Search Results (22)

Herein, we address the peculiar lack of scientific reporting on odor potent chloroanisoles (CAs) in the built environment. We have searched and critically examined sources beyond peer-reviewed scientific journals, namely research conferences, parliamentary records, newspaper articles, and cartoons. We provide evidence that CAs evolved on a large scale in Swedish buildings in the early 1970s and evoked a typical sticky malodor that was attributed to mold and gave rise to the term “mold houses”. The term first appeared in Swedish newspapers in 1978, and the media attention increased rapidly. The malodorous “mold houses” reached the Swedish parliament and led to economic compensation for afflicted homeowners. The “mold houses” became “sick houses” as researchers, predominantly from Sweden, introduced and became world leaders on the “sick buildings syndrome” (SBS). Researchers became aware of the CAs but did not mention them in peer-reviewed articles, just as they did not mention a well-known source of the sticky malodor, namely, legacy preserved wood where CAs were formed through microbial methylation of toxic chlorophenols (CPs). Thus, the mold story from the early 1970s was maintained and prevented the malodorous CAs from becoming recognized as indicators of the presence of hazardous CPs. Our study is the first to report the impact of an indoor malodor, not only on a few people, but on society. Full article

Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent’s sensor readings to calculate action commands to guide the robot to locate the odor source. Compared to traditional ‘olfaction-only’ OSL algorithms, our proposed OSL algorithm integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by non-unidirectional airflow or vision sensing is impaired by environmental complexities. The algorithm leverages the zero-shot multi-modal reasoning capabilities of large language models (LLMs), negating the requirement of manual knowledge encoding or custom-trained supervised learning models. A key feature of the proposed algorithm is the ‘High-level Reasoning’ module, which encodes the olfaction and vision sensor data into a multi-modal prompt and instructs the LLM to employ a hierarchical reasoning process to select an appropriate high-level navigation behavior. Subsequently, the ‘Low-level Action’ module translates the selected high-level navigation behavior into low-level action commands that can be executed by the mobile robot. To validate our algorithm, we implemented it on a mobile robot in a real-world environment with non-unidirectional airflow environments and obstacles to mimic a complex, practical search environment. We compared the performance of our proposed algorithm to single-sensory-modality-based ‘olfaction-only’ and ‘vision-only’ navigation algorithms, and a supervised learning-based ‘vision and olfaction fusion’ (Fusion) navigation algorithm. The experimental results show that the proposed LLM-based algorithm outperformed the other algorithms in terms of success rates and average search times in both unidirectional and non-unidirectional airflow environments. Full article

The present study focused on the use of pouting (Trisopterus luscus), an underutilized gadoid fish species, as a fresh product of potential commercial interest. Accordingly, non-degutted pouting specimens (145–195 g and 15–22 cm) were stored under chilling conditions (0 °C) for microbial, chemical and sensory analyses to evaluate their commercial quality and shelf life. A progressive quality loss (p < 0.05) was detected for this lean species (5.58 g lipids·kg⁻¹ muscle) as the storage time increased, as determined through microbial (aerobes, psychrotrophs and Enterobacteriaceae counts), lipid hydrolysis (free fatty acid value), lipid oxidation (conjugated diene and triene, thiobarbituric acid reactive substance, and fluorescence values) and sensory acceptance assessment. A detailed comparison to related lean fish species revealed that the pouting exhibited a fast quality breakdown under refrigeration conditions. Thus, after 9 d of chilled storage, the psychrotroph counts exceeded the acceptable limits (8.54 log CFU·g⁻¹), and the fish specimens were found to be rejectable, with the sensory panel, external odor and eye appearance being the limiting factors. In contrast, the pouting specimens exhibited high quality after 3 d of storage, with the quality being still acceptable after 6 d. According to the current search for novel, underutilized species, pouting is proposed as a promising source. Full article

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-Avoid Navigation, Vision-Based Navigation, and Olfaction-Based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-Based Navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms vision-only and olfaction-only methods, reducing average search time by 54% and 30%, respectively. Full article

Dogs’ (Canis lupus familiaris) sense of smell is based on a unique anatomy and physiology that enables them to find and differentiate low concentrations of odor molecules. This ability is exploited when dogs are trained as search, rescue, or medical detection dogs. We performed a three-part study to explore the scent detection threshold of 15 dogs to an in-house-made Eucalyptus hydrolat. Here, decreasing concentrations of the hydrolat were tested using a three-alternative forced-choice method until the first incorrect response, which defined the limit of scent detection for each tested dog. Quantitative proton nuclear magnetic resonance spectroscopy was used to identify and measure the contents of ten commercial Eucalyptus hydrolats, which are used in a dog scent training sport called “nose work”. In this study, the dogs’ limit of detection initially ranged from 1:10⁴ to 1:10²³ but narrowed down to 1:10¹⁷–1:10²¹ after a training period. The results show that, with training, dogs learn to discriminate decreasing concentrations of a target scent, and that dogs can discriminate Eucalyptus hydrolat at very low concentrations. We also detected different concentrations of eucalyptol and lower alcohols in the hydrolat products and highlight the importance of using an identical source of a scent in training a dog for participation in canine scent sport competitions and in olfactory research. Full article

Mobile robot olfaction of toxic and hazardous odor sources is of great significance in anti-terrorism, disaster prevention, and control scenarios. Aiming at the problems of low search efficiency and easily falling into a local optimum of the current odor source localization strategies, the paper proposes the adaptive space-aware Infotaxis II algorithm. To improve the tracking efficiency of robots, a new reward function is designed by considering the space information and emphasizing the exploration behavior of robots. Considering the enhancement in exploratory behavior, an adaptive navigation-updated mechanism is proposed to adjust the movement range of robots in real time through information entropy to avoid an excessive exploration behavior during the search process, which may lead the robot to fall into a local optimum. Subsequently, an improved adaptive cosine salp swarm algorithm is applied to confirm the optimal information adaptive parameter. Comparative simulation experiments between ASAInfotaxis II and the classical search strategies are carried out in 2D and 3D scenarios regarding the search efficiency and search behavior, which show that ASAInfotaxis II is competent to improve the search efficiency to a larger extent and achieves a better balance between exploration and exploitation behaviors. Full article

Olfactory dysfunction is a prevalent non-motor symptom in Parkinson’s disease (PD), affecting approximately 65–90% of subjects. PD patients may also report odor perception in the absence of any external source, often referred to as olfactory hallucinations (OHs) or phantosmia. This study aims to explore the current understanding of OHs in PD and offer a comprehensive overview of their prevalence and characteristics. We conducted a systematic search of the literature published on PubMed from inception to July 2023 regarding OHs in PD, following PRISMA guidelines. From the 2875 studies identified through database searching, 29 studies fulfilled the necessary criteria and underwent data extraction. The frequency of OHs in PD patients varies widely, ranging from 0.5% to 18.2%, with female prevalence ranging from 36% to 75% of the patients. Olfactory experiences may vary widely, ranging from pleasant scents to unpleasant odors. Several studies have indicated the concurrent presence of other types of hallucinations alongside phantosmia, especially visual and auditory hallucinations. OHs in PD are a type of hallucination that has been largely overlooked. To gain a deeper understanding of OHs in PD patients, the next crucial step should involve the development and validation of a dedicated questionnaire. Full article

Social media has become a useful instrument and forum for expressing worries about various difficulties and day-to-day concerns. The pertinent postings containing people’s complaints about water quality as an additional source of information can be automatically acquired/retrieved and analyzed using natural language processing and machine learning approaches. In this paper, we search social media for a water quality analysis and propose a scalable messaging system for quality-related issues to the subscribers. We classify the WaterQualityTweets dataset, our newly collected collection, in two phases. In the first phase, tweets are classified into two classes (water quality-related or not). In the second phase, water quality-related issues are classified into four classes (color, illness, odor/taste, and unusual state). The best performance results are BERT and CNN, respectively, for binary and multi-class classification. Also, these issues are sent to different subscribers via a topic-based system with their location and timing information. Depending on the topics that online users are interested in, some information spreads faster than others. In our dataset, we also predict the information diffusion to understand water quality issues’ spreading. The time and effort required for manual comments obtained through crowd-sourcing techniques will significantly decline as a result of this automatic analysis of water quality issues. Full article

In order to improve the precision of gas detection and develop valid search strategies, the improved quantitative identification algorithm in odor source searching was researched based on the gas sensor array. The gas sensor array was devised corresponding to the artificial olfactory system, and the one-to-one response mode to the measured gas was set up with its inherent cross-sensitive properties. The quantitative identification algorithms were researched, and the improved Back Propagation algorithm was proposed combining cuckoo algorithm and simulated annealing algorithm. The test results prove that using the improved algorithm to obtain the optimal solution −1 at the 424th iteration of the Schaffer function with 0% error. The gas detection system designed with MATLAB was used to obtain the detected gas concentration information, then the concentration change curve may be achieved. The results show that the gas sensor array can detect the concentration of alcohol and methane in the corresponding concentration detection range and show a good detection performance. The test plan was designed, and the test platform in a simulated environment in the laboratory was found. The concentration prediction of experimental data selected randomly was made by the neural network, and the evaluation indices were defined. The search algorithm and strategy were developed, and the experimental verification was carried out. It is testified that the zigzag searching stage with an initial angle of 45° is with fewer steps, faster searching speed, and a more exact position to discover the highest concentration point. Full article

There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster–Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a given location is a source. They have potential applications to assist in locating emitting sources using mobile point sensors. However, the performance and limitations of these two algorithms is currently unknown, and a better understanding of their effectiveness under various conditions is required prior to application. To address this knowledge gap, we tested the response of both algorithms to different environmental and odor search parameters. The localization performance of the algorithms was measured using the earth mover’s distance. Results indicate that the IP algorithm outperformed the DS theory algorithm by minimizing source attribution in locations where there were no sources, while correctly identifying source locations. The DS theory algorithm also identified actual sources correctly but incorrectly attributed emissions to many locations where there were no sources. These results suggest that the IP algorithm offers a more appropriate approach for solving the MOSL problem in environments with turbulent fluid flow. Full article

Odor-source localization, by which one finds the source of an odor by detecting the odor itself, is an important ability to possess in order to search for leaking gases, explosives, and disaster survivors. Although many animals possess this ability, research on implementing olfaction in robotics is still developing. We developed a novel algorithm that enables a robot to localize an odor source indoors and outdoors by taking inspiration from the adult male silk moth, which we used as the target organism. We measured the female-localization behavior of the silk moth by using a virtual reality (VR) system to obtain the relationship between multiple sensory stimuli and behavior during the localization behavior. The results showed that there were two types of search active and inactive depending on the direction of odor and wind detection. In an active search, the silk moth moved faster as the odor-detection frequency increased, whereas in the inactive search, they always moved slower under all odor-detection frequencies. This phenomenon was constructed as a robust moth-inspired (RMI) algorithm and implemented on a ground-running robot. Experiments on odor-source localization in three environments with different degrees of environmental complexity showed that the RMI algorithm has the best localization performance among conventional moth-inspired algorithms. Analysis of the trajectories showed that the robot could move smoothly through the odor plume even when the environment became more complex. This indicates that switching and modulating behavior based on the direction of odor and wind detection contributes to the adaptability and robustness of odor-source localization. Full article

Odor source localization (OSL) robots are essential for safety and rescue teams to overcome the problem of human exposure to hazardous chemical plumes. However, owing to the complicated geometry of environments, it is almost impossible to construct the dispersion model of the odor plume in practical situations to be used for probabilistic odor source search algorithms. Additionally, as time is crucial in OSL tasks, dynamically modifying the robot’s balance of emphasis between exploration and exploitation is desired. In this study, we addressed both the aforementioned problems by simplifying the environment with an obstacle region into multiple sub-environments with different resolutions. Subsequently, a framework was introduced to switch between the Infotaxis and Dijkstra algorithms to navigate the agent and enable it to reach the source swiftly. One algorithm was used to guide the agent in searching for clues about the source location, whereas the other facilitated the active movement of the agent between sub-environments. The proposed algorithm exhibited improvements in terms of success rate and search time. Furthermore, the implementation of the proposed framework on an autonomous mobile robot verified its effectiveness. Improvements were observed in our experiments with a robot when the success rate increased 3.5 times and the average moving steps of the robot were reduced by nearly 35%. Full article

Each fall, monarch butterflies in eastern North America undergo an extraordinary long-distance migration to wintering areas in central Mexico, where they remain until returning northward in the spring. Migrants survive the overwintering period by metabolizing lipid reserves accumulated exclusively though floral nectar; however, there is little known about how individuals maximize foraging efficiency in the face of floral environments that constantly change in complex and unpredictable ways along their migratory route. Here, a proboscis extension paradigm is used to investigate the role of cognition during the foraging phase of monarch migration. Male and female migratory butterflies were consecutively trained to discriminate between two color and odor cues and then tested for their ability to simultaneously retain the information on the reward value of each cue in memory without reinforcement over a period of 7 days. To gain further insight into cognitive abilities of monarchs as a migratory species, a second set of captive-reared males and females were tested under harnessed conditions at the same time as wild-caught fall migrants. Results showed that male and female migrants can learn the reward properties of color and odor cues with over 75% accuracy after less than 40 s of exposure and can simultaneously retain visual and olfactory information predicting the availability of floral rewards in memory without reinforcement for at least 7 days. Captive-reared male butterflies also showed the ability to retain visual and olfactory information in long-term memory for 7 days; however, 80% of captive-reared females could not retain color cues in long-term memory for more than 24 h. These novel findings are consistent with the view that monarch butterflies, as a migratory species, have enhancements to long-term memory that enable them to minimize the amount of time and energy wasted searching for suitable nectar sources during their annual fall migration, thereby optimizing migratory performance and increasing the chance of overwinter survival. The possibility that female monarchs undergo a seasonal change in visual long-term memory warrants further empirical investigation. Full article

In this study, we fabricated a novel wearable vibration sensor for insects and measured their wing flapping. An analysis of insect wing deformation in relation to changes in the environment plays an important role in understanding the underlying mechanism enabling insects to dynamically interact with their surrounding environment. It is common to use a high-speed camera to measure the wing flapping; however, it is difficult to analyze the feedback mechanism caused by the environmental changes caused by the flapping because this method applies an indirect measurement. Therefore, we propose the fabrication of a novel film sensor that is capable of measuring the changes in the wingbeat frequency of an insect. This novel sensor is composed of flat silver particles admixed with a silicone polymer, which changes the value of the resistor when a bending deformation occurs. As a result of attaching this sensor to the wings of a moth and a dragonfly and measuring the flapping of the wings, we were able to measure the frequency of the flapping with high accuracy. In addition, as a result of simultaneously measuring the relationship between the behavior of a moth during its search for an odor source and its wing flapping, it became clear that the frequency of the flapping changed depending on the frequency of the odor reception. From this result, a wearable film sensor for an insect that can measure the displacement of the body during a particular behavior was fabricated. Full article

Olfactory systems in animals play a major role in finding food and mates, avoiding predators, and communication. Chemical tracking in odorant plumes has typically been considered a spatial information problem where individuals navigate towards higher concentration. Recent research involving chemosensory neurons in the spiny lobster, Panulirus argus, show they possess rhythmically active or ‘bursting’ olfactory receptor neurons that respond to the intermittency in the odor signal. This suggests a possible, previously unexplored olfactory search strategy that enables lobsters to utilize the temporal variability within a turbulent plume to track the source. This study utilized computational fluid dynamics to simulate the turbulent dispersal of odorants and assess a number of search strategies thought to aid lobsters. These strategies include quantification of concentration magnitude using chemosensory antennules and leg chemosensors, simultaneous sampling of water velocities using antennule mechanosensors, and utilization of antennules to quantify intermittency of the odorant plume. Results show that lobsters can utilize intermittency in the odorant signal to track an odorant plume faster and with greater success in finding the source than utilizing concentration alone. However, the additional use of lobster leg chemosensors reduced search time compared to both antennule intermittency and concentration strategies alone by providing spatially separated odorant sensors along the body. Full article