It is widely recognized among specialists that PCMs (Phase Change Materials) typically have low thermal conductivity, which significantly restricts their commercial use. This study presents alternative, low-cost, yet effective approaches to enhance the average thermal conductivity of a PCM system (a commercially available paraffin wax with a phase change temperature around 40 °C) intended for thermal energy storage. The system contains 600 g of PCM within an annular space around an inner tube, through which heat is either added to or removed from the PCM. Experiments were conducted to assess the effects of water flow rate and temperature, used as the heat transfer fluid, on the system's performance. The flow rate was varied from 2 to 8 L/min, and the temperature was set between 45 and 55 °C. We tested three types of aluminum-based thermal enhancers: a commercial metal foam, a wire mesh, and irregular aluminum flakes (chips) produced as waste from machining processes. The PCM-only sample required the longest time for both charging and discharging, while the PCM with metal foam had the shortest times. The intermediate solutions, using chips and wire mesh, showed moderate phase change times. To evaluate the economic feasibility, we introduced a performance metric based on cost per phase change rate, showing that these two affordable thermal conductivity enhancers could play a vital role in promoting the broader application of latent thermal energy storage technology across various fields.
{"title":"Commercially focused strategies to enhance PCM thermal conductivity in latent thermal energy storage systems","authors":"Giulia Righetti , Kamel Hooman , Claudio Zilio , Dario Guarda , Simone Mancin","doi":"10.1016/j.sctalk.2025.100439","DOIUrl":"10.1016/j.sctalk.2025.100439","url":null,"abstract":"<div><div>It is widely recognized among specialists that PCMs (Phase Change Materials) typically have low thermal conductivity, which significantly restricts their commercial use. This study presents alternative, low-cost, yet effective approaches to enhance the average thermal conductivity of a PCM system (a commercially available paraffin wax with a phase change temperature around 40 °C) intended for thermal energy storage. The system contains 600 g of PCM within an annular space around an inner tube, through which heat is either added to or removed from the PCM. Experiments were conducted to assess the effects of water flow rate and temperature, used as the heat transfer fluid, on the system's performance. The flow rate was varied from 2 to 8 L/min, and the temperature was set between 45 and 55 °C. We tested three types of aluminum-based thermal enhancers: a commercial metal foam, a wire mesh, and irregular aluminum flakes (chips) produced as waste from machining processes. The PCM-only sample required the longest time for both charging and discharging, while the PCM with metal foam had the shortest times. The intermediate solutions, using chips and wire mesh, showed moderate phase change times. To evaluate the economic feasibility, we introduced a performance metric based on cost per phase change rate, showing that these two affordable thermal conductivity enhancers could play a vital role in promoting the broader application of latent thermal energy storage technology across various fields.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"14 ","pages":"Article 100439"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.1016/j.sctalk.2025.100441
Iryna Ruda , Jessica Freiherr
In today's fast-paced world, consuming food while engaging in other activities has become common. Importantly, such distracted eating is associated with increased food intake and rising body weight (Robinson et al., 2013; Van Meer et al., 2022). In this talk, we summarize findings from our studies exploring how cognitive distraction influences taste and odor perception — with a focus on intensity and pleasantness perception — and whether these effects vary by weight status and gender (Ruda et al., 2024a; Ruda et al., 2024b).
Fifty-nine participants formed two study groups — normal-weight (mean BMI = 22.2 kg/m2, range 19.5–24.8 kg/m2) and overweight/obese (mean BMI = 30.3 kg/m2, range 25–39 kg/m2). Participants played a Tetris game while evaluating taste and smell stimuli delivered automatically during the trials.
Our findings indicate that distraction reduced taste intensity perception, particularly in individuals from the overweight/obese group. In contrast to our prior findings, odor intensity perception did not decrease (Hoffmann-Hensel et al., 2017; Schadll et al., 2021). The pleasantness of both taste and odor declined under distraction, an effect most evident in normal-weight participants and particularly pronounced in males.
We hypothesize that this reduction in pleasantness might have a dual effect on eating behavior: it could decrease the perception of palatability of foods, acting as a control against overeating, or conversely, trigger a compensatory drive for more rewarding foods, potentially increasing intake during distraction. These two opposing mechanisms remain to be tested empirically. Our findings highlight the role of chemosensory perception in developing targeted interventions to curb overeating and manage obesity.
在当今快节奏的世界里，一边吃东西一边从事其他活动已经变得很普遍。重要的是，这种分心进食与食物摄入量增加和体重增加有关(Robinson et al., 2013；Van Meer et al., 2022)。在这次演讲中，我们总结了我们的研究结果，探索认知分心如何影响味觉和气味感知-重点是强度和愉悦感-以及这些影响是否因体重状况和性别而变化(Ruda等人，2024a；Ruda et al., 2024b)。59名参与者分成两个研究组——正常体重组（平均BMI = 22.2 kg/m2，范围为19.5-24.8 kg/m2）和超重/肥胖组（平均BMI = 30.3 kg/m2，范围为25-39 kg/m2）。参与者一边玩俄罗斯方块游戏，一边评估在试验中自动传递的味觉和嗅觉刺激。我们的研究结果表明，注意力分散会降低对味觉强度的感知，尤其是在超重/肥胖人群中。与我们之前的研究结果相反，气味强度感知并没有减少(Hoffmann-Hensel等人，2017；Schadll et al., 2021)。在注意力分散的情况下，味觉和嗅觉的愉悦度都有所下降，这一效应在体重正常的参与者中最为明显，在男性中尤为明显。我们假设，这种愉悦感的降低可能会对饮食行为产生双重影响：它可能会降低对食物适口性的感知，起到控制暴饮暴食的作用，或者相反，引发对更有益食物的补偿性驱动，在分心时潜在地增加摄入量。这两种相反的机制仍有待经验检验。我们的研究结果强调了化学感觉知觉在制定有针对性的干预措施以抑制暴饮暴食和控制肥胖方面的作用。
{"title":"Weight status and gender modulate distraction-induced effects on chemosensory perception","authors":"Iryna Ruda , Jessica Freiherr","doi":"10.1016/j.sctalk.2025.100441","DOIUrl":"10.1016/j.sctalk.2025.100441","url":null,"abstract":"<div><div>In today's fast-paced world, consuming food while engaging in other activities has become common. Importantly, such distracted eating is associated with increased food intake and rising body weight (Robinson et al., 2013; Van Meer et al., 2022). In this talk, we summarize findings from our studies exploring how cognitive distraction influences taste and odor perception — with a focus on intensity and pleasantness perception — and whether these effects vary by weight status and gender (Ruda et al., 2024a; Ruda et al., 2024b).</div><div>Fifty-nine participants formed two study groups — normal-weight (mean BMI = 22.2 kg/m<sup>2</sup>, range 19.5–24.8 kg/m<sup>2</sup>) and overweight/obese (mean BMI = 30.3 kg/m<sup>2</sup>, range 25–39 kg/m<sup>2</sup>). Participants played a Tetris game while evaluating taste and smell stimuli delivered automatically during the trials.</div><div>Our findings indicate that distraction reduced taste intensity perception, particularly in individuals from the overweight/obese group. In contrast to our prior findings, odor intensity perception did not decrease (Hoffmann-Hensel et al., 2017; Schadll et al., 2021). The pleasantness of both taste and odor declined under distraction, an effect most evident in normal-weight participants and particularly pronounced in males.</div><div>We hypothesize that this reduction in pleasantness might have a dual effect on eating behavior: it could decrease the perception of palatability of foods, acting as a control against overeating, or conversely, trigger a compensatory drive for more rewarding foods, potentially increasing intake during distraction. These two opposing mechanisms remain to be tested empirically. Our findings highlight the role of chemosensory perception in developing targeted interventions to curb overeating and manage obesity.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"14 ","pages":"Article 100441"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Species conservation in human-dominated landscapes poses more challenges because not only species ecology needs to be understood but also humans' dimension needs to be accounted for. This study aimed to identify conservation hotspots for Asian Small-clawed Otter and Smooth-coated Otter in coastal wetlands of southern Thailand. Camera trap surveys were conducted between 2016 and 2020. Occupancy model was applied to estimate probability of occupancy of two otters in relation to landscape characteristics and human factors. Then Bayesian Belief Network was applied to generated anthropogenic threat levels which was then related with occupancy probabilities to derive conservation hotspots. In total, 1137 camera-trap locations were set up for 26,387 trap-days. Smooth-coated Otter has higher occupancy probabilities on the Andaman where less disturbed wetlands still remain in larger proportion compared to the Gulf. Small-clawed Otter, in contrast, has higher occupancy on the Gulf. Smooth-coated Otter showed strong association with natural habitats, while Asian Small-clawed Otter better adapted with small isolated habitats. A majority of conservation hotspots was located along the Andaman coast and were not protected. In conclusion, otters can adapt and survive in human-dominated modified landscape; however, maintaining good quality of natural habitats and mitigating conflicts still be the main priority for successful otter conservation.
{"title":"Identifying otter conservation hotspots in human-dominated coastal wetlands of peninsular Thailand","authors":"Naruemon Tantipisanuh , Wanlop Chutipong , Anucha Kamjing , Dusit Ngoprasert","doi":"10.1016/j.sctalk.2025.100440","DOIUrl":"10.1016/j.sctalk.2025.100440","url":null,"abstract":"<div><div>Species conservation in human-dominated landscapes poses more challenges because not only species ecology needs to be understood but also humans' dimension needs to be accounted for. This study aimed to identify conservation hotspots for Asian Small-clawed Otter and Smooth-coated Otter in coastal wetlands of southern Thailand. Camera trap surveys were conducted between 2016 and 2020. Occupancy model was applied to estimate probability of occupancy of two otters in relation to landscape characteristics and human factors. Then Bayesian Belief Network was applied to generated anthropogenic threat levels which was then related with occupancy probabilities to derive conservation hotspots. In total, 1137 camera-trap locations were set up for 26,387 trap-days. Smooth-coated Otter has higher occupancy probabilities on the Andaman where less disturbed wetlands still remain in larger proportion compared to the Gulf. Small-clawed Otter, in contrast, has higher occupancy on the Gulf. Smooth-coated Otter showed strong association with natural habitats, while Asian Small-clawed Otter better adapted with small isolated habitats. A majority of conservation hotspots was located along the Andaman coast and were not protected. In conclusion, otters can adapt and survive in human-dominated modified landscape; however, maintaining good quality of natural habitats and mitigating conflicts still be the main priority for successful otter conservation.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"14 ","pages":"Article 100440"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-24DOI: 10.1016/j.sctalk.2025.100438
Charis B. Pasion , Jose Naldrix D. Rivera
Libraries are integral components in the information landscape that provide access to knowledge and resources crucial for learning. The ongoing pervasive digitalization of processes and services across the globe has reshaped industries, enhanced efficiency, and accelerated convenience. This transformation enabled people to access almost any information at the tip of their fingers. However, despite the widespread modernization, libraries still utilize manual processes and mainly offer resources limited to books. This led the researchers to develop the Mobile Library Resources Application to provide a comprehensive application that caters to the needs of the academe in terms of knowledge management and to enable users to access resources through the comfort of their phones. The study resulted being utilized by students for browsing not only books but also materials such as e-books, multimedia materials, and unpublished research using their smartphones. The application was evaluated by IT experts and students which yielded a high E-service quality level. With the ubiquity of smartphones, libraries can leverage this to update their technological infrastructure to provide more diverse materials and optimize their services in a way that users can access relevant information easily using the most prevalent gadget nowadays.
{"title":"Mobile library resources application: An academic library resource knowledge management","authors":"Charis B. Pasion , Jose Naldrix D. Rivera","doi":"10.1016/j.sctalk.2025.100438","DOIUrl":"10.1016/j.sctalk.2025.100438","url":null,"abstract":"<div><div>Libraries are integral components in the information landscape that provide access to knowledge and resources crucial for learning. The ongoing pervasive digitalization of processes and services across the globe has reshaped industries, enhanced efficiency, and accelerated convenience. This transformation enabled people to access almost any information at the tip of their fingers. However, despite the widespread modernization, libraries still utilize manual processes and mainly offer resources limited to books. This led the researchers to develop the Mobile Library Resources Application to provide a comprehensive application that caters to the needs of the academe in terms of knowledge management and to enable users to access resources through the comfort of their phones. The study resulted being utilized by students for browsing not only books but also materials such as e-books, multimedia materials, and unpublished research using their smartphones. The application was evaluated by IT experts and students which yielded a high E-service quality level. With the ubiquity of smartphones, libraries can leverage this to update their technological infrastructure to provide more diverse materials and optimize their services in a way that users can access relevant information easily using the most prevalent gadget nowadays.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"14 ","pages":"Article 100438"},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-19DOI: 10.1016/j.sctalk.2025.100437
Sebastian J. Schlecht , Stephan Weiss
When estimated space-time covariance matrices from finite data, any intersections of ground truth eigenvalues will be obscured, and the exact eigenvalues become spectrally majorised with probability one. In this paper, we propose a novel method for accurately extracting the ground truth analytic eigenvalues from such estimated space-time covariance matrices. The approach operates in the discrete Fourier transform (DFT) domain and groups sufficiently eigenvalues over a frequency interval into segments that belong to analytic functions and then solves a permutation problem to align these segments. Utilising an inverse partial DFT and a linear assignment algorithm, the proposed EigenBone method retrieves analytic eigenvalues efficiently and accurately. Experimental results demonstrate the effectiveness of this approach in reconstructing eigenvalues from noisy estimates. Overall, the proposed method offers a robust solution for approximating analytic eigenvalues in scenarios where state-of-the-art methods may fail.
{"title":"Polynomial eigenvalue decomposition for eigenvalues with unmajorised ground truth – Reconstructing analytic dinosaurs","authors":"Sebastian J. Schlecht , Stephan Weiss","doi":"10.1016/j.sctalk.2025.100437","DOIUrl":"10.1016/j.sctalk.2025.100437","url":null,"abstract":"<div><div>When estimated space-time covariance matrices from finite data, any intersections of ground truth eigenvalues will be obscured, and the exact eigenvalues become spectrally majorised with probability one. In this paper, we propose a novel method for accurately extracting the ground truth analytic eigenvalues from such estimated space-time covariance matrices. The approach operates in the discrete Fourier transform (DFT) domain and groups sufficiently eigenvalues over a frequency interval into segments that belong to analytic functions and then solves a permutation problem to align these segments. Utilising an inverse partial DFT and a linear assignment algorithm, the proposed EigenBone method retrieves analytic eigenvalues efficiently and accurately. Experimental results demonstrate the effectiveness of this approach in reconstructing eigenvalues from noisy estimates. Overall, the proposed method offers a robust solution for approximating analytic eigenvalues in scenarios where state-of-the-art methods may fail.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"14 ","pages":"Article 100437"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.sctalk.2025.100434
Faizan A. Khattak, Ian K. Proudler, Stephan Weiss
In order to extract the analytic eigenvalues from a parahermitian matrix, the computational cost of the current state-of-the-art method grows factorially with the matrix dimension. Even though the approach offers benefits such as proven convergence, it has been found impractical to operate on matrices with a spatial dimension great than four. Evaluated in the discrete Fourier transform (DFT) domain, the computational bottleneck of this method is a maximum likelihood sequence (MLS) estimation, which probes a set of paths of likely associations across DFT bins, and only retains the best of these. In this paper, we investigate an algorithm that remains covered by the existing method's proof of convergence but results in a significant reduction in computation cost by trading the number of retained paths against the DFT length. We motivate this, and also introduce an enhanced initialisation point for the MLS estimation. We illustrate the benefits of scalable analytic extraction algorithm in a number of simulations.
{"title":"Scalable analytic eigenvalue extraction from a parahermitian matrix","authors":"Faizan A. Khattak, Ian K. Proudler, Stephan Weiss","doi":"10.1016/j.sctalk.2025.100434","DOIUrl":"10.1016/j.sctalk.2025.100434","url":null,"abstract":"<div><div>In order to extract the analytic eigenvalues from a parahermitian matrix, the computational cost of the current state-of-the-art method grows factorially with the matrix dimension. Even though the approach offers benefits such as proven convergence, it has been found impractical to operate on matrices with a spatial dimension great than four. Evaluated in the discrete Fourier transform (DFT) domain, the computational bottleneck of this method is a maximum likelihood sequence (MLS) estimation, which probes a set of paths of likely associations across DFT bins, and only retains the best of these. In this paper, we investigate an algorithm that remains covered by the existing method's proof of convergence but results in a significant reduction in computation cost by trading the number of retained paths against the DFT length. We motivate this, and also introduce an enhanced initialisation point for the MLS estimation. We illustrate the benefits of scalable analytic extraction algorithm in a number of simulations.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"13 ","pages":"Article 100434"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.sctalk.2025.100435
Madeline Navarro , Samuel Rey , Andrei Buciulea , Santiago Segarra , Antonio Garcia Marques
This work addresses the task of fair network topology inference from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subpopulations of nodes may not share or receive information equitably. We thus propose an optimization-based approach to accurately infer networks while discouraging biased edges. To this end, we present bias metrics that measure topological demographic parity to be applied as convex penalties, suitable for most optimization-based graph learning methods. Moreover, we encourage equitable treatment for any number of subpopulations of differing sizes. We validate our method on synthetic and real-world simulations using networks with both biased and unbiased connections.
{"title":"Mitigating subpopulation bias for fair graph learning","authors":"Madeline Navarro , Samuel Rey , Andrei Buciulea , Santiago Segarra , Antonio Garcia Marques","doi":"10.1016/j.sctalk.2025.100435","DOIUrl":"10.1016/j.sctalk.2025.100435","url":null,"abstract":"<div><div>This work addresses the task of <em>fair network topology inference</em> from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subpopulations of nodes may not share or receive information equitably. We thus propose an optimization-based approach to accurately infer networks while discouraging biased edges. To this end, we present bias metrics that measure topological demographic parity to be applied as convex penalties, suitable for most optimization-based graph learning methods. Moreover, we encourage equitable treatment for any number of subpopulations of differing sizes. We validate our method on synthetic and real-world simulations using networks with both biased and unbiased connections.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"13 ","pages":"Article 100435"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.sctalk.2025.100433
Antonio Giganti
Natural ecosystems contribute significantly to releasing numerous chemical substances into the atmosphere, including various volatile and semi-volatile compounds. A significant group of these chemicals, emitted predominantly by plants, are known as Biogenic Volatile Organic Compounds (BVOCs). These compounds, such as carbon monoxide and nitric oxide, play a pivotal role in atmospheric processes and have become a key focus of research over the past two decades due to their influence on atmospheric chemistry.
Studying BVOC emissions is essential for numerical evaluations of past, current, and future air quality and climate conditions. To support such studies, quantitative estimations of BVOC emissions are required. As a result, various ground-based measurement techniques have been developed to sample BVOC emissions at multiple scales, from the leaf level to regional and global scales.
However, current BVOC measurements are often limited in space and time, as generating a fine-grained map of BVOC emissions over a large region is costly and time-consuming. Consequently, many existing BVOC emission maps may not be fully suitable for reliable atmospheric, climate, and forecasting model simulations.
My research aims to explore and assess the use of novel AI-based algorithms to improve the spatiotemporal modeling of BVOC emissions. By enhancing these models, we can assist policymakers in developing more effective regulations to address climate change, reduce the environmental impact of industrial activities, and mitigate the harmful effects of emissions on human health.
This technology has practical applications in agriculture, forestry, and urban planning. For instance, understanding gas emissions from crops can help farmers optimize their activities, reducing fertilizer and pesticide use while improving yields. In forestry, better management practices can minimize the environmental impact of logging. In urban planning, accurate gas emission maps can inform the design of green spaces and other urban features, helping reduce emissions and health impacts on city populations.
Additionally, this research can generate dense datasets for atmospheric chemistry, climate, and air quality models. These data can help capture small-scale processes, improve our understanding of BVOC interactions with other chemical compounds, and better quantify emissions caused by abiotic stress and ozone stress.
As the need to tackle atmospheric chemical shifts and climate change intensifies, BVOC emission maps are emerging as critical resources for enhancing our understanding of these compounds' impact on Earth's future. This research marks a vital advancement in pursuing a more sustainable and environmentally conscious future for both present and future generations.
{"title":"Unveiling nature's secrets: Deep learning for enhanced biogenic emission resolution","authors":"Antonio Giganti","doi":"10.1016/j.sctalk.2025.100433","DOIUrl":"10.1016/j.sctalk.2025.100433","url":null,"abstract":"<div><div>Natural ecosystems contribute significantly to releasing numerous chemical substances into the atmosphere, including various volatile and semi-volatile compounds. A significant group of these chemicals, emitted predominantly by plants, are known as Biogenic Volatile Organic Compounds (BVOCs). These compounds, such as carbon monoxide and nitric oxide, play a pivotal role in atmospheric processes and have become a key focus of research over the past two decades due to their influence on atmospheric chemistry.</div><div>Studying BVOC emissions is essential for numerical evaluations of past, current, and future air quality and climate conditions. To support such studies, quantitative estimations of BVOC emissions are required. As a result, various ground-based measurement techniques have been developed to sample BVOC emissions at multiple scales, from the leaf level to regional and global scales.</div><div>However, current BVOC measurements are often limited in space and time, as generating a fine-grained map of BVOC emissions over a large region is costly and time-consuming. Consequently, many existing BVOC emission maps may not be fully suitable for reliable atmospheric, climate, and forecasting model simulations.</div><div>My research aims to explore and assess the use of novel AI-based algorithms to improve the spatiotemporal modeling of BVOC emissions. By enhancing these models, we can assist policymakers in developing more effective regulations to address climate change, reduce the environmental impact of industrial activities, and mitigate the harmful effects of emissions on human health.</div><div>This technology has practical applications in agriculture, forestry, and urban planning. For instance, understanding gas emissions from crops can help farmers optimize their activities, reducing fertilizer and pesticide use while improving yields. In forestry, better management practices can minimize the environmental impact of logging. In urban planning, accurate gas emission maps can inform the design of green spaces and other urban features, helping reduce emissions and health impacts on city populations.</div><div>Additionally, this research can generate dense datasets for atmospheric chemistry, climate, and air quality models. These data can help capture small-scale processes, improve our understanding of BVOC interactions with other chemical compounds, and better quantify emissions caused by abiotic stress and ozone stress.</div><div>As the need to tackle atmospheric chemical shifts and climate change intensifies, BVOC emission maps are emerging as critical resources for enhancing our understanding of these compounds' impact on Earth's future. This research marks a vital advancement in pursuing a more sustainable and environmentally conscious future for both present and future generations.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"13 ","pages":"Article 100433"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.sctalk.2025.100436
Kurt Butler, Marija Iloska, Petar M. Djurić
In this video article, accompanying the paper “On Counterfactual Interventions in Vector Autoregressive Models”, we consider the problem of counterfactual reasoning in a time series setting. Counterfactual reasoning allows us to explore hypothetical scenarios, in which different choices were made in the past, so that we can explore the effects of our actions. However, it is impossible to answer counterfactual questions without first having a causal model.
Here we address the problem using vector autoregressive (VAR) processes. We frame the inference of a causal model as a joint regression task where for inference we use both data with and without interventions. After inferring the causal model, we exploit linearity of the VAR model to make exact predictions about the system under counterfactual interventions. Under this approach, we may measure the total effect of any hypothetical intervention in the past.
{"title":"Counterfactual reasoning with vector autoregressive models","authors":"Kurt Butler, Marija Iloska, Petar M. Djurić","doi":"10.1016/j.sctalk.2025.100436","DOIUrl":"10.1016/j.sctalk.2025.100436","url":null,"abstract":"<div><div>In this video article, accompanying the paper “On Counterfactual Interventions in Vector Autoregressive Models”, we consider the problem of counterfactual reasoning in a time series setting. Counterfactual reasoning allows us to explore hypothetical scenarios, in which different choices were made in the past, so that we can explore the effects of our actions. However, it is impossible to answer counterfactual questions without first having a causal model.</div><div>Here we address the problem using vector autoregressive (VAR) processes. We frame the inference of a causal model as a joint regression task where for inference we use both data with and without interventions. After inferring the causal model, we exploit linearity of the VAR model to make exact predictions about the system under counterfactual interventions. Under this approach, we may measure the total effect of any hypothetical intervention in the past.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"13 ","pages":"Article 100436"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.sctalk.2025.100432
Timothy K. Chung , Pete H. Gueldner , Aakash K. Kottakota , Christian N. Hangey , Jason Y. Lee , Nathan L. Liang , David A. Vorp
The number of medical images taken has continued to increase year over year for an aging population in the United States. It has been shown that patients understand their diagnoses better when shown a 2D or 3D image of their respective diseases. However, clinicians do not regularly show patients their images as it requires additional time and processing. In this experiment, we demonstrate the use of augmented reality to visualize abdominal aortic aneurysms using a previously developed artificial intelligence engine. Our group further expanded the number of cases used for training the stress prediction model to a total of 274 patients (206 used for training or ∼ 5.4 million nodes, and 68 for testing or ∼1.8 million nodes). Medical images undergo automated segmentation, and wall stresses are predicted on the 3D surface of aneurysms to view a heat map. The pipeline includes introducing elements into the Microsoft HoloLens 2 ecosystem to view models and additional analytics, enabling clinicians and patients to view the biomechanical status without the need for a computational or imaging expert. The proposed clinical workflow would allow a local server to process medical imaging data, generate point clouds, predict wall stresses on individual points, and create a 3D model with a colormap to view in augmented reality. The study revealed that neural networks and ensemble boosted tress models predicted the wall stresses more accurately (when compared to ground truth finite element analysis results). The approach is not limited to the HoloLens 2 ecosystem but can be used with other emerging augmented or virtual reality hardware systems.
Summary: Patient understanding of their diagnosis improves when they are shown medical images by clinicians. An artificial intelligence-based clinical workflow has been developed to visualize the biomechanical status using augmented reality, providing additional information to clinicians and patients. Our research improves the tools that are available to clinicians and patients to help provide a better understanding of diagnosis and potentially prognosis.
{"title":"Augmented reality visualization of biomechanical wall stresses on abdominal aortic aneurysms using artificial intelligence","authors":"Timothy K. Chung , Pete H. Gueldner , Aakash K. Kottakota , Christian N. Hangey , Jason Y. Lee , Nathan L. Liang , David A. Vorp","doi":"10.1016/j.sctalk.2025.100432","DOIUrl":"10.1016/j.sctalk.2025.100432","url":null,"abstract":"<div><div>The number of medical images taken has continued to increase year over year for an aging population in the United States. It has been shown that patients understand their diagnoses better when shown a 2D or 3D image of their respective diseases. However, clinicians do not regularly show patients their images as it requires additional time and processing. In this experiment, we demonstrate the use of augmented reality to visualize abdominal aortic aneurysms using a previously developed artificial intelligence engine. Our group further expanded the number of cases used for training the stress prediction model to a total of 274 patients (206 used for training or ∼ 5.4 million nodes, and 68 for testing or ∼1.8 million nodes). Medical images undergo automated segmentation, and wall stresses are predicted on the 3D surface of aneurysms to view a heat map. The pipeline includes introducing elements into the Microsoft HoloLens 2 ecosystem to view models and additional analytics, enabling clinicians and patients to view the biomechanical status without the need for a computational or imaging expert. The proposed clinical workflow would allow a local server to process medical imaging data, generate point clouds, predict wall stresses on individual points, and create a 3D model with a colormap to view in augmented reality. The study revealed that neural networks and ensemble boosted tress models predicted the wall stresses more accurately (when compared to ground truth finite element analysis results). The approach is not limited to the HoloLens 2 ecosystem but can be used with other emerging augmented or virtual reality hardware systems.</div><div>Summary: Patient understanding of their diagnosis improves when they are shown medical images by clinicians. An artificial intelligence-based clinical workflow has been developed to visualize the biomechanical status using augmented reality, providing additional information to clinicians and patients. Our research improves the tools that are available to clinicians and patients to help provide a better understanding of diagnosis and potentially prognosis.</div></div>","PeriodicalId":101148,"journal":{"name":"Science Talks","volume":"13 ","pages":"Article 100432"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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