bluuebunny/crossref_metadata_2025 huggingface dataset:
Rhizosphere and detritusphere habitats modulate expression of soil N-cycling genes during plant development
Authors: Sieradzki Ella T. , Nuccio Erin E. , Pett-Ridge Jennifer , Firestone Mary K.
Published: September 2023

Interactions between plant roots and rhizosphere bacteria modulate nitrogen (N)-cycling processes and create habitats rich in low molecular weight compounds (exudates) and complex organic molecules (decaying root litter) compared to those of bulk soil. Microbial N-cycling is regulated by edaphic conditions and genes from many interconnected metabolic pathways, but most studies of soil N-cycling gene expression have focused on single pathways. Currently, we lack a comprehensive understanding of the interplay between soil N-cycling gene regulation, spatial habitat, and time. We present results from a replicated time series of soil metatranscriptomes; we followed gene expression of multiple N transformations in four soil habitats (rhizosphere, detritusphere, rhizo-detritusphere, and bulk soil) during active root growth for the annual grass,
Avena fatua
. The presence of root litter and living roots significantly altered the trajectories of N-cycling gene expression. Upregulation of assimilatory nitrate reduction in the rhizosphere suggests that rhizosphere bacteria were actively competing with roots for nitrate. Simultaneously, ammonium assimilatory pathways were upregulated in both rhizosphere and detritusphere soil, which could have limited N availability to plants. The detritusphere supported dissimilatory processes DNRA and denitrification. Expression of nitrification genes was dominated by three phylotypes of
Thaumarchaeota
and was upregulated in bulk soil. Unidirectional ammonium assimilation and its regulatory genes (GS/GOGAT) were upregulated near relatively young roots and highly decayed root litter, suggesting N may have been limiting in these habitats (GS/GOGAT is typically activated under N limitation). Our comprehensive analysis indicates that differences in carbon and inorganic N availability control contemporaneous transcription of N-cycling pathways in soil habitats.

IMPORTANCE

Plant roots modulate microbial nitrogen (N) cycling by regulating the supply of root-derived carbon and nitrogen uptake. These differences in resource availability cause distinct micro-habitats to develop: soil near living roots, decaying roots, near both, or outside the direct influence of roots. While many environmental factors and genes control the microbial processes involved in the nitrogen cycle, most research has focused on single genes and pathways, neglecting the interactive effects these pathways have on each other. The processes controlled by these pathways determine consumption and production of N by soil microorganisms. We followed the expression of N-cycling genes in four soil microhabitats over a period of active root growth for an annual grass. We found that the presence of root litter and living roots significantly altered gene expression involved in multiple nitrogen pathways, as well as tradeoffs between pathways, which ultimately regulate N availability to plants.
The impact of Parkinson’s disease-associated gut microbiota on the transcriptome in
Drosophila
Authors: Liu Xin , Yang Meng , Liu Runzhou , Zhou Fan , Zhu Haibing , Wang Xiaoyun
Published: September 2023

Parkinson’s disease (PD) is a common neurodegenerative disease in middle-aged and elderly people, and many studies have confirmed that the disorder of gut microbiota is involved in the pathophysiological process of PD. However, the molecular mechanism of gut microbiota in regulating the pathogenesis of PD is still lacking. In this study, to investigate the impact of PD-associated gut microbiota on host transcriptome, we established various PD models with fecal microbiota transplantation (FMT) in the model organism
Drosophila
followed by integrative data analysis of microbiome and transcriptome. We first constructed rotenone-induced PD models in
Drosophila
followed by FMT in different groups. Microbial analysis by 16S rDNA sequencing showed that gut microbiota from PD
Drosophila
could affect bacterial structure of normal
Drosophila
, and gut microbiota from normal
Drosophila
could affect bacterial structure of PD
Drosophila
. Transcriptome analysis revealed that PD-associated gut microbiota influenced expression patterns of genes enriched in neuroactive ligand-receptor interaction, lysosome, and diverse metabolic pathways. Importantly, to verify our findings, we transplanted
Drosophila
with fecal samples from clinical PD patients. Compared to the control,
Drosophila
transplanted with fecal samples from PD patients had reduced microbiota
Acetobacter
and
Lactobacillus
, and differentially expressed genes enriched in diverse metabolic pathways. In summary, our results reveal the influence of PD-associated gut microbiota on host gene expression, and this study can help better understand the link between gut microbiota and PD pathogenesis through gut-brain axis.

IMPORTANCE

Gut microbiota plays important roles in regulating host gene expression and physiology through complex mechanisms. Recently, it has been suggested that disorder of gut microbiota is involved in the pathophysiological process of Parkinson’s disease (PD). However, the molecular mechanism of gut microbiota in regulating the pathogenesis of PD is still lacking. In this study, to investigate the impact of PD-associated gut microbiota on host transcriptome, we established various PD models with fecal microbiota transplantation in the model organism
Drosophila
followed by integrative data analysis of microbiome and transcriptome. We also verified our findings by transplanting
Drosophila
with fecal samples from clinical PD patients. Our results demonstrated that PD-associated gut microbiota can induce differentially expressed genes enriched in diverse metabolic pathways. This study can help better understand the link between gut microbiota and PD pathogenesis through gut-brain axis.
The essential role of intestinal microbiota in cytomegalovirus reactivation
Authors: Kohda Chikara , Ino Satoshi , Ishikawa Hiroki , Kuno Yoshihiro , Nagashima Ryuichi , Iyoda Masayuki
Published: September 2023

Human cytomegalovirus (HCMV) is a member of Herpesviridae. It has been reported that HCMV is reactivated in the breast milk of HCMV-seropositive lactating women. As we have reported various aspects of the roles of indigenous microbiota, its role in the murine CMV (MCMV) reactivation was examined in this study. MCMV was latently infected in the salivary gland, mammary tissues, and colon in the pregnant mice. When the salivary gland, mammary tissues, and colon were removed 5 days after delivery, MCMV reactivation of latent infection in each organ was confirmed by the detection of MCMV IE1 mRNA using reverse transcription-quantitative PCR. MCMV reactivation was observed in 100% of the mice during pregnancy. Next, for the elimination of intestinal microbiota, the pregnant mice were treated with low-dose or high-dose non-absorbable antibiotics. Although the numbers of aerobe/anaerobe in cecal content in low-dose antibiotic-treated mice were comparable to those in untreated controls, high-dose antibiotic treatment decreased the number of aerobe/anaerobe microbes from ca.9.0 Log10 to ca.3.0 Log10 (cfu/g). However, it could not be confirmed in 16S rRNA analysis that specific bacterial phylum or genus was eliminated by this high-dose treatment. Interestingly, MCMV reactivation was also observed in 100% of low-dose antibiotic-treated mice, whereas, in high-dose antibiotic-treated mice, MCMV reactivation was not observed in the salivary gland or colon. MCMV IE1 mRNA was detected only in 33% of the mammary tissues of those high-dose-treated mice. These results suggest that the indigenous microbiota played a crucial role in the reactivation of latent infection.

IMPORTANCE

Human cytomegalovirus (HCMV) infection via breast milk is a serious problem for very preterm infants such as developing a sepsis-like syndrome, cholestasis, or bronchopulmonary dysplasia, among others. It has been reported that HCMV is reactivated in the breast milk of HCMV-seropositive lactating women. In this study, the roles of indigenous microbiota in the murine CMV (MCMV) reactivation were examined using a mouse model. In MCMV latently infected mice, MCMV reactivation was observed in 100% of the mice during pregnancy. For the elimination of intestinal microbiota, MCMV-latent mice were treated with non-absorbable antibiotics. After delivery, MCMV reactivation was not observed in antibiotic-treated mice. This result suggested that the indigenous microbiota played a crucial role in the reactivation of latent infection.
Cellular localization of the hybrid pyruvate/2-oxoglutarate dehydrogenase complex in the actinobacteriumCorynebacterium glutamicum
Authors: Sundermeyer Lea , Folkerts Jan-Gerrit , Lückel Benita , Mack Christina , Baumgart Meike , Bott Michael
Published: September 2023

For many bacterial proteins, specific localizations within the cell have been demonstrated, but enzymes involved in central metabolism are usually considered to be homogenously distributed within the cytoplasm. Here, we provide an example for a spatially defined localization of a unique enzyme complex found in actinobacteria, the hybrid pyruvate/2-oxoglutarate dehydrogenase complex (PDH-ODH). In non-actinobacterial cells, PDH and ODH form separate multienzyme complexes of megadalton size composed of three different subunits, E1, E2, and E3. The actinobacterial PDH-ODH complex is composed of four subunits, AceE (E1p), AceF (E2p), Lpd (E3), and OdhA (E1oE2o). Using fluorescence microscopy, we observed that inCorynebacterium glutamicum, all four subunits are co-localized in distinct spots at the cell poles, and in larger cells, additional spots are present at mid-cell. These results further confirm the existence of the hybrid complex. The unphosporylated OdhI protein, which binds to OdhA and inhibits ODH activity, was co-localized with OdhA at the poles, whereas phosphorylated OdhI, which does not bind OdhA, was distributed in the entire cytoplasm. Isocitrate dehydrogenase and glutamate dehydrogenase, both metabolically linked to ODH, were evenly distributed in the cytoplasm. Based on the available structural data for individual PDH-ODH subunits, a novel supramolecular architecture of the hybrid complex differing from classical PDH and ODH complexes has to be postulated. Our results suggest that localization at the poles or at mid-cell is most likely caused by nucleoid exclusion and results in a spatially organized metabolism in actinobacteria, with consequences yet to be studied.

IMPORTANCE

Enzymes involved in the central metabolism of bacteria are usually considered to be distributed within the entire cytoplasm. Here, we provide an example for a spatially defined localization of a unique enzyme complex of actinobacteria, the hybrid pyruvate dehydrogenase/2-oxoglutarate dehydrogenase (PDH-ODH) complex composed of four different subunits. Using fusions with mVenus or mCherry and fluorescence microscopy, we show that all four subunits are co-localized in distinct spots at the cell poles, and in larger cells, additional spots were observed at mid-cell. These results clearly support the presence of the hybrid PDH-ODH complex and suggest a similar localization in other actinobacteria. The observation of a defined spatial localization of an enzyme complex catalyzing two key reactions of central metabolism poses questions regarding possible consequences for the availability of substrates and products within the cell and other bacterial enzyme complexes showing similar behavior.
Prediction of mammalian virus cross-species transmission based on host proteins
Authors: Zhang Zheng , Lu Congyu , Mo Bocheng , Bai Kehan , Ge Xing-Yi , Deng Li , Peng Yousong
Published: September 2023

Most emerging viruses are spilled over from mammals. Understanding the mechanism of virus cross-species transmission and identifying zoonotic viruses before their emergence are critical for the prevention and control of newly emerging viruses. This study systematically investigated the host proteins associated with the cross-species transmission of mammalian viruses based on 1,271 pairs of virus-mammal interactions including 382 viruses from 33 viral families and 73 mammal species from 11 orders. Numerous host proteins were found to contribute to the cross-species transmission of mammalian viruses. Host proteins potentially contributing to virus cross-species transmission are specific to viral families, and few overlaps of such host proteins are observed in different viral families. Based on these host proteins, the random-forest (RF) models were built to predict the cross-species transmission potential of mammalian viruses. Moderate performance was obtained when using all viruses together. However, when modeling by viral family, the performance of the RF models varied much among viral families. In 13 viral families such as
Flaviviridae
,
Retroviridae,
and
Poxviridae
, the AUC of the RF model was greater than 0.8. Finally, the contribution of virus receptors to cross-species transmission was evaluated, and the virus receptor was found to have a minor effect in predicting the cross-species transmission of mammalian viruses. The study deepens our understanding of the mechanism of virus cross-species transmission and provides a framework for predicting the cross-species transmission of mammalian viruses.

IMPORTANCE

Emerging viruses pose serious threats to humans. Understanding the mechanism of virus cross-species transmission and identifying zoonotic viruses before their emergence are critical for the prevention and control of emerging viruses. This study systematically identified host factors associated with cross-species transmission of mammalian viruses and further built machine-learning models for predicting cross-species transmission of the viruses based on host factors including virus receptors. The study not only deepens our understanding of the mechanism of virus cross-species transmission but also provides a framework for predicting the cross-species transmission of mammalian viruses based on host factors.
N-euro Predictor
Authors: Shao Qijia , Wang Jian , Zhou Bing , Tran Vu An , Krishnan Gurunandan , Nayar Shree
Published: September 2023

Jitter and lag severely impact the smoothness and responsiveness of user experience on vision-based human-display interactive systems such as phones, TVs, and VR/AR. Current manually-tuned filters for smoothing and predicting motion trajectory struggle to effectively address both issues, especially for applications that have a large range of movement speed. To overcome this, we introduce N-euro, a residual-learning-based neural network predictor that can simultaneously reduce jitter and lag while maintaining low computational overhead. Compared to the fine-tuned existing filters, N-euro improves prediction performance by 36% and smoothing performance by 42%. We fabricated a Fish Tank VR system and an AR mirror system and conducted a user experience study (n=34) with the real-time implementation of N-euro. Our results indicate that the N-euro predictor brings a statistically significant improvement in user experience. With its validated effectiveness and usability, we expect this approach to bring a better user experience to various vision-based interactive systems.
PATCH
Authors: Wang Juexing , Wang Guangjing , Zhang Xiao , Liu Li , Zeng Huacheng , Xiao Li , Cao Zhichao , Gu Lin , Li Tianxing
Published: September 2023

Recent advancements in deep learning have shown that multimodal inference can be particularly useful in tasks like autonomous driving, human health, and production line monitoring. However, deploying state-of-the-art multimodal models in distributed IoT systems poses unique challenges since the sensor data from low-cost edge devices can get corrupted, lost, or delayed before reaching the cloud. These problems are magnified in the presence of asymmetric data generation rates from different sensor modalities, wireless network dynamics, or unpredictable sensor behavior, leading to either increased latency or degradation in inference accuracy, which could affect the normal operation of the system with severe consequences like human injury or car accident. In this paper, we propose PATCH, a framework of speculative inference to adapt to these complex scenarios. PATCH serves as a plug-in module in the existing multimodal models, and it enables speculative inference of these off-the-shelf deep learning models. PATCH consists of 1) a Masked-AutoEncoder-based cross-modality imputation module to impute missing data using partially-available sensor data, 2) a lightweight feature pair ranking module that effectively limits the searching space for the optimal imputation configuration with low computation overhead, and 3) a data alignment module that aligns multimodal heterogeneous data streams without using accurate timestamp or external synchronization mechanisms. We implement PATCH in nine popular multimodal models using five public datasets and one self-collected dataset. The experimental results show that PATCH achieves up to 13% mean accuracy improvement over the state-of-art method while only using 10% of training data and reducing the training overhead by 73% compared to the original cost of retraining the model.
PulmoListener
Authors: Bhalla Sejal , Liaqat Salaar , Wu Robert , Gershon Andrea S. , de Lara Eyal , Mariakakis Alex
Published: September 2023

Prior work has shown the utility of acoustic analysis in controlled settings for assessing chronic obstructive pulmonary disease (COPD) --- one of the most common respiratory diseases that impacts millions of people worldwide. However, such assessments require active user input and may not represent the true characteristics of a patient's voice. We propose PulmoListener, an end-to-end speech processing pipeline that identifies segments of the patient's speech from smartwatch audio collected during daily living and analyzes them to classify COPD symptom severity. To evaluate our approach, we conducted a study with 8 COPD patients over 164 ± 92 days on average. We found that PulmoListener achieved an average sensitivity of 0.79 ± 0.03 and a specificity of 0.83 ± 0.05 per patient when classifying their symptom severity on the same day. PulmoListener can also predict the severity level up to 4 days in advance with an average sensitivity of 0.75 ± 0.02 and a specificity of 0.74 ± 0.07. The results of our study demonstrate the feasibility of leveraging natural speech for monitoring COPD in real-world settings, offering a promising solution for disease management and even diagnosis.
Exploring the Opportunities of AR for Enriching Storytelling with Family Photos between Grandparents and Grandchildren
Authors: Li Zisu , Feng Li , Liang Chen , Huang Yuru , Fan Mingming
Published: September 2023

Storytelling with family photos, as an important mode of reminiscence-based activities, can be instrumental in promoting intergenerational communication between grandparents and grandchildren by strengthening generation bonds and shared family values. Motivated by challenges that existing technology approaches encountered for improving intergenerational storytelling (e.g., the need to hold the tablet, the potential view detachment from the physical world in Virtual Reality (VR)), we sought to find new ways of using Augmented Reality (AR) to support intergenerational storytelling, which offers new capabilities (e.g., 3D models, new interactivity) to enhance the expression for the storyteller. We conducted a two-part exploratory study, where pairs of grandparents and grandchildren 1) participated in an in-person storytelling activity with a semi-structured interview 2) and then a participatory design session with AR technology probes that we designed to inspire their exploration. Our findings revealed insights into the possible ways of intergenerational storytelling, the feasibility and usages of AR in facilitating it, and the key design implications for leveraging AR in intergenerational storytelling.
Society's Attitudes Towards Human Augmentation and Performance Enhancement Technologies (SHAPE) Scale
Authors: Villa Steeven , Niess Jasmin , Schmidt Albrecht , Welsch Robin
Published: September 2023

Human augmentation technologies (ATs) are a subset of ubiquitous on-body devices designed to improve cognitive, sensory, and motor capacities. Although there is a large corpus of knowledge concerning ATs, less is known about societal attitudes towards them and how they shift over time. To that end, we developed The Society's Attitudes Towards Human Augmentation and Performance Enhancement Technologies (SHAPE) Scale, which measures how users of ATs are perceived. To develop the scale, we first created a list of possible scale items based on past work on how people respond to new technologies. The items were then reviewed by experts. Next, we performed exploratory factor analysis to reduce the scale to its final length of thirteen items. Subsequently, we confirmed test-retest validity of our instrument, as well as its construct validity. The SHAPE scale enables researchers and practitioners to understand elements contributing to attitudes toward augmentation technology users. The SHAPE scale assists designers of ATs in designing artifacts that will be more universally accepted.
mmStress
Authors: Liang Kun , Zhou Anfu , Zhang Zhan , Zhou Hao , Ma Huadong , Wu Chenshu
Published: September 2023

Long-term exposure to stress hurts human's mental and even physical health,and stress monitoring is of increasing significance in the prevention, diagnosis, and management of mental illness and chronic disease. However, current stress monitoring methods are either burdensome or intrusive, which hinders their widespread usage in practice. In this paper, we propose mmStress, a contact-less and non-intrusive solution, which adopts a millimeter-wave radar to sense a subject's activities of daily living, from which it distills human stress. mmStress is built upon the psychologically-validated relationship between human stress and "displacement activities", i.e., subjects under stress unconsciously perform fidgeting behaviors like scratching, wandering around, tapping foot, etc. Despite the conceptual simplicity, to realize mmStress, the key challenge lies in how to identify and quantify the latent displacement activities autonomously, as they are usually transitory and submerged in normal daily activities, and also exhibit high variation across different subjects. To address these challenges, we custom-design a neural network that learns human activities from both macro and micro timescales and exploits the continuity of human activities to extract features of abnormal displacement activities accurately. Moreover, we also address the unbalance stress distribution issue by incorporating a post-hoc logit adjustment procedure during model training. We prototype, deploy and evaluate mmStress in ten volunteers' apartments for over four weeks, and the results show that mmStress achieves a promising accuracy of ~80% in classifying low, medium and high stress. In particular, mmStress manifests advantages, particularly under free human movement scenarios, which advances the state-of-the-art that focuses on stress monitoring in quasi-static scenarios.
SmarCyPad
Authors: Wu Yi , Villalobos Luis Alonso González , Yang Zhenning , Croisdale Gregory Thomas , Karataş Çağdaş , Liu Jian
Published: September 2023

Cycling is an efficient and effective way to improve one's overall fitness level, such as cardiovascular fitness, stamina, lower body strength, and body fat percentage. To improve fitness performance, real-time cycling fitness tracking can not only allow cyclists to better control their energy outputs but also help push workout intensity and keep users accountable for their fitness progress. However, existing bike sensors (e.g., the ones mounted to bike's wheel hub or crank arm) are only limited to measuring cycling cadence and speed. Although several recent studies relying on on-body sensors or cameras can provide more fine-grained information (e.g., riding position and knee joint angle), they would either require inconvenient setups or raise serious privacy concerns. To circumvent these limitations, in this paper, we propose SmarCyPad, an innovative smart seat pad that can continuously and unobtrusively track five cycling-specific metrics, including cadence, per-leg stability, leg strength balance, riding position, and knee joint angle of the cyclist. Specifically, we embed conductive fabric sensors in the seat pad to sense the pressure applied to the bike's seat exerted by the cyclist's gluteal muscles. A series of signal processing algorithms are developed to estimate the pedaling period from the sensed pressure signal and further derive the cycling cadence, per-leg stability, and leg strength balance. Additionally, we leverage a deep learning model to detect the cyclist's riding position and reconstruct the cyclist's knee joint angles via linear regression. The sensors and the system prototype are manufactured from scratch leveraging off-the-shelf materials, and the total cost is less than $50. Extensive experiments involving 15 participants demonstrate that SmarCyPad can accurately estimate the cycling cadence with an average error of 1.13 rounds per minute, quantify the cycling stability for each leg, detect cycling imbalance, distinguish five riding positions with an accuracy of 96.60%, and continuously track the knee joint angle with an average mean error as low as 9.58 degrees.
Interaction Proxy Manager
Authors: Huang Tian , Yu Chun , Shi Weinan , Wang Bowen , Yang David , Zhu Yihao , Li Zhaoheng , Shi Yuanchun
Published: September 2023

Emerging terminals, such as smartwatches, true wireless earphones, in-vehicle computers, etc., are complementing our portals to ubiquitous information services. However, the current ecology of information services, encapsulated into millions of mobile apps, is largely restricted to smartphones; accommodating them to new devices requires tremendous and almost unbearable engineering efforts. Interaction Proxy, firstly proposed as an accessible technique, is a potential solution to mitigate this problem. Rather than re-building an entire application, Interaction Proxy constructs an alternative user interface that intercepts and translates interaction events and states between users and the original app's interface. However, in such a system, one key challenge is how to robustly and efficiently "communicate" with the original interface given the instability and dynamicity of mobile apps (e.g., dynamic application status and unstable layout). To handle this, we first define UI-Independent Application Description (UIAD), a reverse-engineered semantic model of mobile services, and then propose Interaction Proxy Manager (IPManager), which is responsible for synchronizing and managing the original apps' interface, and providing a concise programming interface that exposes information and method entries of the concerned mobile services. In this way, developers can build alternative interfaces without dealing with the complexity of communicating with the original app's interfaces. In this paper, we elaborate the design and implementation of our IPManager, and demonstrate its effectiveness by developing three typical proxies, mobile-smartwatch, mobile-vehicle and mobile-voice. We conclude by discussing the value of our approach to promote ubiquitous computing, as well as its limitations.
PLUS: A Semi-automated Pipeline for Fraud Detection in Public Bids
Authors: Brandão Michele A. , Reis Arthur P. G. , Mendes Bárbara M. A. , De Almeida Clara A. Bacha , Oliveira Gabriel P. , Hott Henrique , Gomide Larissa D. , Costa Lucas L. , Silva Mariana O. , Lacerda Anisio , Pappa Gisele L.
Published: September 2023

The diversity of sources and formats of public bidding documents makes collecting, processing, and organizing such documents challenging from the point of view of data analysis. Thus, the development of approaches to deal with such data is relevant since the analysis of them allows to expand of the inclusion of people as they have more access to public decisions and expenditures, increase transparency in the public sector, and give citizens a greater sense of responsibility for having different points of view on the government’s performance in meeting its public policy goals. In this context, we propose PLUS, a semi-automated pipeline for fraud detection in public bids. PLUS comprises a heuristic meta-classifier for bidding documents and a data quality module. Both modules present promising results after a proof of concept, reinforcing the relevance of PLUS for automating the bidding process investigation. Then, we present two applications of PLUS on real-world data: the construction of audit trails for fraud detection and a price database for overpricing detection. Such applications evidence a significant reduction of specialists’ work searching for irregularities in public bids.
Validating Synthetic Usage Data in Living Lab Environments
Authors: Breuer Timo , Fuhr Norbert , Schaer Philipp
Published: September 2023

Evaluating retrieval performance without editorial relevance judgments is challenging, but instead, user interactions can be used as relevance signals. Living labs offer a way for small-scale platforms to validate information retrieval systems with real users. If enough user interaction data are available, then click models can be parameterized from historical sessions to evaluate systems before exposing users to experimental rankings. However, interaction data are sparse in living labs, and little is studied about how click models can be validated for reliable user simulations when click data are available in moderate amounts.

This work introduces an evaluation approach for validating synthetic usage data generated by click models in data-sparse human-in-the-loop environments like living labs. We ground our methodology on the click model’s estimates about a system ranking compared to a reference ranking for which the relative performance is known. Our experiments compare different click models and their reliability and robustness as more session log data become available. In our setup, simple click models can reliably determine the relative system performance with already 20 logged sessions for 50 queries. In contrast, more complex click models require more session data for reliable estimates, but they are a better choice in simulated interleaving experiments when enough session data are available. While it is easier for click models to distinguish between more diverse systems, it is harder to reproduce the system ranking based on the same retrieval algorithm with different interpolation weights. Our setup is entirely open, and we share the code to reproduce the experiments.
Hierarchical Distribution-aware Testing of Deep Learning
Authors: Huang Wei , Zhao Xingyu , Banks Alec , Cox Victoria , Huang Xiaowei
Published: September 2023

With its growing use in safety/security-critical applications, Deep Learning (DL) has raised increasing concerns regarding its dependability. In particular, DL has a notorious problem of lacking robustness. Input added with adversarial perturbations, i.e.,
Adversarial Examples (AEs)
, are easily mispredicted by the DL model. Despite recent efforts made in detecting AEs via state-of-the-art attack and testing methods, they are normally input distribution–agnostic and/or disregard the perceptual quality of adversarial perturbations. Consequently, the detected AEs are irrelevant inputs in the application context or noticeably unrealistic to humans. This may lead to a limited effect on improving the DL model’s dependability, as the testing budget is likely to be wasted on detecting AEs that are encountered very rarely in its real-life operations.

In this article, we propose a new robustness testing approach for detecting AEs that considers both the feature-level distribution and the pixel-level distribution, capturing the perceptual quality of adversarial perturbations. The two considerations are encoded by a novel hierarchical mechanism. First, we select test seeds based on the density of feature-level distribution and the vulnerability of adversarial robustness. The vulnerability of test seeds is indicated by the auxiliary information, which are highly correlated with local robustness. Given a test seed, we then develop a novel genetic algorithm–based local test case generation method, in which two fitness functions work alternatively to control the perceptual quality of detected AEs. Finally, extensive experiments confirm that our holistic approach considering hierarchical distributions is superior to the state-of-the-arts that either disregard any input distribution or only consider a single (non-hierarchical) distribution, in terms of not only detecting imperceptible AEs but also improving the overall robustness of the DL model under testing.
AudioGuard: Omnidirectional Indoor Intrusion Detection Using Audio Device
Authors: Wang Tianben , Li Zhangben , Yan Honghao , Liu Xiantao , Liu Boqin , Li Shengjie , Ma Zhongyu , Hu Jin , Zhang Daqing , Gu Tao
Published: September 2023

Indoor intrusion detection is a critical task for home security. Previous works in intrusion detection suffer from the problems such as blind spots in non-line-of-sight (NLOS) areas, restricted device locations, massive offline training required, and privacy concern. In this article, we design and implement an omnidirectional indoor intrusion detection system, named
AudioGuard
, using only a pair of speaker and microphone.
AudioGuard
is able to detect both line-of-sight (LOS) and NLOS intrusions. Our observation of acoustic signal propagation in an indoor environment shows that there exist abundant multipath reflections and human movement introduces Doppler shift in echo signals. We hence capture periodical Doppler shift caused by intruder's walking motion to detect intrusion. Specifically, we first extract the Doppler shift embedded in echo signals, and we then propose a periodicity polarization method to cancel out the impact of the change of radial angle and the distance on periodicity of Doppler shift. Finally, we detect intrusion by measuring periodicity of Doppler shift over time. Extensive experiments show that
AudioGuard
achieves a miss report rate of 0% and 1.75% for LOS and NLOS intrusion, respectively, and a false alarm rate of 4.17%.
Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review
Authors: Marvasti-Zadeh S. Mojtaba , Goodsman Devin , Ray Nilanjan , Erbilgin Nadir
Published: September 2023

Bark beetle outbreaks can have serious consequences on forest ecosystem processes, biodiversity, forest structure and function, and economies. Thus, accurate and timely detection of bark beetle infestations in the early stage (known as green-attack detection) is crucial to mitigate the further impact, develop proactive forest management activities, and minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep learning (DL)) can provide a great alternative to the current approaches that primarily rely on aerial surveys and field surveys, which can be impractical over vast areas. Existing approaches that exploit RS and ML/DL exhibit substantial diversity due to the wide range of factors involved. This article provides a comprehensive review of past and current advances in green-attack detection from three primary perspectives: bark beetle and host interactions, RS, and ML/DL. In contrast to prior efforts, this review encompasses all RS systems and emphasizes ML/DL methods to investigate their strengths and weaknesses. We parse existing literature based on multi- or hyperspectral analyses and distill their knowledge based on bark beetle species and attack phases with a primary emphasis on early stages of attacks, host trees, study regions, RS platforms and sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices, ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks and architectures. Although DL-based methods and the random forest algorithm showed promising results, highlighting their potential to detect subtle changes across visible, thermal, and short-wave infrared spectral regions, their effectiveness remains limited, and high uncertainties persist due to the subtle distinctions between healthy and attacked trees. To inspire novel solutions to these shortcomings, we delve into the principal challenges and opportunities from different perspectives, enabling a deeper understanding of the current state of research and guiding future research directions.
150 Years of Coevolution Research: Evolution and Ecology of Yucca Moths (Prodoxidae) and Their Hosts
Authors: Smith Christopher Irwin , Leebens-Mack James H.
Published: September 2023

Yucca moths ( Tegeticula and Parategeticula) are specialized pollinators of yucca plants, possessing unique, tentacle-like mouthparts used to actively collect pollen and deposit it onto the flowers of their hosts. The moths' larvae feed on the developing seeds and fruit tissue. First described in 1873, the yucca–yucca moth pollination system is now considered the archetypical example of a coevolved intimate mutualism. Research conducted over the past three decades has transformed our understanding of yucca moth diversity and host plant interactions. We summarize the current understanding of the diversity, ecology, and evolution of this group, review evidence for coevolution of the insects and their hosts, and describe how the nature of the interaction varies across evolutionary time and ecological contexts. Finally, we identify unresolved questions and areas for future research.
Computational Fluid Dynamics Simulation Assessment of Inlet Configuration Influence on Enhancing Swirl Flow Microbubble Generator Performance
Authors: Al-Azzawi Ali , Al-Shimmery Abouther , Alshara Ahmed , Mohammed Mohammed Razzaq
Published: September 2023

In this study, a CFD simulation analysis was used to predict the characteristics of a swirl flow as a reference to optimize a new design of microbubble generator. To examine the impact of the inlet design, three different configurations of the inlet type were applied, namely single inlet, double inlet, and tangent-circle inlet. The performance of the microbubble generator was characterized in terms of swirl velocity, pressure drop in radial position, and pressure distribution along the central axis of the microbubble generator. Generally, the CFD analysis succeeded to visualize the hypothetical bath of the flow streamlines inside the microbubble generators. The results illustrated that the swirl flow in the tangent-circle inlet was able to generate a negative pressure zone in the central area of the generator (i.e., self-suction mechanism). In addition, the tangent-circle inlet showed a high-pressure drop compared with the single inlet microbubble generator. Although the double inlet microbubble generator illustrated a high-pressure drop between the inlet and the outlet, the streamlines distribution was focused only on the top part of the microbubble generator. This was a reason why the self-suction mechanism was not well defined.
Harmonic assessment on two photovoltaic inverter modes and mathematical models on low voltage network power quality
Authors: Penangsang Ontoseno , Wibowo Rony Seto , Aryani Ni Ketut , Prasetyo Mario Dwi , Arianto Marcel Nicky , Lutfi Aulia Amjad
Published: September 2023

Power quality is a crucial aspect of designing a large-scale photovoltaic power plant, particularly regarding harmonics caused by inverter switching. This research aimed to analyze harmonics in a system using electrical transient analyzer program (ETAP) Power Station 20.5.0 to uncover the effect of irradiance on the inverters’ power quality running at 85% and 100% power factors. We analyzed both voltage and current total harmonic distortion (THDi and THDv) from the simulation and compared them with the mathematical model. Moreover, we analyzed the effect of changes in irradiance level on harmonics and reactive power penetration, which influenced power losses in transformers and cables. Inverters at 85% power factor experienced an increase in THDi, whereas those at 100% power factor decreased. Inverters with 85% power factor experienced more frequent switching, causing more prominent distortion. The magnitude of THDv increased proportionally with the rise of irradiance level. Inverters at 85% had a higher THDv value because of the excessive reactive power compensation when irradiance rose. Irradiance level had an inverse relationship with system losses since high irradiance levels led to lower losses as less power was required through transmission lines and transformers. Moreover, losses at 85% power factor were higher since the high harmonics caused additional losses.
Interconnecting industrial multi-microgrids using bidirectional hybrid energy links
Authors: Lourido Walter Naranjo , Sanz Fredy Alberto , Rodriguez Umaña Luis Alfredo
Published: September 2023

Sharing and exchange energy among nearby industrial microgrids are crucial, especially with high energy requirements for their production targets and costly energy storage systems that may be oversized for their operations. Facilitating energy exchange can provide an economic advantage for industrial production by utilizing cheaper energy sources and reducing production costs. This manuscript presents an efficient approach for transferring large energy packets with minimal energy losses using high-voltage direct current (HVDC) energy transmission. The manuscript methodology focuses on implementing an industrial multi-microgrid using a modular multilevel converter. This converter utilizes two power link channels: a three-phase AC and an HVDC link, creating a hybrid energy transmission between microgrids. When a substantial amount of energy to transfer, the HVDC method enhances overall efficiency by reducing copper losses and mitigating issues associated with the AC link, such as harmonics and skin effects. The modular multilevel converter topology offers high flexibility and the use of fewer converters. Additionally, the HVDC link eliminates distance restrictions for energy transfer between industrial microgrids. A case study illustrates the functionality of this topology, demonstrating optimized power transfer and decreased energy losses. This methodology allows industrial microgrids to enhance energy efficiency and productivity while minimizing operational costs.
Power quality disturbance mitigation in grid connected photovoltaic distributed generation with plug-in hybrid electric vehicle
Authors: Harish Basaralu Nagasiddalingaiah , Surendra Usha
Published: September 2023

In the last twenty years, electric vehicles have gained significant popularity in domestic transportation. The introduction of fast charging technology forecasts increased the use of plug-in hybrid electric vehicle and electric vehicles (PHEVs). Reduced total harmonic distortion (THD) is essential for a distributed power generation system during the electric vehicle (EV) power penetration. This paper develops a combined controller for synchronizing photovoltaic (PV) to the grid and bidirectional power transfer between EVs and the grid. With grid synchronization of PV power generation, this paper uses two control loops. One controls EV battery charging and the other mitigates power quality disturbances. On the grid connected converter, a multicarrier space vector pulse width modulation approach (12-switch, three-phase inverter) is used to mitigate power quality disturbances. A Simulink model for the PV-EV-grid setup has been developed, for evaluating voltage and current THD percentages under linear and non-linear and PHEV load conditions and finding that the THD values are well within the IEEE 519 standards.
Optimization algorithms for steady state analysis of self excited induction generator
Authors: Athamnah Ibrahim , Anagreh Yaser , Anagreh Aysha
Published: September 2023

The current publication is directed to evaluate the steady state performance of three-phase self-excited induction generator (SEIG) utilizing particle swarm optimization (PSO), grey wolf optimization (GWO), wale optimization algorithm (WOA), genetic algorithm (GA), and three MATLAB optimization functions (fminimax, fmincon, fminunc). The behavior of the output voltage and frequency under a vast range of variation in the load, rotational speed and excitation capacitance is examined for each optimizer. A comparison made shows that the most accurate results are obtained with GA followed by GWO. Consequently, GA optimizer can be categorized as the best choice to analyze the generator under various conditions.
Design and implementation a novel single switch high gain DC-DC converter based on coupled inductor with low-ripple input current
Authors: Barmoudeh Farid , Ajami Ali , Sutikno Tole
Published: September 2023

A novel high-gain and high-efficiency direct current to direct current (DC-DC) converter is introduced in this paper. The presented converter is suitable for low-voltage renewable energy resources such as photovoltaic (PV) and fuel cell (FC). The existence of series inductance with the input source ensures continuous and low-ramp input current, which is important for extracting maximum power from resources. Using coupled inductor technology and an intermediate capacitor in the suggested converter leads to a high gain voltage. In the presented topology for recovering energy from the leakage inductor, reducing voltage stress on the power switch, and so decreasing overall converter losses, a passive clamp circuit is used. The suitable operation range of duty cycle in the converter, besides the leakage inductor, decreases the problem of reverse recovery in diodes. The low value of the leakage inductor and the low volume and cost of the proposed converter are due to the low turn ratio of the coupled inductor. Details of the operation principles of the proposed converter have been discussed in this paper. The presented simulation and laboratory prototype results verify the theoretical analysis and performance of the suggested topology.
Adversarial attack driven data augmentation for medical images
Authors: Pervin Mst. Tasnim , Tao Linmi , Huq Aminul
Published: September 2023

An important stage in medical image analysis is segmentation, which aids in focusing on the required area of an image and speeds up findings. Fortunately, deep learning models have taken over with their high-performing capabilities, making this process simpler. The deep learning model’s reliance on vast data, however, makes it difficult to utilize for medical image analysis due to the scarcity of data samples. Too far, a number of data augmentations techniques have been employed to address the issue of data unavailability. Here, we present a novel method of augmentation that enabled the UNet model to segment the input dataset with about 90% accuracy in just 30 epochs. We describe the us- age of fast gradient sign method (FGSM) as an augmentation tool for adversarial machine learning attack methods. Besides, we have developed the method of Inverse FGSM, which im- proves performance by operating in the opposite way from FGSM adversarial attacks. In comparison to the conventional FGSM methodology, our strategy boosted performance up to 6% to 7% on average. The model became more resilient to hostile attacks because to these two strategies. An innovative implementation of adversarial machine learning and resilience augmentation is revealed by the overall analysis of this study.