‐to‐detection data during two different repeated‐measures design occupancy surveys for four amphibians and compared the performance of time‐to‐detection methods to the other designs using the location (potential bias) and precision of posterior distributions for occurrence parameters. We further used results of time‐to‐detection surveys to optimize survey design. Time‐to‐detection methods performed best for species that are widespread and have high detection probabilities and rates, but performed less well for cryptic species with lower probability of occurrence or whose detection was strongly affected by survey conditions. In all cases, single surveys were most efficient in terms of person‐hours expended, but under some conditions the survey duration required to achieve high detection probabilities would be prohibitively long for a single survey. Regardless of occupancy survey design, time‐to‐detection methods provide important information that can be used to optimize surveys, allowing researchers and resource managers to efficiently achieve monitoring and conservation goals. Collecting time‐to‐detection data while conducting repeated‐measures occupancy surveys requires only small modifications to field methods but could have large benefits in terms of time spent surveying in the long term. [ABSTRACT FROM AUTHOR]

The sugarcane aphid, Melanaphis sacchari (Zehntner), has emerged as a serious pest of sorghum in the United States. Field trials were conducted in Louisiana and South Carolina in 2016–2018 to investigate its population characteristics and distribution patterns in relation to four sample unit sizes (three circular and one leaf based). Sugarcane aphid populations usually progressed through a phase of rapid rise followed by a phase of rapid decline within a span of 5–6 wk, with peak density determined by sorghum cultivars and climatic conditions. Peak population densities for susceptible cultivars were 1.9–14.9× that for resistant cultivars on a per plant basis. Melanaphis sacchari tended to concentrate on the lower green leaf nodes early in the infestation, with the distribution shifting toward higher green leaf nodes as the infestation progressed. Aphid densities per cm2 at the basal and middle sections were about twice as high as at the distal section of leaves. The proportions of infested sample units were fitted to the Wilson–Room binomial model that incorporates the effect of density on clumping pattern. For a specific sample unit size, clumping patterns were similar across cultivars, years, and leaf positions, but varied across infestation stages. For a fixed aphid density per sample unit, the proportion of infested sample units decreased with increasing sample unit size. For a fixed aphid density per cm2, proportion infested increased with increasing sample unit size, indicating less clumping with a larger sample unit size. Field sampling time and efficiency between samplers were quantified. [ABSTRACT FROM AUTHOR]

Capture–recapture methods are a common tool in ecological statistics, which have been extended to spatial capture–recapture models for data accompanied by location information. However, standard formulations of these models can be unwieldy and computationally intractable for large spatial scales, many individuals, and/or activity center movement. We provide a cumulative series of methods that yield dramatic improvements in Markov chain Monte Carlo (MCMC) estimation for two examples. These include removing unnecessary computations, integrating out latent states, vectorizing declarations, and restricting calculations to the locality of individuals. Our approaches leverage the flexibility provided by the nimble R package. In our first example, we demonstrate an improvement in MCMC efficiency (the rate of generating effectively independent posterior samples) by a factor of 100. In our second example, we reduce the computing time required to generate 10,000 posterior samples from 4.5 h down to five minutes, and realize an increase in MCMC efficiency by a factor of 25. These approaches can also be applied generally to other spatially indexed hierarchical models. We provide R code for all examples, an executable web‐appendix, and generalized versions of these techniques are made available in the nimbleSCR R package. [ABSTRACT FROM AUTHOR]

The ranked-set sampling technique has been generalized so that a more efficient estimator may be obtained. This technique allows more than one unit from each set to be quantified. Consequently, the number of units to be sampled may be reduced significantly and as a result, the corresponding cost would also be reduced. The generalized ranked-set sampling technique is applied in the estimation of parameters of the half logistic distribution. New estimators are proposed which include linear minimum variance unbiased estimators and ranked-set sample estimators. The coefficients, variances and relative efficiencies are tabulated. The estimators are compared to the best linear unbiased estimator of the parameters. Sample design strategy is also considered.

Bioaerosol sampling is a critical step toward characterizing airborne pathogen exposure risks for many respiratory infectious diseases in human-to-human or human-to-animal outbreaks. The integrated performance-parameter design and sampling efficiency of aerosol-to-hydrosol samplers (cyclones) has been rarely published or studied. This review focuses on the determination of cyclone performance-parameter design and impact factors of the sampling efficiency, discusses their mechanisms of collection and describes their advantages and drawbacks. The crucial design parameters of cyclones in different applications are then discussed in detail. We also discuss how the aerosol-to-hydrosol sampling efficiency is intrinsically related to the performance parameters for bioaerosol cyclone design. In the final section of this review, scientific questions and challenges for future work are presented to encourage high collection efficiency design and high-quality cyclones. [ABSTRACT FROM AUTHOR]

Hard ticks (Acari: Ixodidae) act as important vectors of zoonotic pathogens. For instance, Borrelia burgdorferi s.l. spirochetes pose a severe health risk as aetiological agents of Lyme borreliosis. Commonly, to study the abundance of questing (host-seeking) ticks, a 1 m2 piece of cloth is dragged over vegetation for a determined distance. Here, we designed a tick-sampling study to estimate the sampling efficiency of this standard method. We established 10 m dragging transects in a hemiboreal mixed forest patch in SW Finland for a 5-day monitoring period. Five of the transects were cloth-dragged 3× a day, whereas another five transects were dragged 6× a day in a manner that after each morning, midday and afternoon dragging, a second dragging was conducted on the same transect immediately. Captured Ixodes ricinus ticks were subsequently analysed for tick-borne pathogens. The initial population size of nymphal ticks on a transect was approximated by the accumulated nymph catch from the dragging sessions. The sampling efficiency of the cloth dragging was low, as a single dragging in a previously untouched vegetation strip always caught less than 12% (mean 6%) of the estimated population of active nymphs that were assumed to be questing during the study. Clear results were not found for daily activity rhythm, as ticks were caught in all daily dragging sessions. Approximately every third nymph and every second adult carried a pathogen, but nothing indicated that the occurrence of a pathogen affected the likelihood of the tick being caught by cloth dragging. Our results suggest that only a minority of active ticks can be caught by a single cloth dragging. The abundance estimates in many tick investigations might thus be downward biased. [ABSTRACT FROM AUTHOR]

Multi-objective reinforcement learning (MORL) algorithms aim to approximate the Pareto frontier uniformly in multi-objective decision making problems. In the scenario of deep reinforcement learning (RL), gradient-based methods are often adopted to learn deep policies/value functions due to the fast convergence speed, while pure gradient-based methods can not guarantee a uniformly approximated Pareto frontier. On the other side, evolution strategies straightly manipulate in the solution space to achieve a well-distributed Pareto frontier, but applying evolution strategies to optimize deep networks is still a challenging topic. To leverage the advantages of both kinds of methods, we propose a two-stage MORL framework combining a gradient-based method and an evolution strategy. First, an efficient multi-policy soft actor-critic algorithm is proposed to learn multiple policies collaboratively. The lower layers of all policy networks are shared. The first-stage learning can be regarded as representation learning. Secondly, the multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is applied to fine-tune policy-independent parameters to approach a dense and uniform estimation of the Pareto frontier. Experimental results on three benchmarks (Deep Sea Treasure, Adaptive Streaming, and Super Mario Bros) show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Sampling efficiency is crucial to overcome the data crisis in biodiversity and to understand what drives the distribution of rare species.Adaptive niche‐based sampling (ANBS) is an iterative sampling strategy that relies on the predictions of species distribution models (SDMs). By predicting highly suitable areas to guide prospection, ANBS could improve the efficiency of sampling effort in terms of finding new locations for rare species. Its iterative quality could potentially mitigate the effect of small and initially biased samples on SDMs.In this study, we compared ANBS with random sampling by assessing the gain in terms of new locations found per unit of effort. The comparison was based on both simulations and two field surveys of mountain birds.We found an increasing probability of contacting the species through iterations if the focal species showed specialization in the environmental gradients used for modelling. We also identified a gain when using pseudo‐absences during first iterations, and a general tendency of ANBS to increase the omission rate in the spatial prediction of the species' niche or habitat.Overall, ANBS is an effective and flexible strategy that can contribute to a better understanding of distribution drivers in rare species. [ABSTRACT FROM AUTHOR]

Arthropod population field studies undertaken to understand the impacts and population dynamics of the target organism rely on sampling methods that provide accurate measurements of population density. Unsuitable methods may underestimate or provide widely variable measures of population density. The suitability of three vacuum sampling methods: a domestic vacuum cleaner, Vortis™, and blower‐vac (G‐vac), plus heat extraction of turves were compared for sampling common species in an intensively grazed irrigated dairy pasture. Each method used a different approach to sampling with the number of samples per method based on protocols that had been used in grasslands. Overall, for adults of the weevil Listronotus bonariensis, no method provided a consistently high mean density, while for adults of the weevil Sitona obsoletus, the vacuum cleaner and turf heat extraction methods gave generally higher mean densities. For predatory beetles and lacewings, heat extraction was the most effective for density measurements, while for ladybirds and spiders, the Vortis™ provided the best estimate of mean density. Increasing pasture dry matter (kg Dm/ha) generally had a significant negative effect on S. obsoletus density but not for L. bonariensis. While no method was consistently superior than another, the vacuum cleaner and heat extraction methods generally provided higher mean densities. The G‐vac generally produced the lowest density estimates, but the sampling protocol meant a larger collection area per unit effort and therefore the probability of detection was higher compared with the three other methods. In conclusion, the study showed that the optimal sampling method depends on the taxa targeted, and more than one method may be required to measure density and diversity of species in both natural and modified grasslands. [ABSTRACT FROM AUTHOR]

Bell’s inequality provides a remarkable way to test the consistency between quantum mechanics and classic local realistic theory. However, experimental demonstrations of the loophole-free Bell test are challenging and only recently have been demonstrated with bipartite systems. A central obstacle for the photonic system is that the sampling efficiency, including the collection and detection efficiencies, must be above a certain threshold. We here generalize two-particle Eberhard’s inequality to the n-particle systems and derive a Bell-type inequality for multi-particle systems, which significantly relaxes this threshold. Furthermore, an experimental proposal to achieve a multi-partite Bell test without the fair sampling assumption is presented for the case of three particles. For any given value of the sampling efficiency, we give the optimal configurations for actual implementation, the optimal state, the maximum background noise that the system can tolerate, and the lowest fidelity of the quantum state. We believe our work can serve as a recipe for experimentalists planning to violate local realism using a multi-partite quantum state without the sampling loophole.