bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Disease (Water Bubble Disease) of the Giant Freshwater Prawn Macrobrachium rosenbergii Caused by Citrobacter freundii: Antibiotic Treatment and Effects on the Antioxidant Enzyme Activity and Immune Responses
Authors: Zhao Caiyuan , Wen Huagen , Huang Shengsheng , Weng Shaoping , He Jianguo
Published: August 2022

The giant freshwater prawn, Macrobrachium rosenbergii, is an important and economical aquaculture species widely farmed in tropical and subtropical areas of the world. A new disease, “water bubble disease (WBD)”, has emerged and resulted in a large loss of M. rosenbergii cultured in China. A water bubble with a diameter of about 7 mm under the carapace represents the main clinical sign of diseased prawns. In the present study, Citrobacter freundii was isolated and identified from the water bubble. The optimum temperature, pH, and salinity of the C. freundii were 32 °C, 6, and 1%, respectively. A challenging experiment showed that C. freundii caused the same typical signs of WBD in prawns. Median lethal dose of the C. freundii to prawn was 104.94 CFU/g. According to the antibiogram tests of C. freundii, florfenicol and ofloxacin were selected to evaluate their therapeutic effects against C. freundii in prawn. After the challenge with C. freundii, 86.67% and 72.22% survival of protective effects against C. freundii were evaluated in the oral florfenicol pellets and oral ofloxacin pellets feding prawns, respectively, whereas the mortality of prawns without fed antibiotics was 93%. After antibiotic treatment and C. freundii infection, the activities of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), glutathione S-transferase (GST), malondialdehyde (MDA), acid phosphatase (ACP), alkaline phosphatase (ALP), and lysozyme (LZM) in the hemolymph and hepatopancreas of the prawns and the immune-related gene expression levels of Cu/Zn-SOD, CAT, GPx, GST, LZM, ACP, anti-lipopolysaccharide factor, crustin, cyclophilin A, and C-type lectin in hepatopancreas were all significantly changed, indicating that innate immune responses were induced by C. freundii. These results can be beneficial for the prevention and control of C. freundii in prawns.
Quercetin Attenuates Quinocetone-Induced Cell Apoptosis In Vitro by Activating the P38/Nrf2/HO-1 Pathway and Inhibiting the ROS/Mitochondrial Apoptotic Pathway
Authors: Dai Chongshan , Zhang Qinzhi , Shen Linjie , Sharma Gaurav , Jiang Haiyang , Wang Zhanhui , Shen Jianzhong
Published: August 2022

Quinocetone (QCT), a member of the quinoxaline 1,4-di-N-oxides (QdNOs) family, can cause genotoxicity and hepatotoxicity, however, the precise molecular mechanisms of QCT are unclear. This present study investigated the protective effect of quercetin on QCT-induced cytotoxicity and the underlying molecular mechanisms in human L02 and HepG2 cells. The results showed that quercetin treatment (at 7.5–30 μM) significantly improved QCT-induced cytotoxicity and oxidative damage in human L02 and HepG2 cells. Meanwhile, quercetin treatment at 30 μM significantly inhibited QCT-induced loss of mitochondrial membrane potential, an increase in the expression of the CytC protein and the Bax/Bcl-2 ratio, and an increase in caspases-9 and -3 activity, and finally improved cell apoptosis. Quercetin pretreatment promoted the expression of the phosphorylation of p38, Nrf2, and HO-1 proteins. Pharmacological inhibition of p38 significantly inhibited quercetin-mediated activation of the Nrf2/HO-1 pathway. Consistently, pharmacological inhibitions of the Nrf2 or p38 pathways both promoted QCT-induced cytotoxicity and partly abolished the protective effects of quercetin. In conclusion, for the first time, our results reveal that quercetin could improve QCT-induced cytotoxicity and apoptosis by activating the p38/Nrf2/HO-1 pathway and inhibiting the ROS/mitochondrial apoptotic pathway. Our study highlights that quercetin may be a promising candidate for preventing QdNOs-induced cytotoxicity in humans or animals.
The Burden of Impaired Serum Albumin Antioxidant Properties and Glyco-Oxidation in Coronary Heart Disease Patients with and without Type 2 Diabetes Mellitus
Authors: Piarulli Francesco , Banfi Cristina , Brioschi Maura , Altomare Alessandra , Ragazzi Eugenio , Cosma Chiara , Sartore Giovanni , Lapolla Annunziata
Published: August 2022

Human serum albumin (HSA) has an important antioxidant activity due to the presence of the reduced cysteine at position 34, which represents the most abundant free thiol in the plasma. In oxidative-based diseases, HSA undergoes S-thiolation (THIO-HSA) with changes in the antioxidant function of albumin that could contribute to the progression of the disease. The aim of this study was to verify, for the first time, the different burdens of THIO-HSA, glycated HSA (GLY-HSA), and advanced glycation end products (AGE) accumulation both in type 2 diabetes mellitus (T2DM) patients and in non-diabetic patients, with or without coronary heart disease (CHD). In this study, we assessed the presence of modified forms of HSA, THIO-HSA, and GLY-HSA by means of mass spectrometry in 33 patients with both T2DM and CHD, in 31 patients with T2DM and without CHD, in 30 patients without diabetes with a history of CHD, and 27 subjects without diabetes and CHD. All the patients’ anthropometric and clinical data were recorded including age, sex, duration of diabetes, body mass index (BMI), blood pressure, and history of CHD defined with anamnestic data. Metabolic parameters, such as fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), lipids, pentosidine, AGE, receptor for advanced glycation end-products (RAGE) and its soluble form (sRAGE), were measured. AGE and pentosidine are significantly higher in T2DM patients with and without CHD with respect to non-diabetic patients with CHD and control subjects. RAGE levels are significantly higher in T2DM patients with respect to non-diabetic patients, and among T2DM patients, the group with CHD showed significantly higher RAGE levels than those without CHD (217 ± 171 pg/mL and 140 ± 61 pg/mL, respectively). Albumin isoforms discriminate between non-diabetic patients with CHD and T2DM patients with and without CHD and control subjects, with GLY-HSA levels higher in T2DM with and without CHD, and THIO-HSA higher in CHD patients without T2DM. Finally, we demonstrated that the oxidized forms of HSA can increase the expression of the inflammatory cytokine Tumor Necrosis Factor-alpha (TNFα) in monocytic cells. In patients with CHD, GLY-HSA and THIO-HSA have a different prevalent distribution, the first one prevailing in patients with T2DM and the second one in patients without T2DM. These findings suggest that albumin quality and homeostasis balance between glyco-oxidation and thiolation might have an impact on the antioxidant defense system in cardiovascular diseases.
Antiproliferative and Pro-Oxidant Effect of Polyphenols in Aqueous Leaf Extract of Passiflora alata Curtis on Activated T Lymphocytes from Non-Obese Diabetic (NOD SHILT/J) Mice
Authors: Colomeu Talita Cristina , de Figueiredo Daniella , de Matos da Silva Priscila , Fernandes Luís Gustavo Romani , Zollner Ricardo de Lima
Published: August 2022

The antioxidant, anti-inflammatory and antiproliferative properties of Passiflora alata Curtis are due to the presence of polyphenols in its composition. Our previous work showed that non-obese diabetic (NOD) mice undergoing treatment with aqueous leaf extract of P. alata present reduced insulitis in the pancreas, possibly due to its anti-inflammatory properties. However, depending on the concentration and their ability to interact with other molecules, these phenolic compounds may promote oxidation reactions in some cellular components, such as proteins and lipids, thus presenting a pro-oxidant effect. The present work aimed to evaluate the in vitro effects of aqueous leaf extract of P. alata and its polyphenols (vitexin, isoorientin, rutin and catechin) on lymphocyte proliferation and viability, the cell cycle and oxidative stress. Our results showed that T lymphocytes stimulated with concanavalin A mitogen (ConA) and in the presence of IC50 concentrations of P. alata extract and polyphenols undergo cell injury via inhibition of proliferation, with these effects being more pronounced concerning CD4+ T cells (P. alata, 3.54 ± 0.34%; isoorientin, 57.07 ± 6.4%; vitexin, 16.95 ± 1.11%; catechin, 37.9 ± 4.2% and rutin, 40.14 ± 4.5%), compared to the non-treated group (77.17 ± 6.29) (p < 0.0001 for all comparisons). This process includes late apoptosis/necrosis induction (P. alata, 77.5 ± 0.7%; vitexin, 83 ± 3.3%; isoorientin, 83.8 ± 1.4%; catechin, 83 ± 1.9% and rutin, 74.9 ± 3.2, while the control presented 53.6% ± 3.1 (p < 0.0001 for all comparisons)) and mitochondrial depolarization leading to cell-death induction. Furthermore, an in vitro model of a mixed culture of NOD mice T cells with a mouse pancreatic beta-cell line (MIN6) showed increased intracellular nitric oxide and lipid peroxidation in NOD T cells submitted to P. alata extract (46.41 ± 3.08) compared to the untreated control group (33.57 ± 1.99, p = 0.01315). These results suggest that aqueous leaf extract of P. alata and the polyphenols in these leaves represent a target for translational research showing the plant’s benefits for developing new drugs with immunomodulatory properties against inflammatory diseases such as diabetes mellitus.
Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion
Authors: Wang Tong , Xu Xin , Pan Hongxia , Chang Xuefang , Yuan Taotao , Zhang Xu , Xu Hongzhao
Published: August 2022

Given the problems of low accuracy and complex process steps currently faced by the field of fault diagnosis, a fault diagnosis method based on multi-sensor data weighted fusion (MSDWF) combined with depth-wise separable convolutions (DWSC) is proposed. The method takes into account the temporal and spatial information contained in multi-sensor data and can realize end-to-end bearing fault diagnosis. MSDWF is committed to comprehensively characterizing the state information of bearings, and the weighted operation of the multi-sensor data is to establish the interactive information to tap into the inline relationship in the data; DWSC equipped with residual connection is used to realize the decoupling of the channel and spatial correlation of the data. In order to verify the proposed method, the data obtained by a different number of sensors with weighted and unweighted states are used as the input of DWSC, respectively, for comparison, and finally, the effectiveness of MSDWF is verified. Through the comparison between different fault diagnosis methods, the method based on MSDWF and DWSC shows better stability and higher accuracy. Finally, when facing different experimental datasets, the method has similar performance, which shows the stability of the method on different datasets.
ATP-Binding Cassette (ABC) Transporters in Fusarium Specific Mycoparasite Sphaerodes mycoparasitica during Biotrophic Mycoparasitism
Authors: Kim Seon Hwa , Vujanovic Vladimir
Published: August 2022

Recent transcriptomic profiling has revealed importance membrane transporters such as ATP-binding cassette (ABC) transporters in fungal necrotrophic mycoparasites. In this study, RNA-Seq allowed rapid detection of ABC transcripts involved in biotrophic mycoparasitism of Sphaerodes mycoparasitica against the phytopathogenic and mycotoxigenic Fusarium graminearum host, the causal agent of Fusarium head blight (FHB). Transcriptomic analyses of highly expressed S. mycoparasitica genes, and their phylogenetic relationships with other eukaryotic fungi, portrayed the ABC transporters’ evolutionary paths towards biotrophic mycoparasitism. Prior to the in silico phylogenetic analyses, transmission electron microscopy (TEM) was used to confirm the formation of appressorium/haustorium infection structures in S. mycoparasitica during early (1.5 d and 3.5 d) stages of mycoparasitism. Transcripts encoding biotrophy-associated secreted proteins did uncover the enrolment of ABC transporter genes in this specific biocontrol mode of action, while tandem ABC and BUB2 (non-ABC) transcripts seemed to be proper for appressorium development. The next-generation HiSeq transcriptomic profiling of the mycoparasitic hypha samples, revealed 81 transcripts annotated to ABC transporters consisting of a variety of ABC-B (14%), ABC-C (22%), and ABC-G (23%), and to ABC-A, ABC-F, aliphatic sulfonates importer (TC 3.A.1.17.2), BtuF, ribose importer (TC 3.A.1.2.1), and unknown families. The most abundant transcripts belonged to the multidrug resistance exporter (TC 3.A.1.201) subfamily of the ABC-B family, the conjugate transporter (TC 3.A.1.208) subfamily of the ABC-C family, and the pleiotropic drug resistance (PDR) (TC 3.A.1.205) subfamily of the ABC-G family. These findings highlight the significance of ABC transporter genes that control cellular detoxification against toxic substances (e.g., chemical pesticides and mycotoxins) in sustaining a virulence of S. mycoparasitica for effective biotrophic mycoparasitism on the F. graminearum host. The findings of this study provide clues to better understand the biotrophic mycoparasitism of S. mycoparasitica interacting with the Fusarium host, which implies that the ABC transporter group of key proteins is involved in the mycoparasite’s virulence and multidrug resistance to toxic substances including cellular detoxification.
Effect of Grit Size on Airborne Particle Concentration and Size Distribution during Oak Wood Sanding
Authors: Dado Miroslav , Salva Jozef , Schwarz Marián , Vanek Miroslav , Bustin Lucia
Published: August 2022

Adverse health effects caused by exposure to airborne particles have been detected in recent years, however there is little knowledge about exposure to ultrafine particles with a diameter <100 nm. In this study, particle number concentration and size distribution in a range of particle diameters from 10 nm to 10 µm were determined during oak wood sanding. A hand-held orbit sander in combination with three types of grit size (P60, 120 and 240) of sandpaper were used. Measurements were obtained using a portable particle size distribution analyzer and an optical particle size spectrometer, carried out at 15-min intervals for each treatment by static sampling in the breathing zone. We also compared the optical particle size spectrometer to the aerosol monitor in order to evaluate the mass concentration of airborne particles in the range of 1 to 10 µm in diameter. Sanding paper with the finest grit, P240, showed a significantly higher number concentration of ultrafine particles, compared with P60 and P120 grits. The differences among particular grit size were statistically significant for microparticles. The size distribution of particles during sanding was not affected by grit size. For each grit size, apparent peak values of ultrafine and microparticle number concentrations were determined at approximately 15 nm, and 0.1 µm, respectively. Optical particle size spectrometer and aerosol monitor showed comparable results of mass concentration for the respirable fraction.
Cause Analysis of Pier Inclination of An Expressway Interchange Bridge: Case Study
Authors: Fu Qingsong , Shi Xuefei , Ma Haiying
Published: August 2022

An expressway interchange bridge was completed in 2016 in China. In 2019, disease phenomenon, including pier inclination, excessive support slip, and expansion joint damage, were found in the ramp bridge, which influenced the bridge safety and operation. This article conducts a forensic engineering field investigation and uses finite element modeling, revealing the process of disease occurrence according to the displacement cooperation relations in the pier–support–girder region. This research concludes that the main technical causes of the bridge’s disease include: (1) eccentric compression on the pier during construction and operation due to an improper design change and the asynchronous construction process; (2) asymmetric foundation settlement caused by the temporary load during construction and the weight of the filling soil during operation. Finally, the ethical factors leading to the disease are summarized based on technical causes, which can alert professional engineers to problems that should be considered in the design and construction of high-pier bridges with a soft foundation.
Efficient Detection of Defective Parts with Acoustic Resonance Testing Using Synthetic Training Data
Authors: Heinrich Matthias , Valeske Bernd , Rabe Ute
Published: August 2022

Analyzing eigenfrequencies by acoustic resonance testing enables a fast screening of components regarding structural defects. The eigenfrequencies of each specific part depend on the general geometric and material properties, including tolerable part-to-part variations, as well as on possible structural flaws. Separating good parts from defective ones is not straightforward and each application-specific sorting algorithm is usually found from experimental training data. However, there are limitations and training data collection may be intricate. We worked on this challenge focusing on machine-made model parts varying slightly in geometry. The application objective was the eigenfrequency-based detection of parts featuring a through-hole test defect drilled into some of the parts and enlarged stepwise. The eigenfrequencies were measured concomitantly. Unlike the industry standard, our approach is based on synthetic training data created mainly by simulation techniques, which resulted in a principally satisfactory classification of the good and defective parts. However, the parts with small defects were not identified from the eigenfrequencies alone, due to overlaying geometric variations. In order to counteract such noise and to improve defect detection based on synthetic training data, the specific actual part geometry was used, in the sense of additional a priori information. A multimodal data evaluation model showed a clearly enhanced sorting power.
SGXDump: A Repeatable Code-Reuse Attack for Extracting SGX Enclave Memory
Authors: Yoon HanJae , Lee ManHee
Published: August 2022

Intel SGX (Software Guard Extensions) is a hardware-based security solution that provides a trusted computing environment. SGX creates an isolated memory area called enclave and prevents any illegal access from outside of the enclave. SGX only allows executables already linked statically to the enclave when compiling executables to access its memory, so code injection attacks to SGX are not effective. However, as a previous study has demonstrated, Return-Oriented Programming (ROP) attacks can overcome this defense mechanism by injecting a series of addresses of executable codes inside the enclave. In this study, we propose a novel ROP attack, called SGXDump, which can repeat the attack payload. SGXDump consists only of gadgets in the enclave and unlike previous ROP attacks, the SGXDump attack can repeat the attack payload, communicate with other channels, and implement conditional statements. We successfully attacked two well-known SGX projects, mbedTLS-SGX and Graphene-SGX. Based on our attack experiences, it seems highly probable that an SGXDump attack can leak the entire enclave memory if there is an exploitable memory corruption vulnerability in the target SGX application.
Single/Multi-Objective Optimization Design and Numerical Studies for Lead-to-Supercritical Carbon Dioxide Heat Exchanger Based on Genetic Algorithm
Authors: Li Liangxing , Zhao Haoxiang , Zhao Jiayuan , Li Xiangyu
Published: August 2022

Single-/multi-objective optimization based on genetic algorithm is employed in the present study to conduct an optimization design for the primary heat exchanger (HE) in a lead-cooled fast reactor (LFR), where the liquid lead and supercritical carbon dioxide (SCO2) are the working fluids on the heat side and cold side of HE, respectively. A preliminary model of HE was first theoretically calculated by the subsection model based on equal heat transfer power, and an optimization design of HE was then performed based on genetic algorithm, where the entropy generation number and total pumping power were adopted as objective functions. Moreover, the numerical simulation based on Ansys-Fluent software was also performed to study the flow and heat transfer performances of working fluids in the optimized heat exchanger. The results show that the irreversible loss of HE is reduced by 25% after single-objective optimization. The heat transfer and hydraulic performance of optimized HE can be optimized together with multi-objective optimization based on a non-dominated sorting genetic algorithm II (NSGA-II). In addition, the field synergy angle of SCO2 decreases, which indicates the improvement on the comprehensive performance of HE. The present work is helpful for the design of a primary heat exchanger in LFR.
An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos
Authors: Feng Deying , Yang Jie , Wei Yanxia , Xiao Hairong , Zhang Laigang
Published: August 2022

Existing person search methods mainly focus on searching for the target person using database images. However, this is different from real-world surveillance videos which involve a temporal relationship between video frames. To solve this problem, we propose an efficient person search method that employs spatio-temporal features in surveillance videos. This method not only considers the spatial features of persons in each frame, but also utilizes the temporal relationship of the same person between adjacent frames. For this purpose, the spatial features are extracted by combining Yolo network with Resnet-50 model, and the temporal relationship is processed by gated recurrent unit. The spatio-temporal features are generated by the following average pooling layer and used to represent persons in the videos. To ensure search efficiency, locality sensitive hashing is used to organize massive spatio-temporal features and calculate the similarity. A surveillance video database is also constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves search accuracy while ensuring search efficiency.
From Biodeterioration to Creativity: Bioreceptivity of Spruce Pine 87 Glass Batch by Fungi
Authors: Rodrigues Alexandra , Alves Margarida , Gutierrez-Patricio Sara , Miller Ana Z. , Macedo Maria Filomena
Published: August 2022

The bioreceptivity, and the consequent biodeterioration of contemporary glass, used by artists worldwide, was studied. The two main objectives were: first, to verify if fungi with some culture media would produce more damages than the same fungi without a nutritional source, and to verify if the two genera of fungi produce the same damage on the same glass. Colourless glass samples with Spruce Pine 87 Batch (SPB-87) composition were inoculated with two distinct fungal species, Penicillium chrysogenum and Aspergillus niger, separately: (i) half with fungal spores (simulating primary bioreceptivity), and (ii) half with fungi in a small portion of culture media (simulating organic matter that can be deposited on exposed glassworks, i.e., secondary bioreceptivity). The alteration of glass surfaces were analysed by Optical Microscopy, SEM-EDS and µ-Raman. The mycelium of Penicillium chrysogenum generated a higher amount of fingerprints, stains and iridescence, whereas Aspergillus niger produced more biopitting and crystals on the glass surface. However, both species damaged the glass to different degrees in 4 and 6 months after the inoculation, producing physico-chemical damage (e.g., iridescence, biopitting), and chemical alterations (e.g., depletion and deposition of elements and crystals). The primary bioreceptivity experiment of glass samples inoculated with Aspergillus niger results in less damage than in the case of secondary bioreceptivity, being almost similar for Penicillium chrysogenum. The new and in-depth understanding of the bioreceptivity and deterioration of post-modern glass art and cultural heritage provided here is of paramount importance for the scientific, conservation and artistic communities—to protect glass cultural materials, or seen by artists as innovative and inspirational ways of creating glass art in the future.
Improved Electrical Stimulation-Based Exercise Model to Induce Mice Tibialis Anterior Muscle Hypertrophy and Function
Authors: Alves Paula Ketilly Nascimento , Silvestre João G. , Sougey Wenddy Wyllie Damascena , Cruz André , Moriscot Anselmo Sigari
Published: August 2022

Efficient and suitable animal models directed to skeletal muscle hypertrophy are highly needed; nevertheless, the currently available models have limitations, such as restricted hypertrophy outcome and prolonged protocols; thus, additional research is required. In this study, we developed an improved muscle training protocol for mice by directly stimulating the tibialis anterior (TA) muscle motor point using electrical stimulation. C57BL/6 adult male mice were separated into four groups: CTR (control groups for one and two weeks), ES1 (electrical stimulation for one week), and ES2 (electrical stimulation for two weeks). Following muscle training, TA was taken for further examination. The results demonstrated a steady increase in the fiber cross-sectional area as a result of muscle training (ES1, 14.6% and ES2, 28.9%, p < 0.0001). Two weeks of muscle training enhanced muscle mass and maximal tetanic force by 18 (p = 0.0205) and 30%, respectively (p = 0.0260). To assess the tissue remodeling response in this model, we evaluated satellite cell activity and observed an increase in the number of Pax-7-positive nuclei after one and two weeks of muscle training (both >2-fold, p < 0.0001). In addition, we observed an increase in the number of positive nuclei for MyoD after two weeks (2.6-fold, p = 0.0057) without fiber damage. Accordingly, phosphorylation of mTOR and p70 increased following two weeks of muscle training (17%, p = 0.0215 and 66%, p = 0.0364, respectively). The results indicate that this muscle training strategy is appropriate for promoting quick and intense hypertrophy.
Improving Path Loss Prediction Using Environmental Feature Extraction from Satellite Images: Hand-Crafted vs. Convolutional Neural Network
Authors: Sani Usman Sammani , Malik Owais Ahmed , Lai Daphne Teck Ching
Published: August 2022

There is an increased exploration of the potential of wireless communication networks in the automation of daily human tasks via the Internet of Things. Such implementations are only possible with the proper design of networks. Path loss prediction is a key factor in the design of networks with parameters such as cell radius, antenna heights, and the number of cell sites that can be set. As path loss is affected by the environment, satellite images of network locations are used in developing path loss prediction models such that environmental effects are captured. We developed a path loss model based on the Extreme Gradient Boosting (XGBoost) algorithm, whose inputs are numeric (non-image) features that influence path loss and features extracted from images composed of four tiled satellite images of points along the transmitter to receiver path. The model can predict path loss for multiple frequencies, antenna heights, and environments such that it can be incorporated into Radio Planning Tools. Various feature extraction methods that included CNN and hand-crafted and their combinations were applied to the images in order to determine the best input features, which, when combined with non-image features, will result in the best XGBoost model. Although hand-crafted features have the advantage of not requiring a large volume of data as no training is involved in them, they failed in this application as their use led to a reduction in accuracy. However, the best model was obtained when image features extracted using CNN and GLCM were combined with the non-image features, resulting in an RMSE improvement of 9.4272% against a model with non-image features only without satellite images. The XGBoost model performed better than Random Forest (RF), Extreme Learning Trees (ET), Gradient Boosting, and K Nearest Neighbor (KNN) based on the combination of CNN, GLCM, and non-image features. Further analysis using the Shapley Additive Explanations (SHAP) revealed that features extracted from the satellite images using CNN had the highest contribution toward the XGBoost model’s output. The variation in values of features with output path loss values was presented using SHAP summary plots. Interactions were also observed between some features based on their dependence plots from the computed SHAP values. This information, when further explored, could serve as the basis for the development of an explainable/glass box path loss model.
Integrated Carbonate Rock Type Prediction Using Self-Organizing Maps in E11 Field, Central Luconia Province, Malaysia
Authors: Jiménez Soto Grisel , Abdul Latiff Abdul Halim , Ben Habel Wael , Bing Bing Saw , Poppelreiter Michael
Published: August 2022

Reducing uncertainty in 3D carbonate rock type distribution is a critical factor that profoundly impacts field development for hydrocarbon or carbon capture and storage (CCS) projects. Miocene carbonate reservoirs in the Central Luconia offshore region are economically important global gas reservoirs. The nature of these carbonate rocks can be visually distinct in the core and the multiscale reservoir heterogeneity might vary in scale from the 100-m scale to the sub-millimeter scale. This work presents a series of steps workflow to obtain spatial information about the organization scheme of carbonate rock types, and capture the most important petrophysical and sedimentary controls on rock property distribution in the E11 field, a carbonate buildup, located in Central Luconia Province, offshore Malaysia. The spatial data were generated from a supervised neural Kohonen algorithm. The rock types predicted with this workflow were propagated using IPSOM probabilized self-organizing maps SOM. This tool is used for classifying multivariate data samples according to “patterns” or multivariate responses. The workflow includes several steps: A Step 1—Core data description, B Step 2—Thin section description, C Step 3—Well log interpretation, and D Step 4—IPSOM probabilized self-organizing maps for facies prediction SOM. The depth plots of the predicted rock type showed close correspondence to the core-based rock types in terms of the stratigraphic organization of tight and reservoir layers, proportions, and juxtaposition. This result is sufficient to merit the application of the rock type logs into a future porosity model of the E11 field, and to understand the lateral and vertical distribution of tight and reservoir rock types of distribution. The results can be used to build a future realistic digital twin of the subsurface, and in digital geological modeling.
A Modular and Semantic Approach to Personalised Adaptive Learning: WASPEC 2.0
Authors: Apoki Ufuoma Chima , Crisan Gloria Cerasela
Published: August 2022

The ubiquity of smart devices and intelligent technologies embedded in e-learning settings fuels the drive to tackle the grand challenge of personalised adaptive learning. Personalised adaptive learning, which combines the core concepts of personalised learning and adaptive learning, attempts to take individual needs and features into account for personal development through adaptive adjustment. Personalised adaptive learning is supported at its heart by efficient real-time monitoring of the learning process and robust managerial capabilities, which are driven by data, as well as human intuition. The absence of reusable personalised content and logic is one of the key limitations of systems that adopt personalised learning. This is mostly due to the fact that business logic is frequently entangled with the system’s primary functionality. As a result, such systems are unable to interact with other systems that do not adhere to identical design standards. The application of modular frameworks and the semantic web has the potential to be leading technologies that foster reusable personalised content and systems that can efficiently share information. WASPEC, a modular framework for personalised adaptive learning, is evaluated in this paper. An improved architecture, WASPEC 2.0, ensuring more flexibility is also presented in the concluding sections.
Occurrence Characteristics of Inorganic Nitrogen in Groundwater in Silty-Clay Riparian Hyporheic Zones under Tidal Action: A Case Study of the Jingzi River in Shanghai, China
Authors: Cai Yi , Xing Jingwen , Huang Ruoyao , Ruan Xike , Zhou Nianqing , Yi Dongze
Published: August 2022

For comprehending the effect of tidal action on nitrogen cycle in silty-clay riparian hyporheic zones, the synchronous monitoring of water level and water quality was carried out along a test transect during a spring tidal period from 21 to 23 October 2021. Moreover, the permeability and chemical composition of soil samples from drilled holes were measured. Subsequently, the spatiotemporal variation of inorganic nitrogen concentrations in the groundwater in the riparian hyporheic zone was investigated during the study period, and the potential reason was discussed. It is shown that the delayed response time of groundwater level in the silty-clay riparian zone to the tide-driven fluctuation of the river stage increased with distance from the shore and reached 3.0 h at the position 3.83 m away from the shore. The continuous infiltration of the river water under tide action contributed to the aerobic and neutral riparian hyporheic zone conductive to nitrification. Within 4 m away from the bank, the dominant inorganic nitrogen form changed from NO3−-N to NH4+-N, upon increasing the distance from the bank. Additionally, the removal of nitrogen could occur in the riparian hyporheic zone with aerobic and neutral environment under the conjoint control of nitrification, microbial assimilation, and aerobic denitrification.
Chinese Named Entity Recognition of Geological News Based on BERT Model
Authors: Huang Chao , Wang Yuzhu , Yu Yuqing , Hao Yujia , Liu Yuebin , Zhao Xiujian
Published: August 2022

With the ongoing progress of geological survey work and the continuous accumulation of geological data, extracting accurate information from massive geological data has become increasingly difficult. To fully mine and utilize geological data, this study proposes a geological news named entity recognition (GNNER) method based on the bidirectional encoder representations from transformers (BERT) pre-trained language model. This solves the problems of traditional word vectors that are difficult to represent context semantics and the single extraction effect and can also help construct the knowledge graphs of geological news. First, the method uses the BERT pre-training model to embed words in the geological news text, and the dynamically obtained word vector is used as the model’s input. Second, the word vector is sent to a bidirectional long short-term memory model for further training to obtain contextual features. Finally, the corresponding six entity types are extracted using conditional random field sequence decoding. Through experiments on the constructed Chinese geological news dataset, the average F1 score identified by the model is 0.839. The experimental results show that the model can better identify news entities in geological news.
Changed Detection Based on Patch Robust Principal Component Analysis
Authors: Zhu Wenqi , Zhang Zili , Zhao Xing , Fu Yinghua
Published: August 2022

Change detection on retinal fundus image pairs mainly seeks to compare the important differences between a pair of images obtained at two different time points such as in anatomical structures or lesions. Illumination variation usually challenges the change detection methods in many cases. Robust principal component analysis (RPCA) takes intensity normalization and linear interpolation to greatly reduce the illumination variation between the continuous frames and then decomposes the image matrix to obtain the robust background model. The matrix-RPCA can obtain clear change regions, but when there are local bright spots on the image, the background model is vulnerable to illumination, and the change detection results are inaccurate. In this paper, a patch-based RPCA (P-RPCA) is proposed to detect the change of fundus image pairs, where a pair of fundus images is normalized and linearly interpolated to expand a low-rank image sequence; then, images are divided into many patches to obtain an image-patch matrix, and finally, the change regions are obtained by the low-rank decomposition. The proposed method is validated on a set of large lesion image pairs in clinical data. The area under curve (AUC) and mean average precision (mAP) of the method proposed in this paper are 0.9832 and 0.8641, respectively. For a group of small lesion image pairs with obvious local illumination changes in clinical data, the AUC and mAP obtained by the P-RPCA method are 0.9893 and 0.9401, respectively. The results show that the P-RPCA method is more robust to local illumination changes than the RPCA method, and has stronger performance in change detection than the RPCA method.
An Algorithm for Generating Virtual Sources in Dynamic Virtual Auditory Display Based on Tensor Decomposition of Head-Related Impulse Responses
Authors: Zhao Tong , Xie Bosun , Zhu Jun
Published: August 2022

Dynamic virtual auditory displays (VADs) are increasingly used for generating various auditory objects and scenes in virtual and augmented reality. Dynamic VADs are required to generate virtual sources in various directions and distances by using HRTF- or HRIR-based binaural synthesis. In the present work, an algorithm for improving the efficiency and performance of binaural synthesis in dynamic VAD is proposed. Based on tensor decomposition, a full set of near-field HRIRs is decomposed as a combination of distance-, direction-, and time-related modes. Then, binaural synthesis in VAD can be implemented by a common set of time mode-related convolvers or filters associated with direction- and distance-related weights. Dynamic binaural signals are created by updating the weights rather than updating the HRIR-based convolvers, which enables the independent control of virtual source distance and direction and avoids the audible artifact caused by updating the HRIR-based convolvers. An example of implementation indicates that a set of eight common convolvers or filters for each ear is enough to synthesize the binaural signals with sufficient accuracy. The computational efficiency of simultaneously generating multiple virtual sources is improved when the number of virtual sources is larger than eight. A virtual-source localization experiment validates the algorithm.
Thermodynamics of Extra-Toughness and Hidden-Length in Polymeric Materials with Sacrificial Bonds
Authors: Jorge Do Marco Romain Jorge Do , Giordano Stefano
Published: August 2022

Sacrificial bonds have been observed in several biological materials and structures and can increase their toughness, i.e., their resistance to fracture. They provide a reversible mechanism for dissipating mechanical energy before the possible system rupture. From a structural point of view, sacrificial bonds consist of short polymer chains that short-circuit parts of a main macromolecular chain (generating hidden lengths) and absorb energy by breaking them instead of the main chain. The toughness increase due to the presence of sacrificial bonds is typically named extra-toughness. Here, we developed a statistical mechanics and thermodynamics-based theory able to estimate the force–extension relation for chains with sacrificial bonds and to calculate the corresponding extra-toughness. The model is useful to better understand the sacrificial bond effects in biomaterials but also to apply the biomimetic paradigm and foster the development of high-performance artificial polymeric materials.
AI Makes Crypto Evolve
Authors: Zolfaghari Behrouz , Koshiba Takeshi
Published: August 2022

The recent literature reveals a dichotomy formed by a coevolution between cryptography and Artificial Intelligence (AI). This dichotomy consists of two sides, namely Crypto-Influenced AI (CIAI) and AI-Influenced Cryptography (AIIC). While it is pertinent to investigate this dichotomy from both sides, the first side has already been studied. In this review, we focused on AIIC. We identified and analyzed the stages on the evolutionary path of AIIC. Moreover, we attempted to anticipate what the future may hold for AIIC given the impact of quantum computing on the present and the future of AI.
Spatial and Temporal Analysis of Extreme Climate Events over Northeast China
Authors: Yu Xingyang , Ma Yuanyuan
Published: August 2022

In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen’s slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses.
Winter and Wildfire Season Optical Characterization of Black and Brown Carbon in the El Paso-Ciudad Juárez Airshed
Authors: Lara Pamela , Fitzgerald Rosa M. , Karle Nakul N. , Talamantes Jose , Miranda Miranda , Baumgardner Darrel , Stockwell William R.
Published: August 2022

Black (EBC) and Brown (BrC) Carbon are ubiquitous constituents of atmospheric particulate matter that affect people’s health, disrupt ecosystems, and modulate local and global climate. Tracking the local deposition and sources of these aerosol particles is essential to better understanding their multidimensional environmental impact. The main goal of the current study is to measure the absorption coefficient (Babs) of particles within the Planetary Boundary Layer (PBL) of the El Paso (US)–Ciudad Juárez (Mexico) airshed and assess the contribution of black and brown carbon particles to the optical absorption. Measurements were taken during a summer, wildfire, and winter season to evaluate the optical properties of BC and non-volatile BrC. The winter season presented a variation from the background Babs in the late evening hours (3:00 PM to midnight) due to an increase in biomass burning driven by lower temperatures. The wildfire season presents the greatest variation in the Babs from the background absorption due to EBC- and BrC-rich smoke plumes arriving at this region from the US West seasonal wildfires. It was found that the international bridges’ vehicular traffic, waiting time to cross back and forth between both cities, added to other local anthropogenic activities, such as brick kiln emissions in Ciudad Juarez, have created a background of air pollution in this region. These pollutants include carbon monoxide, sulfur dioxide, nitrogen and nitric oxides, coarse and fine particulate matter dominated by BC and BrC. The absorption coefficients due to EBC and BrC of this background constitute what we have called a baseline EBC and BrC. Aided by two photoacoustic Extinctiometers (PAX), operating at 405 nm and 870 nm wavelengths, connected to a 340 °C thermal denuder to remove volatile organics, the optical properties were documented and evaluated to identify the impact of long-range transported emissions from western wildfires. The Single Scattering Albedo and the Absorption Ångstrom exponent were calculated for the winter and summer season. The Angstrom exponent showed a decrease during the wildfire events due to the aging process. The High-Resolution Rapid Refresh Smoke model, HRRR, and the Hybrid Single-Particle Lagrangian Integrated Trajectory model, HYSPLIT, were used to estimate the sources of the particles. In addition, a Vaisala Ceilometer was employed to study the vertical profile of particulate matter within the planetary boundary layer.
An Explainable Machine Learning Framework for Forecasting Crude Oil Price during the COVID-19 Pandemic
Authors: Gao Xinran , Wang Junwei , Yang Liping
Published: August 2022

Financial institutions, investors, central banks and relevant corporations need an efficient and reliable forecasting approach for determining the future of crude oil price in an effort to reach optimal decisions under market volatility. This paper presents an innovative research framework for precisely predicting crude oil price movements and interpreting the predictions. First, it compares six advanced machine learning (ML) models, including two state-of-the-art methods: extreme gradient boosting (XGB) and the light gradient boosting machine (LGBM). Second, it selects novel data, including user search big data, digital currencies and data on the COVID-19 epidemic. The empirical results suggest that LGBM outperforms other alternative ML models. Finally, it proposes an interpretable framework for facilitating decision making to interpret the prediction results of complex ML models and for verifying the importance of various features affecting crude oil price. The results of this paper provide practical guidance for participants in the crude oil market.
Hybrid Fuzzy C-Means Clustering Algorithm Oriented to Big Data Realms
Authors: Pérez-Ortega Joaquín , Roblero-Aguilar Sandra Silvia , Almanza-Ortega Nelva Nely , Frausto Solís Juan , Zavala-Díaz Crispín , Hernández Yasmín , Landero-Nájera Vanesa
Published: August 2022

A hybrid variant of the Fuzzy C-Means and K-Means algorithms is proposed to solve large datasets such as those presented in Big Data. The Fuzzy C-Means algorithm is sensitive to the initial values of the membership matrix. Therefore, a special configuration of the matrix can accelerate the convergence of the algorithm. In this sense, a new approach is proposed, which we call Hybrid OK-Means Fuzzy C-Means (HOFCM), and it optimizes the values of the membership matrix parameter. This approach consists of three steps: (a) generate a set of n solutions of an x dataset, applying a variant of the K-Means algorithm; (b) select the best solution as the basis for generating the optimized membership matrix; (c) resolve the x dataset with Fuzzy C-Means. The experimental results with four real datasets and one synthetic dataset show that HOFCM reduces the time by up to 93.94% compared to the average time of the standard Fuzzy C-Means. It is highlighted that the quality of the solution was reduced by 2.51% in the worst case.
New Results about Radius of Convexity and Uniform Convexity of Bessel Functions
Authors: Cotîrlă Luminiţa-Ioana , Kupán Pál Aurel , Szász Róbert
Published: August 2022

We determine in this paper new results about the radius of uniform convexity of two kinds of normalization of the Bessel function Jν in the case ν∈(−2,−1), and provide an alternative proof regarding the radius of convexity of order alpha. We then compare results regarding the convexity and uniform convexity of the considered functions and determine interesting connections between them.