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Статті в журналах з теми "Smell"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 26 жовтня 2024

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1

Ahammed, Toukir, Sumon Ahmed, and Mohammed Shafiul Alam Khan. "Do Missing Link Community Smell Affect Developers Productivity: An Empirical Study." Knowledge Engineering and Data Science 4, no. 1 (August 1, 2021): 29. http://dx.doi.org/10.17977/um018v4i12021p29-37.

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Missing link smell occurs when developers contribute to the same source code without communicating with each other. Existing studies have analyzed the relationship of missing link smells with code smell and developer contribution. However, the productivity of developers involved in missing link smell has not been explored yet. This study investigates how productivity differs between smelly and non-smelly developers. For this purpose, the productivity of smelly and non-smelly developers of seven open-source projects are analyzed. The result shows that the developers not involved in missing link smell have more productivity than the developers involved in smells. The observed difference is also found statistically significant.

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2

Guggulothu, Thirupathi, and Salman Abdul Moiz. "Detection of Shotgun Surgery and Message Chain Code Smells using Machine Learning Techniques." International Journal of Rough Sets and Data Analysis 6, no. 2 (April 2019): 34–50. http://dx.doi.org/10.4018/ijrsda.2019040103.

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Code smell is an inherent property of software that results in design problems which makes the software hard to extend, understand, and maintain. In the literature, several tools are used to detect code smell that are informally defined or subjective in nature due to varying results of the code smell. To resolve this, machine leaning (ML) techniques are proposed and learn to distinguish the characteristics of smelly and non-smelly code elements (classes or methods). However, the dataset constructed by the ML techniques are based on the tools and manually validated code smell samples. In this article, instead of using tools and manual validation, the authors considered detection rules for identifying the smell then applied unsupervised learning for validation to construct two smell datasets. Then, applied classification algorithms are used on the datasets to detect the code smells. The researchers found that all algorithms have achieved high performance in terms of accuracy, F-measure and area under ROC, yet the tree-based classifiers are performing better than other classifiers.

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3

DiGiovanna, Brett. "Smell." Annals of Internal Medicine 137, no. 9 (November 5, 2002): 768. http://dx.doi.org/10.7326/0003-4819-137-9-200211050-00015.

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4

Liu, Huihui, Bixin Li, Yibiao Yang, Wanwangying Ma, and Ru Jia. "Exploring the Impact of Code Smells on Fine-Grained Structural Change-Proneness." International Journal of Software Engineering and Knowledge Engineering 28, no. 10 (September 25, 2018): 1487–516. http://dx.doi.org/10.1142/s0218194018500432.

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Code smells are used to describe the bad structures in the source code, which could hinder software maintainability, understandability and changeability. Nowadays, scholars mainly focus on the impact of smell on textual change-proneness. However, in comparison to textual changes, structural changes could better reveal the change nature. In practice, not all code change types are equally important in terms of change risk severity levels, and software developers are more interested in particular changes relevant to their current tasks. Therefore, we investigate the relationship between smells and fine-grained structural change-proneness to solve these issues. Our experiment was conducted on 11 typical open source projects. We first employed Fishers exact test and Mann–Whitney test to explore whether smelly files (affected by at least one smell type) had higher structural change-proneness than other files, and whether files with more smell instances are more likely to undergo structural changes, respectively. Multivariate logistic regression model was built to study the relation between each kind of smell and change-proneness with respect to five change categories. Our results showed that: (1) in most cases, smelly files were more prone to structural changes and files with more smell instances tend to undergo higher structural changes; (2) quite a few smell types were related to structural change-proneness, particularly, Refused Parent Bequest (RPB), Message Chains (MCH), Divergent Change (DIVC), Feature Envy (FE) and Shotgun Surgery (SS) increased structural changes for some change categories. However, when controlling the file size Lines of Code (LOC), significant change-proneness of some smells disappeared or the magnitude of significance decreased more or less.

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5

Spinney, Laura. "You Smell Flowers, I Smell Stale Urine." Scientific American 304, no. 2 (February 2011): 26. http://dx.doi.org/10.1038/scientificamerican0211-26a.

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6

Beek, Walter E. A. van. "The dirty smith: smell as a social frontier among the Kapsiki/Higi of north Cameroon and north-eastern Nigeria." Africa 62, no. 1 (January 1992): 38–58. http://dx.doi.org/10.2307/1160063.

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AbstractAmong the Kapsiki/Higi of the Mandara mountains the division between black-smith and non-smith pervades society. Blacksmiths dominate technical and ritual specialisations—including the forge—and through their association with death are considered dirty. One way in which this opposition is expressed is through the definition of smell.Using ideophones, the Kapsiki distinguish fourteen types of smell, each associated with specific ‘smelly’ objects, animals or persons (i.e. blacksmiths). The definition of smells by blacksmiths, however, is different from that of non-smiths; also the women of both ‘castes’ define smell differently from the men. Whereas men use the definition of smell to accentuate the gap between smith and non-smith, the women tend to mediate the division.In Kapsiki culture smell is not associated with notions of evil or witchcraft. It is, however, tied in with burial, which in Kapsiki culture entails protracted exposure to a decomposing corpse. The connection smith-corpse may be one reason for the smelly definition of the smith. Another may be the notion of ambivalence and the tendency to draw strict dividing lines between social groups. Smell in Kapsiki seems to stress borderline situations and the mutual dependence of opposing groups.

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7

Counsell, S., R. M. Hierons, H. Hamza, S. Black, and M. Durrand. "Exploring the Eradication of Code Smells: An Empirical and Theoretical Perspective." Advances in Software Engineering 2010 (March 17, 2010): 1–12. http://dx.doi.org/10.1155/2010/820103.

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Code smells reflect code decay, and, as such, developers should seek to eradicate such smells through application of “deodorant” in the form of one or more refactorings. However, a relative lack of studies exploring code smells either theoretically or empirically when compared with literature on refactoring suggests that there are reasons why smell eradication is neither being applied in anger, nor the subject of significant research. In this paper, we present three studies as supporting evidence for this stance. The first is an analysis of a set of five, open-source Java systems in which we show very little tendency for smells to be eradicated by developers; the second is an empirical study of a subsystem of a proprietary, C# web-based application where practical problems arise in smell identification and the third, a theoretical enumeration of smell-related refactorings to suggest why smells may be left alone from an effort perspective. Key findings of the study were that first, smells requiring application of simple refactorings were eradicated in favour of smells requiring more complex refactorings; second, a wide range of conflicts and anomalies soon emerged when trying to identify smelly code; an interesting result with respect to comment lines was also observed. Finally, perceived (estimated) effort to eradicate a smell may be a key factor in explaining why smell eradication is avoided by developers. The study thus highlights the need for a clearer research strategy on the issue of code smells and all aspects of their identification and measurement.

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8

Schulz, Stefan, and Jeroen S. Dickschat. "Bacterial volatiles: the smell of small organisms." Natural Product Reports 24, no. 4 (2007): 814. http://dx.doi.org/10.1039/b507392h.

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9

P, Clarós, Cygan A, Portela A, Pérez R, Marimon X, Gabarró M, and Gil J. "An Update on Smell and Sensuality." Journal of Clinical Otorhinolaryngology 3, no. 4 (November 4, 2021): 01–10. http://dx.doi.org/10.31579/2692-9562/036.

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Анотація:

When thinking of smell, we usually consider it only as one of the five senses. Compared to the rest of senses, smell has been underestimated. By conducting a research, we will be able to discover how mistaken we are. The human sense of smell is more powerful than it's usually given credit for and plays a major role in human health and behavior than many experts in the field are aware of the sense of smell is present in our daily activities and, depending on the gender, perception may differ, as well as from the anatomical structure of the olfactory organ between genders to the stimulus of the odor. The fragrances determine our everyday food choices, places where we choose to spend with our life partner. Scents can determine our sexual behavior, in building infant-parent connection and create our habits as well. It can also warn us against the selection of food, persons, also death. In this case, the loss of smell can be related with an olfactory organ dysfunction, but as well can have an endocrine, genetic or psychical basis. The olfactory dysfunction can change our habits and basically our whole life. It is irrefutable that while talking about smell which we consider as one of the senses, we can talk about sensuality which broadly covers the relationship between smell and perception of reality. Sensuality in a general sense can be defined as the perception of the surrounding things through the senses, as sensory pleasure, and unlike sexuality, which can be translated as the reception of biological, psychological and physical stimuli. Aim of the study: Currently all of our senses are mostly well known. We are trying to consider the topic from many different sides to make ourselves more and more advanced. We are trying to unite a couple of subjects to prove that we can connect one with another to explore how advanced our organism is. In this research we are trying to look closely at the two different topics and glue them together. Trying to improve and update the connection between the sense of smell and sensuality. This is possible due to the relationship between the smell and the part of our brain responsible for memories and memory, i.e. the limbic system. In this way, we can process the aroma stimulus into memories, we associate smells with specific situations, places, people or things [1]. Therefore, the objectives we have in this study are the following: The nose as an olfactory organ and the anatomical differences as to structure; the olfactory system is strictly connected with the sense of smell; sex, as a gender, something that make us different from each other; physical and psychological disorders and influence of smell. Taking into consideration the amount of work and research on the sense of smell and the still uncertain issues related to it, it is certain how important it is for life and science to fully understand its properties. Over time, with the development of science and technology, there is an increasing wish to learn about such a complicated machine as the human body. Material and methods: In the first part of this research, we will gather all the information that is commonly available so far in the international bibliography, as well as the achievements and utilities obtained to date. Following we will analyse all the new concepts that exist on the topic of sense of smell in connection with sex and sensuality, also how the smell can change due to various disorders and try to summarise it based on the latest research.

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10

Sandouka, Rana, and Hamoud Aljamaan. "Python code smells detection using conventional machine learning models." PeerJ Computer Science 9 (May 29, 2023): e1370. http://dx.doi.org/10.7717/peerj-cs.1370.

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Code smells are poor code design or implementation that affect the code maintenance process and reduce the software quality. Therefore, code smell detection is important in software building. Recent studies utilized machine learning algorithms for code smell detection. However, most of these studies focused on code smell detection using Java programming language code smell datasets. This article proposes a Python code smell dataset for Large Class and Long Method code smells. The built dataset contains 1,000 samples for each code smell, with 18 features extracted from the source code. Furthermore, we investigated the detection performance of six machine learning models as baselines in Python code smells detection. The baselines were evaluated based on Accuracy and Matthews correlation coefficient (MCC) measures. Results indicate the superiority of Random Forest ensemble in Python Large Class code smell detection by achieving the highest detection performance of 0.77 MCC rate, while decision tree was the best performing model in Python Long Method code smell detection by achieving the highest MCC Rate of 0.89.

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11

Dekhanova, Olga A. "The Characterological Functions of Untidiness in the Works of Fyodor Dostoevsky and His Contemporaries." Dostoevsky and World Culture. Philological journal, no. 2 (2022): 144–74. http://dx.doi.org/10.22455/2619-0311-2022-2-144-174.

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The article is the continuation of a study on Dostoevsky’s olfactory in the cultural and historical context of the second half of the 19th century. The involvement of public consciousness in the formation of new hygienic standards led to fundamental changes in the olfactory of Russian literature. The gradual overcoming of aesthetic prohibitions made it possible to use unpleasant odors associated with human activity as an artistic detail with negative assessment. Smell rating (pleasant/ unpleasant) is a cultural phenomenon that largely depends on the historical, national, religious, and economic characteristics of a society. In addition, the assessment of smell is also one of the elements of tidiness, a historically established sign system that determines a group identity. Deviations from established cultural norms (untidiness) determine the social boundaries of society at the olfactory level as well. The smell of untidiness, to which Dostoevsky’s contemporaries actively address, consist a specific stench of unwashed body, smells of sweat, urine and other biological secretions, the smell of unclean clothes, the stinking smell of breath, including the smell of wine fumes. In addition, the use of smells inappropriate for the bodily sphere or the smells of illness for the comparative description of “smells like” forms an extensive negative metaphorical field. Dostoevsky’s use of olfactory details to define his characters follows, on the one hand, general patterns, and on the other, has a number of personal features, including his close attention to the cleanliness of the characters’ underwear. This detail is present in almost every description of appearance. Direct references to the connection between unclean underwear and unpleasant odor can be found in a very small group of descriptions, nevertheless, indications of unclean underwear and the untidy appearance of a character have olfactory properties, as they generate a sense of expected unpleasant smell. In addition, the article considers the peculiarity of the use of negative body odors in the novel The Brothers Karamazov by Dostoevsky and in Leskov’s stories.

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12

Williams, Joshua. "The Smell." Theatre Topics 31, no. 2 (2021): 195–98. http://dx.doi.org/10.1353/tt.2021.0036.

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13

Ronnett, Gabriele V., and Robert D. Barber. "Reconstructing Smell." Molecular Neurobiology 21, no. 3 (2000): 161–74. http://dx.doi.org/10.1385/mn:21:3:161.

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14

Davidson, Terence M., Claire Murphy, and Alfredo A. Jalowayski. "Smell impairment." Postgraduate Medicine 98, no. 1 (July 1995): 107–18. http://dx.doi.org/10.1080/00325481.1995.11946020.

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15

Henkin, RobertI. "DISORDERED SMELL." Lancet 332, no. 8616 (October 1988): 901–2. http://dx.doi.org/10.1016/s0140-6736(88)92493-2.

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16

AMATO, IVAN. "YOU SMELL." Chemical & Engineering News 87, no. 41 (October 12, 2009): 50–54. http://dx.doi.org/10.1021/cen-v087n041.p050.

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17

Williams, Caroline. "Smell signals." New Scientist 200, no. 2685 (December 2008): 38–41. http://dx.doi.org/10.1016/s0262-4079(08)63099-7.

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18

Maggioni, Emanuela, Robert Cobden, Dmitrijs Dmitrenko, Kasper Hornbæk, and Marianna Obrist. "SMELL SPACE." ACM Transactions on Computer-Human Interaction 27, no. 5 (October 5, 2020): 1–26. http://dx.doi.org/10.1145/3402449.

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19

Hsu, Yen-Chia, Jennifer Cross, Paul Dille, Michael Tasota, Beatrice Dias, Randy Sargent, Ting-Hao (Kenneth) Huang, and Illah Nourbakhsh. "Smell Pittsburgh." ACM Transactions on Interactive Intelligent Systems 10, no. 4 (December 3, 2020): 1–49. http://dx.doi.org/10.1145/3369397.

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20

Cosier, Susan. "Smell Similarity." Scientific American Mind 19, no. 4 (August 2008): 11. http://dx.doi.org/10.1038/scientificamericanmind0808-11a.

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21

MORIIZUMI, TOYOSAKA. "Sensing technology on tastes and smell. Smell sensors." Journal of the Institute of Electrical Engineers of Japan 123, no. 2 (2003): 104–7. http://dx.doi.org/10.1541/ieejjournal.123.104.

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22

Manigault, Laila Kiara, and Shem Unger. "Tadpoles and Turtles: Can You Smell that Smell?" International Journal of Undergraduate Research and Creative Activities 15, no. 1 (April 11, 2023): 1. http://dx.doi.org/10.7710/2168-0620.0387.

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23

Hasrullah, Benrad Edwin Simanjuntak, Marhaposan Situmorang, Syahrul Humaidi, and Marzuki Sinambela. "Response Characteristics of Sensor Array of Fresh Meat Detection Circuit using Conducting Polymer Sensor." IOP Conference Series: Earth and Environmental Science 1083, no. 1 (September 1, 2022): 012030. http://dx.doi.org/10.1088/1755-1315/1083/1/012030.

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Abstract Consumption of meat is needed by society. The meat needed is of course fresh and quality meat. However, the quality of meat sold in the community varies greatly. Fresh and quality meat is meat that is odorless, not slimy, does not change color in certain areas. Meat that is not fresh and not of good quality is smelly meat. The odor is described as a fishy smell, a foul smell, contains sulfur and smells like ammonia. Because it is dangerous to consume rotten meat, it is necessary to have an automatic meat detection device so that it can ensure the meat is fresh and of good quality. This tool replaces the human nose with an odor sensor as a bad smell detector.

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24

Villar, Paulina Maria Angela, Ryan Chua, and Ruby Robles. "Smell Training in Prolonged COVID-19 PostInfectious Olfactory Dysfunction: A Case Report." Philippine Journal of Otolaryngology Head and Neck Surgery 36, no. 1 (May 30, 2021): 37. http://dx.doi.org/10.32412/pjohns.v36i1.1655.

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ABSTRACT Objective: To report the case of a woman who underwent smell training for post-infectious olfactory dysfunction presumably from COVID-19. Methods: Design: Case Report Setting: Tertiary Private Training Hospital Patient: One Result: A 41-year-old woman who developed olfactory dysfunction attributed to COVID-19 underwent smell training. At baseline, her responses were mostly “no smell,” and those reported as “can smell a bit” were rated as distorted. After three months, she could now smell items that she previously could not smell, but these smells were still distorted. At the time of this writing, she was on her 4th month of smell training. Conclusion: Although we cannot rule out spontaneous resolution of anosmia in our patient, we would like to think that smell training contributed to her recovery of smell.

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25

Sharma, Pratiksha, and Er Arshpreet Kaur. "Design of testing framework for code smell detection (OOPS) using BFO algorithm." International Journal of Engineering & Technology 7, no. 2.27 (August 6, 2018): 161. http://dx.doi.org/10.14419/ijet.v7i2.27.14635.

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Detection of bad smells refers to any indication in the program code of a execution that perhaps designate a issue, maintain the software and software evolution. Code Smell detection is a main challenging for software developers and their informal classification direct to the designing of various smell detection methods and software tools. It appraises 4 code smell detection tool in software like as a in Fusion, JDeodorant, PMD and Jspirit. In this research proposes a method for detection the bad code smells in software is called as code smell. Bad smell detection in software, OOSMs are used to identify the Source Code whereby Plug-in were implemented for code detection in which position of program initial code the bad smell appeared so that software refactoring can then acquire position. Classified the code smell, as a type of codes: long method, PIH, LPL, LC, SS and GOD class etc. Detection of the code smell and as a result applying the correct detection phases when require is significant to enhance the Quality of the code or program. The various tool has been proposed for detection of the code smell each one featured by particular properties. The main objective of this research work described our proposed method on using various tools for code smell detection. We find the major differences between them and dissimilar consequences we attained. The major drawback of current research work is that it focuses on one particular language which makes them restricted to one kind of programs only. These tools fail to detect the smelly code if any kind of change in environment is encountered. The base paper compares the most popular code smell detection tools on basis of various factors like accuracy, False Positive Rate etc. which gives a clear picture of functionality these tools possess. In this paper, a unique technique is designed to identify CSs. For this purpose, various object-oriented programming (OOPs)-based-metrics with their maintainability index are used. Further, code refactoring and optimization technique are applied to obtain low maintainability Index. Finally, the proposed scheme is evaluated to achieve satisfactory results. The results of the BFOA test defined that the lazy class caused framework defects in DLS, DR, and SE. However, the LPL caused no framework defects what so ever. The consequences of the connection rules test searched that the LCCS (Lazy Class Code Smell) caused structured defects in DE and DLS, which corresponded to the consequences of the BFOA test. In this research work, a proposed method is designed to verify the code smell. For this purpose, different OOPs based Software Metrics with their MI (Maintainability Index) are utilized. Further Code refactoring and optimization method id applied to attained the less maintainability index and evaluated to achieved satisfactory results.

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26

Sotto-Mayor, Bruno, Amir Elmishali, Meir Kalech, and Rui Abreu. "Exploring Design smells for smell-based defect prediction." Engineering Applications of Artificial Intelligence 115 (October 2022): 105240. http://dx.doi.org/10.1016/j.engappai.2022.105240.

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27

Kim, Dong Kwan. "Finding Bad Code Smells with Neural Network Models." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (December 1, 2017): 3613. http://dx.doi.org/10.11591/ijece.v7i6.pp3613-3621.

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Code smell refers to any symptom introduced in design or implementation phases in the source code of a program. Such a code smell can potentially cause deeper and serious problems during software maintenance. The existing approaches to detect bad smells use detection rules or standards using a combination of different object-oriented metrics. Although a variety of software detection tools have been developed, they still have limitations and constraints in their capabilities. In this paper, a code smell detection system is presented with the neural network model that delivers the relationship between bad smells and object-oriented metrics by taking a corpus of Java projects as experimental dataset. The most well-known object-oriented metrics are considered to identify the presence of bad smells. The code smell detection system uses the twenty Java projects which are shared by many users in the GitHub repositories. The dataset of these Java projects is partitioned into mutually exclusive training and test sets. The training dataset is used to learn the network model which will predict smelly classes in this study. The optimized network model will be chosen to be evaluated on the test dataset. The experimental results show when the modelis highly trained with more dataset, the prediction outcomes are improved more and more. In addition, the accuracy of the model increases when it performs with higher epochs and many hidden layers.

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28

Codabux, Zadia, Kazi Zakia Sultana, and Byron J. Williams. "The Relationship Between Code Smells and Traceable Patterns — Are They Measuring the Same Thing?" International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (November 2017): 1529–47. http://dx.doi.org/10.1142/s0218194017400095.

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It is important to maintain software quality as a software system evolves. Managing code smells in source code contributes towards quality software. While metrics have been used to pinpoint code smells in source code, we present an empirical study on the correlation of code smells with class-level (micro pattern) and method-level (nano-pattern) traceable code patterns. This study explores the relationship between code smells and class-level and method-level structural code constructs. We extracted micro patterns at the class level and nano-patterns at the method level from three versions of Apache Tomcat, three versions of Apache CXF and two J2EE web applications namely PersonalBlog and Roller from Stanford SecuriBench and then compared their distributions in code smell versus noncode smell classes and methods. We found that Immutable and Sink micro patterns are more frequent in classes having code smells compared to the noncode smell classes in the applications we analyzed. On the other hand, LocalReader and LocalWriter nano-patterns are more frequent in code smell methods compared to the noncode smell methods. We conclude that code smells are correlated with both micro and nano-patterns.

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29

Canniford, Robin, Kathleen Riach, and Tim Hill. "Nosenography." Marketing Theory 18, no. 2 (October 2, 2017): 234–48. http://dx.doi.org/10.1177/1470593117732462.

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Nosenography is a theoretical and methodological commitment to uncover the presences and practices of smell, an often-ignored sensory feature of market and consumption spaces. Drawing on prior social science theorizations of smell as well as contemporary sensory marketing practices, we develop a framework to understand how smell features in spatial assemblages of bodies, locations and experiences. Extending theorizations of product smells and ambient smells, we show how this framework can guide knowledge of the sensing, practice and management of smell and space. We explain that smell is a dynamic and unruly force that (i) encodes spaces with meaning, (ii) identifies bodies with spaces, and (iii) punctuates the temporal experience of space as it changes. Nosenography reaffirms that spaces of consumption are multisensory and that this quality should be further acknowledged in figuring market spaces as dynamic and contested assemblages of heterogeneous constituents.

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30

Hsieh, Julien W., Andreas Keller, Michele Wong, Rong-San Jiang, and Leslie B. Vosshall. "SMELL-S and SMELL-R: Olfactory tests not influenced by odor-specific insensitivity or prior olfactory experience." Proceedings of the National Academy of Sciences 114, no. 43 (October 10, 2017): 11275–84. http://dx.doi.org/10.1073/pnas.1711415114.

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Smell dysfunction is a common and underdiagnosed medical condition that can have serious consequences. It is also an early biomarker of neurodegenerative diseases, including Alzheimer’s disease, where olfactory deficits precede detectable memory loss. Clinical tests that evaluate the sense of smell face two major challenges. First, human sensitivity to individual odorants varies significantly, so test results may be unreliable in people with low sensitivity to a test odorant but an otherwise normal sense of smell. Second, prior familiarity with odor stimuli can bias smell test performance. We have developed nonsemantic tests for olfactory sensitivity (SMELL-S) and olfactory resolution (SMELL-R) that use mixtures of odorants that have unfamiliar smells. The tests can be self-administered by healthy individuals with minimal training and show high test–retest reliability. Because SMELL-S uses odor mixtures rather than a single molecule, odor-specific insensitivity is averaged out, and the test accurately distinguished people with normal and dysfunctional smell. SMELL-R is a discrimination test in which the difference between two stimulus mixtures can be altered stepwise. This is an advance over current discrimination tests, which ask subjects to discriminate monomolecular odorants whose difference in odor cannot be quantified. SMELL-R showed significantly less bias in scores between North American and Taiwanese subjects than conventional semantically based smell tests that need to be adapted to different languages and cultures. Based on these proof-of-principle results in healthy individuals, we predict that SMELL-S and SMELL-R will be broadly effective in diagnosing smell dysfunction.

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31

Magdi, Yoan Levia, Guntur Bayu Bima Pratama, and Erial Bahar. "RESULTS OF INTRAVENOUS ALINAMIN AND ALCOHOL EXAMINATION RESULTS IN PATIENTS COMPLAINTS OF SNACTING DISORDERS AT RSMH PALEMBANG." Jurnal Kedokteran dan Kesehatan : Publikasi Ilmiah Fakultas Kedokteran Universitas Sriwijaya 10, no. 2 (May 25, 2023): 221–29. http://dx.doi.org/10.32539/jkk.v10i1.21350.

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Smell is a chemical substance mixed in the air that humans perceive with their sense of smell. Loss of smell or loss of smell has become very important in recent years. Currently, research on odor is very interesting because it can determine the type of odor, as well as the function of smell. Examination of the intravenous smell test and alcohol smell test is one of the gold standard examinations that can be done to determine the type of smell disorder. To determine the concordance between the results of the intravenous alinamin smell test and the alcohol smell test in patients with olfactory disorders at RSMH Palembang. Observational and analytic research using cross sectional. Data collection was carried out using the medical records of RSUP Dr. Mohammad Hoesin Palembang for the period October 2022 to January 2023. Data were analyzed with IBM SPSS 25. In this study, there were 49 patients with complaints of smell disturbances to the THTBKL department of RSUP Dr. Mohammad Hoesin Palembang. The mean age in the study was 33 years old with the most vulnerable population aged <20 years (26.5%). With the majority of the female sex (57.1%). While the majority of the work is mostly students (26.5%) and complains of gradual disturbance smells (91.8%). Most of the patients who came with complaints of smell disturbances were patients with sinonasal masses (53.1%). The correlation between intravenous alinamin and alcohol smell tests using the Spearman correlation test showed a very strong correlation (r=0.908) and in the conformity test using Cohen's kappa value obtained was 1.000 which means that perfect agreement was reached between the two tests. There is a concordance in the examination results between the alinamin smell test and the alcohol smell test in patients with complaints of smell disorders.

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32

Kelley, Nicholas J., and Adrienne L. Crowell. "Self-Reported Sense of Smell Predicts Disgust Sensitivity and Disgust Reactivity." Journal of Individual Differences 39, no. 4 (October 2018): 191–95. http://dx.doi.org/10.1027/1614-0001/a000263.

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Abstract. Two studies tested the hypothesis that self-reported sense of smell (i.e., metacognitive insight into one’s olfactory ability) predicts disgust sensitivity and disgust reactivity. Consistent with our predictions two studies demonstrated that disgust correlates with self-reported sense of smell. Studies 1 and 2 demonstrated, from an individual difference perspective, that trait-like differences in disgust relate to self-reported sense of smell. Physical forms of disgust (i.e., sexual and pathogen disgust) drove this association. However, the association between self-reported sense of smell and disgust sensitivity is small, suggesting that it is likely not a good proxy for disgust sensitivity. The results of Study 2 extended this finding by demonstrating that individual differences in self-reported sense of smell influence how individuals react to a disgusting olfactory stimulus. Those who reported having a better sense of smell (or better insight into their olfactory ability) found a disgusting smell significantly more noxious as compared to participants reporting having a poor sense of smell (or poor insight into their olfactory ability). The current findings suggest that a one-item measure of self-reported sense of smell may be an effective tool in disgust research.

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33

Gao, Ya-Juan, Chiung-Ling Wang, Min-Ling Huang, and Wei Guo. "A New Perspective of Sustainable Perception: Research on the Smellscape of Urban Block Space." Sustainability 14, no. 15 (July 27, 2022): 9184. http://dx.doi.org/10.3390/su14159184.

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The smell of space is inseparable from the sustainable development of the living environment. The research on olfactory perception and smell landscape has a positive effect on landscape design and urban planning and contributes to the formation and design optimization of unique urban memory. This study combines urban smell tracking experiments with Internet social media data analysis to classify smells in the old city center of Guangzhou, China, and analyzes the study within the inner ring and six historic districts. Based on the research results, the smell map was drawn, and the reliability of the smell map was tested through social data and semantic analysis. The emotional score heat map of smell and emotion in six regions was constructed, highlighting the impact of smell in key neighborhoods on the environment. In the conclusion to the study, the thematic routes of green urban design are proposed: sightseeing routes, cultural routes, and food routes, as well as improvement strategies to promote the integration of smell and urban operation activities and the sustainable development of urban regional characteristics.

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34

Niu, Xiao Yi, Yu Wen Bai, and Xue Wang. "The Modification of Mechanical Properties of Changbai Mountain Smell Pine Used by Floor." Advanced Materials Research 430-432 (January 2012): 866–68. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.866.

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In this experiment, do smell pine impregnated pressure experiments with small molecules phenolic resin. Prepare different small molecules and phenolic resin by orthogonal.Handle the specimens. Compare the treated material with the smell pine. The optimum condition is the temperature of 60°C, PVA: P (mass ratio) = 3%, P: F: NaOH (molar ratio) = 1:1.2:0.3, the absolute pressure 2.5 Mpa, the temperature of 120 °C hot 15 minutes under the conditions. While smell pine laminated veneer lumber bending strength increased 67.53%.

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35

HIRONAKA, Hiromi. "Smell of PCBs." Journal of Japan Association on Odor Environment 36, no. 6 (2005): 316–22. http://dx.doi.org/10.2171/jao.36.316.

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36

Muller, Theresa. "Smell Ya Later." Proceedings of the Water Environment Federation 2016, no. 2 (January 1, 2016): 543–67. http://dx.doi.org/10.2175/193864716821123233.

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37

Stokel-Walker, Chris. "Smell the roses." New Scientist 255, no. 3400 (August 2022): 31. http://dx.doi.org/10.1016/s0262-4079(22)01489-0.

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38

Baker, Gerald, and P. Weiss. "What's That Smell?" Science News 161, no. 22 (June 1, 2002): 351. http://dx.doi.org/10.2307/4013337.

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39

Ha, Hong Kyu. "Smell and Disgust." Journal of Gamsung 22 (March 31, 2021): 29–57. http://dx.doi.org/10.37996/jog.22.2.

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40

Christensen, Damaris. "What's That Smell?" Science News 155, no. 20 (May 15, 1999): 316. http://dx.doi.org/10.2307/4011407.

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41

Bates, Jane. "A bad smell." Nursing Standard 24, no. 19 (January 13, 2010): 25. http://dx.doi.org/10.7748/ns.24.19.25.s32.

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42

Jacob, Tim. "Learning to smell." Physiology News, Autumn 2004 (September 1, 2004): 18–19. http://dx.doi.org/10.36866/pn.56.18.

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43

Wolff, Christian G. ""What's That Smell?"." Primary Care Companion to The Journal of Clinical Psychiatry 05, no. 01 (February 1, 2003): 47–48. http://dx.doi.org/10.4088/pcc.v05n0109.

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44

Reid, Alastair, and Pablo Neruda. "Sense of Smell." Grand Street, no. 64 (1998): 13. http://dx.doi.org/10.2307/25008288.

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45

Mirsky, Steve. "Patient, Smell Thyself." Scientific American 275, no. 2 (August 1996): 25. http://dx.doi.org/10.1038/scientificamerican0896-25.

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46

BARNETT, S. A. "SMELL AND TASTE." Developmental Medicine & Child Neurology 5, no. 5 (November 12, 2008): 516–17. http://dx.doi.org/10.1111/j.1469-8749.1963.tb10708.x.

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47

Batty, Clare. "What's That Smell?" Southern Journal of Philosophy 47, no. 4 (December 2009): 321–48. http://dx.doi.org/10.1111/j.2041-6962.2009.tb00164.x.

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48

Sharp, Nicola. "A bad smell⇓." BMJ 328, no. 7453 (June 12, 2004): s240. http://dx.doi.org/10.1136/bmj.328.7453.s240.

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49

Welberg, Leonie. "A lingering smell?" Nature Reviews Neuroscience 15, no. 1 (December 20, 2013): 1. http://dx.doi.org/10.1038/nrn3660.

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50

Rodriguez, Tori. "You Smell Angry." Scientific American Mind 22, no. 6 (December 28, 2011): 13. http://dx.doi.org/10.1038/scientificamericanmind0112-13c.

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