Miracle-dz/UniFAR-FTUs huggingface dataset:
A Composite Noise Removal Network Based on Multi-domain Adaptation

—Addressing the limitation of conventional single-scene image denoising algorithms in filtering mixed environmental disturbances, and recognizing the drawbacks of cascaded image enhancement algorithms, which have poor real-time performance and high computational demands, The composite weather adaptive denoising network (CWADN) is proposed. A Cascade Hourglass Feature Extraction Network is constructed with a visual attention mechanism to extract characteristics of rain, fog, and low-light noise from authentic natural images. These features are then transferred from their original real distribution domain to a synthetic distribution domain using a deep residual convolutional neural network. The generator and style encoder of the adversarial network work together to adaptively remove the transferred noise through a combination of supervised and unsupervised training, this approach achieves adaptive denoising capabilities tailored to complex natural environmental noise. Experimental results demonstrate that the proposed denoising network yields a high signal-to-noise ratio while maintaining excellent image fidelity. It effectively prevents image distortion, particularly in critical target areas. Additionally, it adapts to various types of mixed noise, making it a valuable tool for preprocessing images in advanced machine vision algorithms such as target recognition and tracking.
LAD-YOLO: A Lightweight YOLOv5 Network for Surface Defect Detection on Aluminum Profiles

—In this paper, we leverage the advantages of YOLOv5 in target detection to propose a highly accurate and lightweight network, called LAD-YOLO, for surface defect detection on aluminum profiles. The LAD-YOLO addresses the issues of computational complexity, low precision, and a large number of model parameters encountered in YOLOv5 when applied to aluminum profiles defect detection. LAD-YOLO reduces the model parameters and computation while also decreasing the model size by utilizing the ShuffleNetV2 module and depthwise separable convolution in the backbone and neck networks, respectively. Meanwhile, a lightweight structure called "Ghost_SPPFCSPC_group", which combines Cross Stage Partial Network Connection Operation, Ghost Convolution, Group Convolution and Spatial Pyramid Pooling-Fast structure, is designed. This structure is incorporated into the backbone along with the Convolutional Block Attention Module (CBAM) to achieve lightweight. Simultaneously, it enhances the model's ability to extract features of weak and small targets and improves its capability to learn information at different scales. The experimental results show that the mean Average Precision (mAP) of LAD-YOLO on aluminum profiles defect datasets reaches 96.9%, model size is 6.64MB, and Giga Floating Point Operations (GFLOPs) is 5.5. Compared with YOLOv5, YOLOV5s-MobileNetv3, and other networks, LAD-YOLO proposed in this paper has higher accuracy, fewer parameters, and lower floating-point computation.
A Novel Artifact Removal Strategy and Spatial Attention-based Multiscale CNN for MI Recognition

—The brain-computer interface (BCI) based on motor imagery (MI) is a promising technology aimed at assisting individuals with motor impairments in regaining their motor abilities by capturing brain signals during specific tasks. However, non-invasive electroencephalogram (EEG) signals collected using EEG caps often contain large numbers of artifacts. Automatically and effectively removing these artifacts while preserving task-related brain components is a key issue for MI de-coding. Additionally, multi-channel EEG signals encompass temporal, frequency and spatial domain features. Although deep learning has achieved better results in extracting features and de-coding motor imagery EEG (MI-EEG) signals, obtaining a high-performance network on MI that achieves optimal matching of feature extraction, thus classification algorithms is still a challenging issue. In this study, we propose a scheme that combines a novel automatic artifact removal strategy with a spatial attention-based multiscale CNN (SA-MSCNN). This work obtained independent component analysis (ICA) weights from the first subject in the dataset and used K-means clustering to determine the best feature combination, which was then applied to other subjects for artifact removal. Additionally, this work designed an SA-MSCNN which includes multiscale convolution modules capable of extracting information from multiple frequency bands, spatial attention modules weighting spatial information, and separable convolution modules reducing feature information. This work validated the performance of the proposed model using a real-world public dataset, the BCI competition IV dataset 2a. The average accuracy of the method was 79.83%. This work conducted ablation experiments to demonstrate the effectiveness of the proposed artifact removal method and SA-MSCNN network and compared the results with outstanding models and state-of-the-art (SOTA) studies. The results confirm the effectiveness of the proposed method and provide a theoretical and experimental foundation for the development of new MI-BCI systems, which is very useful in helping people with disabilities regain their independence and improve their quality of life
Sensitivity of dispersion model forecasts of volcanic ash clouds to the physical characteristics of the particles

This study examines the sensitivity of atmospheric dispersion model forecasts of volcanic ash clouds to the physical characteristics assigned to the particles. We show that the particle size distribution (PSD) used to initialise a dispersion model has a significant impact on the forecast of the mass loading of the ash particles in the atmosphere. This is because the modeled fall velocity of the particles is sensitive to the particle diameter. Forecasts of the long‐range transport of the ash cloud consider particles with diameters between 0.1 μm and 100 μm. The fall velocity of particles with diameter 100 μm is over 5 orders of magnitude greater than a particle with diameter 0.1 μm, and 30 μm particles fall 88% slower and travel up to 5× further than a 100 μm particle. Identifying the PSD of the ash cloud at the source, which is required to initialise a model, is difficult. Further, aggregation processes are currently not explicitly modeled in operational dispersion models due to the high computational costs associated with aggregation schemes. We show that using a modified total grain size distribution (TGSD) that effectively accounts for aggregation processes improves the modeled PSD of the ash cloud and deposits from the eruption of Eyjafjallajökull in 2010. Knowledge of the TGSD of an eruption is therefore critical for reducing uncertainty in quantitative forecasts of ash cloud dispersion. The density and shape assigned to the model particles have a lesser but still significant impact on the calculated fall velocity. Accounting for the density distribution and sphericity of ash from the eruption of Eyjafjallajökull in 2010, modeled particles can travel up to 84% further than particles with default particle characteristics that assume the particles are spherical and have a fixed density.
Exploring Deposition Techniques and Supramolecular Arrangement in Thin Films for Sensor Applications

In recent decades, many research efforts have been dedicated to finding highly sensitive devices for fast and reliable identification and quantification of an expanding range of analytes. As a result, there has been an increased number of publications dedicated to this area and a consequent increase in the number of review papers on the subject. However, unlike most review articles, we chose to explore the impact of supramolecular arrangement (or deeper, when possible, approaching the molecular organization) and assembly variables on sensing performance. This review briefly discusses the methods used to determine the molecular organization of thin films. We also examine various deposition techniques, including Langmuir-Blodgett, Langmuir-Schaefer, Layer-by-Layer assembly, electrodeposition, and spray pyrolysis, describing mainly (but not limited to) the advances in the last five years in developing thin films for sensors, with a particular emphasis on how the supramolecular arrangement can influence the sensing properties of these films.
Global and along‐strike variations of source duration and scaling for intermediate‐depth and deep‐focus earthquakes

The systematic behavior of earthquake rupture as a function of earthquake magnitude and/or tectonic setting is a key in our understanding of the physical mechanisms involved during earthquake rupture. Geophysical evidence suggests that although deep earthquakes—including intermediate‐depth and deep—are similar to shallow ones, the mechanism involved during deep earthquakes is different from that of shallow ones. In particular, the magnitude and depth dependence of scaled duration, a measure of earthquake rupture duration, has led to controversy of what controls deep earthquake behavior. Here we estimate scaled source durations for 600 intermediate‐depth and deep‐focus earthquakes recorded at teleseismic distances and show deviation from self‐similar scaling. No depth dependence is observed which we interpret as due to little differences between intermediate‐depth and deep‐focus earthquake mechanisms. The data show no correlation between durations and plate age or thermal parameters, suggesting that the thermal properties of the plate have little effect on source durations. We nevertheless report differences in average source duration and scaling between subduction zones and along‐strike variations of source durations that more closely resemble the geometry of subduction (flat or steep subduction) rather than plate age.
Snapshot of the Matuyama‐Brunhes reversal process recorded in 40Ar/39Ar‐dated lavas from Guadeloupe, West Indies

We obtain new 40Ar/39Ar ages for three lavas that record part of the Matuyama‐Brunhes geomagnetic field reversal process on Guadeloupe. These lavas record a reversed‐transitional‐reversed magnetostratigraphy and yield a weighted mean isochron age of 785.2 ± 5.2 ka (2σ analytical uncertainty) relative to an age for the Fish Canyon sanidine standard of 28.201 Ma. This age is of greater accuracy than ages obtained previously by K‐Ar dating. These lavas may record directional fluctuations occurring between the Matuyama‐Brunhes precursor and the final directional reversal. Such variations have been observed in some globally distributed coupled paleomagnetic and oxygen isotope marine sedimentary records of the Matuyama‐Brunhes reversal. Previous paleointensity estimates from these lavas indicate reduced, but fluctuating field strength during this time.
Diel cycle of lake‐air CO2 flux from a shallow lake and the impact of waterside convection on the transfer velocity

Two years of eddy covariance measurements of lake carbon dioxide (CO2) fluxes reveal a diel cycle with higher fluxes during night. Measurements of partial pressure in the air (pCO2a) and in the water (pCO2w), during 4 months, show that the high nighttime fluxes are not explained by changes in the difference between pCO2a and pCO2w. Analyzing the transfer velocity (k600,meas), which is a measure of the efficiency of the gas transfer, with respect to wind speed, shows that variations in wind speed do not explain the diel cycle. During nighttime, when the fluxes are high, the wind is normally low. Thus, a solely wind‐based parameterization of the transfer velocity (ku,CC) results in large errors compared to k600,meas, especially for wind speeds lower than 6 m s−1. The mean absolute percentage error between ku,CC and k600,meas is 79%. By subtracting ku,CC from k600,meas, we investigate how waterside convection influence k600,meas. Our results show that the difference (k600,meas − ku,CC) increases with increasing waterside convection. Separating the transfer velocity parameterization in two parts, one depending on the wind speed and one depending on waterside convection, the mean absolute percentage error compared to the measurements reduces to 22%. The results in this paper show that the high nighttime CO2 fluxes can, to a large extent, be explained by waterside convection and that a transfer velocity parameterization based on both wind speed and waterside convection better fits the measurements compared to a parameterization based solely on wind speed.
Neogene stress field in SW Japan and mechanism of deformation during the Sea of Japan opening

We present new structural data from SW Japan and discuss the mechanism of deformation during the opening of the Sea of Japan. We studied the Miocene basins at the southern margin of the Sea of Japan (northern coast of SW Japan) and of the Median Tectonic Line (MTL) by means of fault slip analysis to determine stress field directions during the opening of the Sea of Japan in early and middle Miocene time. On the southern margin, the stress field was extensional or transtensional with a NW-SE trending extension direction. The MTL was a normal fault in Shikoku during early middle Miocene and probably a normal fault with a left-lateral component in central Japan before having been strained by the collisions of the Tanzawa and Izu blocks with central Japan. We combine our results with published data for the Sea of Japan area and show that early and middle Miocene stress field directions are remarkably consistent on the eastern, southern, and western margins of the Sea of Japan with σHmax trending NE-SW and σHmin trending NW-SE. The stress regime is transpressional or transtensional on the eastern and western margin and almost purely extensional on the southern margin. This stress field distribution is in agreement with the model of the opening of the Sea of Japan in an extensional transfer zone between two N-S right-lateral strike-slip zones proposed by Jolivet et al. (1991). Analogue modelings and kinematic reconstructions showed that this model accounts for 20° to 30° of clockwise rotation in SW Japan, while paleomagnetic rotations reached 40° to 50° during the opening. We present structural data showing that SW Japan was strained during the rotation and did not behave as a rigid block. Our observations suggest that rotations of small blocks may have occurred. We propose that SW Japan has been sheared between the right-lateral strike-slip zones which bound the Sea of Japan and that the missing 10° to 30° of rotation are to be found in its internal deformation. In our model, the MTL is a normal fault with a strike-slip component rotating in a right-lateral shear zone.
Evaluation of spatial and temporal variability of snow cover in a large mountainous basin in iran

Determination of snow characteristics in mountainous basins is difficult due to the complex spatial and temporal variability of snow cover. Accurate representation of snow cover variations in space and time is an important factor in snowmelt modeling, hydrological forecasts, water resources planning, and drought management. This study demonstrates how remotely sensed data can complement the measurements of ground hydro-meteorological data to simulate the spatial and temporal variations of snow cover characteristics in a mountainous basin. In this paper, we studied Karun basin, located in the south west of Iran, because of its importance in accumulating large snow reserves, and subsequently contributing snowmelt to the total runoff. Snow cover variability was simulated by extraction of maps of snow cover indices using remotely sensed data. Contribution of snowmelt to the runoff was determined using a seasonal water balance model as well as estimations based on indirect approaches by modeling variables such as critical temperature, which is an important variable in snow studies. Agreement between indirect approaches used in this paper is an encouraging result that shows the reliability of the procedure where snow data is scarce. The results of correlation analysis between topographic and meteorological variables with snow cover indices suggested that elevation is the single most important variable on large-scale snow variability.
Balancing the Use of Behavioural Research and Design Science Research to Solve the Relevance Problem in Marketing Research

Contemporary marketing research has a value problem. Claims for the managerial impact of research appear in practically every research article. Nevertheless, managers in the field do not consider scientific outputs as relevant in helping them to address the multiple challenges that organizations face. Marketing typically conducts behavioural research, aiming to understand and explain real-world problems. Other disciplines, such as engineering, focus primarily on building solutions to solve practical problems. Such practice is often termed design science. This study proposes that marketing research should focus more on building solutions, hence calling for a better balance between behavioural and design research. An improved balance between these two paradigms in marketing should increase the value of academic research to practice. Four typical case studies are presented to illustrate key differences between design science and behavioural science.
Tilapia lake virus causes mitochondrial damage: a proposed mechanism that leads to extensive death in fish cells

Background Tilapia lake virus (TiLV), also known as Tilapinevirus tilapiae, poses a significant threat to tilapia aquaculture, causing extensive mortality and economic losses. Understanding the mechanisms and pathogenesis of TiLV is crucial to mitigate its impact on this valuable fish species. Methodology In this study, we utilized transmission electron microscopy to investigate the ultrastructural changes in E-11 cells following TiLV infection. We also examined the presence of TiLV particles within the cells. Cellular viability and mitochondrial functions were assessed using MTT and ATP measurement assays and mitochondrial probes including JC-1 staining and MitoTracker™ Red. Results Our findings provide novel evidence demonstrating that TiLV causes cytotoxicity through the destruction of mitochondria. Transmission electron micrographs showed that TiLV particles were present in the cytoplasm of E-11 cells as early as 1 h after infection. Progressive swelling of mitochondria and ultrastructural damage to the cells were observed at 1, 3 and 6 days post-infection. Furthermore, losses of mitochondrial mass and membrane potential (MMP) were detected at 1 day after TiLV inoculation, as determined by mitochondrial probes. The results of the MTT assay also supported the hypothesis that the cell deaths in E-11 cells during TiLV infection may be caused by the disruption of mitochondrial structure and function. Conclusions Our study reveals the significant role of mitochondrial disruption in contributing to cellular death during the early stages of TiLV infection. These findings advance the understanding of TiLV pathogenesis and further enhance our knowledge of viral diseases in fish.
Cooperation in an Arrow-Karlin Type Supply Chain

In this paper we apply cooperative game theory concepts to analyze vertical supply chains. The bullwhip effect in a two-stage supply chain (supplier-manufacturer) in the framework of the Arrow-Karlin model with linear-convex cost functions is considered. It is assumed that both firms minimize their relevant costs, and two cases are examined: the supplier and the manufacturer minimize their relevant costs in a decentralized and in a centralized (cooperative) way. The question of how to share the savings of the decreased bullwhip effect in the centralized (cooperative) model is answered by transferable utility cooperative game theory tools.
Evaluation of Antioxidant and Oxidative Stress Activity of Carthamus tinctorius L. Extract in Lung Cancer A549 Cells

Background: C. tinctorius (safflower) natural food colorant and has been used to control high blood pressure, supress oxidation, cancer immunosuppressive drug, inhibits blood clots, dilate blood vessels, and as neuroprotective agents. Several investigations indicate the relationship between oxidative processes and emergence of cancer. The current investigation data confirms the antiproliferative activity of Safflower against A549 cancer cell line but little is known about their antioxidant mechanisms. Materials and Methods: Nitric Oxide (NO) and Lipid Peroxidation (LPO) levels were measured in order to explore the antioxidant properties of safflower extract. Lactate Dehydrogenase (LDH), reduced Glutathione (GSH), Superoxide Dismutase (SOD) and Catalase (CAT) were measured by UV-spectrophotometric analysis. Further, apoptotic key regulator BAX and SMAC gene expression were also analysed. Results: The dose-dependent antioxidant activity of Safflower extract induced apoptosis in A549 cells. There was significant increase in LPO, NO, LDH, SOD and CAT (* p < 0.0001) activities except GSH. Our data confirmed that safflower treated A549 cells inhibits the oxidation by scavenging free radicals and induced apoptosis by increased gene expression BAX and SMAC. Conclusion: The current findings indicates safflower extract might be potential alternative medicine for the treatment of lung cancer.
Towards combining web classification and web information extraction: a case study

Web content analysis often has two sequential and separate steps: Web Classification to identify the target Web pages, and Web Information Extraction to extract the metadata contained in the target Web pages. This decoupled strategy is highly ineffective since the errors in Web classification will be propagated to Web information extraction and eventually accumulate to a high level. In this paper we study the mutual dependencies between these two steps and propose to combine them by using a model of Conditional Random Fields (CRFs). This model can be used to simultaneously recognize the target Web pages and extract the corresponding metadata. Systematic experiments in our project OfCourse for online course search show that this model significantly improves the F1 value for both of the two steps. We believe that our model can be easily generalized to many Web applications.
Three E2F target-related genes signature for predicting prognosis, immune features, and drug sensitivity in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is extremely malignant and difficult to treat. The adenoviral early region 2 binding factors (E2Fs) target pathway is thought to have a major role in tumor growth. This study aimed to identify a predictive E2F target signature and facilitate individualized treatment for HCC patients. Methods: We constructed an E2F target-related gene profile using univariate COX and LASSO regression models and proved its predictive efficacy in external cohorts. Furthermore, we characterized the role of the E2F target pathway in pathway enrichment, immune cell infiltration, and drug sensitivity of HCC. Results: Lasso Cox regression created an E2F target-related gene signature of GHR, TRIP13, and CDCA8. HCC patients with high risk were correlated with shorter survival time, immune evasion, tumor stem cell characteristics and high sensitivity to Tipifarnib and Camptothecin drugs. Conclusion: Hepatocellular carcinoma prognosis was predicted by an E2F target signature. This finding establishes the theoretical usefulness of the E2F target route in customized identification and treatment for future research.
VisualRDR: A general framework for creating, maintaining and learning of ripple down rules for Information Extraction

The problem facing the industry and the common user today is that of an information glut. Large amounts of useful information, in various forms, are being generated mostly for human consumption. This deluge of information requires us to find ways of gathering information from such unstructured sources with as little manual intervention as possible. This has been made possible by information extraction. In this paper we propose a novel knowledge-based approach to information extraction. We demonstrate how this knowledge for information extraction can be accrued in an incremental fashion.