bluuebunny/crossref_metadata_2025 huggingface dataset:
A Clustering Approach for Atmospheric Phase Error Correction in Ground-Based SAR Using Spatial Autocorrelation
Authors: Qi Yaolong , Hui Jiaxin , Hou Ting , Huang Pingping , Tan Weixian , Xu Wei
Published: July 2024

When using ground-based synthetic aperture radar (GB-SAR) for monitoring open-pit mines, dynamic atmospheric conditions can interfere with the propagation speed of electromagnetic waves, resulting in atmospheric phase errors. These errors are particularly complex in rapidly changing weather conditions or steep terrain, significantly impacting monitoring accuracy. In such scenarios, traditional regression model-based atmospheric phase correction (APC) methods often become unsuitable. To address this issue, this paper proposes a clustering method based on the spatial autocorrelation function. First, the interferogram is uniformly divided into multiple blocks, and the phase consistency of each block is evaluated using the spatial autocorrelation function. Then, a region growing algorithm is employed to classify each block according to its phase pattern, followed by merging adjacent blocks based on statistical data. To verify the feasibility of the proposed method, both the traditional regression model-based method and the proposed method were applied to deformation monitoring of an open-pit mine in Northwest China. The experimental results show that for complex atmospheric phase scenarios, the proposed method significantly outperformed traditional methods, demonstrating its superiority.
Wavelet Transforms Significantly Sparsify and Compress Tactile Interactions
Authors: Slepyan Ariel , Zakariaie Michael , Tran Trac , Thakor Nitish
Published: July 2024

As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object grasping dataset (548 sensor tactile glove, grasping 26 objects). In addition, we investigated which dimension wavelet transform—1D, 2D, or 3D—would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics.
Rotation Error Prediction of CNC Spindle Based on Short-Time Fourier Transform of Vibration Sensor Signals and Improved Weighted Residual Network
Authors: Song Lin , Tan Jianying
Published: July 2024

The spindle rotation error of computer numerical control (CNC) equipment directly reflects the machining quality of the workpiece and is a key indicator reflecting the performance and reliability of CNC equipment. Existing rotation error prediction methods do not consider the importance of different sensor data. This study developed an adaptive weighted deep residual network (ResNet) for predicting spindle rotation errors, thereby establishing accurate mapping between easily obtainable vibration information and difficult-to-obtain rotation errors. Firstly, multi-sensor data are collected by a vibration sensor, and Short-time Fourier Transform (STFT) is adopted to extract the feature information in the original data. Then, an adaptive feature recalibration unit with residual connection is constructed based on the attention weighting operation. By stacking multiple residual blocks and attention weighting units, the data of different channels are adaptively weighted to highlight important information and suppress redundancy information. The weight visualization results indicate that the adaptive weighted ResNet (AWResNet) can learn a set of weights for channel recalibration. The comparison results indicate that AWResNet has higher prediction accuracy than other deep learning models and can be used for spindle rotation error prediction.
IoT-Based Heartbeat Rate-Monitoring Device Powered by Harvested Kinetic Energy
Authors: Nekui Olivier Djakou , Wang Wei , Liu Cheng , Wang Zhixia , Ding Bei
Published: July 2024

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method.
Bridging Convolutional Neural Networks and Transformers for Efficient Crack Detection in Concrete Building Structures
Authors: Yadav Dhirendra Prasad , Sharma Bhisham , Chauhan Shivank , Dhaou Imed Ben
Published: July 2024

Detecting cracks in building structures is an essential practice that ensures safety, promotes longevity, and maintains the economic value of the built environment. In the past, machine learning (ML) and deep learning (DL) techniques have been used to enhance classification accuracy. However, the conventional CNN (convolutional neural network) methods incur high computational costs owing to their extensive number of trainable parameters and tend to extract only high-dimensional shallow features that may not comprehensively represent crack characteristics. We proposed a novel convolution and composite attention transformer network (CCTNet) model to address these issues. CCTNet enhances crack identification by processing more input pixels and combining convolution channel attention with window-based self-attention mechanisms. This dual approach aims to leverage the localized feature extraction capabilities of CNNs with the global contextual understanding afforded by self-attention mechanisms. Additionally, we applied an improved cross-attention module within CCTNet to increase the interaction and integration of features across adjacent windows. The performance of CCTNet on the Historical Building Crack2019, SDTNET2018, and proposed DS3 has a precision of 98.60%, 98.93%, and 99.33%, respectively. Furthermore, the training validation loss of the proposed model is close to zero. In addition, the AUC (area under the curve) is 0.99 and 0.98 for the Historical Building Crack2019 and SDTNET2018, respectively. CCTNet not only outperforms existing methodologies but also sets a new standard for the accurate, efficient, and reliable detection of cracks in building structures.
A Novel Electrical Equipment Status Diagnosis Method Based on Super-Resolution Reconstruction and Logical Reasoning
Authors: Ping Peng , Yao Qida , Guo Wei , Liao Changrong
Published: July 2024

The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system’s situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults.
Manoeuvre Target Tracking in Wireless Sensor Networks Using Convolutional Bi-Directional Long Short-Term Memory Neural Networks and Extended Kalman Filtering
Authors: Peng Duo , Xie Kun , Liu Mingshuo
Published: July 2024

Aiming at the problem that traditional wireless sensor networks produce errors in the positioning and tracking of motorised targets due to noise interference, this paper proposes a motorised target tracking method with a convolutional bi-directional long and short-term memory neural network and extended Kalman filtering, which is trained by using the simulated RSSI value and the actual target value of motorised targets collected from the convolutional bi-directional neural network to the sensor anchor node, so as to obtain a more accurate initial value of the manoeuvre target, and at the same time, the extended Kalman filtering method is used to accurately locate and track the real-time target, so as to obtain the accurate positioning and tracking information of the real-time target. Through experimental simulation, it can be seen that the algorithm proposed in this paper has good tracking effect in both linear manoeuvre target tracking scenarios and non-linear manoeuvre target tracking scenarios.
Broad Learning System under Label Noise: A Novel Reweighting Framework with Logarithm Kernel and Mixture Autoencoder
Authors: Shen Jiuru , Zhao Huimin , Deng Wu
Published: July 2024

The Broad Learning System (BLS) has demonstrated strong performance across a variety of problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is highly sensitive to label noise. To enhance the robustness of BLS in environments with label noise, a function called Logarithm Kernel (LK) is designed to reweight the samples for outputting weights during the training of BLS in order to construct a Logarithm Kernel-based BLS (L-BLS) in this paper. Additionally, for image databases with numerous features, a Mixture Autoencoder (MAE) is designed to construct more representative feature nodes of BLS in complex label noise environments. For the MAE, two corresponding versions of BLS, MAEBLS, and L-MAEBLS were also developed. The extensive experiments validate the robustness and effectiveness of the proposed L-BLS, and MAE can provide more representative feature nodes for the corresponding version of BLS.
Research on Monitoring Assistive Devices for Rehabilitation of Movement Disorders through Multi-Sensor Analysis Combined with Deep Learning
Authors: Xu Zhenyu , Wu Zijing , Wang Linlin , Ma Ziyue , Deng Juan , Sha Hong , Wang Hong
Published: July 2024

This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabilitation progress by distinguishing gait characteristics under different walking modes. Equipped with accelerometers and six-axis force sensors, the device monitors body symmetry and upper limb strength during rehabilitation. Data were collected from normal and abnormal walking groups. A knee joint limiter was applied to subjects to simulate different levels of movement disorders. Features were extracted from the collected data and analyzed using a CNN. The overall performance was scored with Random Forest Model weights. Significant differences in average acceleration values between the moderately abnormal (MA) and severely abnormal (SA) groups (without vehicle assistance) were observed (p < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups (p > 0.05). Force sensor data showed good concentration in the normal walking group and more scatter in the SA-V group. The CNN and Random Forest Model accurately recognized gait conditions, achieving average accuracies of 88.4% and 92.3%, respectively, proving that the method mentioned above provides more accurate gait evaluations for patients with movement disorders.
Cooperative Motion Optimization Based on Risk Degree under Automatic Driving Environment
Authors: Liu Miaomiao , Zhu Mingyue , Yao Minkun , Li Pengrui , Tang Renjing , Deng Hui
Published: July 2024

Appropriate traffic cooperation at intersections plays a crucial part in modern intelligent transportation systems. To enhance traffic efficiency at intersections, this paper establishes a cooperative motion optimization strategy that adjusts the trajectories of autonomous vehicles (AVs) based on risk degree. Initially, AVs are presumed to select any exit lanes, thereby optimizing spatial resources. Trajectories are generated for each possible lane. Subsequently, a motion optimization algorithm predicated on risk degree is introduced, which takes into account the trajectories and motion states of AVs. The risk degree serves to prevent collisions between conflicting AVs. A cooperative motion optimization strategy is then formulated, incorporating car-following behavior, traffic signals, and conflict resolution as constraints. Specifically, the movement of all vehicles at the intersection is modified to achieve safer and more efficient traffic flow. The strategy is validated through a simulation using SUMO. The results indicate a 20.51% and 11.59% improvement in traffic efficiency in two typical scenarios when compared to a First-Come-First-Serve approach. Moreover, numerical experiments reveal significant enhancements in the stability of optimized AV acceleration.
A Novel Part Refinement Tandem Transformer for Human–Object Interaction Detection
Authors: Su Zhan , Yang Hongzhe
Published: July 2024

Human–object interaction (HOI) detection identifies a “set of interactions” in an image involving the recognition of interacting instances and the classification of interaction categories. The complexity and variety of image content make this task challenging. Recently, the Transformer has been applied in computer vision and received attention in the HOI detection task. Therefore, this paper proposes a novel Part Refinement Tandem Transformer (PRTT) for HOI detection. Unlike the previous Transformer-based HOI method, PRTT utilizes multiple decoders to split and process rich elements of HOI prediction and introduces a new part state feature extraction (PSFE) module to help improve the final interaction category classification. We adopt a novel prior feature integrated cross-attention (PFIC) to utilize the fine-grained partial state semantic and appearance feature output obtained by the PSFE module to guide queries. We validate our method on two public datasets, V-COCO and HICO-DET. Compared to state-of-the-art models, the performance of detecting human–object interaction is significantly improved by the PRTT.
Geomagnetic Disturbances and Pulse Amplitude Anomalies Preceding M > 6 Earthquakes from 2021 to 2022 in Sichuan-Yunnan, China
Authors: Li Xia , Qu Rui , Ji Yingfeng , Feng Lili , Zhu Weiling , Zhu Ye , Liao Xiaofeng , He Manqiu , Feng Zhisheng , Fan Wenjie , He Chang , Wang Weiming , Faheem Haris
Published: July 2024

Compelling evidence has shown that geomagnetic disturbances in vertical intensity polarization before great earthquakes are promising precursors across diverse rupture conditions. However, the geomagnetic vertical intensity polarization method uses the spectrum of smooth signals, and the anomalous waveforms of seismic electromagnetic radiation, which are basically nonstationary, have not been adequately considered. By combining pulse amplitude analysis and an experimental study of the cumulative frequency of anomalies, we found that the pulse amplitudes before the 2022 Luding M6.8 earthquake show characteristics of multiple synchronous anomalies, with the highest (or higher) values occurring during the analyzed period. Similar synchronous anomalies were observed before the 2021 Yangbi M6.4 earthquake, the 2022 Lushan M6.1 earthquake and the 2022 Malcolm M6.0 earthquake, and these anomalies indicate migration from the periphery toward the epicenters over time. The synchronous changes are in line with the recognition of previous geomagnetic anomalies with characteristics of high values before an earthquake and gradual recovery after the earthquake. Our study suggests that the pulse amplitude is effective for extracting anomalies in geomagnetic vertical intensity polarization, especially in the presence of nonstationary signals when utilizing observations from multiple station arrays. Our findings highlight the importance of incorporating pulse amplitude analysis into earthquake prediction research on geomagnetic disturbances.
ARAware: Assisting Visually Impaired People with Real-Time Critical Moving Object Identification
Authors: Surougi Hadeel , Zhao Cong , McCann Julie A.
Published: July 2024

Autonomous outdoor moving objects like cars, motorcycles, bicycles, and pedestrians present different risks to the safety of Visually Impaired People (VIPs). Consequently, many camera-based VIP mobility assistive solutions have resulted. However, they fail to guarantee VIP safety in practice, i.e., they cannot effectively prevent collisions with more dangerous threats moving at higher speeds, namely, Critical Moving Objects (CMOs). This paper presents the first practical camera-based VIP mobility assistant scheme, ARAware, that effectively identifies CMOs in real-time to give the VIP more time to avoid danger through simultaneously addressing CMO identification, CMO risk level evaluation and classification, and prioritised CMO warning notification. Experimental results based on our real-world prototype demonstrate that ARAware accurately identifies CMOs (with 97.26% mAR and 88.20% mAP) in real-time (with a 32 fps processing speed for 30 fps incoming video). It precisely classifies CMOs according to their risk levels (with 100% mAR and 91.69% mAP), and warns in a timely manner about high-risk CMOs while effectively reducing false alarms by postponing the warning of low-risk CMOs. Compared to the closest state-of-the-art approach, DEEP-SEE, ARAware achieves significantly higher CMO identification accuracy (by 42.62% in mAR and 10.88% in mAP), with a 93% faster end-to-end processing speed.
Gendered Gaps in the Adoption of Climate-Smart Agriculture in Africa and How to Overcome Them
Authors: Boudalia Sofiane , Dawit Teweldebirhan Mahilet , Ariom Thaddaeus Obaji , Diouf Ndèye Seynabou , Nambeye Eva , Mwatitha Gondwe Therese , Mbo’o-Tchouawou Michele , Okoth Sheila A. , Huyer Sophia
Published: July 2024

In Africa, the agricultural sector contributes approximately 10–20% of the total anthropogenic greenhouse gas (GHG) emissions. It presents rapid growth as a result of rising food demand in Africa, which is being driven by population growth. Consequently, climate change can negatively affect crop yields and livestock production, thus threatening food security. This review highlights the existing gender gaps in African agriculture and discusses the drivers and barriers that maintain gender gaps in climate-smart agriculture (CSA) adoption in African countries. Moreover, the review offers a comprehensive roadmap for the essential measures required to facilitate the widespread uptake of CSA practices among female farmers. Several CSA practices were reported, such as agricultural practices, forest and cropland regeneration practices, water resources, and the use of weather and climate information services. The gender gap in the adoption of CSA practices was influenced by policy legislation, financial resources, social and cultural taboos, and technical determinants such as climate information access. To address this gender gap, scientific-outcome-based research should be used to address gender gaps among female small farmers. In conclusion, to overcome the gender gap in CSA adoption in Africa, this review recommends the use of a gender-responsive approach, the development of scientific research-driven measures, and the prioritization of gender equality in governments’ agendas in the context of climate change uncertainty.
The Role of Carbon Emissions on Inward Foreign Direct Investment: A Nonlinear Dynamic Panel Data Analysis
Authors: Gök Adem , Ashraf Ayesha , Jasinska Elzbieta
Published: July 2024

An increase in carbon emissions (CO2) may increase inward foreign direct investment (FDI) in developing countries since they are seen as pollution havens because of lax environmental regulations (pollution haven hypothesis). Developed countries may also attract FDI since stringent environment regulations in these countries working to reduce emissions might be more attractive to foreign investors concerned with their repute from a green perspective. A rise in CO2 emissions in developed countries therefore deters inward FDI (green haven hypothesis). The existing empirical studies investigate the empirical validity of these hypotheses by focusing on the impacts of environmental policies and regulations on FDI and have yet to produce conclusive results. We examined the effect of CO2 emissions on FDI and provide a more accurate and novel way of investigating the empirical validity of the pollution haven hypothesis against the green haven hypothesis. Specifically, we examined the non-linear effects of CO2 emissions on inward FDI in a sample of 124 countries over the period 1997–2022. The results indicate that CO2 emissions have an inverted-U-shaped relationship with FDI, confirming our hypotheses that higher CO2 emissions in countries with lax environmental standards attract FDI while environmental degradation in countries with stringent environmental standards deter FDI.
Robustness Assessment of the Metro System: A Case Study of Bucharest, Romania
Authors: Ebrahimi Golshanabadi Roozbeh , Popa Mihaela
Published: July 2024

Metro network systems play an important role in urban transportation but they are vulnerable to various risks and disruptions. Having a comprehensive understanding of these risks is essential for their operation. In this paper, we aimed to assess the operational robustness of the Bucharest metro system. We employed graph theory and concepts. Using available data specific to the Bucharest metro system operation, we analyzed the type of disruptions across metro lines and stations. As expected, the two subway lines that sub-cross the city center experienced the greatest number of disruptions. Several stations on Line M1, that sub-cross the central urban area, were found to be particularly vulnerable. In our analysis, the results are as follows: the robustness indicator for the metro network, rT, is 0.1538, and the effective graph conductance (CG) is 0.1. These reveal that the Bucharest metro network can withstand up to 45% of node failures before collapsing. This indicates moderate robustness, suggesting that while the network can handle specific disruptions, targeted improvements are necessary to enhance overall resilience.
Prenatal Arsenic Exposure on DNA Methylation of C18ORF8 and ADAMTS9 Genes of Newborns from the POSGRAD Birth Cohort Study
Authors: Lerma-Treviño Carolina , Hernández-Cadena Leticia , Acosta-Montes Jorge Octavio , Hernández-Montes Georgina , Alvarado-Cruz Isabel , Romieu Isabelle , Barraza-Villarreal Albino
Published: July 2024

Exposure to arsenic (As) is a public health problem associated with cancer (skin and colon) and it has been reported that epigenetic changes may be a potential mechanism of As carcinogenesis. It is pertinent to evaluate this process in genes that have been associated with cancer, such as ADAMTS9 and C18ORF8. Gestation and delivery data were obtained from the POSGRAD study. Exposure to As was measured in urine during pregnancy. Gene methylation was performed by sodium bisulfite sequencing; 26 CpG sites for the C18ORF8 gene and 21 for ADAMTS9 were analyzed. These sites are located on the CpG islands near the start of transcription. Sociodemographic characteristics were obtained by a questionnaire. The statistical analysis was performed using multiple linear regression models adjusted for potential confounders. Newborns with an As exposure above 49.4 μg g−1 showed a decrease of 0.21% on the methylation rate in the sites CpG15, CpG19, and CpG21 of the C18ORF8 gene (adjusted ß = −0.21, p-value = 0.02). No statistically significant association was found between prenatal exposure to As and methylation of the ADAMTS9 gene. Prenatal exposure to As was associated with decreased DNA methylation at the CpG15, CpG19, and CpG21 sites of the C18ORF8 gene. These sites can provide information to elucidate epigenetic mechanisms associated with prenatal exposure to As and cancer.
Exposure of Young Children to Permethrin and Cypermethrin Insecticides in the Residential Environment
Authors: Sirikanyaporn Siriporn , Nankongnab Noppanun , Kongtip Pornpimol , Siri Sukhontha , Suk William Alfred , Woskie Susan Renee
Published: July 2024

The aims of this study were to evaluate the exposure to permethrin and cypermethrin of young children aged between 2 and 5 years in Nakhon Pathom and Sing Buri provinces, Thailand. A questionnaire that included general demographic information, household characteristics, insecticide usage and exposure-related behavior in children was used to interview parents or family caregivers. Permethrin and cypermethrin concentrations on floor surfaces and children’s hands, as well as their urinary metabolites, were analyzed by gas chromatography coupled with mass spectrometry. The results showed that permethrin and cypermethrin were detected in 62% and 83% of the children’s hand wipe samples, with geometric mean (GM) levels of 0.02 µg and 0.04 µg, respectively. Permethrin and cypermethrin were detected in 79% and 93% of floor surface wipe samples, with GM levels of 0.90 µg/m2, and 1.49 µg/m2, respectively. For children’s urine, the GM concentrations of cis- and trans-DCCA, 3-PBA, and total pyrethroid metabolites were 0.84, 0.31 and 1.23 nmol/g creatinine, respectively. This study found that household insecticide product usage and having a tile floor were associated with increased permethrin concentrations on the children’s hands and floor surfaces. However, cypermethrin concentrations on floor surfaces were significantly higher in families using aerosol insecticide sprays and insecticide products in the living room and bedroom. The predictors of the total pyrethroid, DCCA and 3-PBA metabolites are permethrin on children’s hands or floor surfaces and cypermethrin on floor surfaces.
Phage-Mediated Digestive Decolonization in a Gut-On-A-Chip Model: A Tale of Gut-Specific Bacterial Prosperity
Authors: Van Nieuwenhuyse Brieuc , Merabishvili Maya , Goeders Nathalie , Vanneste Kevin , Bogaerts Bert , de Jode Mathieu , Ravau Joachim , Wagemans Jeroen , Belkhir Leïla , Van der Linden Dimitri
Published: July 2024

Infections due to antimicrobial-resistant bacteria have become a major threat to global health. Some patients may carry resistant bacteria in their gut microbiota. Specific risk factors may trigger the conversion of these carriages into infections in hospitalized patients. Preventively eradicating these carriages has been postulated as a promising preventive intervention. However, previous attempts at such eradication using oral antibiotics or probiotics have led to discouraging results. Phage therapy, the therapeutic use of bacteriophage viruses, might represent a worthy alternative in this context. Taking inspiration from this clinical challenge, we built Gut-On-A-Chip (GOAC) models, which are tridimensional cell culture models mimicking a simplified gut section. These were used to better understand bacterial dynamics under phage pressure using two relevant species: Pseudomonas aeruginosa and Escherichia coli. Model mucus secretion was documented by ELISA assays. Bacterial dynamics assays were performed in GOAC triplicates monitored for 72 h under numerous conditions, such as pre-, per-, or post-bacterial timing of phage introduction, punctual versus continuous phage administration, and phage expression of mucus-binding properties. The potential genomic basis of bacterial phage resistance acquired in the model was investigated by variant sequencing. The bacterial “escape growth” rates under phage pressure were compared to static in vitro conditions. Our results suggest that there is specific bacterial prosperity in this model compared to other in vitro conditions. In E. coli assays, the introduction of a phage harboring unique mucus-binding properties could not shift this balance of power, contradicting previous findings in an in vivo mouse model and highlighting the key differences between these models. Genomic modifications were correlated with bacterial phage resistance acquisition in some but not all instances, suggesting that alternate ways are needed to evade phage predation, which warrants further investigation.
Are Viruses Taxonomic Units? A Protein Domain and Loop-Centric Phylogenomic Assessment
Authors: Caetano-Anollés Gustavo
Published: July 2024

Virus taxonomy uses a Linnaean-like subsumption hierarchy to classify viruses into taxonomic units at species and higher rank levels. Virus species are considered monophyletic groups of mobile genetic elements (MGEs) often delimited by the phylogenetic analysis of aligned genomic or metagenomic sequences. Taxonomic units are assumed to be independent organizational, functional and evolutionary units that follow a ‘natural history’ rationale. Here, I use phylogenomic and other arguments to show that viruses are not self-standing genetically-driven systems acting as evolutionary units. Instead, they are crucial components of holobionts, which are units of biological organization that dynamically integrate the genetics, epigenetic, physiological and functional properties of their co-evolving members. Remarkably, phylogenomic analyses show that viruses share protein domains and loops with cells throughout history via massive processes of reticulate evolution, helping spread evolutionary innovations across a wider taxonomic spectrum. Thus, viruses are not merely MGEs or microbes. Instead, their genomes and proteomes conduct cellularly integrated processes akin to those cataloged by the GO Consortium. This prompts the generation of compositional hierarchies that replace the ‘is-a-kind-of’ by a ‘is-a-part-of’ logic to better describe the mereology of integrated cellular and viral makeup. My analysis demands a new paradigm that integrates virus taxonomy into a modern evolutionarily centered taxonomy of organisms.
The Novel Role of the NLRP3 Inflammasome in Mycotoxin-Induced Toxicological Mechanisms
Authors: Liao Chengshui , Xu Fengru , Yu Zuhua , Ding Ke , Jia Yanyan
Published: July 2024

Mycotoxins are secondary metabolites produced by several fungi and moulds that exert toxicological effects on animals including immunotoxicity, genotoxicity, hepatotoxicity, teratogenicity, and neurotoxicity. However, the toxicological mechanisms of mycotoxins are complex and unclear. The nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) family pyrin domain containing 3 (NLRP3) inflammasome is a multimeric cytosolic protein complex composed of the NLRP3 sensor, ASC adapter protein, and caspase-1 effector. Activation of the NLRP3 inflammasome plays a crucial role in innate immune defence and homeostatic maintenance. Recent studies have revealed that NLRP3 inflammasome activation is linked to tissue damage and inflammation induced by mycotoxin exposure. Thus, this review summarises the latest advancements in research on the roles of NLRP3 inflammasome activation in the pathogenesis of mycotoxin exposure. The effects of exposure to multiple mycotoxins, including deoxynivalenol, aflatoxin B1, zearalenone, T-2 toxin, ochratoxin A, and fumonisim B1, on pyroptosis-related factors and inflammation-related factors in vitro and in vivo and the pharmacological inhibition of specific and nonspecific NLRP3 inhibitors are summarized and examined. This comprehensive review contributes to a better understanding of the role of the NLRP3 inflammasome in toxicity induced by mycotoxin exposure and provides novel insights for pharmacologically targeting NLRP3 as a novel anti-inflammatory agent against mycotoxin exposure.
Chronic Heat Stress Induces Oxidative Stress and Induces Inflammatory Injury in Broiler Spleen via TLRs/MyD88/NF-κB Signaling Pathway in Broilers
Authors: Chen Haoxiang , Wang Feiyao , Wu Xingyue , Yuan Songchen , Dong Huili , Zhou Chenyang , Feng Siliang , Zhao Zhanqin , Si Lifang
Published: July 2024

The spleen is the largest peripheral immune organ of the organism, accounting for 25% of the total lymphoid tissue of the body. During HS, the spleen is damaged due to the elevated environment, which seriously affects life performance and broilers’ health. This study aimed to investigate the mechanism of chronic HS damage to broiler spleen tissues. The broilers were typically raised until they reached 21 days of age, after which they were arbitrarily allocated into two groups: an HS group and a cntrol group. The HS group was subjected to a temperature of 35 °C for 10 h each day, starting at 21 days of age. At 35 and 42 days of age, spleen and serum samples were obtained from the broilers. The results showed that after HS, a significant decrease in productive performance was observed at 42 days of age (p < 0.01), and the spleen index, and bursa index were significantly decreased (p < 0.01). T-AOC of the organism was significantly decreased (p < 0.05), GSH-PX, SOD, and CAT antioxidant factors were significantly decreased (p < 0.01), and MDA was significantly elevated (p < 0.01). HS also led to a significant increase in cytokines IL-6, TNF-α, and INF-γ and a significant decrease in IL-4 in the spleen. The histopathologic results showed that the spleen’s red-white medulla was poorly demarcated. The cells were sparsely arranged after HS. After HS, the expression of TLRs, MYD88, and NF-κB genes increased significantly. The expression of HSP70 increased significantly, suggesting that HS may induces an inflammatory response in broiler spleens through this signaling pathway, which may cause pathological damage to broiler spleens, leading to a decrease in immune function and progressively aggravating HS-induced damage with the prolongation of HS.
Modeling the Hydraulic Fracturing Processes in Shale Formations Using a Meshless Method
Authors: Xiang Ziru , Yu Shuyang , Wang Xiangyu
Published: July 2024

Complex bedding properties and in situ stress conditions of shale formation lead to complex hydraulic fracturing morphologies. However, due to the limitations of traditional numerical methods, the simulation of hydraulic fracturing in shale formation still needs further development. Based on this, the liquid–solid interaction modes and the SPH governing equations considering liquid–solid interaction force have been introduced. The smoothing kernel function in the traditional SPH method is improved by introducing the fracture mark ξ, which can realize the simulation of rock hydraulic fracturing processes. The stress boundary of the SPH method is applied by stress mapping of “stress particles”, and the feasibility and correctness of the method are verified by two numerical examples. Then, the simulation of hydraulic fracturing processes of bedding shale formations are carried out. With the increase of horizontal stress ratio, the total number of damaged particles decreases, but the initiation and extension pressure increase gradually. The initiation stress of small bedding dip angles (θ < 45°) is larger than that of big bedding dip angles (θ > 45°). The hydraulic fracture propagation range at low horizontal stress ratio is wider and the fracture is along the direction of maximum principal stress, while the hydraulic fracture propagation range at high horizontal stress ratio is limited to the perforation. The hydraulic fracture will propagate through the bedding with small dip angles. However, when the bedding dip angle is larger, the hydraulic fracture will propagate along the bedding direction.
Bacterioplankton Community Diversity of a Portuguese Aquifer System (Maciço Calcário Estremenho)
Authors: de Figueiredo Daniela R. , Condesso de Melo Maria T. , Saraiva Pedro P. , Oliveira Joana , Gonçalves Ana M. M. , Reboleira Ana Sofia P. S. , Polónia Ana R. M. , Abrantes Nelson , Cleary Daniel F. R.
Published: July 2024

Climate change may increase the vulnerability of aquifers to contamination through extreme precipitation and extended drought periods. Therefore, the understanding of groundwater ecosystem dynamics is crucial, with bacterial assemblages playing a major role in biogeochemical cycles. The present research describes a geospatial study targeting the bacterial community structure of groundwaters from the largest karst aquifer in Portugal (the Maciço Calcário Estremenho), integrating hydrogeochemical and bacterial diversity data. A total of 22 samples were analyzed from a set of 11 geographically sparsely distributed groundwater sources in dry vs. wet seasons. The 16S rRNA gene barcoding data revealed bacterial community variability across samples in space and time. The phylum Proteobacteria was dominant across all samples (from 44 to 92% of total sequence reads), mainly represented by the classes Alphaproteobacteria (orders Sphingomonadales, BD7–3, Rhizobiales and Rhodospirillales), Betaproteobacteria (orders Burkholderiales, Rhodocyclales, Nitrosomonadales), Gammaproteobacteria (orders Pseudomonadales, Xanthomonadales, Alteromonadales, Legionellales) and Deltaproteobacteria (orders Myxococcales, Spirobacillales). Variation in the bacterial community was primarily attributed to parameters such as redox conditions (DO, ORP), Fe, Mn, SO4, PO4, Sr and Cl, but also some minor and trace elements (Al, V, Cr, Cu, Pb). Our results provide novel insights into bacterial diversity in relation to groundwater hydrogeochemistry. The strong dominance of OTUs related to bacterial taxa associated with nitrification/denitrification also highlights a potentially important role of these assemblages on nutrients (nitrogen sources) and groundwater quality dynamics at this karstic aquifer system. Moreover, the integration of bacterial assemblages information is emphasized as central for water quality monitoring programs.
Simulation of Flow and Pressure Loss in the Example of the Elbow
Authors: Smyk Emil , Stopel Michał , Szyca Mikołaj
Published: July 2024

One of the most basic issues in fluid mechanics is the description of flow in closed flows; more precisely, the calculation of pressure drops and the description of the flow form. Therefore, in this paper, the numerical simulation of the flow through the elbow was presented. This case was used to comprehensively describe the most important phenomena that should be taken into account during closed flows. The elbow was chosen as one of the most frequently used fittings in practice. The simulation was made with ANSYS Fluent, with the use of the turbulent model k-ω, SIMPLE simulation method, and at Reynolds number Re=500−100,000. The minor and major pressure loss were presented and discussed in the paper. The minor loss coefficient at the high Reynolds number was equal to around 0.2, which is close to the value of 0.22 used in engineering calculations. The influence of the Reynolds number on the shift of the stream separation point in the elbow was described. The secondary flow in the elbow was observed and the vortex structure was discussed and shown with the use of the Q-criterion (Q iso surface for level 0.005). This analysis allowed us to better visualize and describe the complex flow structure observed in the investigated case.
Predicting Water Inflow in Tunnel Construction: A Fracture Network Model with Non-Darcy Flow Considerations
Authors: Hu Ke , Yao Liang , Liao Jianxing , Wang Hong , Luo Jiashun , Xu Xiangdong
Published: July 2024

Fractures are widely distributed in karst areas, and when flow rates are high, they exhibit complex nonlinear behavior that cannot be accurately described by Darcy’s law. In this work, a hydro-mechanical coupling model based on a discrete fracture network is proposed to predict tunnel water inflow, accounting for the impact of non-Darcy flow. The model’s feasibility has been validated by comparing it with experimental results and the field measurements of flow rates at the Bodaoling Tunnel in Guizhou, China. The results show that Darcy flow tends to overestimate water inflow by approximately 25% compared to non-Darcy flow. The non-Darcy effect grows with the increase in initial fracture width and empirical constant q. When q exceeds 8.77 × 10−6, the growth rate of the Forchheimer number along the fracture width slowed down, and the inhibitory effect of non-Darcy flow on flow became gentle. Additionally, in a complex fracture network, the inflow rate limited by non-Darcy flow at one point drives the water flow through a connect fracture to another point, which increases the difficulty in water inflow prediction. This work highlights the importance of non-Darcy flow and fracture networks when accurately predicting water inflow in tunnels.
Diseño de una Minicentral Hidroeléctrica en la Provincia de Manabí
Authors: Sandoval Pedauga Paola , Jara Alvear José Estuardo
Published: July 2024

La emisión de dióxido de carbono al medio ambiente provocada por la generación de energía con fuente de combustibles fósiles, ha generado gran preocupación a nivel mundial. En Ecuador la generación es mayormente con energía renovable, mediante el uso de las Centrales Hidroeléctricas, ubicadas principalmente en las vertientes del Amazonas. Sin embargo, en los períodos de estiaje (sequía) se hace necesario complementar la generación con las Centrales Termoeléctricas que usan combustibles fósiles. Esta situación sucede más frecuente en las provincias de la costa, cuya demanda está más alejada de las centrales hidroeléctricas existentes. Por lo antes expuesto, y tomando como línea base el Proyecto Propósito Múltiple Chone, se propone el Diseño de una Minicentral Hidroeléctrica en la provincia de Manabí, como generación distribuida renovable, buscando una alternativa más limpia y económica. En la misma, se utilizará el enfoque del concepto NEXOS, que busca la interrelación entre agua, alimento y energía. Adicionalmente se busca mejorar la calidad de vida del sector El Pueblito de Río Grande (área de estudio), el cual tiene frecuentes interrupciones en el suministro del servicio de energía eléctrica. Las variables analizadas fueron la selección y dimensionamiento de los diferentes elementos, obras de ingeniería civiles, equipos eléctricos y la generación de energía hidroeléctrica, cuyos resultados demostraron que con una altura promedio neta de 24,76 m y caudal diseño de 16 m3/s, utilizando la toma de obra del túnel de desagüe y dos turbinas tipo Francis de 3MW cada una, es viable diseñar una minicentral hidroeléctrica con una potencia máxima 4,54 MW y capacidad de generación de energía promedio de 28,34 GWh anuales, energía suficiente para abastecer la demanda del sector el Pueblito de Río Grande.
Diseño conceptual de clínica móvil para atención primaria de salud
Authors: Aguilar Gutiérrez Marcos Vinicio
Published: July 2024

Las clínicas móviles se han convertido en una alternativa viable para llevar la atención médica primaria a las comunidades rurales, por lo que el gobierno de Manabí tiene interés en fomentar el uso de estas clínicas para la atención de la salud en las comunidades de difícil acceso. Tomando en cuenta esta problemática, el presente trabajo tiene como objetivo realizar el diseño conceptual de una clínica móvil para la atención primaria de la salud en las comunidades rurales de difícil acceso de la provincia Manabí, para lo cual en una etapa inicial se elaboró el diseño conceptual basado en la metodología de Ullman, que incluyó la definición de la composición estructural de la clínica, mobiliario, selección del tipo de chasis y tablero indicador. Se realizó, además un análisis de integridad estructural mediante el método de elementos finitos. Los resultados finales permitieron el diseño conceptual de una clínica móvil a partir de la transformación de un bus convencional de transporte de pasajeros, bajo criterios científicamente fundamentados cumpliendo con las normativas establecidas por el MSP para este tipo de clínicas, se comprobó que la estructura es capaz de conservar su integridad estructural ante la acción de las cargas actuantes durante su conducción y prestación de servicio (FS=1,92). Finalmente se puede concluir que el diseño se abordó de manera integral permitiendo la obtención de un prototipo virtual de una clínica móvil que cumple con los requisitos de funcionalidad, resistencia, ergonomía, requeridos para la prestación de los servicios de atención de salud primaria.