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Journal articles on the topic 'Environments'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Uchiyama, Seiichi, Kazuyuki Takehira, Toshitada Yoshihara, Seiji Tobita, and Tomohiko Ohwada. "Environment-Sensitive Fluorophore Emitting in Protic Environments." Organic Letters 8, no. 25 (December 2006): 5869–72. http://dx.doi.org/10.1021/ol062490r.

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2

Klint, P. "A meta-environment for generating programming environments." ACM Transactions on Software Engineering and Methodology 2, no. 2 (April 1993): 176–201. http://dx.doi.org/10.1145/151257.151260.

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3

Ellis-Evans, J. C., V. Galchenko, J. Laybourn-Parry, A. P. Mylnikov, and W. Petz. "Environmental characteristics and microbial plankton activity of freshwater environments at Kongsfjorden, Spitsbergen (Svalbard)." Fundamental and Applied Limnology 152, no. 4 (January 10, 2001): 609–32. http://dx.doi.org/10.1127/archiv-hydrobiol/152/2001/609.

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4

Cuba, Lee, Irwin Altman, and Abraham Wandersman. "Neighborhood and Community Environments: Human Behavior and Environment." Contemporary Sociology 18, no. 3 (May 1989): 391. http://dx.doi.org/10.2307/2073854.

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5

Rolfes, Stefan, and Maria-João Rendas. "Statistical environment representation for navigation in natural environments." Robotics and Autonomous Systems 41, no. 2-3 (November 2002): 129–36. http://dx.doi.org/10.1016/s0921-8890(02)00260-9.

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6

Aztiria, Asier, Juan Carlos Augusto, Rosa Basagoiti, Alberto Izaguirre, and Diane J. Cook. "Discovering frequent user--environment interactions in intelligent environments." Personal and Ubiquitous Computing 16, no. 1 (October 7, 2011): 91–103. http://dx.doi.org/10.1007/s00779-011-0471-4.

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7

Champion, Erik, and Andrew Dekker. "Biofeedback and Virtual Environments." International Journal of Architectural Computing 9, no. 4 (December 2011): 377–95. http://dx.doi.org/10.1260/1478-0771.9.4.377.

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This paper explains potential benefits of indirect biofeedback used within interactive virtual environments, and reflects on an earlier study that allowed for the dynamic modification of a virtual environment's graphic shaders, music and artificial intelligence, based on the biofeedback of the player. The aim was to determine which augmented effects aided or discouraged engagement in the game. Conversely, biofeedback can help calm down rather than stress participants, and attune them to different ways of interacting within a virtual environment. Other advantages of indirect biofeedback might include increased personalization, thematic object creation, atmospheric augmentation, filtering of information, and tracking of participants' understanding and engagement. Such features may help designers create more intuitive virtual environments with more thematically appropriate interaction while reducing cognitive loading on the participants. Another benefit would be more engaged clients with a better understanding of the richness and complexity of a digital environment.

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Nga, Nguyen Thi Thanh, Tadashi Shinohara, and Le Thi Hong Lien. "Effects of Environment Factors on Stress Corrosion Cracking of Austenitic Stainless Steels in Atmospheric Environments." Zairyo-to-Kankyo 66, no. 6 (2017): 209–13. http://dx.doi.org/10.3323/jcorr.66.209.

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9

Gabrys, Jennifer. "Sensors experiencing environments, environments becoming computational." Dialogues in Human Geography 9, no. 1 (March 2019): 121–24. http://dx.doi.org/10.1177/2043820618808668.

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10

Alexander, Samuel Allen, Michael Castaneda, Kevin Compher, and Oscar Martinez. "Extending Environments to Measure Self-reflection in Reinforcement Learning." Journal of Artificial General Intelligence 13, no. 1 (October 1, 2022): 1–24. http://dx.doi.org/10.2478/jagi-2022-0001.

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Abstract We consider an extended notion of reinforcement learning in which the environment can simulate the agent and base its outputs on the agent’s hypothetical behavior. Since good performance usually requires paying attention to whatever things the environment’s outputs are based on, we argue that for an agent to achieve on-average good performance across many such extended environments, it is necessary for the agent to self-reflect. Thus weighted-average performance over the space of all suitably well-behaved extended environments could be considered a way of measuring how self-reflective an agent is. We give examples of extended environments and introduce a simple transformation which experimentally seems to increase some standard RL agents’ performance in a certain type of extended environment.

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11

Akbarzai , Darya Khan, Suhilla Nisar , and Lina Mohammadi. "Genotype × Environment interaction studies in lentil under Afghanistan environments." Journal of Innovative Agriculture 8, no. 2 (June 30, 2021): 39. http://dx.doi.org/10.37446/jinagri/rsa/8.2.2021.39-46.

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12

Tai, G. C. C., and T. R. Tarn. "EVALUATION OF VARIETIES: GENOTYPES, ENVIRONMENTS AND GENOTYPE-ENVIRONMENT INTERACTIONS." Acta Horticulturae, no. 619 (November 2003): 31–34. http://dx.doi.org/10.17660/actahortic.2003.619.3.

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13

Reeth, F., K. Coninx, S. Backer, and E. Flerackers. "Realizing 3D Visual Programming Environments within a Virtual Environment." Computer Graphics Forum 14, no. 3 (August 1995): 361–70. http://dx.doi.org/10.1111/1467-8659.1430361.

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14

Van Reeth, F., K. Coninx, S. De Backer, and E. Flerackers. "Realizing 3D Visual Programming Environments within a Virtual Environment." Computer Graphics Forum 14, no. 3 (August 1995): 361–70. http://dx.doi.org/10.1111/j.1467-8659.1995.cgf143_0361.x.

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15

Irons, William. "Adaptively relevant environments versus the environment of evolutionary adaptedness." Evolutionary Anthropology: Issues, News, and Reviews 6, no. 6 (1998): 194–204. http://dx.doi.org/10.1002/(sici)1520-6505(1998)6:6<194::aid-evan2>3.0.co;2-b.

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16

BAILENSON, JEREMY N., JIM BLASCOVICH, ANDREW C. BEALL, and BETH NOVECK. "Courtroom Applications of Virtual Environments, Immersive Virtual Environments, and Collaborative Virtual Environments." Law Policy 28, no. 2 (April 2006): 249–70. http://dx.doi.org/10.1111/j.1467-9930.2006.00226.x.

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17

Heydarian, Arsalan, Joao P. Carneiro, David Gerber, Burcin Becerik-Gerber, Timothy Hayes, and Wendy Wood. "Immersive virtual environments versus physical built environments: A benchmarking study for building design and user-built environment explorations." Automation in Construction 54 (June 2015): 116–26. http://dx.doi.org/10.1016/j.autcon.2015.03.020.

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18

Kaiser, Florian G., and Carmen Keller. "Disclosing Situational Constraints to Ecological Behavior: A Confirmatory Application of the Mixed Rasch Model* * The original data upon which this paper is based are available at www.hhpub.com/journals/ejpa." European Journal of Psychological Assessment 17, no. 3 (September 2001): 212–21. http://dx.doi.org/10.1027//1015-5759.17.3.212.

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Summary: The present paper explores whether differential endorsement probabilities are related to the behavioral consequences of certain environments, rather than being solely indicative of method bias. One's living environment is among the more salient contextual factors affecting ecological performance. By applying the mixed Rasch model confirmatorily, we aimed at disclosing valid situational influences that can be held responsible for facilitating and constraining people's ecological behaviors in three particular residential environments. A cross-sectional survey of 895 Swiss residents living in various residential contexts revealed that living in urban and rural environments resulted in three distinguishable arrays of behavioral consequences, each the result of situational factors, such as an environment's particular performance opportunities, the predominant features of its public transportation systems and its respective social climate. Remarkably, an environment's being suburban has no apparent uniform significance for people's ecological behavior.

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19

Winograd, Terry. "From programming environments to environments for designing." Communications of the ACM 38, no. 6 (June 1995): 65–74. http://dx.doi.org/10.1145/203241.203259.

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20

Baril, C. P., J.-B. Denis, and P. Brabant. "Selection of environments using simultaneous clustering based on genotype × environment interaction." Canadian Journal of Plant Science 74, no. 2 (April 1, 1994): 311–17. http://dx.doi.org/10.4141/cjps94-059.

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Cluster analysis is used to classify genotypes and environments to decompose and interpret genotype × environment (GE) interaction. A simultaneous clustering method is applied to wheat-yield data collected over 8 yr in seven locations, with two agronomic treatments per location. This approach evidenced redundancies among the used environments constituting the Institut National de la Recherche Agronomique series of experiments in northern France. The aim is to reduce the number of environments without losing GE interaction. A graphical method based on the decreasing mean square of GE interaction is proposed to provide a cutting criterion of the cluster procedure. The comparison of groupings made independently for successive years suggested the removal of some environments, hence providing rational savings in the breeding program. Lastly, the simultaneous two-way clustering procedure is compared with the common one-way clustering procedure. Key words: Cluster analysis, genotype × environment interaction, pattern analysis, series of experiments, wheat

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21

Uddin, Md Saleh, Md Sultan Alam, Nasrin Jahan, Kazi Md Wayaz Hossain, and Md Ali Newaz. "Genotype x environment interaction of wheat genotypes under salinity environments." Asian Journal of Medical and Biological Research 3, no. 1 (April 14, 2017): 38–43. http://dx.doi.org/10.3329/ajmbr.v3i1.32034.

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Genotypes x environment interaction as well as stability of performance were determined for grain yield and yield contributes of 12 wheat genotypes under four salinity levels of environments (control, 8, 12, 16 dS/m). Significant genotype-environment interaction (linear) for days to heading, plant height, number of spikes per plant and grains per spikes, 1000-grain weight and grain yield per plant at 1% level of probability when tested against pooled deviation. Both the environment (linear) and genotype x environment (linear) components of variation for stability were also significant indicating that prediction of the genotypes on the environment appeared feasible for all the characters. The variance due to pooled deviation was significant for only days to heading. Considering all the three stability parameter, genotype G11 was found most stable among all the genotypes for grain weight of wheat. Among the genotypes G11, G22, G24, G33 and G40 were most desirable for yield per plant. The genotype G32 showed more responsiveness to changing environment and was suited only for highly favorable environments. Based on three stability parameters, G11, G22 and G37 were the most stable and desirable genotypes with reasonable good yield among the all.Asian J. Med. Biol. Res. March 2017, 3(1): 38-43

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22

Souza, Vander Fillipe de, Pedro César de Oliveira Ribeiro, Indalécio Cunha Vieira Júnior, Isadora Cristina Martins Oliveira, Cynthia Maria Borges Damasceno, Robert Eugene Schaffert, Rafael Augusto da Costa Parrella, Kaio Olimpio das Graças Dias, and Maria Marta Pastina. "Exploring genotype × environment interaction in sweet sorghum under tropical environments." Agronomy Journal 113, no. 4 (June 8, 2021): 3005–18. http://dx.doi.org/10.1002/agj2.20696.

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23

Hattori, Hiroaki, Keizaburo Takagi, and Takao Watanabe. "Evaluation of a rapid environment adaptation algorithm in adverse environments." Journal of the Acoustical Society of America 100, no. 4 (October 1996): 2792. http://dx.doi.org/10.1121/1.416496.

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24

Korn, Samuel. "An Environment of Environments: MAN transFORMS—Curatorial Modes, Designs, Structures." Architectural Theory Review 23, no. 1 (January 2, 2019): 59–89. http://dx.doi.org/10.1080/13264826.2019.1616659.

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25

Leflon, M., C. Lecomte, A. Barbottin, M. H. Jeuffroy, N. Robert, and M. Brancourt-Hulmel. "Characterization of Environments and Genotypes for Analyzing Genotype × Environment Interaction." Journal of Crop Improvement 14, no. 1-2 (September 13, 2005): 249–98. http://dx.doi.org/10.1300/j411v14n01_11.

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26

Xue, BingKan, Pablo Sartori, and Stanislas Leibler. "Environment-to-phenotype mapping and adaptation strategies in varying environments." Proceedings of the National Academy of Sciences 116, no. 28 (June 20, 2019): 13847–55. http://dx.doi.org/10.1073/pnas.1903232116.

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Biological organisms exhibit diverse strategies for adapting to varying environments. For example, a population of organisms may express the same phenotype in all environments (“unvarying strategy”) or follow environmental cues and express alternative phenotypes to match the environment (“tracking strategy”), or diversify into coexisting phenotypes to cope with environmental uncertainty (“bet-hedging strategy”). We introduce a general framework for studying how organisms respond to environmental variations, which models an adaptation strategy by an abstract mapping from environmental cues to phenotypic traits. Depending on the accuracy of environmental cues and the strength of natural selection, we find different adaptation strategies represented by mappings that maximize the long-term growth rate of a population. The previously studied strategies emerge as special cases of our model: The tracking strategy is favorable when environmental cues are accurate, whereas when cues are noisy, organisms can either use an unvarying strategy or, remarkably, use the uninformative cue as a source of randomness to bet hedge. Our model of the environment-to-phenotype mapping is based on a network with hidden units; the performance of the strategies is shown to rely on having a high-dimensional internal representation, which can even be random.

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27

Wannamakok, Wisuwat. "Investigating environments of university makerspaces: perspectives on environment preference approaches." J. of Design Research 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/jdr.2020.10033112.

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28

Wannamakok, Wisuwat. "Investigating environments of university makerspaces: perspectives on environment preference approaches." J. of Design Research 18, no. 1/2 (2020): 80. http://dx.doi.org/10.1504/jdr.2020.112055.

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29

Wade, L. J., C. G. McLaren, L. Quintana, D. Harnpichitvitaya, S. Rajatasereekul, A. K. Sarawgi, A. Kumar, et al. "Genotype by environment interactions across diverse rainfed lowland rice environments." Field Crops Research 64, no. 1-2 (November 1999): 35–50. http://dx.doi.org/10.1016/s0378-4290(99)00049-0.

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30

Delaney, C., and C. Byrd-Bredbenner. "Acculturation Environment and Home Environments of Mothers with Young Kids." Journal of the Academy of Nutrition and Dietetics 118, no. 9 (September 2018): A44. http://dx.doi.org/10.1016/j.jand.2018.06.171.

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31

Dykstra, Elizabeth A., and Robert P. Carasik. "Structure and support in cooperative environments: the Amsterdam Conversation Environment." International Journal of Man-Machine Studies 34, no. 3 (March 1991): 419–34. http://dx.doi.org/10.1016/0020-7373(91)90028-6.

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32

Poldma, Tiiu. "The interior spatial environment: dynamic 0g environments and human places." Personal and Ubiquitous Computing 15, no. 5 (February 25, 2011): 539–50. http://dx.doi.org/10.1007/s00779-010-0348-y.

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33

Shacklett, Brennan, Luc Guy Rosenzweig, Zhiqiang Xie, Bidipta Sarkar, Andrew Szot, Erik Wijmans, Vladlen Koltun, Dhruv Batra, and Kayvon Fatahalian. "An Extensible, Data-Oriented Architecture for High-Performance, Many-World Simulation." ACM Transactions on Graphics 42, no. 4 (July 26, 2023): 1–13. http://dx.doi.org/10.1145/3592427.

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Training AI agents to perform complex tasks in simulated worlds requires millions to billions of steps of experience. To achieve high performance, today's fastest simulators for training AI agents adopt the idea of batch simulation: using a single simulation engine to simultaneously step many environments in parallel. We introduce a framework for productively authoring novel training environments (including custom logic for environment generation, environment time stepping, and generating agent observations and rewards) that execute as high-performance, GPU-accelerated batched simulators. Our key observation is that the entity-component-system (ECS) design pattern, popular for expressing CPU-side game logic today, is also well-suited for providing the structure needed for high-performance batched simulators. We contribute the first fully-GPU accelerated ECS implementation that natively supports batch environment simulation. We demonstrate how ECS abstractions impose structure on a training environment's logic and state that allows the system to efficiently manage state, amortize work, and identify GPU-friendly coherent parallel computations within and across different environments. We implement several learning environments in this framework, and demonstrate GPU speedups of two to three orders of magnitude over open source CPU baselines and 5-33× over strong baselines running on a 32-thread CPU. An implementation of the OpenAI hide and seek 3D environment written in our framework, which performs rigid body physics and ray tracing in each simulator step, achieves over 1.9 million environment steps per second on a single GPU.

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34

Yagi, Akihiro. "PSYCHOPHYSIOLOGICAI ASSESSMENT OF LIGHTING ENVIRONMENTS." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 78, Appendix (1994): 105–6. http://dx.doi.org/10.2150/jieij1980.78.appendix_105.

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35

Toscano, William. "Psychophysiological Studies in Extreme Environments." Polish Journal of Aviation Medicine and Psychology 19, no. 4 (October 2, 2013): 19–24. http://dx.doi.org/10.13174/pjamp.19.04.2013.3.

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36

M, Babul Reddy, and Prof Rahul Pawar. "Cognitive Computing in Cloud Environments." International Journal of Research Publication and Reviews 5, no. 3 (March 2, 2024): 569–73. http://dx.doi.org/10.55248/gengpi.5.0324.0621.

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37

PARVEEN, TAUHIDA, SCOTT TILLEY, WILLIAM ALLEN, GERALD MARIN, and RICHARD FORD. "DETECTING EMULATED ENVIRONMENTS." International Journal of Software Engineering and Knowledge Engineering 22, no. 07 (November 2012): 927–44. http://dx.doi.org/10.1142/s0218194012500258.

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One of the most powerful tools in the hacker's reverse engineering arsenal is the virtual machine. These systems provide a simple mechanism for executing code in an environment in which the program can be carefully monitored and controlled, allowing attackers to subvert copy protection and access trade secrets. One of the challenges for anti-reverse engineering tools is how to protect software within such an untrustworthy environment. From the perspective of a running program, detecting an emulated environment is not trivial: the attacker can emulate the result of different operations with arbitrarily high fidelity. This paper demonstrates a mechanism that is able to detect even carefully constructed virtual environments by focusing on the stochastic variation of system call timings. A statistical technique for detecting emulated environments is presented, which uses a model of normal system call behavior to successfully identify two commonly used virtual environments under realistic conditions.

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38

Rahim Mammadzada, Rufat Mammadzada, Rahim Mammadzada, Rufat Mammadzada. "VIRTUAL TEMPERATURE CONTROL IN UNITY ENVIRONMENT WITH ML AGENTS." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 149, no. 3-4 (June 20, 2024): 51–57. http://dx.doi.org/10.36962/pahtei149032024-51.

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In this paper, an innovative approach to controlling temperature was made. Using Unity as our chosen platform, we crafted a virtual environment, that replicates the real-world temperature change dynamics. For this, a virtual environment mimics an enclosed area with a virtual heat source. Additionally, this environment’s area and air density were also taken into consideration. Then, a virtual sensor and cooler were added for detecting and adjusting the environment temperature. Finally, the ML agent package was integrated for controlling temperature with RL. This integration consisted of adding a script that inherits from the agent class and has environment, sensor, and cooler as variables. In each epi-sode, there was a time duration in which both environment's temperature change was calculated and the reward was set to be given only when the environment's temperature was at a certain range. The empirical findings show the efficiency of our approach, show-casing the system's ability to adapt and optimize temperature regulation parameters over successive iterations. By successfully integrating Unity and ML agents, our study under-scores the transformative potential of harnessing virtual environments and machine learning for advancing temperature control technologies. Keywords: unity, ml agents, temperature control, virtual environment, reinforcement learning.

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39

McDermott, Christina. "Rhotic Environments." Lifespans and Styles 6, no. 2 (December 10, 2020): 44–54. http://dx.doi.org/10.2218/ls.v6i2.2020.5220.

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This study examines if a listener’s exposure to nonrhotic dialects of English affects how they perceive rhoticity in words spoken in a Boston English accent. Listener judgments on the rhoticity of both nonce words and words in phrases were elicited through a 120-question survey. The results suggest that listeners from the United States who grew up in regions where nonrhotic dialects are prevalent perceived /ɹ/ in certain nonrhotic articulations more than their counterparts did.

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40

Gray, Murray, and Michael Hambrey. "Glacial Environments." Transactions of the Institute of British Geographers 20, no. 3 (1995): 395. http://dx.doi.org/10.2307/622665.

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41

Kang, Jian. "Sound Environments." Environments 7, no. 11 (November 11, 2020): 101. http://dx.doi.org/10.3390/environments7110101.

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42

Ponzoni, Maurilio. "Dangerous environments." International Journal of Hematologic Oncology 3, no. 6 (December 2014): 383–85. http://dx.doi.org/10.2217/ijh.14.36.

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43

Clayton, Keith, M. A. J. Williams, D. L. Dunkerley, P. De Dekker, A. P. Kershaw, and T. Stokes. "Quaternary Environments." Geographical Journal 161, no. 2 (July 1995): 219. http://dx.doi.org/10.2307/3059992.

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44

Hanson, Randel D., Nancy Lee Peluso, and Michael Watts. "Violent Environments." Contemporary Sociology 32, no. 2 (March 2003): 223. http://dx.doi.org/10.2307/3089613.

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45

Greaves, Wilfrid. "Damaging Environments." Environment and Society 9, no. 1 (September 1, 2018): 107–24. http://dx.doi.org/10.3167/ares.2018.090108.

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This article theorizes why Indigenous peoples’ security claims fail to be accepted by government authorities or incorporated into the security policies and practices of settler states. By engaging the concepts of securitization and ontological security, I explain how Indigenous peoples are unable to successfully “speak” security to the state. I argue that nondominant societal groups are unable to gain authoritative acceptance for security issues that challenge the dominant national identity. In effect, indigeneity acts an inhibiting condition for successful securitization because, by identifying the state and dominant society as the source of their insecurity, Indigenous peoples’ security claims challenge the ontological security of settler societies. Given the incommensurability of Indigenous and settler claims to authority over land, and the ontological relationship to land that underpins Indigenous identities and worldviews, the inhibiting condition is especially relevant with respect to security claims based on damage to the natural environment.

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46

Hirsch, Eric. "Constructing Environments." Anthropology Today 7, no. 6 (December 1991): 16. http://dx.doi.org/10.2307/3033050.

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47

Chandler. "Political Environments." Qui Parle 19, no. 2 (2011): 299. http://dx.doi.org/10.5250/quiparle.19.2.0299.

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48

Redman, Richard. "Practice Environments." Research and Theory for Nursing Practice 17, no. 1 (January 2003): 87–88. http://dx.doi.org/10.1891/rtnp.17.1.87.53164.

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49

Delaplace, J. "Radiation Environments." Solid State Phenomena 30-31 (January 1992): 169–78. http://dx.doi.org/10.4028/www.scientific.net/ssp.30-31.169.

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50

Vaughn, Sarah E., and Daniel Fisher. "Witnessing environments." HAU: Journal of Ethnographic Theory 11, no. 2 (September 1, 2021): 387–94. http://dx.doi.org/10.1086/716548.

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