Comparison Analysis Research Articles

Exploiting the synergy of SARIMA and XGBoost for spatiotemporal earthquake time series forecasting

AbstractEarthquakes are vibrations that occur on the surface of earth, generating fires, ground shaking, tsunamis, landslides and cracks. These incidents can cause severe damage and loss of life. Accurate earthquake forecasts are critical for anticipating and mitigating these hazards, which can avoid damage to buildings and infrastructure and save lives. To address the challenges given by earthquakes probabilistic nature, this paper presents a hybrid SARIMA–XGBoost approach to earthquake magnitude prediction. The suggested technique consists of a two‐step process: an exploration phase that uses exploratory data analysis, which includes descriptive statistics and data visualisation, and a prediction phase that focusses on forecasting future earthquakes. Using a large significant earthquake dataset spanning 1965–2023, the study intends to gain insights and lessons for more effective earthquake prediction methods. Further, in a comparison analysis, the results of SARIMA‐XGBoost model are compared to those of traditional ARIMA and SARIMA models. The results highlight the superior performance of the hybrid SARIMA–XGBoost model, showcasing a mean absolute error (MAE) of 0.038, a mean squared error (MSE) of 0.0040, and a root mean squared error (RMSE) of 0.068. These metrics collectively underscore the model's enhanced accuracy in forecasting earthquake magnitudes. The notably low values of MAE, MSE and RMSE indicate that our hybrid approach significantly improves prediction accuracy compared to alternative models. By integrating SARIMA's time series (TS) analysis with XGBoost's machine learning (ML) capabilities, the hybrid model reduces forecasting errors more effectively, demonstrating its clear advantage in precision.

Classification-based consensus model considering quantum interference in linguistic distribution environment

Multi-attribute group classification (MAGC) is widely used in dealing with a variety of practical problems. However, cognitive biases, resulting from factors such as irrational behaviors, epistemic uncertainty, and interference phenomena, may critically affect the consensus reaching process (CRP) of MAGC. To eliminate such influences, this study proposes an ordinal classification consensus model in a linguistic environment based on quantum probability theory (QPT), with the aim of classifying the alternatives into several predefined ordinal classifications accepted by a majority of experts. First, the linguistic distribution assessments (LDAs), which can depict the quantitative distribution and qualitative vagueness, are used to express the uncertain preference of experts. Second, the LDAs best-worst method (BWM) and LDAs maximizing deviation model (MDM) are developed to identify the subjective and objective weight of attributes, and then these two types of attributes weight are integrated by quantum-like Bayesian networks (QLBN). Third, a maximum consensus model (MCM), maximizing the consensus level of ordinal classification results of experts, is provided to determine the parameters of QLBN. When the consensus level is lower than the predetermined threshold, a minimum adjustment distance model (MADM) is designed to guide the preference-modification of experts to derive the consensus classification of alternatives. Finally, a risk assessment case study is conducted to verify the feasibility and effectiveness of the proposed method. Sensitivity analysis and comparison analysis are performed to verify the feasibility and effectiveness of the proposed method

Association of brown adipose tissue activity with circulating sex hormones and fibroblast growth factor 21 in the follicular and luteal phases in young women

BackgroundThermogenesis is influenced by fluctuations in sex hormones during the menstrual cycle in premenopausal women. The thermogenic activity and mass of brown adipose tissue (BAT) are regulated by endocrine factors, including sex hormones and fibroblast growth factor 21 (FGF21). However, the relationship between human BAT and these endocrine fluctuations within individuals remains to be elucidated. This study aimed to assess variations in BAT activity between the luteal and follicular phases and identify correlations with circulating levels of sex hormones and FGF21.MethodsHealthy young women were enrolled in an observational study. Measurement of BAT activity and blood analyses were performed in both the follicular and luteal phases. BAT activity was analyzed using thermography with 2-h cold exposure. Plasma 17β-estradiol, progesterone, and FGF21 levels were determined by enzyme-linked immunosorbent assay. A comparative analysis within individuals was conducted in 13 women to compare the follicular and luteal phases. Furthermore, sensitivity analysis was carried out in 21 women during the follicular phase only.ResultsPlasma 17β-estradiol and progesterone levels were significantly higher in the luteal phase, whereas plasma FGF21 level was significantly higher in the follicular phase. Comparison analysis found no significant differences in cold-induced BAT activity between the follicular and luteal phases in young women. Correlation analysis in both comparison and sensitivity analyses found that plasma 17β-estradiol and progesterone levels were not associated with BAT activity, whereas plasma FGF21 levels were significantly and positively correlated with BAT activity only in the follicular phase. In addition, plasma 17β-estradiol levels in the follicular phase were significantly and positively associated with plasma FGF21 levels in both the comparison and sensitivity analyses.ConclusionsThe thermogenic activity of BAT during cold exposure was comparable between the follicular and luteal phases in young women. Higher BAT activity was associated with elevated levels of plasma FGF21 only in the follicular phase, which is related to increased plasma 17β-estradiol levels.

Ultrasound normalized local variance to assess metabolic dysfunction-associated steatotic liver disease

PurposeIncreased prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) highlights a desire for screening with liver ultrasound normalized local variance (NLV). We aimed to assess variations in NLV values measured at different sampling depths and discuss common technical considerations in measuring liver NLV. MethodsWe retrospectively measured liver NLVs at variable depths on ultrasound images pre-recorded in 116 participants who underwent liver magnetic resonance imaging-proton density fat fraction (MRI-PDFF) and ultrasound to screen for MASLD. Liver NLVs were measured and differences at variable depths were tested using one-way analysis of variance (ANOVA) and multiple paired comparisons using post hoc Tukey honestly significant difference (HSD), Scheffé, Bonferroni, and Holm multiple comparisons. Diagnostic performance of NLV values were analyzed by area under the receiver operating characteristics (AUROC) curve. ResultsThe NLV measured at a depth of 10 cm significantly differed from those measured near the liver capsule and at depths of 6 cm and 8 cm (p < 0.001) from the skin. There was no significant difference in NLV value in other paired groups (p > 0.05). The difference in the area under AUROCs for NLVs measured at variable depths was not significant (p > 0.05). ConclusionsThe best diagnostic performance of liver NLV was measured at depth of 8 cm from the skin, although NLV measured at variable depth showed similar diagnostic performance for assessing ≥ mild hepatic steatosis. The study results provide a reference that can be used in the development of standardized scanning protocols and technical considerations in measuring liver NLV.

Interpretable Model-Driven Deep Network for Hyperspectral, Multispectral, and Panchromatic Image Fusion.

Simultaneously fusing hyperspectral (HS), multispectral (MS), and panchromatic (PAN) images brings a new paradigm to generate a high-resolution HS (HRHS) image. In this study, we propose an interpretable model-driven deep network for HS, MS, and PAN image fusion, called HMPNet. We first propose a new fusion model that utilizes a deep before describing the complicated relationship between the HRHS and PAN images owing to their large resolution difference. Consequently, the difficulty of traditional model-based approaches in designing suitable hand-crafted priors can be alleviated because this deep prior is learned from data. We further solve the optimization problem of this fusion model based on the proximal gradient descent (PGD) algorithm, achieved by a series of iterative steps. By unrolling these iterative steps into several network modules, we finally obtain the HMPNet. Therefore, all parameters besides the deep prior are learned in the deep network, simplifying the selection of optimal parameters in the fusion and achieving a favorable equilibrium between the spatial and spectral qualities. Meanwhile, all modules contained in the HMPNet have explainable physical meanings, which can improve its generalization capability. In the experiment, we exhibit the advantages of the HMPNet over other state-of-the-art methods from the aspects of visual comparison and quantitative analysis, where a series of simulated as well as real datasets are utilized for validation.

Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy

PurposeMR-guided Radiation Therapy (MRgRT) enables online adaptation to address intra- and inter-fractional changes. To address the need of high-fidelity synthetic CT (synCT) required for dose calculation, we developed a conditional generative adversarial network (cGAN) for synCT generation from low-field MRI in the brain. Methods and MaterialsSimulation MR-CT pairs from twelve glioma patients imaged with a head and neck surface coil and treated on a 0.35T MR-linac were prospectively included to train the model consisting of a 9-block residual network generator and a PatchGAN discriminator. Four-fold cross validation was implemented. SynCT was quantitatively evaluated against real CT using mean absolute error (MAE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). Dose was calculated on synCT applying original treatment plan. Dosimetric performance was evaluated by dose-volume histogram (DVH) metric comparison and local three-dimensional gamma analysis. To demonstrate utilization in treatment adaptation, longitudinal synCTs were generated for qualitative evaluation, and one offline adaptation case underwent two comparative plan evaluations. Secondary validation was conducted with 9 patients on a different MR-linac using a high-resolution brain coil. ResultsOur model generated high-quality synCTs with MAE, PSNR and SSIM of 70.9±10.4 HU, 28.4±1.5 d.B. and 0.87±0.02 within the field-of-view, respectively. Underrepresented post-surgical anomalies challenged model performance. Nevertheless, excellent dosimetric agreement was observed with the mean difference between real and synCT DVH metrics of -0.07±0.29 Gy for target D95 and within [-0.14, 0.02] Gy for organs at risk. Significant differences were only observed in the right lens D0.01cc with negligible overall difference (<0.13 Gy). Mean gamma analysis pass rates were 92.2%±3.0%, 99.2%±0.7% and 99.9%±0.1% at 1%/1mm, 2%/2mm and 3%/3mm, respectively. Secondary validation yielded no significant differences in synCT performance for whole brain MAE, PSNR, and SSIM with comparable dosimetric results. ConclusionsOur cGAN model generated high-fidelity brain synCTs from low-field MRI with excellent dosimetric performance. Secondary validation suggests great promise of implementing synCTs to facilitate robust dose calculation for online adaptive brain MRgRT.

The efficacy of first and second immunotherapy exposure in patients with recurrent or metastatic cervical cancer.

Immunotherapy has led to changes in cervical cancer guidelines. Therefore, additional biomarkers to identify the ideal patient who would experience the most benefit may be important. We retrospectively collected 208 patients with R/M CC and recorded clinicopathologic information, peripheral blood markers and treatments to analyze the prognostic factors of clinical outcomes. Response rate comparison, univariate, and multivariate analyses were performed to assess the efficacy of different factors. A total of 43.27% patients achieved objective responses, including 18 with complete response and 72 with partial response. Patients receiving first-line immunotherapy had much higher objective response rate (ORR) than the remaining patients (53.8% vs. 34.8%, p = 0.006). CRP >3 ECOG ≥1 and recurrence in 6 months predicted shorter progression free survival (PFS). CRP >3, GLU >6.1 independently predicted unfavorable overall survival (OS). Compared with no antiangiogenic therapy, previous antiangiogenic therapy reduced the median OS by nearly 14 months. Immunotherapy rechallenge was still effective after first immunotherapy failure, and combined with dual-immunotherapy or bevacizumab combined with chemoradiotherapy resulted in a 60.00% or 62.50% ORR, respectively. Patients with squamous cell carcinoma, with stable disease or objective response in the first immunotherapy or without chemotherapy in second immunotherapy had favorable clinical outcome. The baseline CRP levels in serum was an independent factor for PFS and OS of R/M CC patients treated with immunotherapy, and previous antiangiogenic therapy was associated with poor OS. Patients still show response to immunotherapy rechallenge and combined treatment with bevacizumab or candonilimab showed higher response rate than anti-PD-1 after immunotherapy failure.

The effect of ceramides on skin absorption by Raman spectroscopy.

Ceramides are essential epidermal constituents that play a critical role in skin moisturization treatment as a raw material in cosmetics formulation. Recently, ceramides have been known to be frequently applied in various cosmetic formulations. Despite ceramide's beneficial characteristics, academic research regarding ceramides and their skin absorption remains insufficient. Therefore, our study conducted clinical research employing Raman spectroscopy to investigate the effects of ceramides on skin absorption to enhance the understanding of ceramides' dermatological functionality and their topical application in cosmetics science. Twenty healthy individuals with dry skin have participated in this clinical trial. In this double-arm designed trial, the test group received an investigational product with ceramides (5000ppm) and a control group received an investigational product without the ceramides while all other components remained identical. The subjects visited the clinical research center and acclimatized for 30 min in constant humidity and temperature for equilibrium, subsequently conducting a measurement. Before the trial, the research subject's target site (lower arm area) was kept clean, devoid of any cosmetic administering 24h before the trial when investigational product was topically applied. Our findings with Raman spectroscopy statistically demonstrate that skin absorption amount, speed and depth for both groups improved overall (p<0.05) after administration of the investigational product. Notably, the test group received an investigational product with ceramides (5000ppm) indicating superior effectiveness across all parameters compared to a control group from comparison analysis of each parameter (p<0.05). This study concludes that ceramide-containing cosmetics provide a beneficial effect on skin absorption via visual and statistical results of Raman spectroscopy analysis.

Design of concatenative complete complementary codes for CCC-CDMA via specific sequences and extended Boolean functions

A complete complementary code (CCC) consists of M sequence sets with size M. The sum of the auto-correlation functions of each sequence set is an impulse function, and the sum of cross-correlation functions of the different sequence sets is equal to zero. Thanks to their excellent correlation, CCCs received extensive use in engineering. In addition, they are strongly connected to orthogonal matrices. In some application scenarios, additional requirements are made for CCCs, such as recently proposed for concatenative CCC (CCCC) division multiple access (CCC-CDMA) technologies. In fact, CCCCs are a special kind of CCCs which requires that each sequence set in CCC be concatenated to form a zero-correlation-zone (ZCZ) sequence set. However, this requirement is challenging, and the literature is thin since there is only one construction in this context. We propose to go beyond the literature through this contribution to reduce the gap between their interest and our limited knowledge of CCCCs. This paper will employ novel methods for designing CCCCs and precisely derive two constructions of these objects. The first is based on perfect cross Z-complementary pair and Hadamard matrices, and the second relies on extended Boolean functions. Specifically, we highlight that optimal and asymptotic optimal CCCCs could be obtained through the proposed constructions. Besides, we shall present a comparison analysis with former structures in the literature and examples to illustrate our main results.

Short-term outcomes of gastric per-oral endoscopic pyloromyotomy (G-POEM) then and now: a comparison analysis

Summary Background Gastric per-oral endoscopic pyloromyotomy (G-POEM) has been performed for 10 years in the treatment of refractory gastroparesis (Gp). Many studies from the early years of G‑POEM reported short-term outcomes with 60–80% clinical success. Notably, no recent studies have assessed short-term outcomes after a decade of experience with this procedure. In this study, we compare the short-term outcomes of our initial 16 patients undergoing G‑POEM 8 years ago with 16 of our recent patients undergoing G‑POEM. Patients and methods The initial 16 patients who underwent G‑POEM from 06/2015 to 07/2016 (group A; the first 16 G-POEM procedures QC performed) and 16 recent patients who underwent G‑POEM from 07/2021 to 01/2022 (group B; the first 16 procedures performed when QC moved to a different institution) were enrolled in the study. Patients’ demographics, clinical success, procedure time, and length of hospital stay were analyzed and compared between the two groups. All procedures were performed by a single advanced endoscopist (QC). Results Patients’ age, gender, and etiology of gastroparesis were similar between the two groups. There were no differences between the two groups in terms of short-term clinical outcomes. However, there was a significant difference in procedure time and the length of hospital stay between the two groups. There were no adverse events reported for either group A or group B. Conclusion For a single experienced advanced submucosal endoscopist, there were no differences in the short-term clinical outcomes of G‑POEM when comparing the initial timeframe of performing G‑POEM with that after 8 years of experience at the time of the study in a different institution. Notably, the procedure time and the length of hospital stay were significantly shorter after 8 years of practice.

Clinical and Radiographic Outcomes of Cervical Disc Replacement Versus Posterior Endoscopic Cervical Decompression: A Matched-Pair Comparison Analysis.

To compare clinical and radiographic outcomes between 2 motion preservation surgeries, cervical disc replacement (CDR) and posterior endoscopic cervical decompression (PECD), for unilateral cervical radiculopathy. Between February 2018 and December 2020, 60 patients with unilateral cervical radiculopathy who underwent either CDR or PECD were retrospectively recruited as matched pairs. Clinical outcomes included visual analogue scale (VAS) scores for neck and arm pain, Neck Disability Index (NDI), and satisfaction rates. The radiographic outcome was index level motion. Intraoperative data, complications, and hospital stay were collected. Preoperative and postoperative outcomes were compared. Patients undergoing CDR or PECD were included, with 30 cases in each group. Matched pairs were compared in terms of demographic data and preoperative measurements. CDR was associated with shorter operative times, whereas PECD resulted in less intraoperative blood loss. The total complication rate was 5%. NDI and VAS for neck and arm were significantly improved in both groups, with no significant differences between the 2 groups. Satisfaction rates of good and excellent exceeded 87% in both groups. CDR was superior to PECD in the restoration of disc height. Early postoperative follow-up showed no significant difference in terms of index level motion. PECD demonstrated significantly shorter hospital stays and quicker return-to-work times (p<0.05). PECD achieved equivalent clinical and radiologic outcomes compared with CDR when the certain criteria for surgery were met. Both techniques demonstrated the potential to maintain index level motion. Additionally, PECD resulted in less blood loss, shorter hospital stays, and faster return-to-work times. Conversely, CDR offered shorter operative times and better restoration of disc height.

Nurses' Experiences and Perspectives Caring for People With Substance Use Disorder and Their Families: A Qualitative Descriptive Study.

Substance use disorder is a public health crisis that is a financial strain to many healthcare systems and communities, but more importantly, it costs lives. Nurses interact with people experiencing substance use disorders and their families in many settings. Nurses can provide insights into the experiences of working with this population. This descriptive qualitative study aimed to examine nurses' experiences and perspectives on caring for people with substance use disorder and their families. After receiving institutional review approval, purposive sampling was used to recruit registered nurses (n = 16) who worked in a variety of settings, and interviews were conducted. Constant comparison analysis was conducted concurrently with data collection until saturation was reached. Code development and refinement was an iterative process. Three themes were generated from the data. Personal Experiences Affect Professional Practice represented participants varied personal experiences and included two subthemes: Reflecting on Personal Experiences and Seeing the Person Beyond the Substance Use Disorder. A second theme is Professional Experiences Affect Professional Practice, which included two subthemes: Professional Experiences are Stressful and Rewarding and Substance Use Disorder Education Increases Confidence. Finally, Stigma Affects Substance Use Disorder Care is the third theme. Future implications range from the individual engaging in self-reflection, to nursing leadership establishing a framework to incorporate reflection and creating a culture that supports and reinforces these activities. The findings of this study support the need for stigma awareness/reduction education starting in undergraduate nursing programmes, throughout practice, with extension to inter-professional groups and the community.

Comparison of the Effectiveness of Microteaching and School Environmental Introduction (PLP 2) in Developing Teaching Skills for Biology Education Students

The research presented a comparison analysis of the effectiveness of microteaching and PLP 2 in developing teaching skills for biology education students. Microteaching, a commonly used method in higher education, involves simulated teaching sessions where students receive feedback from peers and instructors. On the other hand, PLP 2 offers practical teaching experience in a school environment, providing real challenges and interaction with students in the classroom. This research evaluates and compares how both methods contribute to improving teaching competencies. The main findings indicate that while microteaching focuses on pedagogical techniques and classroom management, PLP 2 emphasizes adaptability to various learning environments and student engagement strategies. The study concludes that integrating both approaches into teacher education programs can better prepare aspiring educators with the comprehensive skills to teach in diverse educational contexts

DeepAIP: Deep learning for anti-inflammatory peptide prediction using pre-trained protein language model features based on contextual self-attention network

Non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, and other immunosuppressants are commonly used medications for treating inflammation. However, these drugs often come with numerous side effects. Therefore, finding more effective methods for inflammation treatment has become more necessary. The study of anti-inflammatory peptides can effectively address these issues. In this work, we propose a contextual self-attention deep learning model, coupled with features extracted from a pre-trained protein language model, to predict Anti-inflammatory Peptides (AIP). The contextual self-attention module can effectively enhance and learn the features extracted from the pre-trained protein language model, resulting in high accuracy to predict AIP. Additionally, we compared the performance of features extracted from popular pre-trained protein language models available in the market. Finally, Prot-T5 features demonstrated the best comprehensive performance as the input for our deep learning model named DeepAIP. Compared with existing methods on benchmark test dataset, DeepAIP gets higher Matthews Correlation Coefficient and Accuracy score than the second-best method by 16.35 % and 6.91 %, respectively. Performance comparison analysis was conducted using a dataset of 17 novel anti-inflammatory peptide sequences. DeepAIP demonstrates outstanding accuracy, correctly identifying all 17 peptide types as AIP and predicting values closer to the true ones. Data and code are available at https://github.com/YangQingGuoCCZU/DeepAIP.

The Impact of Tax Incentives on the Growth of Small and Medium-Sized Enterprises: A Comparative Study Based on Industry

Tax incentive policies have been widely used as a crucial tool to promote the development of Small and Medium-sized Enterprises (SMEs), but their effectiveness varies across different industries and policy types. This study utilizes a comprehensive panel dataset from 2015 to 2023 to investigate the impact of Value-Added Tax (VAT) and income tax incentives on the growth of SMEs in China. Through fixed effects model analysis, we find that both VAT and income tax incentives significantly enhance SMEs' sales revenue and total asset growth. However, the magnitude of these effects differs substantially across industries, with high-tech and information technology service industries benefiting the most. Our industry comparison analysis reveals that the effectiveness of tax incentives is closely related to industry-specific characteristics, such as research and development (R&amp;D) intensity and capital requirements. These findings provide key empirical evidence for optimizing SME tax policies and suggest that a more nuanced, industry-specific approach to tax incentives is needed. This study not only contributes to the literature on SME growth and tax policy effectiveness but also offers important insights for policymakers to refine SME support systems in the context of rapid technological change and economic transformation.

A two-stage distributionally robust maximum expert consensus model with asymmetric costs and risk aversion

The maximum expert consensus model (MECM) emerges as a widely used consensus optimization model in group decision making (GDM). However, contemporary complexities in decision-making environment lead to the asymmetry and uncertainty of adjustment costs of decision-makers (DMs), which are critical during the consensus reaching process (CRP). Additionally, the risks emerging with the uncertainty during CRP should also be analyzed. Therefore, this paper focuses on developing the two-stage distributionally robust MECM (DRO-MECM) with asymmetric adjustment costs under an uncertain environment to improve the CRP. Specifically, we propose a MECM with asymmetric costs. Moreover, we build the two-stage DRO-MECM based on the mean-CVaR under two uncertain scenarios, allowing it to manage uncertain costs effectively while considering the risk preferences of DMs. The first stage aims to maximize the number of DM within consensus and the second stage seeks to minimize the consensus cost. Finally, the applicability of the proposed models is demonstrated by applying them to the allocation of healthcare and security capacity enhancement subsidy funds in China. The efficiency of the models is further corroborated by sensitivity analysis and comparison analysis.

Facilitating integrative and personalized oncology omics analysis with UCSCXenaShiny.

The continuous generation of multi-omics and phenotype data is propelling advancements in precision oncology. UCSCXenaShiny was developed as an interactive tool for exploring thousands of cancer datasets available on UCSC Xena. However, its capacity for comprehensive and personalized pan-cancer data analysis is being challenged by the growing demands. Here, we introduce UCSCXenaShiny v2, a milestone update through a variety of improvements. Firstly, by integrating multidimensional data and implementing adaptable sample settings, we create a suite of robust TPC (TCGA, PCAWG, CCLE) analysis pipelines. These pipelines empower users to conduct in-depth analyses of correlation, comparison, and survival in three modes: Individual, Pan-cancer and Batch screen. Additionally, the tool includes download interfaces that enable users to access diverse data and outcomes, several features also facilitate the joint analysis of drug sensitivity and multi-omics of cancer cell lines. UCSCXenaShiny v2 is an open-source R package and a web application, freely accessible at https://github.com/openbiox/UCSCXenaShiny .

Molecular characterization of ‘Candidatus Phytoplasma australasiaticum’ associated with ornamentals, amaranthus, French bean and sunhemp crops and development of rapid LAMP assay for detection

Phytoplasma is one of the most economically important pathogens, causing significant yield losses in many crops worldwide. During a survey in Bengaluru rural and Chikkabalpura areas of Karnataka, India, a total of 33 symptomatic plant samples including ornamentals, amaranthus, french bean, and sunhemp displaying phytoplasma symptoms were collected and confirmed as infected with phytoplasma through PCR targeting the 16S rRNA and secY genes. Pairwise sequence comparison and phylogenetic analysis of 16S rRNA and secY gene sequences confirmed the presence of ‘Candidatus Phytoplasma australasiaticum’ in antirrhinum, chrysanthemum, china aster, dianthus, amaranthus, french bean, and sunhemp. In-silico restriction fragment length polymorphism (RFLP) analysis indicated that phytoplasma strains infecting Antirrhinum, China aster, Dianthus, French bean, and Sunhemp represented distinct variants within the 16SrII phytoplasma group, with a threshold similarity coefficient of less than 0.98. A loop-mediated isothermal amplification (LAMP) assay was developed for detecting phytoplasma strains infecting the four ornamentals and other studied crops, targeting the phytoplasma 16S rRNA gene sequence with detection in 60 min. The comparative testing of LAMP products using various nucleic acid dyes and a colorimetric dye (hydroxy naphthol blue) confirmed the presence of phytoplasmas in symptomatic samples. This LAMP assay offers superior ease of use, cost-effectiveness, and rapid results for detecting phytoplasmas in symptomatic plants.

Energy bubble entropy guided symplectic geometry mode decomposition for rotating machinery incipient fault feature extraction

Abstract Extracting incipient fault features is a critical aspect of monitoring the rotating machinery operation condition. However, existing methods based on symplectic geometry mode decomposition (SGMD) suffer from limited parameter adaptability and noise robustness. Therefore, this paper proposes an Energy bubble entropy guided SGMD method to extract incipient fault feature. Firstly, the SGMD method is employed to initially separate fault characteristic components from noisy signal. Furthermore, the energy bubble entropy(EbEn) is constructed to evaluate the attributes of incipient feature within the signal, which requires almost no parameter setting with good robustness and computational efficiency. Thirdly, the Empirical Bayes shrinkage method effectively mitigates irrelevant noise and enhances the significance of incipient fault feature. Simulated and experimental signals are employed to substantiate the efficacy of the EbEn guided SGMD method. The comparison analysis with relevant methods exhibits that this method has greater robustness and adaptivity.&amp;#xD;

Mining Comparative Opinions in Portuguese: A Lexicon-based Approach

The constant expansion of e-commerce, recently boosted due to the coronavirus pandemic, has led to a massive increase in online shopping, made by increasingly demanding customers, who seek comments and reviews on the Web to assist in decision-making regarding the purchase of products. In these reviews, part of the opinions found are comparisons, which contrast aspects expressing a preference for an object over others. However, this information is neglected by traditional sentiment analysis techniques and it is not applicable for comparisons, since they do not directly express positive or negative sentiment. In this context, despite efforts in the English language, almost no studies have been done to develop appropriate solutions that allow the analysis of comparisons in the Portuguese language. This work presented one of the first studies on comparative opinion in Portuguese. Four main contributions are (1) A hierarchical approach for detecting comparative opinions, which consists of an initial binary step, which subdivides the regular opinions from the comparatives, to further categorize the comparatives into the five opinion groups: (1) Non-Comparative; (2) Non-Equal Gradable; (3) Equative, (4) Superlative; and (5) Non-Gradable. The results are promising, reaching 87% of Macro-F1 and 0.94 of AUC (Compute Area Under the Curve) for the binary step, and 61% of Macro-F1 in multiple classes; (2) An lexicon algorithm to detect the entity expressed as preferred in comparative sentences, reaching 94% of Macro-F1 for Superlative; (3) Two new datasets with approximately 5,000 comparative and non-comparative sentences in Portuguese; and (4) a lexicon with words and expressions frequently used to make comparisons in the Portuguese language.