Environmental Assumptions Research Articles

A Dynamic Scene Vision SLAM Method Incorporating Object Detection and Object Characterization

Simultaneous localization and mapping (SLAM) based on RGB-D cameras has been widely used for robot localization and navigation in unknown environments. Most current SLAM methods are constrained by static environment assumptions and perform poorly in real-world dynamic scenarios. To improve the robustness and performance of SLAM systems in dynamic environments, this paper proposes a new RGB-D SLAM method for indoor dynamic scenes based on object detection. The method presented in this paper improves on the ORB-SLAM3 framework. First, we designed an object detection module based on YOLO v5 and relied on it to improve the tracking module of ORB-SLAM3 and the localization accuracy of ORB-SLAM3 in dynamic environments. The dense point cloud map building module was also included, which excludes dynamic objects from the environment map to create a static environment point cloud map with high readability and reusability. Full comparison experiments with the original ORB-SLAM3 and two representative semantic SLAM methods on the TUM RGB-D dataset show that: the method in this paper can run at 30+fps, the localization accuracy improved to varying degrees compared to ORB-SLAM3 in all four image sequences, and the absolute trajectory accuracy can be improved by up to 91.10%. The localization accuracy of the method in this paper is comparable to that of DS-SLAM, DynaSLAM and the two recent target detection-based SLAM algorithms, but it runs faster. The RGB-D SLAM method proposed in this paper, which combines the most advanced object detection method and visual SLAM framework, outperforms other methods in terms of localization accuracy and map construction in a dynamic indoor environment and has a certain reference value for navigation, localization, and 3D reconstruction.

Current practices, potentials, challenges, future opportunities, environmental and economic assumptions for Türkiye’s clean and sustainable energy policy: A comprehensive assessment

In today’s world, most countries including Türkiye have met their electricity demand at a dominant rate by burning fossil-based fuels in thermal power plants. However, fossil-fuel reserves have been rapidly depleted, resulting in high volatility in these fuels’ markets, as well as alarming environmental, and economic problems for the governments. In recent years, many governments have started to face these problems and have rapidly transitioned to renewable and alternative carbon-free energy sources in their electricity production variety. However, these belated steps have failed to mitigate the increment in global greenhouse gas emissions against the rapid growth of population and energy demand. In recent years, Türkiye has put a noteworthy challenge to mitigate its dominant use of fossil fuels, reducing its energy dependence, sustaining its economic development, and mitigating the carbon footprint. From this point of view, it is witnessed that many power plants have been established, many of them are currently under construction, especially to produce more electricity in a sustainable way. Accordingly, the present study aims to comprehensively discuss Türkiye’s energy production policy, energy potential and reserves, challenges, future opportunities, and the impacts of the energy sector on the economic and environmental issues for the country. In this framework, it is well-noticed that the country’s future energy production policy has been reasonably changed in order to achieve positive economic and environmental outcomes in the medium and long term.

Invariant Policy Learning: A Causal Perspective.

Contextual bandit and reinforcement learning algorithms have been successfully used in various interactive learning systems such as online advertising, recommender systems, and dynamic pricing. However, they have yet to be widely adopted in high-stakes application domains, such as healthcare. One reason may be that existing approaches assume that the underlying mechanisms are static in the sense that they do not change over different environments. In many real-world systems, however, the mechanisms are subject to shifts across environments which may invalidate the static environment assumption. In this paper, we take a step toward tackling the problem of environmental shifts considering the framework of offline contextual bandits. We view the environmental shift problem through the lens of causality and propose multi-environment contextual bandits that allow for changes in the underlying mechanisms. We adopt the concept of invariance from the causality literature and introduce the notion of policy invariance. We argue that policy invariance is only relevant if unobserved variables are present and show that, in that case, an optimal invariant policy is guaranteed to generalize across environments under suitable assumptions.

How cosmic rays mediate the evolution of the interstellar medium

ABSTRACT We explore the impact of diffusive cosmic rays (CRs) on the evolution of the interstellar medium (ISM) under varying assumptions of supernova explosion environment. In practice, we systematically vary the relative fractions of supernovae (SN) occurring in star-forming high-density gas and those occurring in random locations decoupled from star-forming gas to account for SN from run-away stars or explosions in regions that have been cleared by prior SN, stellar winds, or radiation. We find that in the simple system of a periodic stratified gas layer the ISM structure will evolve to one of two solutions: a ‘peak driving’ state where warm gas is volume filling or a ‘thermal runaway’ state where hot gas is volume filling. CR pressure and transport are important factors that strongly influence the solution state the ISM reaches and have the ability to flip the ISM between solutions. Observable signatures such as gamma-ray emission and H i gas are explored. We find that gamma-ray luminosity from pion decay is largely consistent with observations for a range of model parameters. The thickness of the H i gas layer may be too compact, however, this may be due to a large cold neutral fraction of mid-plane gas. The volume fraction of hot gas evolves to stable states in both solutions, but neither settles to a Milky Way-like configuration, suggesting that additional physics omitted here (e.g. a cosmological circumgalactic medium, radiation transport, or spectrally resolved and spatially varying CR transport) may be required.

A Blueprint for Genetic Determinism

A Blueprint for Genetic Determinism

BUSINESS SUSTAINABILITY THROUGH E-COMMERCE: A MYTH OR REALITY IN NIGERIA

A succinct exploration of business sustainability via an e-commerce platform was examined in the study. Its myth and reality permeate its adoption, use, and acceptability within the context of our economic, social, and environmental assumptions were also validated. To achieve its aim, exploratory methodology, which compared benefits and challenges among the three main elements – business, users, and authority; in the application of the e-commerce platforms and channels in the country was examined. The result showed mixed outcomes. On one side, there are huge benefits to businesses in adopting e-commerce while on the other, challenges such as optimal patronage due to cultural trust gaps, user-confidence, poor connectivity, and government selective policy drive. Based on this outcome, it is concluded that; business sustainability via e-commerce in the country is relatively weak and can be improved upon if, end-users policy drive is taken into serious consideration.

Applicability of calcined sewage sludge instead of Portland cement for coating mortars

Based on the environmental assumptions that permeate the role of researchers in enabling the use of waste in consumer goods, using sewage sludge in human consumption material has become the object of worldwide study because of the potential that it carries. Aiming to fulfill the sustainable role through the creation of mechanisms that favor the ecological coexistence between environment and human beings, the use of the calcined sewage sludge as a potentiator in the properties of the material is made possible through the construction materials, behavior. Coating mortars are currently evaluated by exposing, from the replacement of the cement by the calcined material, an effective applicability with respect to its resistance to deleterious agents. It is understood that to idealize unconventional materials with the use of residues that have high degradation power to the environment, ratifies the attempt to mitigate large and irreversible environmental impacts arising from improper and poorly planned disposal.

Eliminating short-term dynamic elements for robust visual simultaneous localization and mapping using a coarse-to-fine strategy

Visual simultaneous localization and mapping (VSLAM) is one of the foremost principal technologies for intelligent robots to implement environment perception. Many research works have focused on proposing comprehensive and integrated systems based on the static environment assumption. However, the elements whose motion status changes frequently, namely short-term dynamic elements, can significantly affect the system performance. Therefore, it is extremely momentous to cope with short-term dynamic elements to make the VSLAM system more adaptable to dynamic scenes. This paper proposes a coarse-to-fine elimination strategy for short-term dynamic elements based on motion status check (MSC) and feature points update (FPU). First, an object detection module is designed to obtain semantic information and screen out the potential short-term dynamic elements. And then an MSC module is proposed to judge the true status of these elements and thus ultimately determine whether to eliminate them. In addition, an FPU module is introduced to update the extracted feature points according to calculating the dynamic region factor to improve the robustness of VSLAM system. Quantitative and qualitative experiments on two challenging public datasets are performed. The results demonstrate that our method effectively eliminates the influence of short-term dynamic elements and outperforms other state-of-the-art methods.

Testing software's changing features with environment-driven abstraction identification.

ions are significant domain terms that have assisted in requirements elicitation and modeling. To extend the assistance toward requirements validation, we present in this paper an automated approach to identifying the abstractions for supporting requirements-based testing. We select relevant Wikipedia pages to serve as a domain corpus that is independent from any specific software system. We further define five novel patterns based on part-of-speech tagging and dependency parsing, and frame our candidate abstractions in the form of <key, value> pairs for better testability, where the “key” helps locate “what to test”, and the “value” helps guide “how to test it” by feeding in concrete data. We evaluate our approach with six software systems in two application domains: Electronic health records and Web conferencing. The results show that our abstractions are more accurate than those generated by a state-of-the-art technique. While the initial findings indicate our abstractions’ capabilities of revealing bugs and matching the environmental assumptions created manually, we articulate a new way to perform requirements-based testing by focusing on a software system’s changing features. Specifically, we hypothesize that the same feature would behave differently under a pair of opposing environmental conditions and assess our abstractions’ applicability to this new form of feature testing.

Combining Genetic Programming and Model Checking to Generate Environment Assumptions

Software verification may yield spurious failures when environment assumptions are not accounted for. Environment assumptions are the expectations that a system or a component makes about its operational environment and are often specified in terms of conditions over the inputs of that system or component. In this article, we propose an approach to automatically infer environment assumptions for Cyber-Physical Systems (CPS). Our approach improves the state-of-the-art in three different ways: First, we learn assumptions for complex CPS models involving signal and numeric variables; second, the learned assumptions include arithmetic expressions defined over multiple variables; third, we identify the trade-off between soundness and coverage of environment assumptions and demonstrate the flexibility of our approach in prioritizing either of these criteria. We evaluate our approach using a public domain benchmark of CPS models from Lockheed Martin and a component of a satellite control system from LuxSpace, a satellite system provider. The results show that our approach outperforms state-of-the-art techniques on learning assumptions for CPS models, and further, when applied to our industrial CPS model, our approach is able to learn assumptions that are sufficiently close to the assumptions manually developed by engineers to be of practical value.

Can two competing on-demand service platforms be profitable?

Can two competing on-demand service platforms be profitable in equilibrium? This question is well-studied for firms competing purely on price in a single-sided market, but it is not well-understood for competing service platforms that use price to influence passenger demand and wage to influence driver participation in a “two-sided” market. To fill this research gap, we analyze the equilibrium structure of different variants of a 2-stage non-cooperative game in which both platforms use lower prices and waiting time to compete for more customers and higher wages and utilization to entice more providers to participate.To explicate our analysis and isolate the effect of each element, we first examine a parsimonious and stylized model that is based on the case when both firms operate under seven operational assumptions: (1) Non-exclusive providers; (2) Non-exclusive customers; (3) Pure pricing strategies; (4) Constant pricing; (5) Homogeneous services; (6) Homogeneous providers; and (7) Homogeneous customers. We find that only one platform can sustain in a payoff dominant stable equilibrium. Then we examine whether this “winner-take-all” equilibrium would persist when those operational environmental assumptions are relaxed separately. Our analysis reveals that the “winner-take-all” phenomenon continues to persist under promotional pricing strategies, service differentiation, and heterogeneous service providers. Interestingly, we find both platforms can sustain profitably when each platform engages providers (or customers) exclusively, when platforms adopt time-based pricing strategies, or when customers are heterogeneous. Our results offer insights into different operating environments under which both platforms can both sustain profitably in equilibrium.

A point-line-plane primitives fused localization and object-oriented semantic mapping in structural indoor scenes

Visual SLAM (abbreviates ‘simultaneous localization and mapping’) is a promising solution for environment mapping. This study is devoted to a description of a semantically ensembled SLAM framework. For structural indoor scenes, the structured lines and planes can serve as the newly added constraints to improve the positioning accuracy. In this paper, we propose to comprehensively incorporate point-line-plane primitives and construct a tightly coupled camera pose estimator without any environment assumptions. In particular, the maximum number of extracted lines features is numerically determined. We further integrate a lightweight object mapping pipeline with the designed pose estimator. In this pipeline, the leveraging of fitted plane and cuboid landmarks enables an online, CPU-based dense mapping. The tests on ICL-NUIM and TUM benchmark datasets illustrate that, in comparison to ORB-SLAM2, PL-SLAM (Point and Line based SLAM), SP-SLAM (Supposed Plane SLAM) and PLP-SLAM (Point, Line and Plane fused SLAM), our design leads to superior performances in global consistency and system drift elimination. The feature detection and multi-level map reconstruction results are simultaneously provided.

Specifiable robustness in reactive synthesis

When synthesizing a system from a given specification, there is room for automatically adding various requirements, hence improving the resulting system. One such requirement covered extensively in past literature is that of robustness. In particular, the system can fail to read the inputs correctly from the environment, and the environment can fail to satisfy our assumptions about its behavior. Nevertheless, we want the system to still satisfy the specification even under these failures, in some limited way. It has to be limited because it is typically too strong of a requirement to realize the property regardless of the inputs and the environment’s assumptions. In this work, we propose a simple and flexible framework for synthesizing robust systems, where the user defines the required robustness via a temporal robustness specification. For example, the user may specify that the environment is eventually reliable, or input misreadings cannot occur more than k consecutive steps and synthesize a system under this assumption. Furthermore, our framework enables us to specify a temporal recovery specification, which describes how the designer expects the system to recover after a failure of the environment assumptions. We show examples of robust systems that we synthesized with this method using our synthesis tool Party.

Carbonate dissolution cones require more than stemflow funneling from plants

Over geologic timescales, forests have intercepted precipitation and thereby modified the intensity, duration, and spatial patterns of water fluxes to forest soils. Across a range of environmental conditions, persistent focused water flows can dissolve carbonate substrates, and form conical dissolution features—termed “dissolution cones.” These cones generally fill with soils, becoming localized soil (and water) reservoirs occupied by vegetation. A myriad of mechanisms are hypothesized to have formed dissolution cones. Prior work has sought to explain co-located palm trees and modern dissolution cones in tropical unconsolidated carbonates as the result of the chemical action of weakly acidic stemflow funneled by palm canopies down their stems, and into the substrate. Using a geochemical modeling program, PHREEQC, we find that for a range of environmental conditions and favorable assumptions, stemflow is unable to dissolve a benchmark volume of carbonate substrate that typifies tropical dissolution cones. Therefore, dissolution cone formation by abiotic dissolution from stemflow funneling is unlikely to be the chief geomorphic process. Further hypotheses to be tested are discussed.

THE STRUCTURE OF A MODERN SCHOOL – CASE STUDY

The aim of this article is to test and apply the developed methodology of research on the correlation between the physical learning environment and education, analysing Vilnius Geroji Viltis Progymnasium. The article analyses functional-spatial structure and usability of the selected school (applying the principles of post occupancy evaluation (POE), and analyses the school culture and the needs of school community groups, which are compared with modern Lithuanian educational goals and objectives. The functional-spatial structure of the analysed school is compared with the general school model of the 21st century formed in the author’s previous research, which distinguishes 7 features of the physical learning environment that define the quality of the modern learning environment. It also examines the extent to which the current physical school environment satisfies and meets the school culture and community needs. The article provides guidelines for the implementation of the harmony of school culture (values and needs) and its physical environment, which allows each school to self-assess the physical learning environment and its cultural and 21st century school physical environment characteristics and assumptions and opportunities to meet them.

Environmental Pollution and Economic Growth based on the Theory of Environmental Kuznets Curve (EKC) in Iran's Provinces (1997-2017)

Despite the fact that economic growth provides the basis for the progress of societies, it increases pollution and, as a result, destroys the environment and reduces the welfare of societies. On the other hand, preserving and protecting the environment is one of the most important pillars of achieving sustainable development. Regardless of the environmental issue, natural and human resources will be depleted and will have dire consequences on the planet and even in human societies. The environment is considered as a factor, which affects and is affected by both economic growth and sustainable development. In developing countries, overproduction and economic growth generally lead to environmental degradation. In this study, the effects of reverse U-shaped and N-shaped curve of GDP on the surface variable with power two and three on the pollutants emission volume is investigated. Also, nitrogen oxide, carbon monoxide, carbon dioxide, sulfur dioxide, sulfur trioxide using long-term panel econometric methods, including; panel unit root test, Kao co-integration test, FMOLS and DOLS for 30 provinces of the country over 1997-2017 were considered. As a result, estimates suggest that the environmental Kuznets assumptions are the same for all five pollutants, and as GDP increases, pollution first increases, then begins to decline after reaching the peak point, and finally continues to rise by increasing reproduction. In other words, the results of the estimates indicate the confirmation of the N-shaped relationship between the provinces of Iran.

Covid-19, tourism and the advocacy of degrowth

ABSTRACT This article reviews the advocacy of degrowth in tourism, principally in relation to the emerging debates taking place in the light of the impact of Covid-19. It reviews the origins and meaning of degrowth as a development philosophy in order to place the discussion in a broader context. It proceeds to question some of the economic, environmental and cultural assumptions implicit and often explicit in degrowth advocacy with regard to tourism. The article argues that economic growth – in tourism and generally – is a necessary, albeit far from sufficient, condition for addressing the varied problems facing contemporary society manifest in modern tourism. There have been numerous publications, especially in the light of Covid-19, advocating tourism degrowth. There have been none directly questioning the claims made. It is hoped that this article will open up a discursive space for engagement between those who see economic growth as a part of the problem and those who see it as a part of the solution.

Trait-specific testing of the equal environment assumption: The case of school grades and upper secondary school attendance

Objective: This paper tests the equal environment assumption for school grades and upper secondary school attendance and describes the conditions under which violations are problematic.&#x0D; Background: A growing number of sociologists use twin-based research designs, particularly the Classical Twin Design (CTD), to differentiate between genetic and social causes of social inequalities. One key assumption of CTD is that environmental influences are shared by monozygotic and dizygotic twins to the same extent; called the equal environment assumption (EEA). This assumption is frequently contested and the target of concern, because violation can result in an overestimation of heritability and an underestimation of the role of the social environment.&#x0D; Method: Using data from the first wave of the German TwinLife study, the paper illustrates two approaches to test EEA for school grades and enrolment in upper secondary school (Gymnasium). The analysis is based on a sample of twins (N = 1,576) aged ten to twelve years.&#x0D; Results: The results show that the approaches are able to detect violations of EEA (though in different ways), depending on the environmental variables that might causally be involved in trait variance. Only in one case was a violation was observed; it had no effect on heritability estimates.&#x0D; Conclusion: While EEA holds for school grades, violations do not automatically invalidate CTD in case of upper secondary school attendance.

The Augmented Classical Twin Design: Incorporating Genome-Wide Identity by Descent Sharing Into Twin Studies in Order to Model Violations of the Equal Environments Assumption.

The Classical Twin Method (CTM) compares the similarity of monozygotic (MZ) twins with that of dizygotic (DZ) twins to make inferences about the relative importance of genes and environment in the etiology of individual differences. The design has been applied to thousands of traits across the biomedical, behavioral and social sciences and is arguably the most widely used natural experiment known to science. The fundamental assumption of the CTM is that trait relevant environmental covariation within MZ pairs is the same as that found within DZ pairs, so that zygosity differences in within-pair variance must be due to genetic factors uncontaminated by the environment. This equal environments assumption (EEA) has been, and still is hotly contested, and has been mentioned as a possible contributing factor to the missing heritability conundrum. In this manuscript, we introduce a new model for testing the EEA, which we call the Augmented Classical Twin Design which uses identity by descent (IBD) sharing between DZ twin pairs to estimate separate environmental variance components for MZ and DZ twin pairs, and provides a test of whether these are equal. We show through simulation that given large samples of DZ twin pairs, the model provides unbiased estimates of variance components and valid tests of the EEA under strong assumptions (e.g. no epistatic variance, IBD sharing in DZ twins estimated accurately etc.) which may not hold in reality. Sample sizes in excess of 50,000 DZ twin pairs with genome-wide genetic data are likely to be required in order to detect substantial violations of the EEA with moderate power. Consequently, we recommend that the Augmented Classical Twin Design only be applied to datasets with very large numbers of DZ twin pairs (> 50,000 DZ twin pairs), and given the strong assumptions relating to the absence of epistatic variance, appropriate caution be exercised regarding interpretation of the results.

Concordance rate in cattle and sheep between genotypes differing in Illumina GenCall quality score.

Proper quality control of data prior to downstream analyses is fundamental to ensure integrity of results; quality control of genomic data is no exception. While many metrics of quality control of genomic data exist, the objective of the present study was to quantify the genotype and allele concordance rate between called single nucleotide polymorphism (SNP) genotypes differing in GenCall (GC) score; the GC score is a confidence measure assigned to each Illumina genotype call. This objective was achieved using Illumina beadchip genotype data from 771 cattle (12428767 genotypes in total post-editing) and 80 sheep (1557360 SNPs genotypes in total post-editing) each genotyped in duplicate. The called genotype with the lowest associated GC score was compared to the genotype called for the same SNP in the same duplicated animal sample but with a GC score of >0.90 (assumed to represent the true genotype). The mean genotype concordance rate for a GC score of <0.300, 0.300-0.549, and ≥0.550 in the cattle (sheep in parenthesis) was 0.9467 (0.9864), 0.9707 (0.9953), and 0.9994 (0.99997) respectively; the respective allele concordance rate was 0.9730 (0.9930), 0.9849 (0.9976), and 0.9997 (0.99998). Hence, concordance eroded as the GC score of the called genotype reduced, albeit the impact was not dramatic and was not very noticeable until a GC score of <0.55. Moreover, the impact was greater and more consistent in the cattle population than in the sheep population. Furthermore, an impact of GC score on genotype concordance rate existed even for the same SNP GenTrain value; the GenTrain value is a statistical score that depicts the shape of the genotype clusters and the relative distance between the called genotype clusters.