Miracle-dz/UniFAR-FTUs huggingface dataset:
West Foster Creek 2007 Follow-up Habitat Evaluation Procedures (HEP) Report.

A follow-up habitat evaluation procedures (HEP) analysis was conducted on the West Foster Creek (Smith acquisition) wildlife mitigation site in May 2007 to determine the number of additional habitat units to credit Bonneville Power Administration (BPA) for providing funds to enhance and maintain the project site as partial mitigation for habitat losses associated with construction of Grand Coulee Dam. The West Foster Creek 2007 follow-up HEP survey generated 2,981.96 habitat units (HU) or 1.51 HUs per acre for a 34% increase (+751.34 HUs) above baseline HU credit (the 1999 baseline HEP survey generated 2,230.62 habitat units or 1.13 HUs per acre). The 2007 follow-up HEP analysis yielded 1,380.26 sharp-tailed grouse (Tympanuchus phasianellus) habitat units, 879.40 mule deer (Odocoileus hemionus) HUs, and 722.29 western meadowlark (Sturnella neglecta) habitat units. Mule deer and sharp-tailed grouse habitat units increased by 346.42 HUs and 470.62 HUs respectively over baseline (1999) survey results due largely to cessation of livestock grazing and subsequent passive restoration. In contrast, the western meadowlark generated slightly fewer habitat units in 2007 (-67.31) than in 1999, because of increased shrub cover, which lowers habitat suitability for that species.
Online Fault Line Detection in Small-Sample and Streaming Data Environments

This article focuses on medium- and low-voltage networks with neutral grounded through arc-suppression coils. Standard data-driven fault line detection (FLD) approaches assume that the training sample is sufficient, static, and reusable. In practical scenarios, such approaches may be infeasible due to the sporadic and temporary nature of single-phase-to-ground faults, which provide insufficient fault samples and due to large-scale high-speed dynamic data streams associated with measurements. To tackle these issues, this article proposes a novel FLD scheme based on personalized federated learning (PFL) and incremental stochastic configuration networks (SCNs) for small-sample and streaming data environments. Concretely, the SCN, a concise noniterative neural network, is exploited as the FLD classifier. To adapt effectively to dynamic and nonreusable environments, an incremental SCN is proposed that can learn fault features without experiencing forgetting when dealing with streaming data. The proposed FLD scheme based on PFL selectively aggregates fault features from multiple substations. This approach addresses the challenge of limited sample sizes while preserving the personalization of each local model. Extensive experimental results using real data show that the proposed method can significantly improve accuracy when dealing with small samples and continuously learn fault features in streaming data.
Dynamic quantization‐driven stability of networked control systems under DoS attacks

This paper focuses on the stability problem of networked control systems (NCSs) under denial‐of‐service (DoS) attacks and bandwidth limitation. Firstly, the periodic dynamic quantization mechanism is extended to insecure systems, and a periodic‐like quantization strategy is proposed. Due to the designable period, such mechanism allows a trade‐off between better system performance and less energy consumption. With finite quantization levels, a reasonable parametric design is given to avoid quantizer saturation under general energy‐constrained DoS attacks. Secondly, a suitable quantized controller is designed for the system affected by DoS attacks. The relationship between the quantized controller and the DoS attacks is analyzed, and the criterion for NCSs achieving global asymptotic stability is derived. Finally, two numerical examples are given to verify the correctness of the theoretical result.
An Improved Color Cast Detection Method Based on an AB-Chromaticity Histogram

AB-chromaticity histogram analysis works well most of the time, but it may not work well when the color cast is not severe. To overcome this problem, we propose an improved, two-step automatic cast-detection method. First, we compute the RGB color variance to evaluate the quality of the input image. If this variance is very small, we extract near-neutral color areas and compute the local ab-chromaticity histogram. We use this local ab-chromaticity histogram to evaluate the quality of the input image. This method has been tested in ZTE’s video surveillance system. The results show that the proposed method produces better results based on subjective evaluation and is more efficient in various conditions.
Innovative Approaches to Fungal Food Production: Mycelial Pellet Morphology Insights

Mycelia products enhance edible mushrooms in alignment with future sustainability trends. To meet forthcoming market demands, the morphology of mycelial pellets was optimized for direct consumption. Among ten commercial edible mushrooms in Taiwan, Pleurotus sp. was selected for its rapid growth and was identified via an internal transcribed spacer sequence. A combination of Plackett-Burman design and Taguchi’s L9(34) orthogonal table revealed the optimal formula as potato dextrose broth (2.4%), olive oil (2%), calcium carbonate (0.5%), yeast extract (0.75%), and soy flour (0.5%). This led to a biomass increase to 19.9 ± 1.1 g/L, resulting in a 2.17-fold yield increase. To refine morphology, image processing by ImageJ quantified spherical characteristics. The addition of 0.2 to 1.0% Tween 80 enhanced pellet compaction by over 50%. Dilution of the medium improved uniformity (0.85) and conversion rate (42%), yielding mycelial pellets with 2.10 ± 0.52 mm diameters and a yield of 15.1 ± 0.6 g/L. These findings provide an alternative evaluation and application of edible mycelial pellets as future food.
A Comprehensive Review on the Diagnosis of Knee Injury by Deep Learning-Based Magnetic Resonance Imaging

The continual improvement in the field of medical diagnosis has led to the monopoly of using deep learning (DL)-based magnetic resonance imaging (MRI) for the diagnosis of knee injury related to meniscal injury, ligament injury including the cruciate ligaments, collateral ligaments and medial patella-femoral ligament, and cartilage injury. The present systematic review was done by PubMed and Directory of Open Access Journals (DOAJ), wherein we finalised 24 studies conducted on the accuracy of DL MRI studies for knee injury identification. The studies showed an accuracy of 72.5% to 100% indicating that DL MRI holds an equivalent performance as humans in decision-making and management of knee injuries. This further opens up future exploration for improving MRI-based diagnosis keeping in mind the limitations of verification bias and data imbalance in ground truth subjectivity.
Enhancing Seismic Facies Classification Using Interpretable Feature Selection and Time Series Ensemble Learning Model With Uncertainty Assessment

Seismic facies classification is crucial in reservoir evaluation and guiding oil and gas exploration and development. While machine learning and deep learning models have shown promising results in this field, they often lack interpretability, hindering the understanding of their decision-making process. Additionally, the nonuniqueness of classification results and the need for uncertainty evaluation pose challenges. This study proposes an interpretable and high-precision workflow by combining the time series ensemble learning algorithm Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) 2.0 (HC2) and the Shapley additive explanations (SHAP) interpretation method. HC2, which excels in processing time-domain seismic data, leverages multiple base classifiers in diverse domains and employs the cross-validation accuracy weighted probabilistic ensemble (CAWPE) technique to aggregate predictions, enhancing accuracy and generalization. Notably, the probability values from HC2 enable uncertainty quantification, enabling geologists and engineers to consider the model’s uncertainty in decision-making, thereby reducing risks. SHAP facilitates global and local interpretation, enabling the measurement of seismic attribute and elastic parameter importance and optimizing feature combinations for improved classification performance. Experimental results using real data demonstrate the superiority of the proposed workflow over classical machine learning and time series models, specifically in terms of single-well experiment accuracy and lateral continuity of seismic profiles.
Stage shift and relative survival for head and neck cancer during the 2020 COVID-19 pandemic: a population-based study of temporal trends

Objective During the first wave of the COVID-19 pandemic in 2020, non-essential health services were suspended in Belgium, and the public was ordered to socially isolate. Underdiagnosis of cancer during this period was reported worldwide. Certain risk factors for head and neck cancer (HNC) overlap with those for COVID-19 incidence and mortality, making underdiagnosis and subsequent stage shift of this potentially rapidly progressing cancer a major concern. We aimed to analyze incidence, clinical stage at presentation, and survival of patients diagnosed with HNC in 2020 in Belgium, considering recent temporal trends. Methods Using population-based data from the Belgian Cancer Registry (BCR), we extrapolated 2017-2019 trends in incidence, clinical stage, and 1-year relative survival (1yRS) of HNC to create an expected value for 2020 and compared this to the observed value. Results There were 9.5% fewer HNCs diagnosed in 2020, compared to the predicted incidence. Underdiagnosis was larger for males (-11.8%), patients aged 50-64 (-11.2%) and 65-79 (-11.1%), and for oral cavity cancer (-17.6%). Shifts to more advanced stages were observed in larynx and oropharynx tumors and for (male) patients aged 80+. A 2.4 percentage point decline in 1yRS was observed, relative to the increasing trends in 1yRS (2017-2019). Conclusion The COVID-19 pandemic led to underdiagnosis of HNC, resulting in shifts to more advanced stage at presentation in certain subgroups. A stage shift can be expected for the 9.5% of tumors not yet diagnosed at the end of 2020. HNC patients diagnosed in 2020 suffered higher than expected mortality.
Semisupervised Deep Neural Network-Based Cross-Frequency Ground-Penetrating Radar Data Inversion

Ground-penetrating radar (GPR) with different center frequencies can detect defects at different depths with a range of resolutions enabling it to be used for subsurface defect inspection. However, the existing deep learning methods cannot accurately invert the permittivity from GPR data of different frequencies, due to the limited number of labeled GPR images for every center frequency. To tackle this challenge, a semisupervised deep neural network (DNN)-based cross-frequency GPR data inversion method was proposed, which enables the generalized model to be trained on the GPR data with one frequency (source domain) for migration to other frequencies (target domain). The method was trained in a semisupervised manner using a small number of paired GPR data with permittivity labels and a large amount of unlabeled GPR data without corresponding permittivity maps. An adversarial learning mechanism together with a novel random perturbation strategy was designed to improve the global inversion performance for a large-scale structure and avoid a discontinuity in the reconstructed shapes. Furthermore, a mean-teacher architecture is introduced to improve the inversion accuracy of detailed information from the unlabeled GPR data under different perturbation conditions. The ablation and comparative experiments results indicated that the proposed method outperforms other methods and can be effectively generalized to GPR B-Scan data with different frequencies and signal-to-noise ratios. In addition, sandbox model testing was conducted and the results indicate that this method can transfer the knowledge from the synthetic data domain to the real data domain with satisfactory results.
Photothermal Effect of Gold Nanoparticles as a Nanomedicine for Diagnosis and Therapeutics

Gold nanoparticles (AuNPs) have received great attention for various medical applications due to their unique physicochemical properties. AuNPs with tunable optical properties in the visible and near-infrared regions have been utilized in a variety of applications such as in vitro diagnostics, in vivo imaging, and therapeutics. Among the applications, this review will pay more attention to recent developments in diagnostic and therapeutic applications based on the photothermal (PT) effect of AuNPs. In particular, the PT effect of AuNPs has played an important role in medical applications utilizing light, such as photoacoustic imaging, photon polymerase chain reaction (PCR), and hyperthermia therapy. First, we discuss the fundamentals of the optical properties in detail to understand the background of the PT effect of AuNPs. For diagnostic applications, the ability of AuNPs to efficiently convert absorbed light energy into heat to generate enhanced acoustic waves can lead to significant enhancements in photoacoustic signal intensity. Integration of the PT effect of AuNPs with PCR may open new opportunities for technological innovation called photonic PCR, where light is used to enable fast and accurate temperature cycling for DNA amplification. Additionally, beyond the existing thermotherapy of AuNPs, the PT effect of AuNPs can be further applied to cancer immunotherapy. Controlled PT damage to cancer cells triggers an immune response, which is useful for obtaining better outcomes in combination with immune checkpoint inhibitors or vaccines. Therefore, this review examines applications to nanomedicine based on the PT effect among the unique optical properties of AuNPs, understands the basic principles, the advantages and disadvantages of each technology, and understands the importance of a multidisciplinary approach. Based on this, it is expected that it will help understand the current status and development direction of new nanoparticle-based disease diagnosis methods and treatment methods, and we hope that it will inspire the development of new innovative technologies.
Arbuscular mycorrhizal fungi improve selenium uptake by modulating root transcriptome of rice (Oryza sativa L.)

Although selenium (Se) is an essential trace element in humans, the intake of Se from food is still generally inadequate throughout the world. Inoculation with arbuscular mycorrhizal fungi (AMF) improves the uptake of Se in rice (Oryza sativa L.). However, the mechanism by which AMF improves the uptake of Se in rice at the transcriptome level is unknown. Only a few studies have evaluated the effects of uptake of other elements in rice under the combined effects of Se and AMF. In this study, Se combined with the AMF Funneliformis mosseae (Fm) increased the biomass and Se concentration of rice plants, altered the pattern of ionomics of the rice roots and shoots, and reduced the antagonistic uptake of Se with nickel, molybdenum, phosphorus, and copper compared with the treatment of Se alone, indicating that Fm can enhance the effect of fertilizers rich in Se. Furthermore, a weighted gene co-expression network analysis (WGCNA) showed that the hub genes in modules significantly associated with the genes that contained Se and were related to protein phosphorylation, protein serine/threonine kinase activity, membrane translocation, and metal ion binding, suggesting that the uptake of Se by the rice roots may be associated with these genes when Fm and Se act in concert. This study provides a reference for the further exploration of genes related to Se uptake in rice under Fm treatment.
The Interplay Between Maternal and Paternal Senses of Efficacy Moderates the Link Between Perinatal Parental Stress and Family Alliance at 3 Months

SYNOPSIS Objective. Both parents may report high levels of stress during the perinatal period with possible negative consequences for parental well-being and child development. Parental sense of efficacy moderates the effect of stress. To date, no studies have assessed links between parental stress and mother-father-infant interactions in terms of family alliance and the extent to which each parent’s sense of efficacy moderates these links. Design. In this study, 65 dual-parent families answered a questionnaire about parental stress between 36 and 38 weeks of pregnancy and at 3 months after birth. Families were also observed while playing with their 3-month-old infant in the Lausanne Trilogue Play, and they completed a questionnaire about parental efficacy. Results. Structural equation modeling analyses showed that higher maternal or paternal postnatal stress has a direct link with higher family alliance and the links between stress and family alliance are moderated by complex interaction effects between maternal and paternal senses of efficacy. Conclusions. This study shows the necessity of considering the interaction between the senses of self-efficacy of both parents as moderators of parenting stress.
Electromagnetic Characterization of Aqueous Sodium Lactate Solutions and Its Application for Enhanced Concentration Monitoring

This article presents the electromagnetic (EM) properties of sodium lactate (C3H5NaO3)/water mixtures in the dc-to-microwave frequencies range and its exploitation for sensing purposes. EM features of solutions are characterized and modeled, while underlying physical phenomenon are explained. Then, intrinsic properties of the sodium lactate molecule and its consequences upon aqueous solutions are exploited to monitor concentrations. The proposed sensor is based on a coupled-resonators principle and works around 7 GHz, it features ability to extract sodium lactate concentrations in aqueous solutions within the 0–4.2 mol.L−1 range with a relative error lower than 8% if the sample temperature is given. Indeed, in addition to the extraction of the sodium lactate concentration, the proposed sensor also features the ability to monitor, simultaneously, temperature of the sample within the 15 °C–35 °C range. This innovative bi-parameter (sodium-lactate concentration and temperature), radio frequency-based monitoring exhibits precisions better than 5.2% on the sodium lactate concentrations and 12% on the temperature when these parameters are simultaneously extracted.
Atrial Fibrillation Detection With Spectral Manifolds in Low-Dimensional Latent Spaces

Atrial fibrillation (AF) remains a significant health challenge as the number of patients increases with the aging of the population worldwide. AF detection continues to be a significant challenge, particularly in the long term, to improve the quality of life of people suffering from the disease and reduce treatment costs. In recent decades, machine learning and deep learning have provided promising results in classifying heart conditions with moderate interpretability. We hypothesized that the complexity of AF could be handled in low-dimensional latent spaces in terms of non-Gaussian manifolds and that feature selection techniques on an audio-inspired feature set could improve the detection and interpretability of the AF phenomenon. Therefore, we used long-term monitoring databases in which noise-reduction filters preprocessed the signals and extracted their RR-value sequences. From these datasets, several audio spectral characteristics were calculated and used as input vectors for different types of manifold estimators. We also used the least absolute shrinkage and selection operator (LASSO) regression, a feature selection technique. Our experiments showed that these spectral representations of AF segments can yield better-defined low-dimensional embedding manifolds and acceptable intrinsic separability of AF from sinus rhythm. Moreover, including supervised stages and LASSO regression improved the overall performance of this proposal. The performance obtained in classifying AF signals versus normal ones reaches a precision of 91.2%, a recall of 95.5%, and an accuracy of 94.5%. These results pave the way for manifold learning and low-dimensional latent spaces in AF applications, and they support the advantage of using machine learning methods in different AF problems.
Integrated application of UAS-based and geoinformation tools for studying urban mobility patterns in urbanized territories: the case of Sofia, Bulgaria

This article aims to present the developed and applied methodology for the study, mapping, and evaluation of urban mobility patterns in the city of Sofia. The research is based on the combination and integration of GIS-based spatial analysis techniques and high-resolution data obtained through sensors based on state-of-the-art Unmanned Aerial Vehicle systems (UAVs). The results obtained from this study are adapted to their use for the purpose of supporting urban planning processes in the city and could greatly assist in the establishment of more adequate models for solving some of the most pressing problems of urban mobility in the urban area of Sofia.
Latency Compensation and Prediction for Wireless Train to Ground Communication Network Based on Hybrid LSTM Model

This paper proposes a novel method compensate for the communication imperfection of wireless train-to-ground communication network (WT2GCN). WT2GCN has an unavoidable transmission delay, which wastes the expensive investment of wired network and impacts the economic operation of the energy management for the rail transit system. Instead of upgrading the WT2GCN, which will lead to unnecessary costs, this paper has developed an accurate prediction method to forecast the WT2GCN measurement based on the long short-term memory (LSTM) and high-degree polynomial linear regression (HPLR) technique. To capture different characteristics of physical parameters, multiple LSTM and HPLR models are specifically designed to predict various measurements. In addition, the attention mechanism is applied to help exact the key information of features. Then, a steady-state model is derived for a priori evaluation of WT2GCN measurement prediction with non-delayed electric measurements. A detailed implementation illustration of the proposed methodology is also presented. Finally, the developed platform is tested with the actual data and implemented in one practical rail transit project of China Metro. The real-time operating result has fully demonstrated the effectiveness of the proposed method.
El enfoque por competencias en el EEES y sus implicaciones en la enseñanza y el aprendizaje

En este articulo se hace una aproximacion conceptual al termino “competencia” y se analiza su utilizacion en el ambito academico. Partiendo de la descripcion de su genesis en el contexto empresarial, se estudia la comprension y el tratamiento que se le esta dando en la universidad. Finalmente, se analizan algunas implicaciones de dicho concepto sobre la planificacion de la ensenanza y el aprendizaje en el nuevo contexto del Espacio Europeo de Educacion Superior.
Atractylenolide promotes trophoblast cell proliferation and migration in recurrent spontaneous abortion via ERK pathway

Purpose: To investigate the effect of atractylenolide on recurrent spontaneous abortion (RSA). Methods: The HTR-8/SVneo was established as an in vitro cell model of RSA. Cell viability and proliferation were determined using CCK8 and BrdU staining, while cell migration and invasion were determined by cell scratch and transwell assays.Results: Atractylenolide significantly increased cell viability, and enhanced the number of BrdU-positive cells of HTR-8/SVneo (p < 0.01). Atractylenolide also significantly promoted cell migration and invasion (p < 0.01), and increased protein expression of MMP-9, MMP-2, and N-cadherin, but reduced Ecadherin. Atractylenolide also increased the phosphorylation of ERK (p < 0.01). Conclusion: Atractylenolide enhances cell proliferation and migration of HTR-8/SVneo through activation of ERK signaling. Further studies using animal models are recommended to determine the protective role of atractylenolide against RSA, in vivo.